Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications: Emerging Technologies for Connected and Smart Social Objects [1st ed.] 978-3-030-24512-2;978-3-030-24513-9

Table of contents :
Front Matter ....Pages i-x
Social IoT Healthcare (Azadeh Zamanifar)....Pages 1-11
Challenges and Solutions of Using the Social Internet of Things in Healthcare and Medical Solutions—A Survey (Kamel H. Rahouma, Rabab Hamed. M. Aly, Hesham F. Hamed)....Pages 13-30
MIPv6 in Crowdsensing Applications for SIoT Environments (Daniel Minoli, Wei Wang, Benedict Occhiogrosso)....Pages 31-49
Humanizing IoT: Defining the Profile and the Reliability of a Thing in a Multi-IoT Scenario (D. Ursino, L. Virgili)....Pages 51-76
Smart Cities Initiatives to Examine and Explore Urban Social Challenges (Milad Pirayegar Emrouzeh, Gregory Fleet, Robert Moir)....Pages 77-98
Information Integrity for Multi-sensors Data Fusion in Smart Mobility (Doaa Mohey El-Din, Aboul Ella Hassanien, Ehab E. Hassanien)....Pages 99-121
Legal Issues of Social IoT Services: The Effects of Using Clouds, Fogs and AI (Sz. Varadi, G. Gultekin Varkonyi, A. Kertesz)....Pages 123-138
Social Internet of Things and New Generation Computing—A Survey (Hamed Vahdat-Nejad, Zahra Mazhar-Farimani, Arezoo Tavakolifar)....Pages 139-149
Security Threats of Social Internet of Things in the Higher Education Environment (Ahmed A. Mawgoud, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa)....Pages 151-171
Social Networking with Internet of Things Aid Bahraini Medical Professionals’ Decisions Through Their Knowledge Sharing (Anjum Razzaque, Allam Hamdan)....Pages 173-182
An Artificial Intelligence Approach for Enhancing Trust Between Social IoT Devices in a Network (J. Senthil Kumar, G. Sivasankar, S. Selva Nidhyananthan)....Pages 183-196
A Survey of Internet of Things (IoT) in Education: Opportunities and Challenges (Mostafa Al-Emran, Sohail Iqbal Malik, Mohammed N. Al-Kabi)....Pages 197-209
Assessing the Performance of Container Technologies for the Internet of Things Based Application (Ruchika Vyas, Kathiravan Srinivasan, Aswani Kumar Cherukuri, Karan Singh Jodha)....Pages 211-233
Peak-End Rule Promotes Social Capital for Knowledge Management in Thru Social Internet of Things (Anjum Razzaque, Allam Hamdan)....Pages 235-247
Towards Smart Cities: Challenges, Components, and Architectures (Djamel Saba, Youcef Sahli, Brahim Berbaoui, Rachid Maouedj)....Pages 249-286
Smart Cities: The Next Urban Evolution in Delivering a Better Quality of Life (Abdulrahman Sharida, Allam Hamdan, Mukhtar AL-Hashimi)....Pages 287-298
Convergence of Blockchain and IoT: An Edge Over Technologies (T. Choudhary, C. Virmani, D. Juneja)....Pages 299-316
Social Internet of Things in Agriculture: An Overview and Future Scope (Chandan Kumar Panda, Roheet Bhatnagar)....Pages 317-334

Citation preview

Studies in Computational Intelligence 846

Aboul Ella Hassanien Roheet Bhatnagar Nour Eldeen M. Khalifa Mohamed Hamed N. Taha Editors

Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications Emerging Technologies for Connected and Smart Social Objects

Studies in Computational Intelligence Volume 846

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

More information about this series at http://www.springer.com/series/7092

Aboul Ella Hassanien Roheet Bhatnagar Nour Eldeen M. Khalifa Mohamed Hamed N. Taha •

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•

Editors

Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications Emerging Technologies for Connected and Smart Social Objects

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Editors Aboul Ella Hassanien Information Technology Department Faculty of Computers and Artificial Intelligence Cairo University Giza, Egypt Nour Eldeen M. Khalifa Information Technology Department Faculty of Computers and Information Cairo University Giza, Egypt

Roheet Bhatnagar Department of Computer Science and Engineering, Faculty of Engineering Manipal University Jaipur Jaipur, Rajasthan, India Mohamed Hamed N. Taha Information Technology Department Faculty of Computers and Information Cairo University Giza, Egypt

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-24512-2 ISBN 978-3-030-24513-9 (eBook) https://doi.org/10.1007/978-3-030-24513-9 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To the Scientific Research Group in Egypt (SRGE) and my wife Nazah Hassan El-saman

Preface

This book constitutes the refereed contributions from the contributing authors. Social Internet of Things (SIoTs) is very much pertinent in today’s contemporary world and has its reach in almost all the possible domain of modern society. Though SIoT is still in its infancy, its enablers are now well matured, and there are constant efforts to provide solution, from both the conventional and non-conventional methods. When things get smart, the Internet of Things get social! According to Atzori, who has coined the term Social Internet of Things in 2012, defined it as an IoT where things are capable of establishing social relationships with other objects, autonomously with respect to humans, thereby creating a network of objects. SIoT is to augment the capabilities of humans and devices to discover, select and use objects with their services in IoT. The synergies between social network and IoT devices are very much significant and have great potential with many use-cases which could be of great relevance to industry in future, and we are going to see Cyber Physical Social Intelligence. Seeing the relevance of Social IoT and its future, we thought of coming up with an edited book entitled ‘Towards Social Internet of Things: Enabling Technologies, Architectures and Applications’, to address the recent developments, technology enablers, architecture and frameworks and recent work of researchers in our book. This volume is aimed at furthering the know-how on state-of-the-art research challenges, results, architectures and applications in a Social IoT paradigm. We express our sincere thanks to the contributors from all around the globe, all our esteemed reviewers for providing their timely expert opinion and feedbacks and helping us in selecting manuscripts as chapters for our book. We would like to thank all our friends and well-wishers who have helped us in properly publicizing and promoting the call for chapters. Finally, we extend our thanks to Springer

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Publishing Team for supporting us throughout at every stage of the production of this edited book. We hope the readers would equally love the chapters and their contents and appreciate the efforts that have gone into bringing it to reality. Giza, Egypt Jaipur, India Giza, Egypt Giza, Egypt

Aboul Ella Hassanien Roheet Bhatnagar Nour Eldeen M. Khalifa Mohamed Hamed N. Taha

Contents

Social IoT Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Azadeh Zamanifar Challenges and Solutions of Using the Social Internet of Things in Healthcare and Medical Solutions—A Survey . . . . . . . . . . . . . . . . . . Kamel H. Rahouma, Rabab Hamed. M. Aly and Hesham F. Hamed MIPv6 in Crowdsensing Applications for SIoT Environments . . . . . . . . Daniel Minoli, Wei Wang and Benedict Occhiogrosso

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Humanizing IoT: Defining the Profile and the Reliability of a Thing in a Multi-IoT Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Ursino and L. Virgili

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Smart Cities Initiatives to Examine and Explore Urban Social Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Milad Pirayegar Emrouzeh, Gregory Fleet and Robert Moir

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Information Integrity for Multi-sensors Data Fusion in Smart Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Doaa Mohey El-Din, Aboul Ella Hassanien and Ehab E. Hassanien

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Legal Issues of Social IoT Services: The Effects of Using Clouds, Fogs and AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Sz. Varadi, G. Gultekin Varkonyi and A. Kertesz Social Internet of Things and New Generation Computing—A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Hamed Vahdat-Nejad, Zahra Mazhar-Farimani and Arezoo Tavakolifar Security Threats of Social Internet of Things in the Higher Education Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Ahmed A. Mawgoud, Mohamed Hamed N. Taha and Nour Eldeen M. Khalifa

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Social Networking with Internet of Things Aid Bahraini Medical Professionals’ Decisions Through Their Knowledge Sharing . . . . . . . . . 173 Anjum Razzaque and Allam Hamdan An Artificial Intelligence Approach for Enhancing Trust Between Social IoT Devices in a Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 J. Senthil Kumar, G. Sivasankar and S. Selva Nidhyananthan A Survey of Internet of Things (IoT) in Education: Opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Mostafa Al-Emran, Sohail Iqbal Malik and Mohammed N. Al-Kabi Assessing the Performance of Container Technologies for the Internet of Things Based Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Ruchika Vyas, Kathiravan Srinivasan, Aswani Kumar Cherukuri and Karan Singh Jodha Peak-End Rule Promotes Social Capital for Knowledge Management in Thru Social Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Anjum Razzaque and Allam Hamdan Towards Smart Cities: Challenges, Components, and Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Djamel Saba, Youcef Sahli, Brahim Berbaoui and Rachid Maouedj Smart Cities: The Next Urban Evolution in Delivering a Better Quality of Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Abdulrahman Sharida, Allam Hamdan and Mukhtar AL-Hashimi Convergence of Blockchain and IoT: An Edge Over Technologies . . . . . 299 T. Choudhary, C. Virmani and D. Juneja Social Internet of Things in Agriculture: An Overview and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Chandan Kumar Panda and Roheet Bhatnagar

Social IoT Healthcare Azadeh Zamanifar

Abstract Monitoring the vital signs of patients and thus predicting the health status of a patient in the Internet of Things (IoT) healthcare applications is the primary goal of healthcare systems. One common approach in these works is the detection of the activity of the patient (activity recognition) based on sensors in the environment. However, this method requires many sensors to record the patient’s condition, which can be costly and inconvenient. These methods cannot predict the health status of a patient, and can only detect current abnormal behavior. In this chapter we want to survey the works done in predicting the health status of patients in health care with the aids of social IoT.

1 Introduction Healthcare is among the most well-known and popular IoT applications and aims to monitor a patient’s vital signs on a 24/7 basis, eliminating the need for the patient to be hospitalized. Although electronic health (e-health) systems were introduced before the emergence of IoT, in traditional healthcare applications two-way communication between sensors and a remote server is not possible, and a gateway/remote server cannot directly communicate with the sensor nodes. The IoT makes this possible by leveraging existing Internet protocols such as IPv6, which enable the direct addressing of various devices and sensors through the Internet [19]. Social networking can be useful in various areas and play a significant role in sharing information and transferring knowledge. It can be used in managing chronic diseases like diabetes when there is an immediate need to raise awareness of various behavioral aspects of healthy diet, physical exercise and knowledge of self-management. People can be educated and their awareness levels can be increased through information sharing and discussions via mobile social networking applications, which are very convenient and easy to use. The Social Internet of Things represents the extension of Internet of A. Zamanifar (B) Computer Engineering Faculty, University of Science and Culture, Tehran, Iran e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_1

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Things, which has great potential for various applications. Although, it lacks intelligence and cannot comply with the increasing application performance requirements from different domains, agent technology seems to be a promising approach in the implementation of the Social Internet of Things. Applying the social networking principles to the IoT can lead to several advantages [4]: 1. The SIoT structure can be shaped as required to guarantee the network navigability, so as that the discovery of objects and services is performed effectively and the scalability is guaranteed like in the human social networks. 2. A level of trustworthiness can be established for leveraging the degree of interaction among things that are friends. 3. Models designed to study the social networks can be re-used to address IoT related issues (intrinsically related to extensive networks of interconnected objects).

1.1 IoT Healthcare In a healthcare system, various sensors are deployed to monitor the vital signals of the patient(s), including environmental monitoring sensors, and these tend to be inconvenient for the patient and costly. There are two ways to detect and predict a patient’s health status: either with or without activity recognition methods. Non-activity-based recognition methods can be further categorized into two groups, namely conventional and IoT-based methods. In conventional methods, the health data of a given patient are stored and further analyzed by different learning methods, to detect the most significant factors in diseases [31]. This is done using learning methods such as ANN or fuzzy logic. Srinivas et al. applied different classifiers including rule-based approaches, a decision tree, naive Bayes and an artificial neural network to a massive healthcare dataset containing attributes relevant to heart attacks. Their study showed that the naive Bayes approach outperformed other methods. Fuster-Parra et al. considered 23 attributes to increase the accuracy of offline cardiovascular risk assessment. They used multiple classifiers and found that a Bayesian network generated the best accuracy [11]. These solutions do not use the characteristics of the IoT in recognizing an abnormal status. Furthermore, they cannot predict (and can only detect) an abnormal status, and do not update their constructed models according to new data. IoT solutions that are not based on activity recognition methods can only detect the status of the patient. They are inaccurate [30] and unable to predict the abnormal status of the patient. Some recent studies [13, 16] have focused on using support vector machines (SVM) to classify anomalous behavior using a data set based on door sensors within a home, but these are manually annotated. Moreno et al. [30] proposed a solution for detecting abnormal situations in home environments, oriented mainly towards elderly people and those living alone. Kumara et al. [20] have proposed an abstract schema for an IoT healthcare system in which the monitored data of a patient

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are transformed into a social system that clusters patients with similar behaviors for symptom analysis. Their schema includes a prediction system for predicting anomalies; however, this is an abstract schema and is not implemented. The related works either propose only an abstract schema or are not sufficiently accurate; they also need a great deal of historical data to detect an abnormal status for the patient. There are several studies which examine the feasibility of identifying abnormal behavior by finding behavior patterns that are dissimilar to learned normal patterns [17, 32]. Many studies have demonstrated the feasibility of training a classifier to detect a specific event, especially falls [6, 7, 22, 29, 34, 37]. Clustering algorithms have also been used to identify abnormal behavior patterns [21, 24]. Meng et al. propose an online daily habit modeling and anomaly detection (ODHMAD) model, which can perform daily activity recognition, habit modeling and anomaly detection for solitary older adults within their living spaces. ODHMAD consists of an online activity recognition (OAR) model and a dynamic daily habit modeling (DDHM) component. OAR performs online processing of sensor data to identify daily activities and urgent events for the elderly [28]. However, this method cannot predict anomalous behavior and also requires extra sensors to detect the behavior of the patient. However, these prediction methods are important, as they can predict an abnormal status for patients and help them survive. Suryadevara et al. define wellness to monitor the activity of older adults. They carry out real-time activity recognition in elderly patients and determine the wellness function for these patients using appliance-based activities. Six types of sensors must be deployed in the monitoring environment. Based on the data accumulated from the environment and the use of wellness function, they detect abnormal behavior, although they achieve low accuracy. Dohr et al. [10] introduced MobiCare, an architecture for a healthcare system that provides a broad range of health-related services for the efficient health care of mobile patients. These services include: (1) health-related services in medical devices and sensors for remote installation, self-activation, reconfiguration or even self-repair with new health services and applications; (2) secure and reliable dynamic software upgrade or update services, applied to the native code of a clinical device; and (3) remote registration and (re)configuration of body sensors and remote health data services, such as patient health report downloads and diagnosis data uploads with provider servers. However, this system focuses solely on web services and does not have a predictive capability. Gayathri et al. [12] detect an abnormal status for the patient by hierarchically applying a Markovian logic network. They assume there are different sensors within different objects in the house. The goal of their method is to identify the status of the patient by considering: (1) the objects used by the patient; (2) the time of arrival in a room; (3) the duration of stay in a room; (4) the activity of the patient; and (5) the possibility of doing concurrent activities. To this end, they apply two learning methods: one for detecting and classifying the activity, and another for extracting rules that indicate the relationship between these factors and the abnormality of the status. Thus, the overhead in this method is considerable. Furthermore, they do not use ECG data, which is used in IoT healthcare systems.

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Shaji et al. [35] collect body movement activity data, classify it and use it in conjunction with ECG data to detect an abnormal status of the patient. They claim that the activity of a patient reveals this status; however, their method requires that the patient wear four extra sensors for activity recognition, which may be annoying. Although online prediction of a patient’s health status can be achieved based on activity recognition techniques; it needs a considerable number of sensors to record the physical status [5, 8, 33, 35]. It is also inconvenient for patients, and especially elderly people, to wear a high number of sensors. Activity recognition approaches require numerous sensors to monitor the patient’s physical status, as well as contextual information, making this a costly task. Furthermore, it is not convenient for a patient to wear five or six body sensors. Although previous works rely on activity recognition methods to increase the accuracy of mobile node data (such as ECG wave signals) and to detect a patient ’s abnormal health status, their implementation is expensive due to the need for many different sensors, and they are not convenient for the patient [12]. Zamanifar and Nazemi [38] propose an approach for predicting the health status of the patient that does not rely on the use of activity recognition sensors. The proposed IoT-based distributed health status prediction (DHSP) approach predicts future mobile sensor data using a customized version of an HSMM with two outputs. In this paper, a continuous ECG wave signal provides the mobile node data. To achieve a health status prediction capability, the monitoring area is modeled as cells of equal size with a static node at the center of each, as described in [39]. The network is built in such a way that the temporal pattern of the patient’s ECG data in each cell can be extracted, as well as the time of the patient’s arrival and the probable duration of stay within that cell. They build the prediction model, a log of the ECG data and the patient’s location traces is collected by the static leaf nodes over a certain period. The leaves send their collected data to the gateway through the intermediate nodes of the network’s tree [39]. Following this, a prediction model is constructed in the gateway and partitioned into appropriate sub-models, which are routed to their corresponding leaf nodes through the network schema. Therefore, each static leaf node holds the relevant data model in addition to the maximum and minimum thresholds for the mobile sensor node data.

1.2 Social Network and Healthcare Social network is a social structure made of individuals or organizations associated with one or more types of interdependence (friendship, common interests, work, knowledge, prestige, etc.) which are the “nodes” of the network. Networks can be organized to exchange information, knowledge or financial assistance under the various interest groups in universities, workplaces and associations of citizens. Today the most popular and widely used networks are based on application of the Internet as the main ICT. Depending on the method of connection, their field of activity and expertise of those who participate in certain networks Benefits of social media

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in healthcare includes, (1) Public health monitoring, (2) Citizen engagement, (3) Research recruitment and (4) Raise awareness and counter misinformation [26]. According to the authors of [4] “the IoT vision can be completely accomplished as it were in the event that objects are able to participate in an open way”. In this way, in arrange to misuse the potential of the IoT, the authors accept that trust objects and given administrations ought to be effectively discoverable and usable by humans and by other objects. But, the existing arrangements for benefit disclosure in IoT do not scale with the number of things that are anticipated to be associated in IoT. Moreover, although analysts uncover other issues for receiving IoT in reality, different solutions were proposed. In this chapter, we are going center as it were on the potential advertised by social organizing to empower IoT. Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting [14]. Social networking can be useful in various areas and play a significant role in sharing information and transferring knowledge. It can be used in managing chronic diseases like diabetes when there is an immediate need to raise awareness of various behavioral aspects of healthy diet, physical exercise and knowledge of self-management. People can be educated and their awareness levels can be increased through information sharing and discussions via mobile social networking applications, which are very convenient and easy to use. This can rapidly decrease healthcare expenditures in managing such chronic diseases and help people to self-manage their disease effectively [1].

1.3 Social IoT and Healthcare In recent times, there has been a growing interest in building Social Internet of Things (SIoT) [3, 4, 18]. The paradigm of IoT relies mainly on making objects, called things, disappear and weave themselves into the fabric of our daily life for supporting us in carrying out activities. Scalability and heterogeneity are among the major challenges which hinder the wide-scale realization of IoT services in users’ daily lives. In order to address IoT challenges, a new research stream has come forward in the literature as a paradigmatic class of the Cyber-Physical Social Systems (CPSS), which is known as the Social Internet of Things (SIoT). The SIoT builds on the notion underlined by small-world phenomenon where social structure allowing trust-based social relationship among people and objects, in a manner resembling traditional Social Network Services (SNS) is suggested to address IoT challenges. This social structure can improve objects navigability and discovery by narrowing down its scope to a manageable social network of everything. However, since SIoT inherits characteristics from different computing and networking environments (i.e., IoT and SNS) this, actually, increases the quantity and the variety of contextual data that must be handled for adaptive service provisioning in SIoT [2]. Health data mining methods in social media is a pioneer study that utilizes traditional text mining techniques into some personal social media resource for exploring

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useful health information. The popularity of social media allows the websites like Twitter or Facebook become communication hubs where people share life experience. This hub contains a large volume of potential personal health information. While health data mining methods in social media have a promising technical outlook, the issue of security and privacy is a big obstacle to limit its use [9]. An IoT that captures primarily the data of the physical world (e.g., physiological data in the case of health-related activities or applications) is increasingly being complemented by online data and knowledge and data and experiences shared on social media to provide physical, cyber, and social big data that are relevant to improving human experience. For example, an ongoing effort on managing asthma combines a patient’s physiological-data-related feature (a peak flow meter for lung functioning or a Fitbit for activity levels), physical surrounding (indoor air quality, temperature, humidity, etc.), external environmental data (the air quality index and allergens in the environment), and structured information extracted from the physician’s clinical notes for the patient. These multi modal data enable identification of personalized correlations (e.g., high ragweed leads to allergy and later chest tightening when not treated) for a patient. These data, combined with background medical knowledge (here, an asthma control protocol on how symptoms are related to asthma control levels and which medications are generally prescribed for a patient for a given asthma control level) and with information extracted from the patient’s clinical records serving as a baseline (e.g., a doctor’s guidance on which medication to take when certain symptoms present), allow a patient to continue the self-appraisal of his or her medical condition [27]. Zang et al. [40] propose a Social-based Mobile Sybil Detection (SMSD) scheme to detect Sybil attackers from their abnormal contacts and pseudonym changing behaviors. They first define four levels of Sybil attackers in mobile environments according to their attacking capabilities. They then exploit mobile users’ contacts and their pseudonym changing behaviors to distinguish Sybil attackers from normal users. To alleviate the storage and computation burden of mobile users, the cloud server is introduced to store mobile user’s contact information and to perform the Sybil detection. Furthermore, they utilize a ring structure associated with mobile user’s contact signatures to resist the contact forgery by mobile users and cloud servers. In addition, investigating mobile user’s contact distribution and social proximity, they propose a semi-supervised learning with Hidden Markov Model to detect the colluded mobile users. Security analysis demonstrates that the SMSD can resist the Sybil attackers from the defined four levels, and the extensive trace-driven simulation shows that the SMSD can detect these Sybil attackers with high accuracy. Maghawry and Ghoniemy [25] design an intelligent recommender system that is able to deal with such biological data extracted from weareable sensors. They proposes a framework to develop an enhanced intelligent expert advisor based health monitoring and disease awareness system. The proposed framework enables the researchers to design advisory systems that are able to observe physiological signals through the use of different biosensors and integrate it with historical medical data together with the massive data collected from social networks to provide accurate alerts and recommendations for many ailments inspected. The proposed Framework

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is designed to facilitate generic, dynamic and scalable process of integrating different types of social networks and biosensors. Han considered that associated gadgets are able to have social relationship with both individuals and other gadgets. In this approach, a Web API was utilized to guarantee the interaction between online social arrange (OSN) and gadgets. Afterward on, unused functionalities were added to the OSN center, and the result, an widespread OSN of everything, was named ThingsChat. To incorporate social characteristics into the benefit system, a Device Socialization module was included to the design displayed in [15]. This module encourages communication between both gadgets and SIoT, and gadgets and human clients. A domestic portal named ThingsGate is the component that discovers, stores, and transmits gadget administrations to ThingsChat. This module is responsible with client allow authorization to ThingsChat and gadget initialization with social functionalities. In this way the associated gadget gotten to be social substances. ThingsGate plays a imperative part within the realization of the socializing gadget, exceptionally critical for this SIoT platform [15]. In arrange to fit the gadget within the SIoT stage, ThingsGate acts like a mediator within the handle of gadget authorizing and customization. It is the module that registers the gadget in SIoT and includes it to the socialized list, which can be accessed later by client. Message trades between clients and gadgets are facilitated by presence of a NLP (Normal Dialect Handling) interface which changes over user messages into machine clear commands, and bad habit versa. The ordinary interaction between clients and gadgets distinguished by the creator is that the client inquires the device to total different operations and the gadget does the work and a while later replies back to the client. This appears to be comparable to the client/server arrangement handle in multi-agent innovation. Something else, they consider that some of the functionalities on/in this stage can be realized by utilizing operators (for case, NLP). The authors of the [23], characterize an NFC-based intelligent agent for the Social Web of Things (displayed in Fig. 2) and propose a framework for creating this kind of operator, which combines the NFC method with context-acquisition, ontologyknowledgebase, and semantic-adaptation modules, a credit-based motivation plot to empower social participation and to recommend relevant administrations. In arrange to empower the settlement to the questionable real-world environments, “this think about too characterizes cleverly and social functionalities for the proposed NIA to realize three vital capacities: receptive activities, proactive accomplishments, and social participation. In arrange to back context-aware computing, this ponder creates a few components to bolster the capacities of area, time, activity, and social awareness”. The coming about social-advertising framework shows that this system can bolster setting mindfulness within the SIoT situations and a wide run of distinctive functionalities.

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1.4 Discussion Things, the smart objects turn to social objects to boost the pace of IoT emergency and to make it more universal. The relationships of co-location, co-ownership, co-work and parental among friend objects provide a platform to share services, information, computing, and other resources and output. This modern promising paradigm of technology extension is called Social Internet of Things (SIoT). An inevitable aspect of SIoT is the convergence of smart objects and social media that can introduce new social interactions by enabling the things to have their own social networks and interactions. The smart objects can establish their social relationship based on their activities, interest, and profile. In a typical social IoT setting, we treat the devices and services as bots where they can set up relationships between them and modify them over time. This will allow us to seamlessly let the devices cooperate among each other and achieve a complex task. To make such a model work, we need to have many interoperating components with common ontology. We need the below major component: 1. ID: we require a one of a kind strategy of object distinguishing proof. An ID can be assigned to an object based on conventional parameters such as the MAC ID, IPv6 ID, a all inclusive item code, or a few other custom method. 2. Meta data: in conjunction with an ID, we require a few meta information about the gadget that describes its frame and operation. This can be required to set up fitting connections with the gadget and suitably put it within the universe of IoT devices. 3. Security controls: this can be like “friend list” settings on Facebook. An proprietor of a gadget might place restrictions on the sorts of gadgets that can interface to it. These are regularly alluded to as proprietor controls. 4. Service discovery: such a kind of a framework is like a benefit cloud, where we got to have devoted catalogs that store points of interest of gadgets giving certain sorts of administrations. It gets to be exceptionally critical to keep these catalogs up to date such that gadgets can learn approximately other devices. 5. Relationship administration: this module manages relationships with other devices. It also stores the types of devices that a given device should try to connect with based on the type of services provided. For example, it makes sense for a light controller to make a relationship with a light sensor. 6. Service composition: this module takes the social IoT model to a new level. Social networks improve the management of IoT resources, under three approaches: costs, quality and Sustainability). Agent technology is a good candidate in collaborating different entities in social IoT. Using an ontology can help in identifying the structure of the information that is exchanged between parties. As healthcare experts cannot bear to require risks, significant investigate is needed to create SioT at healthcare a reality [36]. Despite the many problems faced by the adoption of SIoT in healthcare, this paradigm promises to deliver to human users and connected devices the possibility to discover, select, and use proper services found through scanning their friends network.

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1.5 Conclusions Bringing the social data along with other related IoT health care data to predict the patient ’s health status is a new area and there are lots of to be done. Social IoT has a great potential for many applications. This chapter also gives an outline of the affect of the SIoT in healthcare, and handles with the characteristic challenges of making the utilize of SIoT a reality within the field. The Social Web of Things has extraordinary potential for healthcare organizations and society. The benefits can be gigantic, both for individuals and for healthcare organizations, on the off chance that the improvement of SIoT is legitimately managed.

References 1. Alanzi, T., et al.: Evaluation of a mobile social networking application for improving diabetes Type 2 knowledge: an intervention study using WhatsApp. J. Comp. Eff. Res. 7(09), 891–899 (2018) 2. Ali, D.H.: A social Internet of things application architecture: applying semantic web technologies for achieving interoperability and automation between the cyber, physical and social worlds. Ph.D. thesis, Institut National des Télécommunications (2015) 3. Atzori, L., Iera, A., Morabito, G.: Siot: giving a social structure to the Internet of things. IEEE Commun. Lett. 15(11), 1193–1195 (2011) 4. Atzori, L., et al.: The social Internet of things (SIoT)-when social networks meet the internet of things: concept, architecture and network characterization. Comput. Netw. 56(16), 3594–3608 (2012) 5. Avci, A., et al.: Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: a survey. In: 2010 23rd International Conference on Architecture of Computing Systems (ARCS), pp. 1–10, Feb 2010 6. Chen, J., et al.: Wearable sensors for reliable fall detection. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005, IEEE-EMBS 2005, pp. 3551–3554. IEEE (2006) 7. Cheng, J., Chen, X., Shen, M.: A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J. Biomed. Health Inform. 17(1), 38–45 (2013) 8. Choudhury, T., et al.: The mobile sensing platform: an embedded activity recognition system. Pervasive Comput. (IEEE) 7(2), 32–41 (2008) 9. Deng, Z., et al.: Life-logging data aggregation solution for interdisciplinary healthcare research and collaboration. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 2315–2320. IEEE (2015) 10. Dohr, A., et al.: The Internet of things for ambient assisted living. In: 2010 Seventh International Conference on Information Technology: New Generations (ITNG), 2010, pp. 804–809. https:// doi.org/10.1109/ITNG.2010.104 11. Fuster-Parra, P., et al.: Bayesian network modeling: a case study of an epidemiologic system analysis of cardiovascular risk. Comput. Methods Programs Biomed. 126, 128–142 (2016). https://doi.org/10.1016/j.cmpb.2015.12.010 12. Gayathri, K.S., Elias, S., Ravindran, B.: Hierarchical activity recognition for dementia care using Markov logic network. Pers. Ubiquitous Comput. 19(2), 271–285 (2015). https://doi. org/10.1007/s00779-014-0827-7 13. Gottfried, B., et al.: Spatial health systems. In: Smart Health, pp. 41–69. Springer (2015)

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14. Griffiths, F., et al.: The impact of online social networks on health and health systems: a scoping review and case studies. Policy Internet 7(4), 473–496 (2015) 15. Han, N.S.: Semantic service provisioning for 6LoWPAN: powering internet of things applications on Web. Ph.D. thesis, Institut National des Télécommunications (2015) 16. Jakkula, V.R., Cook, D.J.: Detecting anomalous sensor events in smart home data for enhancing the living experience. Artif. Intell. Smarter Living 11(201), 1 (2011) 17. Khan, S.S., et al.: Towards the detection of unusual temporal events during activities using HMMs. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1075– 1084. ACM (2012) 18. Koreshoff, T.L., Leong, T.W., Robertson, T.: Approaching a human-centred Internet of things. In: Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, pp. 363–366. ACM (2013) 19. Kulkarni, P., Öztürk, Y.: Requirements and design spaces of mobile medical care. ACM SIGMOBILE Mob. Comput. Commun. Rev. 11(3), 12–30 (2007) 20. Kumara, S., Cui, L.Y., Zhang, J.: Sensors, networks and Internet of things: research challenges in health care. In: Proceedings of the 8th International Workshop on Information Integration on the Web: In Conjunction with WWW 2011, IIWeb ’11, Hyderabad, India, 2:1–2:4. ACM (2011). https://doi.org/10.1145/1982624.1982626. ISBN: 978-1-4503-0620-1 21. Lee, M.-S., et al.: Unsupervised clustering for abnormality detection based on the tri-axial accelerometer. In: ICCAS-SICE, 2009, pp. 134–137. IEEE (2009) 22. Li, Q., et al.: Accurate, fast fall detection using gyroscopes and accelerometerderived posture information. In: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009, BSN 2009, pp. 138–143. IEEE (2009) 23. Lin, C.-H., Ho, P.-H., Lin, H.-C.: Framework for NFC based intelligent agents: a contextawareness enabler for social Internet of things. Int. J. Distrib. Sens. Netw. 10(2), 978951 (2014) 24. Lotfi, A., et al.: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Ambient Intell. Hum. Comput. 3(3), 205–218 (2012) 25. Maghawry, N.E., Ghoniemy, S.: A proposed Internet of everything framework for disease prediction. Int. J. Online Eng. 15(4) (2019) 26. Masic, I., et al.: Social networks in improvement of health care. Materia Socio-Medica 24(1), 48 (2012) 27. Mayer, S., et al.: An open semantic framework for the industrial Internet of things. IEEE Intell. Syst. 32(1), 96–101 (2017) 28. Meng, L., Miao, C., Leung, C.: Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing. Multimed. Tools Appl. 76(8), 10779–10799 (2017) 29. Mirmahboub, B., et al.: Automatic monocular system for human fall detection based on variations in silhouette area. IEEE Trans. Biomed. Eng. 60(2), 427–436 (2013) 30. Moreno-Fernandez-de-Leceta, A., et al.: Real prediction of elder people abnormal situations at home. In: Grana, M., et al. (eds.) International Joint Conference SOCO’16-CISIS’16ICEUTE’16, San Sebastián, Spain, 19–21 October 2016 Proceedings, pp. 31–40. Springer International Publishing, Cham (2017) 31. Nahar, J., et al.: Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst. Appl. 40(4), 1086–1093 (2013) 32. Ordóñez, F.J., de Toledo, P., Sanchis, A.: Sensor-based Bayesian detection of anomalous living patterns in a home setting. Pers. Ubiquitous Comput. 19(2), 259–270 (2015) 33. Peri, D.: Body area networks and healthcare. In: Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful, pp. 301–310. Springer International Publishing, Cham (2014) 34. Rakhecha, S., Hsu, K.: Reliable and secure body fall detection algorithm in a wireless mesh network. In: Proceedings of the 8th International Conference on Body Area Networks, pp. 420–426. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2013)

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35. Shaji, S., Ramesh, M.V., Menon, V.N.: Real-time processing and analysis for activity classification to enhance wearable wireless ECG. In: Proceedings of the Second International Conference on Computer and Communication Technologies, pp. 21–35. Springer (2016) 36. Turcu, C.E., Turcu, C.O.: Social Internet of things in healthcare: from things to social things in Internet of things. In: The Internet of Things: Breakthroughs in Research and Practice, pp. 88–111. IGI Global (2017) 37. Yin, J., Yang, Q., Pan, J.J.: Sensor-based abnormal human-activity detection. IEEE Trans. Knowl. Data Eng. 20(8), 1082–1090 (2008) 38. Zamanifar, A., Nazemi, E.: An approach for predicting health status in IoT health care. J. Netw. Comput. Appl. (2019) 39. Zamanifar, A., Nazemi, E., Vahidi-Asl, M.: A mobility solution for hazardous areas based on 6LoWPAN. In: Mobile Networks and Applications, pp. 1–16 (2017) 40. Zhang, K., et al.: Exploiting mobile social behaviors for Sybil detection. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 271–279. IEEE (2015)

Challenges and Solutions of Using the Social Internet of Things in Healthcare and Medical Solutions—A Survey Kamel H. Rahouma, Rabab Hamed. M. Aly and Hesham F. Hamed

Abstract Social Internet of Things (SIoT) is considered as one of the most attractive topics in the last centuries of researches. It introduces a lot of technologies in different fields based on the new intelligent things of era internet. Furthermore, it plays an important role to update new technologies of healthcare and medical Robotics. In this chapter, we introduce some of the challenges of using the SIoT in healthcare and medical applications. The different methods and solutions utilize big data in different fields. After giving a survey about these challenges and solutions, we introduce two recent applications of our own in healthcare solutions. The first application is for heart diseases diagnosis and the second is for brain tumor diagnosis. The results of the two applications prove the importance of SIoT in healthcare solutions. Keywords Internet of thing (IoT) · Medical robotics · Adaptive neural network (ANN) · Optimization · Fuzzy logics and metaheuristic

1 Introduction Nowadays, a lot of different technologies affect the system design in different fields such as Social Internet of Things (SIoT). IoT is considered an important part in smart technology such as social networking, healthcare, cyber-physical systems and security mechanisms [10]. IoT with the smart things are connected together and they exchange huge data among them. These data are analyzed in different researches based on different methods and techniques. The main analysis of big data is different from a field to another, especially in social networking. There are a lot of surveys which focused on security systems in healthcare and industrial applications [5]. The security challenge and solutions are closely related to the sensors of networking K. H. Rahouma (B) · H. F. Hamed Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt e-mail: [email protected] R. Hamed. M. Aly The Higher Institute for Management Technology and Information, Minia, Egypt © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_2

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(wireless sensor networking). Actually, IoT solves the problem more effectively than the traditional solutions and also improves the use of Social Internet of Things (SIOT). Furthermore, IoT applications are also improved in other fields such as Internet of Medical Things (IoMT) [2], Internet of Nano Things (IoNT) [3], Internet of Mobile Things (IoMBT), Internet of Cloud Things (IoCT), Internet of Drone Things (IoDT), Industrial Internet of Things (IIoT), Internet of Underwater Things (IoUT). All of these applications depend on the target of using it [10]. The general IoT challenges requirements for each application are shown as in Fig. 2. In this chapter, we introduce a survey about solutions of IoT in different fields such as healthcare and medical robotics and then we introduce a new method for using the big medical data based on optimization and machine learning techniques with SIoT. The rest of the chapter is organized as follows. Section 2 gives some security challenges with different SIoT applications with examples from different researches. Section 3 introduces a methodology of two applications of SIoT medical sensors. Section 4 introduces some conclusions (Fig. 1).

Fig. 1 Different types of IoT

Fig. 2 IoT challenges for each application

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2 Security Challenges of SIoT Generally speaking, security is the method to protect information from any attack on the computer system. Security methods differ from an application to another based on the importance of data. The general security frame of network may consist of: (a) (b) (c) (d) (e) (f)

Confidentiality Integrity Authentications Non-repudiation Availability Privacy

Each of these components is discussed in details in Kouicem et al. [10]. Furthermore, authors, in Stergiou et al. [16], introduced the different types of security based on cloud computing techniques and explained how to improve IoT using cloud computing. In Stergiou et al. [16], authors compared the methods of security of IoT and explained in details how to apply them in the cloud computing. They introduced a new algorithm for the key generation based on mobile cloud computing and this achieved the best performance of IoT security. On another hand, SIoT is proved to have the same challenges as cloud computing. In Farris et al. [7], the authors introduced SIoT architecture and improved it using the cloud computing principles to lessen or remove the delay time. The general IoT application is shown in Fig. 3 and it needs high security requirements such as introduced in Fig. 2.

Fig. 3 General SIoT applications

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2.1 Security Requirements and Challenges for Some SIoT Applications In this part, we introduce some various SIoT applications with the security requirements and challenges. Note that, the SIoT needs more security requirements than the IoT systems.

2.1.1

Healthcare

Recently, the smart healthcare is considered one of the most important fields in the smart hospitals. The embedded sensors for human bodies play an important role to save the life of patients in the right time. Indeed, the population is annually increasing in all countries and the smart applications help in emerging situations. The improvement of heart sensor and brain sensor help to diagnose the dangerous diseases [1]. The heart beat sensor with cloud computing and smart phones help doctors to follow the patient form long distances or from a country to another. The security design for emerging situations needs some requirements as follows: (a) Authentications: Only physicians or nurses are able to access the records of patients. (b) Integrity and Confidentiality: This is to guarantee the security of data communication between patient and clinics or hospitals for data exchange. (c) Privacy concerns: This is to hide the important information based on SIoT devices and real time. Furthermore, there are important challenges for healthcare solutions such as: – Mobility. – Resources limitations such as in memory or battery which is very important and it must be considered. – Heterogeneity environments which must be considered in the solution. In Sect. 3, we will introduce some of pervious methods and suggestions for new healthcare methods based on optimization and bioinformatics data techniques.

2.1.2

Transports

Intelligence is very important in design of transporting systems. Actually, the next generation of transporting systems aims to link between people and roads in different fields. IoT and especially SIoT employs vehicle systems and subsystems such as GPS, station subsystems and V2G (vehicle to grid) and it is very important to ensure that electric charge of vehicle is complete or not. SIoT makes transporting very easier and comfortable. To secure different types of vehicles, there are some of security requirements:

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(a) (b) (c) (d)

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Authentications: it is for senders of messages. Availability: to avoid jamming attacks. Privacy: The drivers should protect their privacy from unauthorized observers. Non-repudiation: drivers are responsible for their accidents. Furthermore, there are some important challenges for transports such as:

– High mobility: it is for highly dynamic where there are changes in network topology. – The variety of sources attacks: the exchange of information must be more secured. – Heterogeneity: the variety of entries and sets of the different attacks.

2.1.3

Smart Homes and Smart Cities

Smart homes or smart cities consist of a lot of SIoT or IoT such as in emerging systems or electricity systems or communication systems. Sensors and embedded design contain traffic and adapt weather and smart banking system in smart cities, etc. Added to all of that, there are some requirements for smart homes or cities such as: – Integrity of data: there are sensitive data and take quick decisions to save citizens lives. – Authentications: information origins should be authentic. – Confidentiality: control of sensitive data should be guaranteed. – Availability of information: it is for decision makers and users. Furthermore, there are some more important challenges for smart homes or cities solutions such as: – Scalability: the number of intelligent devices is improved every moment. – Data management issues: this is for the big data which is generated by smart devices and how they can be allocated and accessed. – The huge level of heterogeneity: this is to dedicate different information from all different devices for different applications in smart cities.

2.1.4

Industrials

Recently, SIoT is one of the most practical fields in industrial technology. It is considered as the practical and perfect solutions for a lot of problem in manufacturing. Actually, embedded sensors improved the design of industrials and made it very more powerful and very fast. Recently, the manufacturing, in some industries, has greatly improved using Internet Robot of Things (IRoT). IoT and SIoT aim to provide better control and efficiency. SIoT system in industrials claims an importance of security requirements such as:

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– Authentication: this is mandatory for any parts of manufacturing. – Confidentiality: this is to protect the processing from attack. – Integrity: it is to preserve the integrity of information exchange between SIoT devices and this is the cause of safety in Cyber Physical System (CPS). – Availability: CPS is considered as a new challenge which comes from the DoS’s attacks, disrupt routing protocols, and it prevents the compressions between sensors to send measurement. On another hands, there are some more important challenges for industrials such as: – Cyber Physical attacks: CPS can attack at any time by different viruses. – Scalability: CPS grows continuously, security solutions deal with any expansion. – Resources limitations: In industrial architectures, more SIoT devices are employed which violates the claim of using low cost and present constrains of power. – SCADA based IoT systems have no standardization of protocols where there are 150–200 open standard protocols [10]. – Safety: manufacturing systems need safety for several types of SCADA from different attacks [1]. In the following subsection, we will discuss some of the methodologies of healthcare SIoT applications based on pervious researches.

2.2 Examples of Healthcare SIoT Methodology Recently, Artificial Neural Networks (ANNs) play an important role in SIoT applications, especially, in medical embedded sensors. Furthermore, ANNs introduce new challenge in mobile applications and the development of it. Authors in some researches introduced the using of SIoT such as in Zeng et al. [20]. In Zeng et al. [20], authors described how the embedded sensor recognizes the human activity and understands the human behavior. The authors introduced one of the types of ANNs which is the Convolution Neural Network (CNN). They tried to extract the discriminative features for activity recognitions. They applied a lot of things based on CNN and also local dependency and scale invariance of speech signal. They tried to improve the system using mobile applications and they achieved high accuracy using CNN as shown in Fig. 4. The main steps of work of this paper are: – Extraction of features based on the principles of component analysis. – Deep learning for features recognition. – CNN for classifying the behaviors. The structure of data inputs consists of 64 layers and the convolution output has dimension of 12 and the maximum pooling is 4. On another hand, the system has two hidden layers of dimensions 1024 and 30 (SOFTMAX classifier layer is the top layer) as shown in Fig. 5. The method achieved accuracy up to 100%.

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Fig. 4 System of activity recognitions

Fig. 5 CNN for human activity recognition

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Fig. 6 System block diagram of epilepsy seizure detection

There are a lot of authors introduced other methods based on the SIoT and how to use them in predictions and rearrangements of social things such as attending of patients in hospitals and clinical systems [4]. In [4], authors introduced a study of frameworks for outpatients appointment scheduling (OAS) in a dental clinic. They introduced a system of OAS based on some main steps as follows: – Prediction of the treatment duration using back propagation—ANN. – Simulation of the operations of the dental clinic based on discrete event simulation. – Simulation of the performance of appointment scheduling. The system of this paper served patients and dentists because of: – The providers consist of professional dentists. – The system introduced the variety of medical treatments. On another hand, the method of Support Vector Machine (SVM) has been introduced in a lot of researches as one of the most practical classification methods with IoT healthcare applications. In [15], authors applied SVM with spline to detect the degree of Epilepsy Seizure (ES). Actually, the method of SVM with IoT has achieved a high performance. The IoT sensors for brain helped to detect the brain signals and classify ES by using SVM. Figure 6 shows the block diagram of the system. Datasets were collected from public domains and used in the system and SIoT of online data achieved high accuracy. The SIoT helps in the analysis of big data especially the social and public data. Heart beat sensors with mobile application help to improve the system of diagnosis. In [11], authors applied IoT medical sensors with Raspberry Pi kit to monitor the patient’s body temperatures and movements using Raspberry Pi. Actually, The Raspberry Pi Kit with sensor consist of a set of sensors (respiration, heart beats, and temperatures, as shown in Fig. 7). In Gupta et al. [9], authors introduced the design of IoT smart healthcare kit based on INTEL GALILEO 2ND generation kit. They collected assets of emergency sensors that applied a lot of features to help patients to reduce the number of visits to the clinic by monitoring the blood pressure and heartbeats and help them as much as possible without going to the doctors.

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Fig. 7 Patient Monitoring using Raspberry Pi kit

In Sect. 3, we will introduce a healthcare application based on ARDUINO kit and IoT (heartbeat sensor) and predict the patient’s situation all the time to help doctors for early diagnosis. Furthermore we modify the method to analyze the brain performance information of its features, extracted from the brain MRI images which are obtained from online database. Based on that, we diagnose the brain diseases and predict any future complications through the use of the SIoT.

3 Methodology of Healthcare SIoT Applications 3.1 SIoT for Heart Diagnosis Based on ARDUINO Kit The system consists of four main parts as shown in Fig. 8. The first part is the sensor of heart beat with ARDUINO circuit. This part is considered one of the important parts is the connection between patient and Laptop. This part applies in two test stages: – First stage is the connection between patient with heartbeat sensor, ARDUINO Kit and laptop by wired as shown as Fig. 9. – Second stage is the connection between patient with heart beat sensor, ARDUINO Kit, Bluetooth and laptop as shown in Fig. 10.

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The sensor records the heartbeats of the patient by a Bluetooth unit and we designed software in MATLAB 2017a to read the real-time data from the patient to diagnose the patient case and also to predict the future complications as shown in Fig. 11. The second part in the system block diagram is a preprocessing stage depending on discrete wavelet transform (DWT) to filter and enhance the ECG signal and also to extract features such as in Rahouma et al. [14]. The causes to use DWT are: (a) Recovering the original signal from the noisy signal. (b) Analyzing the signal in time and frequency domain. (c) Representing the function in case it has discontinuities and sharp peaks more efficiently than using only discrete Fourier transform (DFT). The main processing of DWT consists of three parts [14]: – The decomposition process. – Thresholding. – Reconstruction process. After applying the main processing of DWT, we extract the features and detect the main features (RR, QRS, ST, QT, PR). The normal features which we use in diagnosing processing in Table 1. The online databases of heart diseases is playing important part in classification to diagnose the patient diseases. After collecting the features of diseases, we compared with the current features of the patient such as [14]. On another hand, the last stage of the system is prediction. The prediction technique consider one of the most important in early diagnosing [12]. In this system, we applied Linear Prediction Coding Method (LPC). This method introduced in Rahouma et al. [14] and approved high accuracy up to 100%. The formula of the estimated value is [6]: E(X(n + 1)) = −

N  i=0

Fig. 8 System block diagram

ai X(n − i)

(1)

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Fig. 9 Arduino with heart beat sensor without bultooth

Fig. 10 Arduino with heart beat sensor with bluetooth Table 1 Normal features Name of feature wave

Standard normal value (ms)

RR

0.6–1.2

ST

0.08–0.12

QRS

0.08–0.12

PR

0.12–0.2

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Fig. 11 GUI of Heartbeat sensor diagnosis system

In Eq. (1), we want to predict the sample value x(n + 1) using a linear combination of the points, x(n), x(n − 1), x(n − 2), ……, x(n − N) which represent the present and history sample values respectively. It is important to mention here that the estimated sample value {E(X(n + 1))} and the actual computed future value {X(n + 1)} are generally not equal. This can be interpreted as a prediction error. The formula gives this error as: Err(X(n + 1)) = {X(n + 1) − E(X(n + 1))}/X(n + 1)

(2)

where X(n + 1) is the actual next value and E(X(n + 1)) is the predicted one. Notice that, we use the first 80% from the data set for training of Eq. (1) till the ai coefficients are determined. The rest 20% of the data set are then used to predict the future values. This is repeated for all the features. The prediction LPC algorithm applied in our work is given below. Figure 11 shows the results of the system. LPC Algorithm Start (1) Input the present and history values of the features (QRS, PR, RR, QT, ST). (2) For each feature do the following: Start loop (a) Use 80% of data to determine the coefficients [14]. (b) Use the rest 20% of data to estimate the future value of the feature. (c) Extract the next actual value of the feature.

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(d) Calculate the percentage errors between next actual values and the estimation values using the formula: Err = actual value−Estimated value ∗ 100% actual value (e) Compute the accuracy = 100 − Err (5) Print the results End loop End

3.1.1

Results and Discussion of SIoT for Heart Diagnosis Based on ARDUINO Kit

In this section, we present the results which were obtained based on the previous section. As discussed before, we designed the system on MATLAB 2017a. The strategy of the system will be as the following: – Apply sensor kit with the patient and reading data wireless and after that send it to laptop or the personal computer (PC) of the server of clinic or hospitals and can be made it send to online based on internet. – The system will take saved data file of patient from server or laptop and divided into samples. – The recorded data will be sent or recorded from 1 to 10 min. – Such as Rahouma et al. [14], we used 1,000,000 samples, with sampling frequency of 250 samples per second. We divided the samples into groups, each of them corresponds to 10 min, such that we process the samples of the first minute (15,000 samples) of the period and hold the results on the screen for the remaining 9 min. The GUI includes all the results in values and drawings, as shown in Fig. 11: (a) The construction of DWT is decomposition process and reconstruction of the signal. (b) DWT gives low pass and high pass filter coefficients. (c) The approximation coefficients are extracted from the low pass filter coefficients. (d) DWT is considered an excellent method for time and frequency domain analyses. (e) The results when show that some values are abnormal, that is mean that we need to apply the prediction techniques to predict any future complications. (f) The heart rate is computed using RR as: 60/RR = 133.33 bpm. (g) LPC achieved from 95 to 99.9% for future complications based on historical datasets of heart diseases.

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3.2 SIoT for Brain Diagnosis Based on Real Time Clinical Data Signal This system consists of three main parts as shown in Fig. 12 where we will use computer vision techniques. The main stage of the system represents SIoT in the form of an online datasets from MRI equipment where the hospitals make online reports with online datasets and share them with other clinics and hospitals. Also, some hospitals have real time monitoring systems with patients and they save data on some websites. (1) The preprocessing stage consists of two main parts: – A filter processing which is a low pass filter. – A segmentation process which includes: (a) Thresholding and binarization analysis after enhancement. (b) Segmentation processing based on K-means clusters. We have applied the K-means clusters to segment the tumor from the MRI images. Segmentation will help in features extraction and the classification of the tumors degrees. The k-means algorithm divides a set of data into K-groups of a disjoint clusters as in clustering process. The method which we have used to calculate the distance for centroid data is “Euclidean distance” [19]. For example, if there is an image with resolution (x, y) and the cluster is K-numbers, let consider P(x, y) is an input pixel in cluster and ck is the cluster center then the algorithm of K-means will be as the following: (a) Define the number of cluster K. (b) For each pixel, the Euclidean distance (d) is calculated using Eq. (3). d =  p(x, y) − ck  ck =

1  p(x, y) k y∈c x∈c k

Fig. 12 The block diagram of the system

k

(3) (4)

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Fig. 13 K-means clustering for segmentation of brain tumor

(c) Based on distance (d) and attributes of all the pixels are obtained, recalculate the new position of the center based on Eq. (4). (d) Repeat the process until satisfying the minimum error. (e) Extract the segmentation from MRI images as shown in Fig. 13. (2) Features extraction based on Ant Colony Optimization (ACO): In ant colony optimization, the used indirect communication is called a pheromone. The quantity of pheromone is based upon distance, quality, and quantity of food source. The artificial ant colony algorithm is introduced in [8]. The authors have introduced the solution of the ant colony optimization problem and then applied it to solve travelling salesman problem. The target of ant colony optimization is to find the shortest path to the food of ants and travelling salesman achieved it with the shortest time and paths. In this paper, we use the ant colony optimization based on travelling salesman for feature extraction and selection method for binary MRI images of different brain tumor cases [18]. Our algorithm for Ant Colony is based on travelling salesman (ACO-TSP) and pheromone indirect communications [8, 18]. The algorithm is as follows: (a) The probability of the ant moving is calculated by using Eq. (5) where i is the present node for the present step of the ant movement and k is the next node. β

τ α + Ii  p k =  i β τ Nα i + I N i where: β τi  Ii Ni

is heuristic exponential weight and α is pheromone exponential weight. The pervious or past attractive value. adds to transition attractiveness for ants. is the set of nodes connected to point i without last visted.

(b) Reverse:

(5)

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In this case, the internal memory of the virtual ant plays and important role. The ant has reversed the path based on its internal memory. Note that, the ant in her reversed path will be in opposite order and eliminated cycles. The reversed equation of the ant movements is shown in Eq. (6) t t τit+1 j − τi j + τ

(6)

where: τitj is the value of pheromone in step t. τ is the saved value of pheromone in step t and these values can be constant values in some cases. (c) The last step of Ant colony—TSP “Evaporation of pheromone”: = (1 − ρ)τitj τit+1 j

(7)

This method achieved high accuracy in the detection of the optimal features. The following the classification and prediction process which achieved accuracy up to 100%. (3) Classification and prediction method GMDH—method: The group method of data handling (GMDH) is using for classification and prediction for the predicted features of the MRI brain tumor databases. GMDH is the same structure of Polynomial Neural Network (PNN). PNN is the most practical method as a type of Artificial Neural Network (ANN). Furthermore, GMDH is a multilayer network which uses quadratic neurons offering an effective solution to modeling of non-linear systems. It is more practical and accurate in prediction of behavior of the system model [13, 17].

3.2.1

Results and Discussion of SIoT Diagnosis Tumor Method

The system of this paper is based on classifying and predicting the brain tumors images. We applied the low pass filter to enhance the brain tumor images. Furthermore, after the thresholding process and binarization process; we applied K-mean method for the segmentation process. The feature extraction process is based on the tumor optimal segmentation tumor which help in the classification and prediction process. The Ant colony based on travelling salesman (ACO-TSP) achieved the best solutions in classification and prediction process. In ACO-TSP, we created a model of matrix based on the segmented data and after that we used the principle component analysis (PCA) to extract the optimal general features from segmented images and after that we calculated the 7 features from the 100 general features based on equations in Table 2 and the maximum limit of iterations is 500. The model of ACO-TSP

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Table 2 Equations for features extraction (ACO) Name of equations

Equation

Mean

μ=

Standard deviation

Entropy



x

N



σ =

N 

1 N

(xi − μ)2

i=1



ENT = −

i

Root mean square (RMS)

Variance

RMS = σ2 =

1 N

x(i, j)Log2 x(i, j)

i

N 

x 2 (i)

i=1

m−1  n−1 

(i − μ)2 x(i, j)

i=0 j=0

Smoothness Kurtosis

Smoothness = 1 − Ku =

1 σ4

m−1 



1 (1+σ 2 )



(i − μ)4 x(i) − 3

i=0

helped to save time in CPU [8]. Furthermore, we applied the ACO-TSP algorithm for feature extraction at the same laptop system. The result showed that all features of any image needed 5 min for the whole process including the all stages (preprocessing, segmentation, feature extraction, classification, and prediction) which means that for all of the stages. Applying the MATLAB parallel processing tools on the laptop system, we got a total time 2 min for all feature. The accuracy of using ACO-TSP after classification and prediction from 95% up to 99.8%.

4 Conclusions This chapter introduces a survey of the challenges and solutions of the SIoT and some new examples of its applications. The SIoT proved its importance in a lot of applications especially in diagnosing techniques in heart diseases and brain tumor diseases. The big data which is utilized in the SIoT helps to improve practically the predication and classification methods of the different diseases and this gives high accuracy levels which reach up to 100% in some cases.

References 1. AL-mawee, W.: Privacy and Security Issues in IoT Healthcare Applications for the Disabled Users a Survey. p. 50 (2012)

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2. Darwish, Ashraf, Hassanien, Aboul Ella, Elhoseny, Mohamed, Muhamma, Arun Kumar Sangaiah Khan: The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities challenges, and open problems. J. Ambient Intell. Humanized Comput. 1–16, 2017 (2017) 3. Batth, R.S., Nayyar, A., Nagpal, A.: ‘Internet of robotic things: driving intelligent robotics of future—concept, architecture, applications and technologies. In: 2018 4th International Conference on Computing Sciences (ICCS), pp. 151–160. IEEE 4. Chang, W.-J., et al.: Design of a patient-centered appointment scheduling with artificial neural network and discrete event simulation. J. Service Sci. Manag. 11, 71–82 (2018) 5. Choi, J., Shin, Y., Cho, S.: ‘Study on information security sharing system among the industrial IoT service and product provider. In: International Conference on Information Networking, 2018–January, pp. 551–555 6. Disi, M., Al et al.: Intelligent Systems: Models and Applications. Springer International Publishing (2013) 7. Farris, I., et al.: Taking the SIoT down from the cloud: integrating the Social Internet of Things in the INPUT architecture’, IEEE World Forum on Internet of Things, WF-IoT 2015—Proceedings, pp. 35–39 8. Gülcü, S., ¸ et al.: A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem. Soft. Comput. 22(5), 1669–1685 (2018) 9. Gupta, P. et al.: IoT based smart healthcare kit. In: 2016 International Conference on Computational Techniques in Information and Communication Technologies, ICCTICT 2016—Proceedings, pp. 237–242 (2016) 10. Kouicem, D.E. et al.: Internet of Things Security : A Top-Down Survey to Cite This Version : HAL Id : hal-01780365 Internet of Things Security : A Top-Down Survey (2018) 11. Kumar, R., Pallikonda Rajasekaran, M.: An IoT based patient monitoring system using raspberry Pi. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering, ICCTIDE (2016) 12. Loong, J.L.C. et al.: A new approach to ECG biometric systems: a comparative study between LPC and WPD systems. Int. Scholarly Scientif. Res. Innovat. 4(8), 340–345 (2010) 13. Mehri, Y., Soltani, J., Khashehchi, M.: Predicting the coefficient of discharge for piano key side weirs using GMDH and DGMDH techniques. Flow Measurem. Instrument. Elsevier Ltd, 65, 1–6 (2019) 14. Rahouma, K.H., Aly, R.H.M., Hamed, H.F.A.: Analysis of Electrocardiogram for Heart Performance Diagnosis Based on Wavelet Transform and Prediction of Future Complications (2017) 15. Srinivas, M.S.: Epilepsy Seizure Detection Using IoT and Support Vector Machine with Spline, vol. 6(11), pp. 100–103 (2017) 16. Stergiou, C. et al.: Secure Integration of IoT and Cloud Computing. Future Generat. Comput. Syst. Elsevier B.V., 78, 964–975 17. Takao, S., et al.: Deep feedback GMDH-type neural network and its application to medical image analysis of MRI brain images. Artif. Life Robot. Springer Japan 23(2), 161–172 (2018) 18. Wu, Y. et al.: High-order graph matching based on ant colony optimization. Neurocomputing. Elsevier B.V. (2018) 19. Zeinalkhani, L., Ali Jamaat, A., Rostami, K.: Diagnosis of brain tumor using combination of k-means clustering and genetic algorithm. Iranian J. Med. Informat. 7(1), 6 (2018) 20. Zeng, M. et al.: Convolutional neural networks for human activity recognition using mobile sensors. In: Proceedings of the 6th International Conference on Mobile Computing, Applications and Services (2014)

MIPv6 in Crowdsensing Applications for SIoT Environments Daniel Minoli, Wei Wang and Benedict Occhiogrosso

Abstract Crowdsensing can be an enabler of the Social Internet of Things (SIoT), among a plethora of other systems, elements, infrastructure, and applications. Although in the short term crowdsensing can be supported within the traditional wireless cellular infrastructure, in the longer term, it will be an important component of the evolving Smart City paradigm. Given the expected increase of urban populations in the next 35 years, this application not only will assist in the process of “greening the environment” but also make city living more livable. Clearly, mobility is at the core of crowdsensing in particular, and SIoT in general. While several mobility management techniques have emerged, an extensive body of applicable research has been developed in the past twenty years, in the form of the Mobile IPv6 (MIPv6) and related protocols. As of press time over seventy RFCs had been published by the IETF on MIPv6 and related MIPv6 mobility protocols; yet, MIPv6 has received relatively little attention up to now in the IoT. Broad deployment of SIoT will benefit from MIPv6 technologies. This chapter describes key MIPv6 features and its propitious applicability to crowdsensing and SIoT, particularly given 3rd Generation Partnership Project (3GPP) recent adoption of it for some 4G/5G scenarios. Keywords Crowdsensing · Mobile crowdsensing · Social Internet of Things (SIoT) · Mobile IPv6 (MIPv6) · Mobility · Localization · Location based services (LBS) · Wireless · Multimedia encoding · 3GPP

D. Minoli (B) · B. Occhiogrosso DVI Communications Inc., New York, NY, USA e-mail: [email protected] W. Wang Department of Computer Science, San Diego State University, San Diego, CA, USA © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_3

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1 Introduction As noted elsewhere in this book, Social Internet of Things (SIoT) deals with a connected “smarter, smaller” world, spanning the smart home, the smart city, and the smart planet. It provides a common platform for globally interconnected social objects enabling users of all kinds—human and inanimate—to share services, information, computing and other resources. The ultimate goal is to enhance and optimize the Quality of Life (QoL) and Quality of Experience (QoE) of human beings, while advancing socially-focused goals, such as informed, peaceful, productive, culturallyenhanced coexistence with humankind and with nature, especially in the context of social responsibility, respect for the planet, and green philosophies. SIoT aims at integrating social networking concepts into the IoT ecosystem. It is an evolution of the social networks’ (SNs) concept enabling a truly symbiotic interconnection of people to the ubiquitous computing universe that now surrounds us [1–10]. Some take another view to SIoT, as being an emerging paradigm where IoT-endowed devices collaborate with each other to achieve a specified goal by establishing durable logical and physical relationships, thus an SIoT paradigm allows objects to have their own social networks. In this view, connected devices are given social meanings that render them unique and distinguishable from other things, entities, or devices. More specifically, objects establish social relationship by forfeiting their individuality in favor of common interest for federated service(s) to the larger community of objects or entities. Furthermore, in this view SIoT makes use of a service-oriented architecture (SOA) model where a cadre of IoT devices can offer or request atomic services from each other and also collaborate either as a whole or individually [11, 12]. Facilitating autonomous interaction between SNs and the IoT is, as noted, a key emerging application of SIoT; other applications of SIoT include Smart Cities and mobile health (m-Health). After a motivational overview, in Sect. 2 this chapter describes some key approaches to mobility in support of SIoT where there are multimedia streaming applications, which are becoming more common, especially in SNs. Localization mechanisms and location-based services are discussed in Sect. 3. IPv6 applicability to crowdsensing and SIoT, particularly given 3rd Generation Partnership Project’s (3GPP) recent adoption of it for some 4G/5G scenarios, is covered in Sect. 4 of the chapter. Basic MIPv6 constructs are identified in Sect. 5, and a number of advanced MIPv6 protocols applicable to crowdsensing and SIoT are discussed in Sect. 6. Future research directions for both crowdsensing and SIoT are discussed in Sect. 7. Smart wearable technology contributes to the development and propagation of the SIoT, particularly in the SN context. The kind of wearable devices already available in the market, including medical monitoring devices, activity trackers, smart watches, and smart fabrics are proliferating dramatically. Wearable systems can collect data of the person’s lifestyles; wearable fitness and health trackers monitor the person’s biometric parameters such as body temperature, blood pressure, heart rate, expended calories, and sleep cycles. In conjunction with smartphones, tablets or other input devices, the data is typically transmitted to the cloud for specialized analytics. Well-

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known wearable technologies on the market include, but are certainly not limited to, the Apple Watch—that interacts with the user’s iPhones via Bluetooth—Fitbit activity trackers, and Google Glass—effectively a pair of glasses equipped with a built-in computer and a mini display with a prism projector that beams the screen into the user’s (right) eye, along with a camera, a microphone, and a touch pad enabling gesture control. Wearable technology can be used for crowdsensing applications. In turn, crowdsensing can be an enabler of SIoT, among other systems, elements, infrastructure, and applications. Crowdsensing deals with the collection of massive distributed data for aggregation and analysis. In many instances it also deals with the geographic location of that data (i.e., the physical location of the [mobile] sensor where the information in question is being collected). The term Mobile Crowdsensing (MCS) refers to a gamut of crowdsensing applications. In recent years, the broad deployment of smartphones that include a number of on-board sensors has facilitated the opportunity of harvesting a large volume of data, especially in metropolitan areas, thus enabling a batch of MCS-driven applications [13–17]. The number of smartphones in use globally was estimated to be 2.5 billion in 2019. Functional enhancements in smart devices, such as wearables, smartphones, and smartwatches, and connectivity enhancements such as Low Power Wide Area Networks (LP-WAN)—for example, Sigfox and LoRa—and open-air hotspot, are now allowing MCS solutions to materialize, particularly for Smart Cities applications, for both data and video or multimedia streams. According to the United Nations by 2050, cities will have 2.8 billion more people than today; the urban population will comprise 70% of the world’s projected 10 billion inhabitants. This major growth in the community of city dwellers will create urgent issues in urban QoL, infrastructure, transportation, energy consumption, waste management, housing, and healthcare, to list just a few areas of impact. The IoT in general and MCS in particular will be key technological enablers to address the challenges associated with this population shift. Thus, MCS will be a critical component of the evolving Smart City paradigm, which will not only assist in the process of “greening the environment” but also make city living more livable. Sensing devices include smart watches, wearables, fitness devices, e/m-health monitors, gaming systems, smartphones, stationary environmental sensors on poles, and in-vehicle sensors. Many observers characterize the IoT in general, and MCS in particular (with connected wearable and ubiquitous computing as backdrops), as the most transformative and disruptive technology since the commercialization of the Internet: IoT, and the myriad applications it supports, will have impact a large portion of the global economy in the years to come. MCS applications tend to entail community-type sensing. Here, macro-scale phenomena (e.g., air quality, vehicular traffic) are monitored utilizing a large pool of individuals or other entities that automatically supply location-focused information, which is then assembled for system-wide results [18]. For example, the NYC Transit Department of Buses has recently designed a digital radio system to be deployed in thousands of city buses and other city vehicles to support advanced ComputerAided Dispatch and Automatic Vehicle Location (CAD-AVL). This system aims at

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tracking the position of buses and other city vehicles in real-time using the Global Positioning System (GPS) along with cellular services, to provide bus-stop time-ofarrival notification via customers’ smartphones (specifically, when will the next-bus arrive at the bus stop in question), as well as supporting advanced fleet management (using Vehicle Logic Units to monitor tire pressure, engine health and fuel efficiency) throughout the city. The IoT deploys sensors with built-in data collection software, and in some cases actuators, in various objects in the physical world, and wirelessly interconnecting these objects with centralized IT data analysis systems to facilitate monitoring, surveillance, ambient intelligence, control, and remote action control [19]. However, mobility is a key aspect of the emerging IoT systems in general, and Machine to Machine (M2M) applications in particular; mobility is at the core of MCS, especially when the sensing is associated with vehicular mobility, whether in Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) context. While a number of mobility management techniques have emerged, an extensive body of applicable research has been developed in the past twenty years, in the form of the MIPv6 and related specifications. Yet, IPv6 and MIPv6 (including Proxy MIPv6 [PMIPv6] and Fast Handover for MIPv6 [FMIPv6]) have received relatively little attention up to now. This could possibly be related to the amount of nodeavailable power and/or processor complexity; however, in MCS applications where the sensor is a smartphone or in a car, power is less of an actual concern; also, processors associated with smartphones can easily support rather computationally-complex tasks; in addition, low-power IPv6-based protocols have emerged. Mobile devices are typically equipped with embedded sensors, such as GPS, accelerometers, digital compasses, gyroscopes, microphones, cameras, and possibly other environmental sensors, also including (some) e-health biometric elements. MCS allows this sizable community of mobile devices to measure phenomena of general interest over a large metropolitan area, facilitating data collection, analysis, storage, and sharing—this data often being “big data” in nature. MCS leverages the ubiquitous availability of mobile users and/or devices and the availability of well-established wireless infrastructures, especially in city environments, to collect and analyze sensed data without having to deploy a large set of bespoken static sensors. The MCS ecosystem capacitates multi-modal low-cost large-scale sensing in a much more cost-effective and quicker ‘time-to-market’ manner than deploying mission-specific, dedicated, and/or stationary sensors [20]. Mobility management (MM) deals with retaining end-to-end connectivity while a device is in motion. MM is critical to MCS and to a suite of Location-based Services (LBSs). Since, as noted, on-board power is typically less of an issue for body-worn or vehicle-mounted MCS sensors, IPv6 protocols and MIPv6 mobility management techniques may be a useful technology. This is also motivated by the fact that some of the dispersed metro-based ‘fog’ networks may be in distinct Autonomous Systems (that is, different network domains) belonging to different providers. For example, Wi-Fi hotspots owned by different ‘in-town providers’ and a person/car having sensors used in a MCS application, may roam (in different parts of town) across different IP domains (e.g., in cases where, for cost-effectiveness or throughput, Wi-Fi is used, when/where available, by the smartphone in lieu of a cellular link.)

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35

Fig. 1 Smart city-supporting networks possibly located in different autonomous systems

A basic domain may be the “home network (HN)” and the other in-city networks may be the visited (or “foreign”) networks. Vehicular traffic moving at highway or city-street speeds may well benefit from the mechanisms offered by MIPv6. See Fig. 1 for a pictorial example in a Wide Area Network (WAN) context. The rest of the chapter describes key MIPv6 features and its applicability to MCS applications in general and SIoT in particular.

2 Wireless Cross Layer Systems for Crowdsensing MCS applications support both traditional IoT low-rate-low-traffic data sampling and high-rate-high-definition multimedia such as social media video and image sharing [18]. MM where there is a streaming application that has tight Quality of Service (QoS) and/or QoE requirement is more challenging, because packet loss is typically not recoverable and it will degrade QoS/QoE. Such crowdsourcing data diversity may require a redesign of lower layer IoT protocols and scheduling algorithms. The redesign guideline could be mathematically formulated as a resource-constrained quality optimization problem. Here a priority multimedia coding method is introduced which leads to low bandwidth and energy consumption when implemented in an IoT device.

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Let q denote the quality contribution of a multimedia coding unit (a video frame, a picture, or a macroblock) if it is successfully received. A coding unit’s priority weight is the sum of its own quality and the total quality of coding units that depend on it. The dependency is determined by the multimedia codec, such as H.265/264. A set i is defined as the collection of all the coding units decoding dependency refers to unit i. Total N coding units are divided into W priority types with Wk presenting the coding unit set for type k with k = 1,2, …, W. The optimization problem is to find the best unit set with the highest priority weight leading to the highest expectation of decoding quality [21]. ⎧ ⎛ ⎞⎫ ⎬ ⎨  ⎝qi + qj⎠ (1) W∗ = arg max ⎭ ⎩ ∀k j∈i

i∈Wk

For example, in the temporal domain coding of H.264/H.265 video sequences, a coding unit is a frame, and the information is divided into three types with premium I-frames, fewer premium P-frames, and regular B-frames with W = 3. The premium information denotes the intra coding I-frames serving as reference data for other two types of inter coding P-frames and B-frames. In the spatial domain coding of a picture or image, a coding unit is a packet, and all the packets are partitioned into independent premium and regular blocks where W = 2. The premium information presents the crucial contours and shapes, i.e., the low frequency flat areas presented by a small number of large Macro Blocks with many pixels. After the unique pattern of prioritization with importance diversity is established at higher layers, a complex energy-efficient resource allocation problem at lower layers can be formulated, with the goal of maximizing and/or optimizing the received data quality expectation. Let Ai denote the unit set that unit i refers to, ξ be the packet error rate after using typical channel coding methods. ⎞ ⎛   

1   ⎝ 1 − ξj ⎠ (2) qi (1 − ξi ) []k,k=1...W = arg max N ∀k=1...W ∀i∈W j∈A k

i

subject to W 

[]k ≤ Rmax

(3)

k=1

[]k presents the resource matrix for priority class k, where each  denotes a resource parameter such as FEC channel coding rate, source coding unit length after compression, modulation constellation size, ARQ retransmission limit, transmission power, relay selection, etc. Rmax denotes the total resource budget for N coding units. Based on our analysis, Fig. 2 (top) shows the video quality per micro-joule of energy consumption. Due to its effectiveness in controlling the bit errors, a prioritized

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Fig. 2 Video quality per µJ of energy consumption, when (i) utilizing ARQ, prioritized FEC, coding size selection, adaptive modulation, and power control, versus (ii) classical method without priority and resource allocation. Top: Full Control. Bottom Reduced Control

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Forward Error Correction (FEC) scheme that assigns unequal channel coding rates to different video frame categories has the largest impact on improving quality per unit of energy consumption (adaptive modulation ranking is the second choice in terms of quality improvement.) The performance gain of allocating diverse Automatic repeat request (ARQ) retransmissions among prioritized frames in the transmission stream is similar to the gain achieved by packetizing frames utilizing different sizes in the source coding compression process. That is, retransmitting large frames with multiple reattempts has a similar effect on quality improvement to packetizing video information using small coding sizes during the compression process. Figure 2 (bottom) illustrates that removing one resource allocation parameter in the priority-based scheme discussed above results in performance degradation. Specifically, removing ARQ retry—for the purpose of saving computing and communication cost—equates to a small performance reduction. This implies that one can utilize the other four strategies to optimize video quality by only removing ARQ strategy. Abrogating the power control results in the largest performance loss. Compared with the result in the left diagram, the performance gain is minimal if one only uses a power control strategy without other resource allocation mechanisms. However, it has the most dominant role in improving system performance when simultaneously optimizing with the other four resource allocation strategies. From the simulation study we can see, multiple resource allocation parameters should be jointly considered when transmitting IoT data flows and packets with multiple priorities.

3 Localization and LBSs With the popularity of IoT devices and smartphones including integrated sensors such as GPS receivers, accelerometers and cameras, it is now feasible and cost efficient to build MCS applications that gather large scale real-time mobile sensor data [13]. For example, advances in wireless communication technologies and locationaware sensor technologies are fostering the advancement of vehicle networks (VNs). Research has established that MCS can be utilized for quite a few purposes and deliver economic benefits, including personal and public vehicular transportation, traffic monitoring and management, smart cities, and digital mapping. These crowdenabled systems are location-centric and context-aware; they provide location-related information from locations that users visit (e.g., cafes, offices, homes, or schools) as targets for data collection and analysis [22]. LBSs have recently experienced significant growth, while evolving from utilizing a single location to harness the complete trajectory of a vehicle. VNs use inter-vehicular or V2V communications, involving sharing and disseminating information about road conditions, accidents, or other information among vehicles in the vicinity of each other. SIoT principles are applicable in this context, as well as in many other contexts. In addition, modern smartphone MCS could also generate multimedia content such as pictures and videos shared among vehicle users

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to record road condition or to report traffic accidents/incidents. The data collected from mobile phones, sensors, IoT networks and the infrastructures within vehicles can be utilized by MCS applications to facilitate collective intelligence, taking the locations of sensors into account, which enhances the efficiency of information collection. It aims to provide an effective and efficient mechanism for solving specific problems in collaboration with participants in the general public. For example, traffic congestion results in significant economic losses each year in terms of productivity and energy losses, increased pollution, and has a negative impact on the overall quality of life. Using MCS with heterogeneous multimedia data to capture the road conditions allows for real-time transportation system analysis and long-term planning. By using location-based MCS in VNs, traffic anomalies can be detected and represented with multiple forms of data: human mobility such as drivers’ routing behavior, and social media information that people may post when anomalies occur [23]. Moreover, keeping the digital maps up-to-date and capturing all the physical road semantics is very difficult. Traffic is impacted by different road conditions and types such as tunnels, bridges, and crosswalks. The location-based information such as these can be mined to automatically enrich the features of mapping services in a MCS paradigm [24]. Another practical example is to find a good place to park a car, especially in a shopping mall or during a public event. Looking for parking spaces involves anxiety and uncertainty. It wastes limited resources—time, road space, and fuel—when drivers search for parking spaces. These problems could be alleviated if drivers had vacant parking spot information in advance. In addition, people could also temporarily lend their unused parking spots, in a way similar to an Uber business model, and accrue financial compensation. Information of vacant parking places can alternatively be identified and mapped through MCS as direct input from drivers using vehicles’ preinstalled parking sensors [25, 26]. Such information can be gathered with dedicated MCS systems keeping track of the reservation status of parking areas. Such crowd information shared among vehicles can reduce gas consumption and road congestion, alleviating traffic related issues in cities and metropolitan areas [27].

4 IPv6 Applicability to Crowdsensing There is an expectation that the IoT/M2M in general, and MCS systems in particular, will broadly make use of IPv6 in the future, as these applications experience increased market penetration [28, 29]. The (likely/expected) use of IPv6 is buttressed by the abundant unique addresses offered by IPv6, as well as end-to-end connectivity that can be provided in a canonical manner, without supplementary address or status (re)processing. Reprocessing is often required in IPv4 environments via the Network Address Translation mechanism (however, some proposed IoT protocols assume that the object has its own ID that, while it may not be totally unique within a given application, over the totality of the device population it would indeed have a

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unique address [30]). In particular, IPv6 offers a sizable 2128 address space. Some of the useful and applicable capabilities of IPv6 include: (i) Scalability; (ii) simplified connection mechanisms that facilitates the attachment of MCS equipment; and (iii) mobility management, as defined in the MIPv6 family of protocols. The basic MIPv6 is specified in RFC 3775 and a large set of related RFCs define useful extensions—as of press time over 70 RFCs were published by the IETF on MIPv6; thus, MIPv6 is actually a rich family of related standards. MIPv6 and related specifications are directly applicable to MCS. MCS applications that make use of IPv6 should consider header compression (HC) techniques, such as Robust Header Compression (ROHC) defined in RFC 3095/RFC 5795/6846 that can be used in WAN applications; furthermore, RFC 6282 describes a compression format for IPv6 over IEEE 802.15.4 networks [31] (updating the method described in RFC 4944) that can be used in LAN environments. HC is important since the IPv6 header is relatively long at 40 bytes, even without additional Extension Headers which carry optional network layer information (an IPv6 packet may include one or more extension headers). The extension headers defined at press time were: Mobility, Hop-by-Hop Options, Destination Options, Fragment, Routing, Authentication Header, Encapsulating Security Payload, and Destination Options. In MIPv6, a new extension header is introduced called the Mobility Header, which has a new Destination Option and a new Routing Header Type Option. In addition to MIPv6, a number of IPv6-based IoT protocols have been developed. For example, it is well known that in general, IoT nodes (possibly including nodes in MCS applications associated with wearables), have functional and power constraints that impact functions at the upper layers as well as the operation at the physical layer. To address these issues, the IETF defined an IPv6 encapsulation over IEEE 802.15.4, now known as 6LowPAN, for using IPv6 over low power WPANs (as noted, HC was also defined) [32]. 6LowPAN in MCS applications may be used, for example, when the sensors are incorporated in wearables. The IPv6/IEEE 802.15.4 and the 6LoWPAN frame are shown in Fig. 3.

5 MIPv6 Constructs Mobility is pivotal to the IoT in general and MCS in particular. Applications such as fleet management, logistics, e/m-health, smart municipal services, video or signal surveillance, public infrastructure monitoring, among other applications, depend on mobility. MM can be handled by acquiring channels at the physical level, as supported by a zone-level cell handoff (for example cellular handoff to another tower, a Wi-Fi handoff to another AP and so on). This occurs in a transparent fashion to the IP, TCP, UDP layers and other upper layers. Figure 4 (bottom) depicts this traditional connectivity management where end-to-end continuity is maintained via a PHY layer handoff. There are applications and environments where an IP-layer management of mobility is desirable. Mobile IP (MIP, RFC 3344/5944) and MIPv6 (RFC 3775/3776)

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Fig. 3 IPv6 and 6LoWPAN frames in an IEEE 802.15.4 environment

addresses the latter case. Figure 4 (top) depicts the general environment of IP-based MM. MIPv6 allows nodes (known as Mobile Nodes [MNs]) to stay reachable while roaming around various subnetworks. MIPv6 enables a MN to change its point of attachment (POA) to the network without losing higher-layer sessions; this is accomplished utilizing tunneling mechanisms between the MN and a defined Home Agent (HA). Thus, a MN maintains its connectivity to a centralized point, such as a data aggregation site or an analytics site, when roaming from one access router to another. MIPv6 uses the Destination Options header to exchange hand shake messages between MNs and the respective HA. The protocol allows MNs to retain permanent addresses even if they roam and change the POA to the core infrastructure. MIPv6 MM operations deals with movement detection and location update. The basic routing and/or forwarding operation of MIPv6 is depicted in Fig. 5. As shown, there are different entities in the MIPv6 environment: • MN: A mobile node (e.g., an MCS node) that can modify its POA, while still reachable via its “Home Address (HAdr)”. The MN is identified by its HAdr, independently of its current POA. When the MN is not attached to its HN the MN is said to be “away from home”. While roaming, based on the current location of MN, a MN is also associated with an “in-care-of-address” (CoA). Packets or datagrams addressed to a MN’s HAdr are (re) routed to the MN in a transparent fashion using the CoA. When the roaming MN connects to a new network “away

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Fig. 4 Mobility Management Top: IP Layer Management (e.g., MIP, MIPv6) Bottom: Physical Layer Management (e.g., cellular antennas, Wi-Fi APs)

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Fig. 5 Operation of the mobile nodes and the correspondent node (Modified from D. Minoli, Building the Internet of Things with IPv6 and MIPv6, Wiley)

from home”, it sends Binding Updates (BUs) to the HA and to Correspondent Nodes (CNs). • CN: A node with which a MN wishes to communicate or is in the process of communicating. • HA: A router on a MN’s HN with which the current CoA is registered. When the MN is roaming, the HA intercepts transmissions sent to the MN’s HAdr, and after encapsulating them routes to the MN’s CoA. A listing of the CNs to which MN sent a BU are kept by the MN; in turn, the CN learns the position of a MN by processing the BUs (recipients of BUs acknowledge the BU by returning a Binding Acknowledgement [BA]). The MIPv6 protocol enables MM routers (the HA) to cache the binding of a MN’s HAdr with its CoA; this binding is utilized by the HA to support MM. Using the IPv6 Routing Header, the CN performs packet (re)routing to the MN.

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6 Advanced MIPv6 Protocols for Crowdsensing Several extensions/enhancements of the MIPv6 have been proposed and can be utilized in MCS environments. Some of these extensions are briefly surveyed next. The 3rd Generation Partnership Project (3GPP) recently adopted the use of some of these protocols in specific 4G/5G applications; 4G/5G systems are increasingly expected to support MCS and SIoT applications, among many other applications. Network Mobility (NEMO) Basic Support Protocol (RFC3963) is a backward compatible extension of MIPv6. As the network in its entirety roams, NEMO enables Mobile Networks (MONETs) to attach to different points in the core infrastructure while maintaining session continuity for all nodes in the MONET (also called Mobile Network Nodes [MNNs]), thus permitting every node in the MONET to remain reachable. One example might be a mobile Wi-Fi hotspot, say in an automobile or bus, supporting connectivity to various people in the vehicle. A MONET is a subnet that can relocate and attach to any number of points to the core infrastructure. This MONET can only be reached using gateways known as Mobile Routers (MRs); the MRs handle the movement of the MONET. MONETs need at least one MR supporting them; the MR is the default gateway for the MONET. The MR is a router that can change its POA to the core infrastructure without affecting higher layer connections of any attached MNs. The approach entails setting up bi-directional tunnels between the MRs and the NEMO to the core infrastructure and the appropriate HAs. The protocol defined in RFC 3963 is not visible to nodes in the MONET (that is, MNNs located behind the MR); this, in turn implies that the MNNs are not responsible to take any action for MM. The MR does not forward the MONET routes to the infrastructure at its POA; rather, it utilizes the bi-directional tunnel to the HA to exchange routing information; in turn, the HA advertises an aggregation of MONETs to the core infrastructure. Thus, NEMO operates by transferring the MM from MNs to the MR; the MR is capable of changing its POA to the core infrastructure in a transparent manner to the MNs attached to it. The MR runs the NEMO protocol with its HA thus providing routing capabilities for routing between its POA (represented by the CoA) and a subnet that moves with the MR. Proxy Mechanisms (PMIPv6, RFC 5213) defines a protocol that supports network-based MM mechanisms without requiring the MN’s to initiate any MM handshake. In this environment, the network is tasked with managing the IP mobility for the MN. There will be mobility entities (proxies) tasked with the MM of the MN and responsible for the underlying handshake for the nodes under its jurisdiction. The proxy mobility agent undertakes the handshake with the HA and handles the MM on behalf of the MN. Additionally, network systems should preferably be designed to support mobility not only for nodes that are equipped with mobile IP functionality (i.e., run the MIPv6), as well as nodes without the mobility functionality. In fact, PMIPv6 does support this mixed-node environment. PMIPv6 makes use of local mobility anchors (LMAs) and also of mobile access gateways (MAGs). Associations are set up between LMAs and MAGs for transmitting Proxy BUs (PBUs). The mobility entities in the network monitor real-time location of the MNs and start the

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MM handshake to set up the routing information. Specifically, the task of maintaining the MN’s reachability information is with the LMA which also acts as the anchor point for the MN’s HN. The MAG (which is located on the access line where MN is anchored) is the entity that undertakes the MM function for a MN: it is responsible for the detection of the roaming experienced by MN; also, it is responsible for handling binding registrations to the MN’s local mobility anchor. Recently 3GPP has issued specifications that utilize PMIPv6. MM driven by roaming is more likely to be required in a city environment where microcells are typical than in rural environments where macrocells are more typical. 3GPP TS 123 402 v13.5.0 (2016-04) stipulates that certain reference points associated with the Evolved Packet Core architecture can use PMIPv6; this usage is also discussed in 3GPP TS 29.275 (2016-03). 3GPP indicates that the protocol is applicable to various gateway elements including the Packet Data Network Gateway, the Serving Gateway, and the Evolved Packet Data Gateway, in addition to the Trusted Non-3GPP Access functionality.

7 Research and Future Directions MCS has opened opportunities in many areas of research. Although the literature on PMIPv6 for general mobile applications is relatively extensive (including but not limited to [33–47]), the literature, research, and discussion on PMIPv6 applications for MCS and SIoT in particular, and for IoT in general, is rather limited at this juncture (e.g., [48–53]). Therefore, it is of interest to further continue to assess the utility of these MM mechanisms for MCS and the SIoT. Some issues needing further investigation promote the concern for energy limitations of the devices for some applications (for example, with wearables.) Others are concerned with security and distributed computational processing ability and fog/cloud architectures. However, an important feature of the application is in mobility for which this chapter has suggested MIPv6. Although mobile devices supported by the current cellular infrastructure may be sufficient for the MCS application in the short term, it is conceivable that in the long term specialized IoT devices, specifically supporting IP-layer MM, may be needed, as hinted in the 3GPP initiatives in this arena. The interest in this protocol is well-situated given the wide implementation in cellular and wireless networks and the internet. MIPv6 implementation in IoTs, however, require further investigation leading to low power (for example for wearables/immersive computing), low processing-intensive protocol suite which is also compatible with the current infrastructure. We call this “stripped down MIPv6” protocol, which should be investigated and developed further for wider future deployment among billions of IoTs in the landscape. Furthermore, the popularity of MCS has posed new challenges to rethink and redesign lower layer protocols and algorithms. The inborn characteristics of heterogeneous traffic flows and diverse priorities should be considered when designing the full MIPv6 or the “stripped down MIPv6” protocols. When considering the applica-

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tion data priorities, the “stripped down MIPv6” could be designed as “crowdsourcingfriendly” and the users’ QoE or QoL could be enhanced as the output benefit of such designs. Overall, it is desirable and advantageous to continue to develop standards that appropriately cover the core, the fogs, and possibly even the analytics of MCS and SIoT applications in Smart City environments [54]. Finally, the question arises if, in addition to HC, there are any protocol optimization for MIPv6 to support MCS/LBSs, especially considering the incentive and collaboration aspects in MCS. Another research question is whether the users with higher incentive and who may contribute valuable MCS data should be treated in MIPv6 with higher or different priorities. Yet another research question might be whether there is any optimization to MCS mechanisms by considering MIPv6; for example, MIPv6 may give information or hints on user mobility, which can be in turn leveraged to determine the best time for the user to upload their data to the cloud. Topics that will benefit from future research in the SIoT arena per se include ad hoc dynamic collaborative computing (where MIPv6 can play an important role), work on service-oriented architecture to support collaborative autonomous services; evolving wireless technologies, such as 5G cellular; reliable data delivery; and security, privacy, and trust [55–58]. It is conceivable that blockchain mechanisms can become one of several enabling technologies for the SIoT [59] and/or the Secure IoT [60] (aka IoTSec, e.g., [61].)

8 Conclusion MM is often handled by activating new physical channels, for example, a cellular-type cell handoff or a new satellite spot beam. Nonetheless, an IP-level MM is appropriate and efficient, especially when there is a large number of nodes and roaming is very frequent. MIPv6 provides a possible approach to MM with applications to MCS, although it may not be trivial to implement. These mobile technologies can be a catalyst for the widespread deployment of the SIoT. Acknowledgements The authors wish to thank Wen Hseih, Kazem Sohraby and Chonggang Wang for inputs provided.

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Humanizing IoT: Defining the Profile and the Reliability of a Thing in a Multi-IoT Scenario D. Ursino and L. Virgili

Abstract In the last years, things involved in IoT are becoming more and more complex and intelligent. For this reason, they are increasingly showing a behavior looking like the one of humans in social networks. Therefore, recently, several approaches combining the Internet of Thing (IoT) and social networking have been proposed. Thus, it is not out of place to talk about “humanization” of things, i.e., to assume that they show behaviors similar to the one of humans. In such a scenario, defining the profile of a thing and evaluating its reliability are extremely challenging issues. This paper first introduces the MIoT (Multiple IoT) paradigm, conceived to handle complex scenarios where things are organized in several IoTs cooperating with each other. Then, it defines the profile of a thing in a MIoT. Finally, it presents the concept of reliability of a thing in a MIoT. Keywords Internet of Things · Social networking · Multiple IoTs · MIoT · Profile of a thing · Reliability of a thing

1 Introduction The Internet of Things can be considered as one of the most challenging evolutions of the Internet, based on the concept of pervasive computing [1]. In the past, researchers who investigated this issue proposed several approaches implementing the IoT paradigm [2–4]. One of the most challenging families of these approaches leverages the ideas and results of the social networking paradigm [5–7]. It represents IoT as a social network and, based on this choice, exploits Social Network Analysisbased models to empower IoT. One of the most known approaches of this family is certainly the SIoT (Social Internet of Things) paradigm. It empowers things with D. Ursino (B) · L. Virgili Polytechnic University of Marche, Ancona, Italy e-mail: [email protected] L. Virgili e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_4

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social skills, making them more similar to humans [5, 6]. It assumes that five kinds of relationship may exist among things, namely: (i) parental, (ii) co-location, (iii) co-work, (iv) ownership, and (v) social ones. If a node is associated with each thing, an edge is associated with each relationship between things and, finally, all the nodes and the edges linked by the same relationship are considered as joined together, a SIoT can be modeled as a set of five pre-defined networks. Here, some nodes belong to only one network (we call them inner nodes), whereas other ones belong to more networks (we call them cross nodes). The ideas proposed in [5, 6] are challenging; they received, and are still receiving, a great attention in the literature. However, currently, and even more in the future, the number and the variety of the relationships that might connect things could be much higher than those characterizing the relationships considered in [5, 6]. Actually, the possible kinds of relationship between things could vary from one application to another, and it could happen that they are very different from the five ones introduced in [5, 6]. As a consequence, we think that a new paradigm, taking this fact into account, is in order. The new paradigm should consider that, currently, the number and the variety of available things are high. This makes it more appropriate to model this scenario as a set of IoTs interacting with each other, instead of as a unique huge IoT. In [8, 9], we proposed to pass from social networking to social internetworking and introduced the SIS (Social Internetworking System) paradigm, which is capable of modeling a set of social networks interacting with each other. The SIS paradigm extends the single social network one by taking into consideration that: (i) a user can join several social networks; (ii) these joins can frequently vary over time; (iii) the presence of users joining more social networks can allow the cooperation of users not registered to the same social network. In [10], we proved that the key concepts of SIS can also be applied to things (instead of to users) and to relationships between things and we proposed the MIoT (Multiple Internets of Things) paradigm. Roughly speaking, a MIoT is a set of related IoTs cooperating with each other and, therefore, a set of related networks of things. Actually, to provide a more precise definition of MIoT, it is necessary to introduce the concept of instance of a thing in an IoT. Specifically, an instance represents a virtual view of the corresponding thing in an IoT. As a consequence, a thing can have associated several instances, one for each IoT it participates to. The nodes associated with a thing in a MIoT represent its instances in the different IoTs of the MIoT. In the MIoT paradigm, the presence of more instances for one thing is extremely relevant because it allows the definition of cross relationships between the different IoTs of the MIoT. The MIoT paradigm is the reference one for this paper. To determine a relationship between the SIoT and the MIoT paradigms, we observe that, differently from the classical SIoT paradigm, in a MIoT the number of relationships is not defined a priori. All the nodes linked by a given kind of relationship, together with the corresponding edges, represent an IoT of the MIoT. Observe that, under this perspective, SIoT can be seen as a specific case of MIoT in which the number of the possible kinds of relationship is limited to 5 and these kinds are pre-defined.

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Another important trend characterizing the current Internet of Things scenario regards the existence of increasingly sophisticated and intelligent things. These are showing a progressively proactive behavior. This feature, along with the possibility to define a profile of a thing, which registers the kinds of content mostly managed and the types of task mostly performed by it in the past, makes the thing very similar to an intelligent agent. In this last research field, people investigated very much the possibility that an agent behaves like a human and acts on her behalf in several circumstances. This property leads an agent to take different several traits typical of a human or, in other word, to a humanization of an agent. Now, if a thing with the powered features mentioned above becomes an intelligent agents, it is not out of place talking about the humanization of a thing, and, analogously to what happens for people, it is possible to investigate several parameters of a thing in a network of things, such as the reliability, the scope, the socialization capability, etc. This paper aims at providing a contribution in this setting. First of all, it describes the MIoT paradigm, along with an architecture that uses it to organize things in Multiple IoTs. Then, as a first contribution, it introduces the concept of profile of a thing. As the profile of a human, the profile of a thing has two components. The former denotes the past behavior of the thing and can be used, for instance, to support content-based recommendations. The latter reflects its neighbors, i.e., the other things with which it most frequently comes into contact; it can be exploited, for instance, to support collaborative filtering recommendations [11]. As a second contribution, this paper provides an example of “a thing’s humanization” by introducing, and deeply investigating, the concept of reliability of a thing in an IoT and, then, in a MIoT. According to the Concise Oxford Dictionary [12], reliability is “the quality of being trustworthy or of performing consistently well”. In this paper, we use this term to introduce a collective measure of trustworthiness of a thing in a community of other things, based on the transactions these last performed with it. Clearly, there is a strict correlation between these two contributions. In fact, the reliability of a thing (but the same discourse is valid for humans) depends on its past behavior (which is stored in the first component of its profile) and on its relationships with the other things (which is stored in the second component of its profile) of the MIoT. As a consequence, the deep knowledge of the mechanisms leading to the construction of the profile of a thing can play a relevant role in understanding the mechanism underlying the reliability of a thing in a MIoT. This paper is organized as follows: in Sect. 2, we examine related literature. In Sect. 3, we present the MIoT paradigm and the Multi-IoTs architecture based on it. In Sect. 4, we introduce the concept of profile of a thing. In Sect. 5, we investigate the concept of reliability of a thing in a MIoT. In Sect. 6, we present several tests to evaluate the adequacy of the introduced concepts. Finally, in Sect. 7, we draw our conclusions and have a look to future developments of our research efforts.

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2 Related Literature The IoT paradigm was introduced several years ago [1, 13–15]. During this period, this term has been associated with a huge variety of concepts, technologies and solutions. Furthermore, recently, new technologies, such as Big Data [16] and social networking, have been applied to IoT and have contributed, and are still contributing, to modify the definition of this term. The evolution of these technologies certainly will influence what IoT will become in the future [17]. Currently, IoT research is focusing on the capability of connecting each thing to the Internet. This idea of IoT led to the Web of Things (WoT) paradigm [2, 18, 19] and to the application of social networking to the IoT domain [7]. It is possible to foresee that, in the future, these technologies will be combined with other ones, such as Information Centric Networks [3, 20–24] and Cloud [4, 25, 26]. In fact, the strengths of these last ones represent exactly those features necessary to address the weaknesses of the current IoT concept [27]. Some examples of this combination have been already proposed in the literature [28–31]. As for the application of the social networking paradigm to IoT, significant efforts have been performed in the past [5]. Some of the proposed solutions are based on a complex architecture capable of managing services [32–35]. In other cases (e.g., in [36–39]), the authors propose to use the relationships typically characterizing human social networks to share services provided by several things connected through several kinds of relationship. In [5, 6], the authors perform a relevant step forward in this direction. In fact, here, they introduce the SIoT paradigm. This is based on the idea of defining relationships among things without the need of the owner’s intervention. Things can autonomously navigate through the network to find resources and services of their interest provided by other things. Today, social networking is experiencing a continuous increase of the connection level of humans and things. This is paving the way to a “network of networks” scenario and, thus, to passing from social networking to social internetworking. One of the most interesting attempts in this direction is the Social Internetworking System (hereafter, SIS). In a SIS, several human networks are connected to form a network of human networks [8, 9]. A SIS S consists of n social networks {S1 , S2 , · · · , Sn }. It can be modeled by means of a graph G = N , A. Here, N is the set of the nodes of G. A node ni ∈ N represents a user account in one of the social networks of S . A is the set of the arcs of G. It consists of two subsets, namely Af and Am . Af is the set of the friendship arcs; an arc aj = ns , nt , wst  ∈ Af from ns to nt indicates that there is a relationship between ns and nt in such a way that ns can influence nt . wst represents the strength of the influence of ns on nt . Am is the set of me arcs; an arc aj = ns , nt  ∈ Am between ns and nt denotes that they represent two accounts of the same person in two different social networks. The strength of SIS resides in its capability of interconnecting users who join different social networks. In this intriguing scenario, concepts and tools of Social Network Analysis must be modified in such a way as to handle also interactions between users belonging to

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different networks. This paradigm was conceived to guarantee a tradeoff between the autonomy of each network and the increase of the overall power, efficiency and effectiveness obtained thanks to the interaction of the different networks of the SIS. To the best of our knowledge, no architecture similar to SIS has been proposed for networks of things yet. In [7], the authors specify that several issues regarding the SIoT paradigm are still open. In particular, in order to make things capable of establishing heterogeneous social relationships, specific investigations and new approaches are compulsory. Two of the most relevant ones regard the definition of inter-object relationships and the modeling of the new social graphs thus obtained. The former requires the definition of new representation formats for things, as well as the design of methods and technologies allowing an object to crawl the network for finding other (possibly heterogeneous) objects with which it can establish a new connection. The latter implies the analysis of the new social graphs thus obtained in such a way as to characterize them. After having discussed how the MIoT paradigm is related to the IoT one and, even more, to the SIoT paradigm, we now consider the past literature regarding the main contribution of our paper. In particular, in the past computer science research, a very high number of papers addressing trustworthiness and reliability have been proposed. Each of them presents a model that handles these concepts from a specific viewpoint. In many cases, the efficiency and the effectiveness of a proposed model depend on the context on which it is operating. As reported in [40], trustworthiness and reliability models proposed in the literature can be catalogued by taking some features into account, namely: (i) trust classes, (ii) categories of trust semantics, (iii) reputation network architectures, and (iv) reputation compute engine. Trust classes provide different ways to interpret trust semantics; this is necessary because the concept of trust can be seen through different perspectives. In an online environment, participants must have the possibility to correctly interpret ratings, reputation and trust measures. Their semantics can be expressed in terms of specificity or generality, subjectivity or objectivity. Another important issue to address regards the ways the participants to a system exchange ratings and scores. Clearly, the architecture of a system could be centralized or distributed. In the former case, there is a central authority that collects trust information and computes the corresponding reliabilities. In the latter case, no central authority or location exists. As for the reputation computation engine, several categories can be mentioned. Among them, we cite rating summation or average, fuzzy operators and “flow” models. These last compute trust and reputation scores through looped or long chains. Google’s Page Rank [41] belongs to this category. After having provided an overview of trustworthiness and reliability management in online service provision, we now examine how these concepts can be transposed to the IoT context. A well defined reliability system is extremely relevant in IoT for managing the services provided by objects. The concept of reliability in IoT takes into account factors like the goodness of a service as well as the trustworthiness and availability of an object. In this context, available approaches can be catalogued according to the type of architecture that the authors decided to develop. As for this

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issue, it is possible to recognize three different kinds of architecture, namely: (i) centralized [42, 43], (ii) semi-centralized [44] and (iii) distributed ones [45–47]. An interesting issue regarding reliability in an IoT context is addressed in [48]. Here, the authors investigate aspects concerning the reliable and scalable acquisition of big data from IoT domains. Their main focus is maintaining freshness of cache records at the proxy and, at the same time, a low delay and a low energy communications. There are some analogies, at least on the considered topic, between the approach of [48] and our own. However, at the same time, we can recognize several important differences between them. First of all, the approach of [48] operates on an IoT, whereas our own has been conceived for MIoTs. Furthermore, the approach of [48] focuses on a particular aspects of reliability, namely reliability on data acquisition. By contrast, our approach focuses on the reliability of the instances, the objects and the IoTs themselves. These are the reasons why it must adopt principles and algorithms completely different from the ones of [48]. Finally, [49] introduces two trustworthiness and reliability models for the SIoT environment. The former is called subjective trustworthiness. In this case each node computes the trustworthiness of its neighbors by considering its experience and the one of its neighbors. The computation considers the following factors: (i) direct opinion; (ii) indirect opinion; (iii) long-term opinion; (iv) relationship factor; (v) direct opinion in the credibility. The latter is called objective trustworthiness. It is defined in a Peer-to-Peer (P2P) scenario where special nodes, called Pre-Trusted Objects, handle the information of each node. The computation considers the following factors: (i) long-term opinion; (ii) short-term opinion; (iii) relationship factor in credibility; (iv) intelligence in the credibility. In both models, the weight of a node when it provides an opinion is strictly related to its credibility. Therefore, both of them give a heavy weight to recommendations made by “good” friends and a low weight to feedbacks provided by “bad” friends.

3 The MIoT Paradigm In this section, we provide an overview of the MIoT paradigm, described in detail in [10], because it is the reference paradigm that we have used for the definition of the profile and the reliability of a thing in a MIoT. A MIoT M consists of a set of m Internets of Things. Formally speaking: M = {I1 , I2 , · · · , Im } where Ik is an IoT. In Fig. 1, we provide a schematic representation of the corresponding architecture that we will describe in detail below. Let oj be an object of M . We assume that, if oj belongs to Ik , it has an instance ιjk , representing it in Ik . The instance ιjk consists of a virtual view (or, better, a virtual agent) representing oj in Ik . For example, it provides all the other instances of Ik ,

Fig. 1 Schematic representation of the proposed MIoT architecture

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and the users who interact with this IoT, with all the necessary information about oj . Information stored in ιjk is represented according to the format and the conventions adopted in Ik . In M , an object oj has associated a set MDj of metadata. Our model defines a rich set of metadata of an object. This choice is justified by the fact that metadata play a key role in favoring the interoperability of IoTs and of their things, which is the main goal of a MIoT, and, ultimately, the “humanization” of things, which is the main subject of this paper. MDj consists of three different subsets: MDj = MDjD , MDjT , MDjB  Here: • MDjD is the set of descriptive metadata. It denotes the type of oj . To represent and handle descriptive metadata, our approach adopts the taxonomy defined by the IPSO Alliance [50]. • MDjT represents the set of technical metadata. It must be compliant with the object type. In other words, there is a different set of technical metadata for each object type of the taxonomy. Also in this case, our approach adopts the technical metadata of the IPSO Alliance. • MDjB represents the set of behavioral metadata. It regards the behavior of oj and is defined as the union of the sets of the behavioral metadata of the instances of oj . Specifically, let ιj1 , ιj2 , . . . , ιjl , l ≤ m, be the instances of oj belonging to the IoTs of M . Then: l  MDjB = MDjBk k=1

MDjBk is the set of the behavioral metadata of the instance ιjk . In order to describe the structure of MDjBk , we must preliminarily introduce the set tranSetjqk of the metadata associated with the transactions from the instance ιjk of oj in Ik to the instance ιqk of oq in Ik . tranSetjqk is defined as: tranSetjqk = {Tjqk1 , Tjqk2 , · · · , Tjqkv } A transaction Tjqkt ∈ tranSetjqk is defined as a tuple: Tjqkt = reasonjqkt , sourcejqkt , startTmpjqkt , destjqkt , startjqkt , finishjqkt , successjqkt , contentjqkt  Here: • reasonjqkt represents the reason why the transaction occurred; it is determined among a set of predefined values.

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• sourcejqkt denotes the starting node of the path followed by Tjqkt . • startTmpjqkt denotes the starting timestamp of the first transaction of the path of which Tjqkt is a branch; this value is necessary to univocally identify this path because the pair (sourcejqkt , startTmpjqkt ) represents a unique key for it. • destjqkt represents the final node of the path followed by Tjqkt . • startjqkt denotes the starting timestamp of Tjqkt . • finishjqkt indicates the ending timestamp of Tjqkt . • successjqkt denotes whether Tjqkt was successful or not; it is set to true in the affirmative case, to false in the negative one, and to NULL if Tjqkt is still in progress. • contentjqkt indicates the content “exchanged” from ιjk to ιqk during Tjqkt ; it consists of a tuple: contentjqkt = formatjqkt , fileNamejqkt , sizejqkt , authorjqkt , creationTimejqkt , lastU pdTimejqkt , topicsjqkt  Here: – formatjqkt indicates the format of the content exchanged during Tjqkt ; the possible values are: “audio”, “video”, “image” and “text”; – fileNamejqkt denotes the name of the file used to transmit Tjqkt ; – sizejqkt indicates the size in bytes of the content; – authorjqkt represents the author of the content; – creationTimejqkt denotes the creation date of the content; – lastU pdTimejqkt represents the date of the last update of the content; – topicsjqkt indicates the set of the content topics; these are represented by a set of keywords and, for each keyword, by the number of occurrences in which it appears. Formally speaking: 1 1 2 2 w w , nkwjq ), (kwjq , nkwjq ), . . . , (kwjq , nkwjq )} topicsjqkt = {(kwjq kt kt kt kt kt kt

In other words, the set of the topics of Tjqkt consists of w pairs, each composed by a keyword and the corresponding number of its occurrences. We can now define the set of the behavioral metadata MDjBk of an instance ιjk of Ik . Specifically, let ι1k , ι2k , · · · , ιwk be all the instances belonging to Ik . Then: MDjBk =



tranSetjqk

q=1..w,q=j

In other words, the set of the behavioral metadata of an instance ιjk is given by the union of the sets of the metadata of the transactions from ιjk to all the other instances of Ik .1

1 Recall

that, in our model, transactions are directional.

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4 Definition of a Thing’s Profile In this section, we introduce the concepts of instance and object profiles. As pointed out in the Introduction, they represent the first main contribution of this paper. To present them, we must preliminarily introduce the following operators:  • : it takes a set {entitySet1 , entitySet2 , · · · , entitySett } of entity sets in input and carries out their union not eliminating the duplicates but reporting the number of their occurrences. Therefore, it returns a set of pairs {(entity1 , ne1 ), (entity2 , ne2 ), · · · , (entityw , new )} such that the pair  (entityr , ner ) indicates the rth entity and the number of its occurrences. When counts the number of occurrences, it also considers synonymies and homonymies possibly existing among the entities into evaluation. These properties can be computed (for terms, images, etc.) by applying any of the approaches proposed in the past literature for this purpose (see [51] for a survey on them). • avgFileSize: it takes a set of files in input and computes their average size. We are now able to define the profile Pjqk of the relationship existing between two instances ιjk and ιqk , such that a set tranSetjqk = {Trjqk1 , Trjqk2 , · · · , Trjqkv } of transactions were performed from ιjk to ιqk . Specifically: Pjqk = reasonSetjqk , sourceSetjqk , destSetjqk , audioSetjqk , videoSetjqk , imageSetjqk , textSetjqk avgSzAudiojqk , avgSzV ideojqk , avgSzImagejqk , avgSzTextjqk , successFractionjqk , topicSetjqk  Here: • • • • • • • • • • •

 reasonSetjqk =  t=1..v (reasonjqkt ); sourceSetjqk =  t=1..v (sourcejqkt ); destSetjqk =  t=1..v (destjqkt ); audioSetjqk =  t=1..v {fileNamejqkt |formatjqkt = “audio”}; videoSetjqk = t=1..v {fileNamejqkt |formatjqkt = “video”}; = t=1..v {fileNamejqkt |formatjqkt = “image”}; imageSetjqk  textSetjqk = t=1..v {fileNamejqkt |formatjqkt = “text”}; avgSzAudiojqk = AvgFileSizet=1..v {fileNamejqkt |formatjqkt = “audio”}; avgSzV ideojqk = AvgFileSizet=1..v {fileNamejqkt |formatjqkt = “video”}; avgSzImagejqk = AvgFileSizet=1..v {fileNamejqkt |formatjqkt = “image”}; avgSzTextjqk = AvgFileSizet=1..v {fileNamejqkt |formatjqkt = “text”}; |{Trjq |Trjq ∈tranSetjq ,successjq =true}|

kt kt k kt ; • successFraction v  jqk = • topicSetjqk = t=1..v (topicsjqkt ).  We can introduce the operator for compactly representing the set of operations described above to obtain the profile Pjqk of the relationship existing between two instances ιjk and ιqk starting from the corresponding transactions. In this case, all the previous tasks can be formalized by means of one operation as follows:

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Pjqk =



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Tjqkt

t=1..v

Now, let ιjk be the instance of oj in Ik . Let {ι1k , ι2k , · · · , ιzk } be the set of the instances of Ik with which ιjk performed at least one transaction in the past. In this case, the profile Pjk of ιjk can be defined as: Pjk =



Pjqk

q=1..z

Finally, let oj be an object and let {I1 , I2 , · · · , Il } be the set of IoTs which it participates to. Let {ιj1 , ιj2 , · · · , ιjl } be the instances of oj in the IoTs of the MIoT. The profile Pj of oj can be defined as: Pj =



Pjk

k=1..l

In the previous profile definitions it could be possible to introduce some mech anisms taking content obsolescence into account. For this purpose, the operator could be modified in such a way that it does not consider the number of occurrences but the corresponding weights that may decrease with the passing of time. Furthermore, the decrease rate could be tuned taking the sensor typology into consideration. In this case, possibly sophisticated and very accurate algorithms for computing the decrease rate against the passing of time, and strictly depending on the objects of the MIoT, could be defined. Everything we have seen so far regards the profile of an instance from a “contentbased” perspective (i.e., taking its past behavior into account). Beside it, another viewpoint can be considered, i.e., the “collaborative filtering” one (i.e., based on the similarity of the behaviors of the instance neighbors). To adopt this perspective, it is first necessary to represent a MIoT as a graph. In particular, a graph G k = Nk , Ak  can be associated with an IoT Ik . In this case: • Nk is the set of the nodes of G k ; there is a node njk for each instance ιjk ∈ Ik ; • Ak is the set of the arcs of G k ; there is an arc ajqk = (njk , nqk ) if there exists a link from njk to nqk . Based on this notation, a MIoT M can be represented as: M = N , A Here:  • N= m k=1 Nk ;  • A = AI ∪ AC , where AI = m k=1 Ak and AC = {(njk , njq )|njk ∈ Nk , njq ∈ Nq , k = q}.

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AI is the set of the inner arcs (hereafter, i-arcs) of M ; they link instances (of different objects) belonging to the same IoT. AC is the set of cross arcs (hereafter, c-arcs) of M ; they link instances of the same object belonging to different IoTs. Observe that the adoption of the Social Internetworking paradigm (instead of the Social Networking one) as the framework underlying the MIoT paradigm allows us to represent a MIoT not as a “big unique social network” but as a set of interconnected social networks cooperating with each other. This is relevant because, if we adopted the classical Social Network paradigm, the multiplicity and the possible heterogeneity of involved social networks could be lost. Indeed, recall that, in the MIoT paradigm, a node does not represent an object, like in the classical IoT paradigms, but an object instance. As a matter of fact, an object has different instances in different IoTs and, consequently, it could be represented by more nodes in a MIoT. Furthermore, it could have different profiles and behaviors in different IoTs, and this fact could not be handled by a “big unique social network” model. Finally, c-arcs have a semantic totally different from the one of i-arcs, which cannot be reduced to this last ones. Indeed, i-arcs link instances of different objects in the same IoTs, whereas c-arcs link instances of the same objects in different IoTs. From these considerations, it emerges that any model for IoT should explicitly consider c-nodes and c-arcs and, therefore, all the issues investigated by researchers on IoT in the past should be re-considered for MIoTs, and completely different results could be found as well as new research challenges could arise. After having modeled M as a graph, we can define the structural neighborhood sNbh(ιjk ) of ιjk as: sNbh(ιjk) = sNbhout (ιjk) ∪ sNbhin (ιjk) where: sNbhout (ιjk) = {ιqk |(njk , nqk) ∈ AI } and sNbhin (ιjk) = {ιqk |(nqk , njk) ∈ AI } In other words, sNbh(ιjk ) comprises those instances directly connected to ιjk through an incoming or an outgoing arc. Furthermore, we can define the behavioral neighborhood bNbh(ιjk ) of ιjk as: bNbh(ιjk ) = bNbhout (ιjk ) ∪ bNbhin (ιjk ) where: bNbhout (ιjk ) = {ιqk |ιqk ∈ sNbhout (ιjk ), |tranSetjqk | > 0}

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and bNbhin (ιjk ) = {ιqk |ιqk ∈ sNbhin (ιjk ), |tranSetqjk | > 0} In other words, bNbh(ιjk ) comprises those instances directly connected to ιjk , from the structural viewpoint, which shared at least one transaction with ιjk .

5 Reliability In this section, we introduce our concept of reliability in a MIoT, which represents the second main contribution of this paper. The principle underlying the reliability of an instance ιjk in an IoT Ik is that it is directly proportional to the number of instances from which it received transactions in the past, to the reliability of these instances, to the fraction of successful transactions and their oldness. The principle underlying the reliability of an object oj in a MIoT is that it is directly proportional to the fraction of successful transactions that the instances of oj performed in the MIoT and to the reliability of the corresponding objects. Finally, the general idea of the reliability of an IoT Ik in a MIoT is that it is obtained by averaging the reliabilities in the MIoT of the objects having an instance in Ik . In the following, we start by presenting the reliability of an instance in an IoT; then, we proceed with the reliability of an object in the MIoT; finally, we conclude with the definition of the reliability of an IoT in the MIoT.

5.1 Reliability of an Instance in an IoT Let Ik be an IoT and let ιjk be an instance of Ik . The reliability Rjk of ιjk depends on the following factors: • • • •

the number of instances from which ιjk received transactions in the past; the reliability of these instances in Ik ; the fraction of successful transactions that they performed with ιjk ; the oldness of these transactions.

To solve this problem, we define a variant of the PageRank formula [41]. For this purpose, we must preliminarily introduce the following parameters: • • • •

FirstTS(ιqk ) denotes the start timestamp of the first transaction received by ιqk ; CurrentTS is the current timestamp; Rkmax is the maximum reliability of an instance of Ik ; OKTranSetjqk is the fraction of successful transactions in tranSetjqk ; it is set to 0 if no transactions have been performed from ιjk to ιqk yet.

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We are now able to define the formula for the computation of Rjk . In particular:  Rjk = γ + (1 − γ ) ·

ιqk ∈bNbhin (ιjk )

 OKTranSetqjk · Rqk · 1 −

FirstTS(ιqk ) CurrentTS



|bNbhin (ιjk )|

In this formula, γ is the damping factor generally adopted in the PageRank formula. It determines the minimum absolute reliability assigned to an instance of Ik . From a more abstract viewpoint, it determines the fraction of the absolute reliability that ιjk transmits to ιqk . Rjk belongs to the real interval [γ , +∞). In order to obtain a reliability value ranging in the real interval [0, 1] and, at the same time, to normalize the reliabilities of the instances of the different IoTs of the MIoT, we define the relative reliability

jk of ιjk in Ik as follows: R

jk = Rjk R Rkmax

5.2 Reliability of an Object in the MIoT Let oj be an object of a MIoT. Let Mj = {I1 , · · · , Il } be the subset of the IoTs of M containing at least one instance of oj . Let OKTranSetjq be the fraction of the successful transactions from any instance of oj to any instance of oq in the IoTs of the MIoT; it is set to 0 if no transactions have been performed from any instance of oj to any instance of oq . The reliability of oj depends on both the fraction of successful transactions that its instances performed in each IoT of Mj and the reliability of the objects, which the instances performing these transactions refer to. Once again, to formalize this problem, it is possible to define a variant of the PageRank formula, defined as follows: 

 Rj = δ + (1 − δ) ·

k=1..l

ιqk ∈bNbhin (ιjk )

OKTranSetqj · Rq

l · |bNbhin (ιjk )|

In this formula, δ is the damping factor. Its semantics is analogous to the one of γ seen in Sect. 5.1. Also in this case, it is advisable to proceed with the normalization of Rj in such a way that its values belong to the real interval [0, 1]. This task is performed in a way analogous to the one adopted for the instance reliability in Sect. 5.1. In particular, we have that:

j = Rj R R max

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5.3 Reliability of an IoT in the MIoT The reliability of an IoT Ik in a MIoT is given by averaging the reliabilities in the MIoT of the objects having an instance in Ik . If we introduce the set Objk of the objects having an instance in Ik , the reliability

k of Ik in the MIoT is defined as: R

k = R

 j∈Obj

jk R

|Objk |

6 Experimental Campaign In this section, we present several experiments that we performed to evaluate the suitability of our definitions. Specifically, first we describe our testbed and, then, we illustrate our tests, along with the underlying motivations and the obtained results.

6.1 Adopted Testbed In order to perform our experiments, we needed several MIoTs with different sizes, ranging from hundreds to thousands of nodes. Since, currently, real MIoTs having the dimension and the variety managed by our model do not exist yet, we constructed a MIoT simulator. This tool starts from real data and returns simulated MIoTs with certain characteristics specified by the user. The MIoTs created by our simulator follow the paradigm described in Sect. 3. Our MIoT simulator provides the user with a suitable interface allowing her to “personalize” the MIoT to construct by specifying the desired values for several parameters, such as the number of nodes, the maximum number of instances of an object, and so forth. To make “concrete” and “plausible” the created MIoT, our simulator leverages a real dataset concerning the taxi routes in the city of Porto from July 1st 2013 to June 30th 2014. It can be found at the address http://www.geolink.pt/ecmlpkdd2015challenge/dataset.html. Each route contains several Points of Interests corresponding to the GPS coordinates of the vehicle. We partitioned the city of Porto in six areas and associated a real IoT with each area. Our simulator associates an object with a given route recorded in the dataset and an object instance with each partition of a route belonging to an area. It associates a MIoT node with each instance and a c-arc with each pair of instances belonging to the same route. Furthermore, it creates an i-arc between two nodes of the same IoT if the length of the time interval between the corresponding routes is less than a certain threshold tht . The weight of the i-arc indicates the length of this time interval.

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The user can specify the value of tht through the constructor’s interface. Clearly, the higher tht the more connected the constructed MIoT. As for instance profiles, since there are no thing profiles available (indeed, the concept of thing profile is one of the main novelties introduced in this paper), we had to simulate them. However, we aimed at making them as real as possible. For this purpose, we performed a sentiment analysis task for each of the six areas in which we partitioned the city of Porto and for each day which the dataset refers to. To carry out this task, we leveraged IBM Watson on the social media and blogs it uses as default. Having this data at disposal, our simulator assigns to each instance the most common topics (along with the corresponding occurrences) discussed in that area in the day on which the corresponding route took place. The construted MIoTs are returned in a format that can be directly processed by the cypher-shell of Neo4J (see below). The interested reader can find the MIoTs adopted in the experiments described in this section at the address http://daisy.dii.univpm.it/miot/datasets/reliability. We point out that the data about the city of Porto, which we used in our experiment, are to be intended simply as a testbed. We used them because they were sufficiently complex to allow us to construct a significant MIoT. Actually, our approach is general and can operate on any kind of MIoT. We carried out all the tests presented in this section on a server equipped with an Intel I7 Quad Core 7700 HQ processor and 16 GB of RAM with Ubuntu 16.04 operating system. To implement our approaches we adopted: • Python, as programming language; • Neo4J (Version 3.4.5), as underlying DBMS.

6.2 Computation Time Our first test was devoted to evaluate the time necessary to compute reliability by applying our definitions. Indeed, since these definitions apply to large MIoTs, whose IoTs could consist of even hundreds of nodes, it is necessary to verify if, in these situations, the time necessary to return a result is still acceptable. In this experiment, we considered several MIoTs with different numbers of nodes. Given a MIoT M , obtained by means of our MIoT constructor, we considered all its IoTs I1 , · · · , I6 (see Sect. 6.1). First of all, for each instance of an object, we computed its reliability in the corresponding IoTs. In this computation, at the first iteration, we set to 1 the initial reliability of each instance of the MIoT. In other words, we decided to assume an “optimistic” policy that we considered the most reasonable one. Under this policy, each node is totally reliable until proven otherwise. We performed a total number of 600,000 transactions in the MIoT and we recomputed all reliability values every 100 transactions (in the following, we call epoch an interval of 100 transactions). We measured the time that our approach needed for computing the reliability of each instance in its IoT against the number of epochs

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Fig. 2 Average time necessary for the computation of the reliability of an instance in its IoT against the number of epochs for MIoTs with a different number of nodes

for MIoTs having a different number of nodes (ranging from 10 to 1000). Finally, we averaged the obtained computation times for all the instances of the MIoT. The obtained results are reported in Fig. 2. From the analysis of this figure, we can observe that the time necessary to compute the values of instance reliability is always low when the number of epochs is lower than or equal to 2000 and the number of nodes is lower than or equal to 100. When the number of nodes is higher, the computation time increases, even if it is still acceptable for a number of epochs lower than or equal to 2000. When the number of epochs is higher than 2000, the computation time starts to rapidly increase. It tends to become unacceptable when the number of nodes is higher than 500 and the number of epochs is higher than 2000. However, consider that a MIoT consisting of more than 500 things is not generally encountered in real situations. In any case, it would tend to have a high computation time only after 2000 epochs, i.e., after 200,000 transactions. In this case, a possible solution to mitigate the problem could consist in associating a higher number of transactions with each epoch (currently this number is equal to 100). In order to verify the possible dependence of the obtained results from the operating systems and the language adopted to implement our approach, we repeated this experiment with three further configurations, namely: (i) Windows 10 as operating system and Python as programming language; (ii) Ubuntu 16.04 as operating system and Java as programming language; (iii) Windows 10 as operating system and Java as programming language. For all these configurations, the obtained results are very similar to the ones of Fig. 2. The maximum increase in the computation time that we

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obtained was 0.98%; the maximum decrease was 0.42%; the average increase was 0.09%, whereas the average decrease was 0.04%. As illustrated in Sect. 5.2, the definition of the reliability of an object in a MIoT is structurally similar to the definition of the reliability of an instance in an IoT. As a consequence, all the considerations about the computation time that we have illustrated above can be easily extended to this case. As shown in Sect. 5.3, the reliability of an IoT in the MIoT is obtained by averaging the reliabilities of the objects having an instance in it. Generally speaking, the time necessary to perform the computation of the average of a set of object reliabilities is negligible w.r.t. the time necessary to compute these last ones. As a consequence, all the considerations about the computation time that we made for the reliability of an object in the MIoT can be extended to the reliability of an IoT in the MIoT.

6.3 Values and Distributions In order to analyze the variation of the reliability of an instance in an IoT against the number of epochs we applied the way of proceeding described in Sect. 6.2. The corresponding results are reported in Fig. 3. From the analysis of this figure, we can observe that, as a general trend, the reliability value is unstable for a time period corresponding to approximatively 1000

Fig. 3 Average values of the reliability of an instance in its IoT against the number of epochs for MIoTs with a different number of nodes

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Fig. 4 Distribution of the reliability of an instance in its IoT after 1000, 2000 and 3000 epochs for the MIoT with 100 instances

epochs. After that period, the reliability value becomes stable and generally high (in particular, for the MIoTs into examination, it is comprised between 0.82 and 0.92). Observe that the reliability value is higher for smaller networks. This reflects a general trend also observed in social networks of humans and, more in general, for communities of people. In fact, in a small community, the corresponding members tend to trust each other more. Actually, at a deeper investigation, the real discriminant factor is the number of transactions performed by each instance. Given a certain number of epochs, e.g. 2000 (corresponding to an overall number of 200,000 transactions), in a small network, the number of transactions involving a given entity is higher than the one involving the same entity when it belongs to a greater community. Generally, with the increase of the number of transactions performed each other, two entities (e.g., two persons in a community or to things in an IoT) tend to increase their trustworthiness each other. This, ultimately, reflects in an increase of the reliability of both of them. We also computed the distributions of the reliability values after 1000, 2000 and 3000 epochs. We performed this computation for the MIoT with 100 instances adopted in the previous experiments. The obtained results are reported in Fig. 4. This figure represents a further confirmation of what we observed in Fig. 3. Indeed, we can note that the shape of the distribution does not significantly change over time but, as the number of epochs increases, the distribution values move to the right. This phenomenon is much more evident when passing from 1000 to 2000 epochs than when passing from 2000 to 3000 ones. Finally, the standard deviations of the instance reliability values after 1000, 2000 and 3000 epochs are 0.032, 0.031 and 0.033, respectively. The extremely low and constant values of the standard deviations evidence that the obtained results are trustworthy and stable over time. A similar procedure can be applied to object reliability, whose values against the number of epochs are reported in Fig. 5 and whose value distributions are reported in Fig. 6. From the analysis of Fig. 5, we can observe that the object reliability values are generally smaller than the corresponding ones of instance reliability, even if they are still acceptable. This is explained by the fact that object reliabilities refer to the whole MIoT and not to a single IoT, i.e., to a larger and more variegate scenario

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Fig. 5 Average values of the reliability of an object in the MIoT against the number of epochs for MIoTs with a different number of nodes

Fig. 6 Distribution of the reliability of an object in the MIoT with 100 instances after 1000, 2000 and 3000 epochs

than the one characterizing the evaluation of instance reliabilities. In this context, it is clearly more difficult for an object to acquire and maintain trustworthiness. The distributions of Fig. 6 confirm these observations. Indeed, in this case, we can observe that the distribution shape is roughly the same after 1000, 2000 and 3000 epochs, but, differently from instance reliability values distributions, it moves to the right only very slightly when the number of epochs increases. In other words, in this scenario, the object reliability values show only a very small increase over time. The reasons are the same as the ones that we mentioned to explain Fig. 5. Finally, the value of the standard deviation after 1000, 2000 and 3000 epochs are 0.157, 0.166 and 0,162, respectively. These values are higher than the ones characterizing the standard deviation of instance reliability, even if they are still acceptable and constant over time. This evidences that the object reliability scenario is certainly more difficult to handle than the instance reliability one, even if it can be still maintained under control.

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Finally, as for the reliability of an IoT in the MIoT, it is obtained by averaging the reliability values of the objects having an instance in it. As a consequence, the corresponding values and distributions are very similar to the ones we have illustrated for the objects in the MIoT. Therefore, due to space limitations, we do not report them here.

6.4 Resilience This experiment was performed for evaluating the robustness of our reliability definition against possible anomalies in the behavior of one or more things. In particular, we considered two possible extreme anomalies. The former assumed that, for a fraction X % of the instances, the reliability is wrongly set to 1 independently of the real result of the transactions performed by them, and all the transactions regarding these instances are reported as successful independently of their real results (we call them “positive anomalies” in the following). The latter assumed an opposite behavior and, therefore, that for a fraction Y % of the instances, the corresponding reliability is wrongly set to 0 and all the transactions concerning these instances are reported as failed independently of their real results (we call them “negative anomalies” in the following). With regard to this experiment, we point out that, owing to the reliability formulas shown in Sects. 5.1 and 5.2, a wrong value of the reliability of an instance or of an object also affects the reliability of the other instances or objects. In other words, in principle, an anomaly could propagate (and even increase) its negative effects throughout the MIoT; these effects could become unmanageable, especially if the approach to evaluate has a null or a very low resilience to anomalies. We performed this experiment on the MIoT consisting of 100 instances that we had constructed to carry out the tests described above. First we investigated positive anomalies. In particular, we computed the average instance reliability values in absence of anomalies and in presence of an increasing fraction of them. The corresponding results are reported in Fig. 7. This figure evidences that, when the number of epochs is less than 1000, our instance reliability definition is very resilient to positive anomalies. In particular, even 30% of positive anomalies does not cause significant variations of the resilience values (specifically, the maximum variation is 0.04). This fact has both a positive and a negative aspect. As a matter of facts, on the one side, our reliability definition shows a high robustness; however, on the other side, it could become excessive in such a way as to make it incapable of recognizing positive anomalies. Actually, this last risk does not happen because, after 1000 epochs, the variation of the reliability values becomes increasingly marked also for a small fraction of positive anomalies. Therefore, if we consider Fig. 7 as a whole, we can say that our reliability definition is robust but, at the same time, after a sufficient number of epochs, capable of recognizing positive anomalies. After this, we investigated negative anomalies. Also in this case, we computed the average instance reliability values in absence of anomalies and in presence of

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Fig. 7 Average values of the reliability of an instance against the increase of positive anomalies for the MIoT with 100 instances

Fig. 8 Average values of the reliability of an instance against the increase of negative anomalies for the MIoT with 100 instances

an increasing fraction of them. The obtained results are reported in Fig. 8. From the analysis of this figure, we can observe significant decreases of the reliability values already for a small number of epochs and for a small fraction of negative anomalies. This denotes that our reliability definition is much more sensible to negative

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anomalies than to positive ones. We also observe that, differently from the case of positive anomalies, the resilience values and variations do not significantly change over time. A final interesting result is that, when passing from 20 to 30% of negative anomalies, the reliability values show a very significant decrease so that they start to become unacceptable. Such low values are never reached in presence of positive anomalies, thus denoting, again, that our reliability definition is more sensible to negative anomalies than to positive ones. An analogous reasoning can be drawn for the resilience of an object or of an IoT in the MIoT.

7 Conclusion In this paper, we have seen that things are becoming increasingly sophisticated and intelligent showing a behavior that looks like the one of users in social networks. Therefore, it is not only possible, but also extremely valuable, to combine Internet of Things and social networking. Furthermore, it is not out of place to talk about thing “humanization”, which implies to assume that they have a profile like humans and that this profile can be exploited in many situations where human profiles are generally adopted. After these premises, we have illustrated the Multiple IoTs (MIoT) paradigm and, then, we have presented the two main contributions of this paper, namely the definition of a thing’s profile and the reliability of a thing. We have also proposed several experiments confirming the adequacy of our idea. This paper must not be considered as an ending point; on the contrary, it is a starting point for future research efforts. Indeed, the definition of a thing’s profile and the usage of paradigms, like MIoT, coupling IoT and social internetworking, could pave the way to the extension, to the IoT context, of many research themes already analyzed for social networks. For instance, it could be possible: (i) to model the concepts of “scope” of a thing in an IoT and, then, in a MIoT; (ii) to develop “team building” approaches aiming at constructing teams of things to perform a certain activity; (iii) to investigate new forms of centrality of a thing in a MIoT based on both its position and its profile. Actually, these are just three of the many possible future developments of our research efforts in such a rapidly evolving and very promising scenario. Acknowledgements This work was partially funded by the Department of Information Engineering at the Polytechnic University of Marche under the project “A network-based approach to uniformly extract knowledge and support decision making in heterogeneous application contexts” (RSAB 2018).

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Smart Cities Initiatives to Examine and Explore Urban Social Challenges Milad Pirayegar Emrouzeh, Gregory Fleet and Robert Moir

Abstract Smart cities initiatives are closely tied up with social aspects of urban and rural communities. The potential of the smart city concept in addressing social challenges has not been clearly explored in previous studies and projects. Poverty is one of the most critical issues in many small- and medium-sized Canadian cities. This chapter introduces a practical smart solution to help address the generational poverty challenge in the City of Saint John, Canada. The outcome suggests that decision makers and key stakeholders can employ real-time data to better understand and serve the needs and demands of vulnerable individuals and marginalized population in poor communities by using a foodbank platform. Future studies can focus on developing a social network for key stakeholders, target groups, and decision makers in order to be in a consistent and real-time connection with each other. Keywords Smart city · Poverty · Food bank platform · Saint john

1 Introduction Canada is recognized for its high quality of life and outstanding standards of living, but “statistics show that nearly 4.9 million people are living below the poverty line and 1.2 million of these are children under the age of eighteen” [1]. While national and provincial poverty-reduction projects have had a considerable effect on decreasing or controlling the poverty rate across the country, they have not significantly addressed the causes of poverty. It remains unclear what the roots and causes of poverty are in the marginalized communities. One of the most significant challenges in the City of Saint John is poverty. Several public and private organizations like The Municipality of Saint John, Government of M. P. Emrouzeh (B) Urban and Community Studies Institute, University of New Brunswick, Saint John, Canada e-mail: [email protected] G. Fleet · R. Moir Faculty of Business, University of New Brunswick, Saint John, Canada © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_5

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New Brunswick, Human Development Council, Living Saint John, and Enterprise Saint John have been involved in this challenge. They have invested in poverty reduction projects for many years, but the outcomes were not completely successful, and solutions have not been realized. Many previous studies and reports about the generational poverty challenge [2] in the city have relied on one single economic factor: income. Therefore, the recommendations are based on this factor. Yet, poverty is a multi-dimensional challenge, which encompasses social, educational, cultural, governmental, and economic aspects. More recently, national and provincial reports including “Tackling Poverty Together” and “Overcoming Poverty Together” are empirical studies based on the multi-dimensional nature of poverty. The smart city, as a concept, which covers a wide range of environmental to economic aspects of a community, aims to use modern technologies and the Internet of Things (IOT) as the facilitators to address urban social phenomena. The question can be asked how the smart city concept might be employed to solve a challenge in cities through using the IOT. “It is anticipated that smart city solutions, with the help of instrumentation and interconnection of mobile devices, sensors and actuators allowing real-world urban data to be collected and analyzed, will improve the ability to forecast and manage urban flows and push the collective intelligence of cities forward” [3]. The main objective of this study, therefore, is making a connection between the IOT initiatives, as the key enablers of smart cities ideas, to address a critical urban social challenge: the generational poverty issue in the City of Saint John. Through the use of smart city indicators, we believe there will opportunities for city authorities and stakeholders to overcome social issues and enhance the quality of life, to improve economic conditions, and to provide better services. This chapter aims to employ smart cities initiatives to recommend practical solutions to address the generational poverty challenge in the City of Saint John. In the next section we will look more closely at a number of poverty definitions as well as some specific studies, especially those conducted in our local region. We will then turn to topic of smart cities, surveying the literature to help define its criteria and indicators. Finally, we explore our practical, smart solution: an idea for bringing data analytics to the generational poverty challenge in the city, and the use of food distribution centers (food banks) as data collection centers.

2 Poverty There is no agreement amongst researchers on the definition of poverty. The definition varies in different areas of studies. “The standard distinction in the literature is between absolute and relative definitions of poverty. The former approach focuses on the lack of basic necessities while the latter emphasizes inadequacy compared to average living standards” [4]. Besides this classification, the Government of Canada provides a definition of poverty: “Living in low income and lacking sufficient finan-

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cial resources to afford adequate food, shelter, clothing, transportation and other necessities of economic and social well-being” [5]. The main purpose here is to explain the meaning of poverty related to the goals of this study. “Poverty has probably always been understood as a multidimensional problem, yet traditionally it has been measured with one dimension: income” [6]. Although “Income is undoubtedly a good proxy of the living standard of an individual or a family” [7], it cannot address vulnerable individuals’ challenges and issues. There is no agreement upon an official poverty line as a measurement tool in Canada, and thus, a challenge for researchers and authorities is how to measure poverty. “The Statistics Canada Low Income Cut-Off (LICO) has most often been used to measure poverty. The LICO is essentially an income threshold below which a family will likely devote a larger share of its income on food, shelter and clothing than would the average family” [8]. Besides reviewing the definition of poverty, it is crucial to recognize the target groups of a study who have been categorized as poor individuals, as recommendations may vary as applied to different groups. Women and children are two major groups, which have been the main focus of many poverty projects. “Despite many international agreements affirming their human rights, women are still much more likely than men to be poor and illiterate” [9]. “Investment in children is a widely shared priority, and this has to some degree been reflected in the construction of indicators of poverty and social exclusion” [10]. The Millennium Development Goals (MDG) is a reliable source examining several studies suggest seven indicators to study the human poverty in a society, with a clear focus on women and children” [11]: • • • • • • •

Proportion of population living below $1 (PPP) per day; Prevalence of underweight children under-5 years of age (%); Proportion of population below minimum level of dietary energy consumption; Infant mortality rate; Under-5 mortality rate per 1,000 live births; Maternal deaths per 100,000 live births; and Number of tuberculosis cases per 100,000 population.

Another cluster of indicators is provided by Parekh et al. [12]. The central context of their study is family income. They reviewed roots of poverty through five categories with their indicators: • Low Income: Low income and inequality; and Child poverty • The Recession: Unemployment and worklessness; and Debt • Child and young adult well-being: Economic circumstances; Education; Health; and Exclusion • Adult well-being: Economic circumstances; Health; and Crime • Communities and services: Neighborhoods; and Access to services. A third standard, the Multidimensional Poverty Index (MPI), is a significant indicator to measure the rate of the poverty. Its main focus is on three aspects of a society, which are health, education, and the standard of living. To achieve a better result,

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the provided solutions to solve the poverty challenges should be evaluated regarding these three dimensions and target groups, which can be women, children, or any other categories. The health and education dimensions consist of two indicators each, but the standard of living is measured by six indicators: asset, cooking fuel, electricity, drinking water, floor, and sanitation. Despite some work worldwide to provide a richer measurement standard for poverty, research in Canada is mostly limited to income as a key factor. Therefore, the poverty rate in cities is also formed by income. Low-Income Measures (LIMs) are well-known indicators, which are used to measure the level of the poorness in Canada. “LIMs are a fixed percentage (50%) of adjusted median family income where adjusted indicates a consideration of family needs. The family size adjustment used in calculating the LIMs reflects the precept that family needs increase with family size. A census family is considered to be low-income when their income is below the LIM for their family type and size” [13]. Poverty-reduction projects at the national and provincial level have concentrated on income to recognize poor rural and urban communities. Some of them have provided practical recommendations to overcome this challenge, which encompass multi-dimensional solutions. In the following section, five reports with multidisciplinary perspectives will be reviewed. The goal of investigating these projects is threefold: (a) recognizing the economic condition of the City of Saint John among Canadian communities; (b) identifying roots of poverty in the provincial level in order to explore them in the city; and (c) examining previous (successful and failed) programs. The following five examples illustrate the main purposes of these projects, their outcome and correlations to the economic condition of the City of Saint John.

2.1 Tackling Poverty Together [14] The Tackling Poverty Together is a national project which aims to hear directly from Canadian suffering from poverty. The project includes six case studies across Canada: Saint John, Trois-Rivières, Regent Park (Toronto), Winnipeg, Yellowknife, and Tisdale. The focus of the project is on six federal government programs, including Canada Child Benefit, Canada Learning Bond, Guaranteed Income Supplement, Canada Pension Plan Disability Benefit, Working Income Tax Benefit, and Homelessness Partnering Strategy. According to the project, the most vulnerable target groups are: single parents, people with mental and health and addiction issues, people with disabilities, seniors, indigenous people, youth, recent immigrants, racialized people, low-wage workers, and unattached individuals aged 45–64 years. Besides the six programs, there are other programs at national and local levels that also make a big difference in poor families’ conditions, including healthy food, affordable housing, education, employment, community centres, indigenous services and programs, life skills, parenting and early childhood, information and referrals, partnerships and networks.

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The Tackling Poverty Together project notes some substantial points about Saint John: – The high concentrations of poverty in particular neighborhoods. For example, Crescent Valley has a 77% child poverty rate. – The feedback of target groups in response to the success1 of Canada Child Benefit, Canada Learning Bond, Guaranteed Income Supplement, Canada Pension Plan Disability Benefit, and Working income tax benefit programs2 in Saint John are respectively 62%, 46%, 77%, 51%, and 31%.

2.2 The New Brunswick Economic and Social Inclusion Plan [15] In 2009, New Brunswick started to adopt a poverty-reduction plan, becoming the sixth province across Canada to do so. New Brunswickers, including non-profits, business owners, government, and people living in poverty were invited to collaborate and share their ideas in order to develop a comprehensive plan. The main purpose of the plan is to develop strategic initiatives and plan to decrease poverty and assist New Brunswickers to become more self-sufficient. According to the 2009 report, 22 action items are identified to overcome poverty in the province. These action plans are categorized in four main groups: (a) Opportunities for Being (meeting basic needs); (b) Opportunities for Becoming (life-long learning and skills acquisition); (c) Opportunities for Belonging (community participation); and (d) Delivery and Accountability. The focal points of action plans indicate the roots and causes of poverty in the province, which convey causes of poverty in the city. Besides the 22 action plans, another significant point of this report is related to conducted interviews with target groups: A Choir of Voices in 2009. Participants of the public engagement events have noted some of the most significant causes of poverty in the province.

2.3 A Choir of Voices [16] A Choir of Voices is a report of the public engagement initiative in the poverty reduction process, which identified some critical issues and also recommended some solutions to overcome poverty. Based on this report, the most substantial causes of poverty in the province are education, the cost of post-secondary education, job opportunities, job skills, income, the social assistance system, the generational nature of poverty, addiction and mental health issues, the cost of living, affordable housing, child-care 1 The success of the programs has been measured based on target groups’ responses to this question:

Do you find these programs helpful? is not any report regarding the Homelessness Partnering Strategy.

2 There

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spaces, and benefits in many jobs. The whole data gathering process included three phases: public dialogues, roundtable, and final forum. Phase 1 aimed to engage participants to talk about poverty, its definition, and its causes in the province. The outcome of this phase is most closely related to the purposes of this paper. The responses to a particular question cover the main point of the paper: What Causes Poverty? Participants’ feedback to this question includes 132 different causes of poverty in the province. These answers shape the structure of the questionnaire of this paper.

2.4 The New Brunswick Economic and Social Inclusion Plan [17] This plan is the second five-year action plan after the 2009–2013 report. It is based on a series of public engagement sessions, which provided an opportunity for New Brunswickers to discuss economic and social inclusion in the province. The main concept of developing the plan is not a “how to” plan but a “what can be done” plan to decrease poverty rate and enhance the quality of life. The outcome of the plan emphasizes community capacity-building and includes 28 priority actions, divided into four pillars: – “Community Empowerment includes actions addressing community development, communication and networking and volunteerism. – Learning includes actions addressing child and youth education and adult education, training, and preparation for work. – Economic Inclusion includes actions addressing participation in the labour market and business activity. – Social Inclusion includes actions addressing food security and healthy food availability, housing, and transportation” [17]. These action plans and the four sections represent the main causes of poverty in the province, which, most likely, can be extended to the City of Saint John.

2.5 The Face of Child Poverty in New Brunswick [18] The Human Development Council releases a poverty-driven report annually in partnership with Campaign 2000. This report contains some data and information about poverty rate, child poverty rate, single parents, families with children in poverty, and other indicators related to the poverty condition in the province. The 2017 report includes some remarkable points about New Brunswick, and eight cities in the province (Table 1). Besides the information presented in Table 1, some additional points are worth noting:

Smart Cities Initiatives to Examine … Table 1 Poverty data in Canada and New Brunswick [19]

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Indicator

Location

Data

Poverty rate (%)

Canada

13.9

New Brunswick

14.5

Saint John

19.4

Families with children in poverty (%)

Canada

13.4

New Brunswick

14.7

Children poverty rate (%)

Canada

17.4

New Brunswick

20.3

Saint John

30

– Income inequality: the average income for the poorest 10% of New Brunswick families is $22,059. This number for the richest 10% families is $207,582. – One in five children in New Brunswick is living in poverty. – Half of all children in single parent families are living in poverty in New Brunswick compared to one in ten children in couple headed families. – One out of four children under the age of six in New Brunswick is living in poverty. – Half of all racialized children in New Brunswick are living in poverty. – Two out of five indigenous children are living in poverty in New Brunswick. The poverty-oriented data in the City of Saint John is in a critical condition in comparison to other cities in the province and across the country as well. Employing the most practical methods in order to explore the roots and causes of poverty in the city to achieve a better understanding of the related economic, social, cultural, and educational items is a key factor. In the following sections, smart cities initiatives will be addressed by reviewing the definition, criteria, and indicators of this nascent concept. The ultimate goal here is to explore the relationship between the smart city concept and the generational poverty challenge in the City of Saint John in order to identify a practical smart solution.

3 Smart City 3.1 The Definition of Smart Cities Smart cities must be separated from the intelligent city and the creative city. These other two concepts have emerged from a top-down approach and a community-based process respectively. “Smart cities are both creative and intelligent…the archetype of a smart city varies according to the identity and resources of the city” [20].

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The concept of smart cities is a fuzzy concept, which encompasses a wide range of disciplines related to cities, such as economic, environmental, and social aspects of an urban area. The main tool to shape a smart city is the technology, and it plays a significant role. “Smart Cities base their strategy on the use of information and communication technologies in several fields such as economy, environment, mobility and governance to transform the city infrastructure and services” [21]. Meijer and Bolivar analyzed three categories of ideal-typical definition of smart cities [22]: – The smart city as a city using technologies – The smart city as a city with smart people (who are well-educated; and trained to cooperate in urban projects) – The smart city as a city with smart collaboration (a city which invites all stakeholders and citizens to be involved in an interactive process of urban planning, management, and design). Some research papers are based upon one of these definitions, and some are designed with a combination of two or three of them. According to the mentioned definitions of smart cities, Meijer and Bolivar offered a new explanation: “the smartness of a city refers to its ability to attract human capital and to mobilize this human capital in collaborations between the various (organized and individual) actors through the use of information and communication technologies” [22]. By human capital they tried to address the value of individuals’ collaboration, and their power to guide planners and managers to first realize and then solve urban challenges. To conclude a better understanding, the definition of smart cities is shown in Table 2, which is designed by Albino et al. to demonstrate several aspects of the related debates [23]. The rows are sorted by researchers based on three categories: technology-driven definitions (five resources), technology as a tool to develop urban dimensions (eight resources); and multidisciplinary aspects of smart cities (ten resources). This table clearly shows various aspects of the smart city concept, which can potentially assist decision makers to address urban social challenges. Although there are many definitions of smart cities, the definition that applies most accurately to this study according to its goals and themes is: The smart city is a concept, which aims to employ technology in social, economic, environmental, and governmental dimensions whether to solve a problem or increase the strengths in cities. Its main purpose is to provide more efficient services for citizens, and make the city more intelligent, interconnected, and sustainable. The concept devotes some suggestions regarding a wide range of subjects, such as green energy; sustainable development; social capital; ICTs; quality of life; urban management; and transportation.

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Table 2 Definitions of smart cities Definition

Resource

Smart city as a high-tech intensive and advanced city that connects people, information and city elements using new technologies in order to create a sustainable, greener city, competitive and innovative commerce, and an increased life quality

Bakici et al. (2012)

Being a smart city means using all available technology and resources in an intelligent and coordinated manner to develop urban centers that are at once integrated, habitable, and sustainable

Barrionuevo et al. (2012)

A community of average technology size, interconnected and sustainable, comfortable, attractive, and secure

Lazaroiu and Roscia (2012)

A smart city is based on intelligent exchanges of information that flow between its many different subsystems. This flow of information is analyzed and translated into citizen and commercial services. The city will act on this information flow to make its wider ecosystem more resource efficient and sustainable. The information exchange is based on a smart governance operating framework designed to make cities sustainable

Gartner (2011)

Smart city [refers to] a local entity—a district, city, region or small country—which takes a holistic approach to employ[ing] information technologies with real-time analysis that encourages sustainable economic development

IDA (2012)

A city is smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance

Caragliu et al. (2011)

Smart cities will take advantage of communications and sensor capabilities sewn into the cities’ infrastructures to optimize electrical, transportation, and other logistical operations supporting daily life, thereby improving the quality of life for everyone

Chen (2010)

Two main streams of research ideas: (1) smart cities should do everything related to governance and economy using new thinking paradigms and (2) smart cities are all about networks of sensors, smart devices, real-time data, and ICT integration in every aspect of human life

Cretu (2012)

Smart community—a community which makes a conscious decision to aggressively deploy technology as a catalyst to solving its social and business needs—will undoubtedly focus on building its high-speed broadband infrastructures, but the real opportunity is in rebuilding and renewing a sense of place, and in the process a sense of civic pride. […] Smart communities are not, at their core, exercises in the deployment and use of technology, but in the promotion of economic development, job growth, and an increased quality of life. In other words, technological propagation of smart communities isn’t an end in itself, but only a means to reinvent cities for a new economy and society with clear and compelling community benefit

Eger (2009)

(continued)

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Table 2 (continued) Definition

Resource

A city well performing in a forward-looking way in economy, people, governance, mobility, environment, and living, built on the smart combination of endowments and activities of self-decisive, independent, and aware citizens. Smart city generally refers to the search and identification of intelligent solutions which allow modern cities to enhance the quality of the services provided to citizens

Giffinger et al. (2007)

A smart city, according to International Council for Local Environmental Initiatives (ICLEI), is a city that is prepared to provide conditions for a healthy and happy community under the challenging conditions that global, environmental, economic, and social trends may bring

Guan (2012)

A city that monitors and integrates conditions of all of its critical infrastructures, including roads, bridges, tunnels, rails, subways, airports, seaports, communications, water, power, even major buildings, can better optimize its resources, plan its preventive maintenance activities, and monitor security aspects while maximizing services to its citizens

Hall (2000)

A city connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city

Harrison et al. (2010)

(Smart) cities as territories with high capacity for learning and innovation, which is built-in the creativity of their population, their institutions of knowledge creation, and their digital infrastructure for communication and knowledge management

Komninos (2011)

Smart cities are the result of knowledge-intensive and creative strategies aiming at enhancing the socio-economic, ecological, logistic, and competitive performance of cities. Such smart cities are based on a promising mix of human capital (e.g. skilled labor force), infrastructural capital (e.g. high-tech communication facilities), social capital (e.g. intense and open network linkages) and entrepreneurial capital (e.g. creative and risk-taking business activities)

Kourtit and Nijkamp (2012)

Smart cities have high productivity as they have a relatively high share of highly educated people, knowledge-intensive jobs, output-oriented planning systems, creative activities, and sustainability-oriented initiatives

Kourtit et al. (2012)

The application of ICT with their effects on human capital/education, social and relational capital, and environmental issues is often indicated by the notion of smart city

Lombardi et al. (2012)

Smart Cities initiatives try to improve urban performance by using data, information, and information technologies (IT) to provide more efficient services to citizens, to monitor and optimize existing infrastructure, to increase collaboration among different economic actors, and to encourage innovative business models in both the private and public sectors

Marsal-Llacuna et al. (2014)

(continued)

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Table 2 (continued) Definition

Resource

A smart city infuses information into its physical infrastructure to improve conveniences, facilitate mobility, add efficiencies, conserve energy, improve the quality of air and water, identify problems, and fix them quickly, recover rapidly from disasters, collect data to make better decisions, deploy resources effectively, and share data to enable collaboration across entities and domains

Nam and Pardo (2011)

Creative or smart city experiments […] aimed at nurturing a creative economy through investment in quality of life which in turn attracts knowledge workers to live and work in smart cities. The nexus of competitive advantage has […] shifted to those regions that can generate, retain, and attract the best talent

Thite (2011)

Smart cities of the future will need sustainable urban development policies where all residents, including the poor, can live well and the attraction of the towns and cities is preserved. […] Smart cities are cities that have a high quality of life; those that pursue sustainable economic development through investments in human and social capital, and traditional and modern communications infrastructure (transport and information communication technology); and manage natural resources through participatory policies. Smart cities should also be sustainable, converging economic, social, and environmental goals

Thuzar (2011)

The use of Smart Computing technologies to make the critical infrastructure components and services of a city—which include city administration, education, healthcare, public safety, real estate, transportation, and utilities—more intelligent, interconnected, and efficient

Washburn et al. (2010)

A smart city is understood as a certain intellectual ability that addresses several innovative socio-technical and socio-economic aspects of growth. These aspects lead to smart city conceptions as “green” referring to urban infrastructure for environment protection and reduction of CO2 emission, “interconnected” related to revolution of broadband economy, “intelligent” declaring the capacity to produce added value information from the processing of city’s real-time data from sensors and activators, whereas the terms “innovating”, “knowledge” cities interchangeably refer to the city’s ability to raise innovation based on knowledgeable and creative human capital

Zygiaris (2013)

Source [23]

3.2 Criteria of Smart Cities As mentioned before, smart city is a wide and comprehensive concept. Criteria help the study to be narrower and more categorized. Although smart cities have been studied for three decades, their criteria are still a debatable topic. The following paragraphs review the relevant resources to smart cities to identify its criteria. Some

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factors are used to ensure the validity of resources, such as the publishing source, whether the publishing source is peer-reviewed, from similar publications of the author, timeliness, and additional resources provided by the author. In addition to these resources, some basic publications are used to increase the reliability of the research. Hasan et al. focused on the technological dimension of smart cities [24]. Regarding this dimension, they surveyed a wide range of applications to design a smart city: “smart metering: monitoring smart grids and smart garbage bins; surveillance and security: secure access and monitoring in city buildings and neighborhoods; infrastructure management: load sensing for critical infrastructures, managing historical sites; city automation: smart parking system, traffic monitoring, real-time travel and route updates; and E-health: health updates by wearable health monitor” [24]. Caragliu, Del Bo, and Nijkamp summarized the characteristics of a smart city along four dimensions: “Utilization of networked infrastructure to improve economic and political efficiency and enable social, cultural and urban development; business-led urban development; social inclusion of various urban residents in public services; high-tech and creative industries along with knowledge networks, voluntary organizations, crime free environments, after dark entertainment economy; social and relational capital in urban development; and social and environmental sustainability” [25]. This classification is a multi-dimensional one, which tries to cover several aspects of cities besides technology like the two following categories. Chourabi et al. mentioned eight clusters of indicators, including technology; people and communities; governance; policy; the economy; management and organization; built infrastructure; and the natural environment [26]. Batty et al. proposed these seven indicators as urban issues for the smart city, now and in the future: city services; citizens; transport; communication; water; business; and energy [27]. Neirotti et al. categorized the indicators into two major groups, and twelve minor indicators [28]. Their goal was to comprise different dimensions of cities like the previous classifications, but into hard and soft categories. Hard – – – – – – – –

Energy Grids Public lighting, natural resources, and water management Waste management Environment Transport, mobility, and logistics Office and residential buildings Healthcare Public Security

Soft – Education and culture – Social inclusion and welfare

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– Public administration and (e-) government – Economy. Naphade et al. believed that there is an urgent need for cities worldwide to become smarter regarding how they manage their infrastructure and resources to respond to the current and future needs of their citizens [29]. Their definition of smart cities derives from a need perspective, which is more related to this study, and is different from other researchers’ perceptions. Concurrent trends in urbanization, economic growth, technological progress, and environmental sustainability are the major categories for this newfound approach. Harrison et al. described the criteria for cities to be smarter [29]. These criteria are more practical than the previous ones as they were concluded from actual project experience: traffic control and demand balancing; air pollution; electrical energy usage and pricing management; the structural health of city infrastructure; extreme water events; crime and public safety; and entrepreneurial issues. The following three classifications explain the six conventional criteria of smart cities. Vanolo explained the term smart city by distinguishing six conceptually distinct characteristics: smart economy; smart mobility; smart governance; smart environment; smart living; and smart people [30]. Lombardi et al. [31] classified the criteria of smart cities into six major clusters, and studied them into two categories: civil society and industry. The criteria are: smart economy; smart mobility; smart environment; smart people; smart living; and smart governance. “These six dimensions connect with traditional regional and neoclassical theories of urban growth and development. In particular, the dimensions are based on theories of regional competitiveness, transport and ICT economics, natural resources, human and social capital, quality of life, and participation of citizens in the governance of cities” [31]. Albino et al. described the key elements of smart cities, according to eight resources from 1997 to 2012 [23]. In another part of their article, they illustrated the criteria of smart cities relying on two resources. One of them is Lambordi et al’s article, which is mentioned in the last paragraph. The other one is Lazaroiu and Roscia’s article, which explained the criteria in details: Pollution, Innovative spirits, CO2 , Transparent governance, Sustainable resource management, Education facilities, Health conditions, Sustainable, innovative and safe public transportation, Pedestrian areas, Cycle lanes, Green areas, Production of solid municipal waste, GWh household, Fuels, Political strategies and perspectives, Availability of ICT infrastructure, Flexibility of labor market. A unique category of criteria of smart cities does not exist. Every study reviewed these criteria according to one specific dimension of their research. The central point of the related research is the smartness of cities, but the second variable of them varies from technology, governance, planning, management, transportation to health, education, and pollution. Table 2 infers the criteria of smart cities derived from the various reviewed resources. All criteria are listed in the first column, while the second column provides their sources. More than fifty unique criteria are recognized from

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Table 3 The criteria of smart cities Criteria

Resource

Infrastructure

Caragliu et al. (2011), Chourabi et al. (2012), Batty et al. (2012), Neirotti et al. (2014), Harrison et al. (2010), Albino et al. (2015)

Economy

Caragliu et al. (2011), Chourabi et al. (2012), Batty et al. (2012), Neirotti et al. (2014), Naphade et al. (2011), Harrison et al. (2010), Vanolo (2013), Lombardi et al. (2012), Albino et al. (2015)

Society

Caragliu et al. (2011), Chourabi et al. (2012), Batty et al. (2012), Neirotti et al. (2014), Vanolo (2013), Lombardi et al. (2012)

Technology

Caragliu et al. (2011), Chourabi et al. (2012), Naphade et al. (2011), Albino et al. (2015)

Education

Caragliu et al. (2011), Neirotti et al. (2014), Vanolo (2013), Albino et al. (2015)

Environment

Caragliu et al. (2011), Chourabi et al. (2012), Batty et al. (2012), Neirotti et al. (2014), Naphade et al. (2011), Harrison et al. (2010), Vanolo (2013), Lombardi et al. (2012), Albino et al. (2015)

Governance

Chourabi et al. (2012), Neirotti et al. (2014), Vanolo (2013), Lombardi et al. (2012), Albino et al. (2015)

Management

Chourabi et al. (2012), Naphade et al. (2011), Harrison et al. (2010), Hasan et al. (2013), Albino et al. (2015)

Transportation

Batty et al. (2012), Neirotti et al. (2014), Harrison et al. (2010), Hasan et al. (2013), Vanolo (2013), Lombardi et al. (2012), Albino et al. (2015)

Energy

Batty et al. (2012), Neirotti et al. (2014), Harrison et al. (2010)

Health care

Neirotti et al. (2014), Harrison et al. (2010), Hasan et al. (2013), Vanolo (2013), Albino et al. (2015)

Public safety and security

Neirotti et al. (2014), Harrison et al. (2010), Hasan et al. (2013), Albino et al. (2015)

the literature, but Table 3 indicates the most frequently mentioned ones, which are twelve key elements of smart cities. Some of the twelve criteria have common features, which can be combined into a single criterion. For example, management and governance are two criteria, which are used interchangeably in the literature, and can be categorized as one criterion. The outcomes are like the well-known classification of the criteria of smart cities, which are used in past projects, and research. This chapter intends to use this classification with an attention to the twelve criteria. The employed criteria in previous projects and studies are people, governance, living, economy, mobility, and environment. The twelve criteria can be categorized into the six according to their characteristics: – – – – –

People: society; education Governance: management; governance; public safety and security; infrastructure Living: health care Economy Mobility: transportation

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– Environment: energy. Technology is a key element of the classification, which can be merged to any of these criteria, as it plays a significant role to design a smart city. The point is that implementing the technological dimension of smart cities should not be the main goal of the study. Criteria are major components of smart cities. Reviewing their subsets is the next step to study the concept in detail. These subsets are smart city indicators, which provide opportunities for researchers to focus on contents of main criteria.

3.3 Indicators of Smart Cities In this section, we define indicators as a subset of smart city criteria. For example, the European Smart Cities is a valuable database of indicators of smart cities. These indicators are analyzed and concluded from smart city projects in more than 90 cities in Europe. They classified the indicators based on the introduced six criteria. The following points illustrate the indicators of smart cities used by them: – People: Education; Lifelong Learning; Ethnic Plurality; and Open-Mindedness, – Governance: Political Awareness; Public and Social Services; and Efficient & Transparent Administration, – Living: Cultural and Leisure Facilities; Health Conditions; Individual Security; Housing Quality; Education Facilities; Touristic Attractiveness; and Social Cohesion, – Economy: Innovative Spirit; Entrepreneurship; City Image; Productivity; Labor Market; and International Integration, – Mobility: Local Transport System; (Inter-)National Accessibility; ICTInfrastructure; and Sustainability of the Transport System, and – Environment: Air Quality; Ecological Awareness; and Sustainable Resource Management Lombardi et al. analyzed the six criteria and concluded that there were close correlations between some of these criteria as follows [31]: people and education; governance and e-democracy; living and security and quality; economy and industry; mobility, and logistics and infrastructures; and environment and efficiency and sustainability. To achieve a better understanding of indicators, concentration on these combinations of criteria would be helpful. Dirks et al. also surveyed the components of smart cities through these indicators [32]. They used a slightly different classification of criteria than the six noted ones, but the reviewed indicators are valuable for this study: Public service management; Local government administration; Health and education; Public safety; Government services; Business environment; Administrative burdens; Cars, roads; Public transport; Airports, seaports; Broadband, wireless; Phones, computers; Sanitation; Freshwater supplies; Seawater; Oil, gas; Renewable energy; Nuclear energy.

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Schaffers et al. summarized the indicators of smart cities into three main major clusters [3]. They categorized the criteria and indicators from the perspective of city challenges, and their solutions: (a) Innovation Economy—intelligent city clusters: manufacturing, business services, health, and tourism; intelligent city districts: central business district (CBD), techno park, mall, university campus, port area, and airport city; and new company’s creation/intelligent incubators; (b) City Infrastructure and Utilities—smart transport, mobility and parking; broadband, Digital Subscriber Line (DSL), Fiber To The Home (FTTH), wi-fi, embedded systems; energy saving/smart grid; and environment monitoring, real time alert, safety; and (c) Governance—government services to citizens; decision making/participation/direct democracy; and monitoring & measurement: the city as database. For this chapter, a set of reasonable indicators must be developed using interviews, questionnaires, and surveys, which can be used and duplicated in other small and medium sized cities. The main criteria are the ones discussed in the previous paragraphs. For example, in some cases the economic dimension of the city is dominant, and in other cases its environmental aspect is the main focus of the study. Based on previous studies and successful projects, paying attention to the following three suggestions can help guide this study: – Every study about cities is an interdisciplinary project, which requires an interdisciplinary vision. So, concentrating on a particular subject should not steer researchers away from studying other aspects of cities. – In any step of designing a smart city, its technological dimension must be highlighted. – The stakeholders (authorities) related to each indicator must be defined to implement smart city initiatives (see Table 4). Table 4 explains stakeholders’ roles and responsibilities in various categories including policy makers, regulators, developers, owners, and operators. The table defines their main tasks and obligations.

4 Food Bank Platform: A Smart Solution There is currently no integrated data platform to recognize and analyze causes and reasons of poverty in the City of Saint John. In another study [34], the authors conducted a two-phase survey to identify: (1) causes and reasons of poverty, and (2) the role of key stakeholders in addressing poverty. They discovered that the most critical challenges of studying poverty entail: (a) having access to data (and more importantly real-time data), including information about low-income families (e.g., education level, location, job status, health condition, and family information); (b) understanding the role of key stakeholders; and (c) an integrated connection between decision makers and people suffering from poverty. This is not a problem restricted to Saint John; there is no integrated real-time database related to low-income families in the world. A lack of such an integrated

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Table 4 Stakeholders roles and responsibilities Policy

Regulators

Developers

Owners

Operators

Governments at all levels set policies: – Federal – State – Local – Regional – European Union – United Nations Think Tanks, consultants, the public, NonGovernmental Organizations (NGOs), universities, and other all influence policy

Regulators influence and create policy, as well as monitor policy adherence Semigovernment agencies and NGOs often perform a quasiregulatory role in that they influence policy

Developers include real estate, utilities, transportation, and city services Developers contract with architects, designers, consultants, and general contractors, as well as arrange financing Developers may be speculative and hand off assets to owners, such as pension-fund owners

Owners include real estate, utilities, transportation, and city services entitles Owners/developers may be the same entity Owners often own assets long term (e.g. pension funds/infrastructure funds) Owners often appoint third parties to manage assets

Operators comprise various groups, such as: – Real estate and facilities managers who act on behalf of the owner – Governmentowned public entities, such as water, power, and transportation – Private operators of utilities, transportation, and city services

Source [33]

database is the linkage between my doctoral thesis and my proposed postdoctoral study. The first step in creating an integrated database is identifying the most relevant source of data. Food banks are one of the most important places to have access to target groups. In addition, food banks could become a more significant channel for stakeholders to share and present their projects, studies, recommendations, services, and products. The key stakeholders in Saint John are not systematically connected with each other, and their objectives often overlap. This problem is not just restricted to Saint John, and it is a common challenge for poverty-reduction projects across the world [8]. An interconnected relationship has multidimensional influences on poverty-reduction projects, including preventing overlaps, leading stakeholders towards mutual goals, creating connections between target groups’ demands and donors/volunteers, and informing policy makers about existing challenges. In addition, identifying the relevant metrics is an important step in creating a database platform [35]. Policy makers and key stakeholders across the world often rely on the poverty rate as the main indicator in poverty-reduction projects [36], without having any knowledge of or input from other aspects of the issue such as education level, job status, health condition, and family information about target groups. On the other hand, the Millennium Development Goals (MDG) comprise seven indicators to study the human poverty in a society [11]. Similarly, the Multidimensional Poverty Index

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(MPI) is another index that is used to measure the poverty rate. The MPI focuses on three dimensions: health, education, and the standard of living [37]. Poverty is clearly a multidimensional construct [6] and concentrating only on limited factors can lead to inappropriate conclusions and recommendations. This issue is even more dramatic in Canada, where Low-Income Measures (LIMs) are employed to measure the level of the poorness in the country based on a single factor: income [13]. Overall, these measurement factors do not represent causes and roots of poverty in a community. Thus, having access to a real-time database of these various dimensions will help us better understand the roots and reasons of poverty and will also decrease financial and human resources expenses of decision makers. Through the application of relevant technologies (software and data gathering platforms), integrating data gathered from related stakeholders, as well as data compilation and analytics, these real-time data will assist three major groups: suppliers (key stakeholders), providers (food bank managers/employees), and end users (lowincome families and other users of food banks). This database will collect various information based on several factors relevant to the causes and roots of poverty, and at the same time provide real-time data for decision makers and researchers. Although this platform will be designed for food banks, the data gathering could be applied to other relevant places (e.g., women’s shelters). Decision and policy makers, academics, donors, and volunteers could also find beneficial information from the database. Real-time data and its analysis will facilitate the process of decision making [38]. “The success of poverty reduction programs depends largely on the use of quality data to help determine the nature and extent of poverty and to properly design and implement strategies for alleviating poverty in a particular context” [39]. As indicated above, food banks are substantial sources of data to understand and identify challenges of low-income families. They are one of the best sources to have access to vulnerable individuals, from understanding their basic needs to recognizing their personal and family problems, and potentially offering solutions to assist them in a wide variety of ways (e.g. find a job, increase their level of education, and develop their skills). A significant part of the problem is related to data mismanagement, including a lack of an integrated system to gather data, which results in wasted and uncollected information. In other words, various sorts of data do exist at food banks, and the challenge is collecting and exporting them in a systematic and comprehensive way. Food banks, at the same time, have been facing critical issues in managing their inputs and outputs for several years. In March 2016, 863,492 people received food from food banks in Canada, up 1.3 percent from the same time last year, and 28 percent from March 2008 [40]. This lack of efficiency is clearly one contributor to the $31 billion worth of food that ends up in Canadian landfills or composters each year [41]. Therefore, food banks could also benefit from a data platform to create a high-efficient and low-cost (time, human resource, and money) input and output management system. This platform would also serve to generate useful consumption and trend data related to target groups.

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The main objective of the proposed platform is not simply building an online system for food banks, but it is creating a network for target groups and the variety of key stakeholders. Therefore, this project will create a multi-dimensional platform, which aims to provide a better opportunity for: – Food bank employees to manage their inputs and outputs automatically and have access to real-time data of existing and on-demand food – Government and policymakers to have access to real-time data of families suffering from poverty in order to make reasonable decisions and policy changes based on their age, gender, location, marital status, and other critical factors – Academics to analyze collected data and recommend practical solutions and evidence-based and policy suggestions based on peer-reviewed up-to-date scientific research – Donors/Volunteers to identify and donate on demand items – Target groups to share their challenges to find suitable solutions – Service providers to work together to help address local issues. The spillover benefits of initiating this data collection and analytics project can also assist the understanding and addressing of the target groups’ medical conditions. This platform will gather data that can provide governments with tools to decrease medical expenses by analyzing the relationship between target groups’ medical history and the regular (or offered) food basket contents. Another challenge for decision makers is the move from a reaction-based service model to one that is proactive and informed about the needs of the region they are serving. The availability of realtime, location-based data related to low-income families would greatly assist with the identification of the most reliable and useful action plans. This proposal would gather ongoing and behavioral data from food bank users through regular surveys. The notable point of this project is not just the idea of using data to improve operations and efficiency, it also using this data to push our thinking and innovate additional services into new and modern areas of assistance. Moving forward, external source and databases can also be integrated into this project in order to expand the beneficial aspects of both datasets. For example, if target groups voluntarily share their Medicare number, this number could be used as a key identifier for government and decision makers to make a connection between target groups’ information and the Medicare database. Locally, the New Brunswick Institute for Research, Data, and Training (NB-IRDT) provides provincial health data for researchers to facilitate projects and assist the government. (Note: Medicare is only used as an example of how a unique identifier and its associated database could assist with this project.) The main idea of creating a database for food banks truly is a novel project. When possible, the project can employ existing technologies and knowledge provided by previous research and studies. For example, Liu et al. [42] have recently designed a poverty management information system in China. Their project has focused on various indicators and different technological aspects of such a database. Although their project did not concentrate on food banks, it covers the basic demands, questions, and technological issues of developing a data management platform for low-income

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families. A primary agreement is already in place with Saint John Community Food Basket to implement the project in this pilot location. A professional development team is also in place to cover the technical dimension of the project.

5 Conclusion Poverty is a critical issue for communities, which negatively impacts the cultural, social, economic, and health (mental and physical) dimensions of its citizens. Our understanding of previous poverty-reduction projects suggests that the most important challenges are: (a) having access to data (and more importantly real-time data), specifically information about low-income families (e.g., education level, location, job status, health condition, and family information); (b) understanding the role of key stakeholders and local service groups; and (c) an integrated connection between decision-makers and people suffering from poverty. Food banks, themselves, can be substantial sources of data to understand and identify the challenges of low-income families. In fact, they are probably one of the best sources to have access to disadvantaged individuals. Food banks, though, have been facing critical issues in managing their operations for several years (including volunteer staffing, and more importantly, their inputs and outputs of inventory). Therefore, an automated inventory platform in food banks could create a high-efficient and lowcost input and output management system. In addition, a correlation of inventory data with historic usage data and donor offerings can bring simple advantages to these volunteer service organizations, as well as government policymakers. For example, having access to real-time data of low-income families and people suffering from poverty has become an issue for government at the local, provincial, and national level. Donors to and volunteers at food banks also face a similar challenge. They usually look for information related to demand for food, clothing, or services. Today, many low-income families face a segregated system when it comes to seeking services or solutions for their issues. Given their backgrounds, social level, and income it is almost impossible for them to afford the cost of a visit to a professional lawyer or psychologist. Food banks are regular locations for low-income families to address their critical needs. Currently, there is not any systematic connection between the service offered through food baskets and their medical history. This project is extendable for shelters, hospitals, and other major sources of vulnerable target groups. The ultimate goals are (a) collecting target groups’ (users) data by completing a onetime registration form in order to track inputs and outputs of food banks, and to notify donors; (b) analysing users’ various challenges, including level of education, employment status, mental or physical disabilities, and other critical barriers, and share the data with decision makers; and (c) offering a suitable food basket, based on individuals’ medical history.

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References 1. Dallas-smith, S.: Central Alberta Poverty Reduction Alliance Poverty Awareness Survey Executive Summary, April 2018, pp. 1–12 2. Noble, S.: POVERTY 101 : Looking for Answers (2018) 3. Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., Oliveira, A.: Smart cities and the future internet: towards cooperation frameworks for open innovation. Lecture Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 6656, 431–446 (2011) 4. Gyles, C.: Poverty in Canada. 2nd ed. The Fraser Institute. Vancouver: The Fraser Institute, pp. 1–281 (2009) 5. Government of Canada. Towards a Poverty Reduction Strategy (2016) 6. Alkire, S., Santos, M.E.: A multidimensional approach: poverty measurement & beyond. Soc. Indic. Res. 112(2), 239–257 (2013) 7. Brandolini, A., Magri, S., Smeeding, T.M.: Asset-Based Measurement of Poverty. Banca D’Italia (2010) 8. Bryant, T., Raphael, D.: The politics of poverty. Soc. Altern. 32(1), 1–64 (2013) 9. United Nations Population Fund (UNFPA). Gender equality | UNFPA—United Nations Population Fund [Internet]. 2017 [cited 2017 July 19]. http://www.unfpa.org/gender-equality 10. Marlier, E., Atkinson, A.B.: Indicators of poverty and social exclusion in a global context. J. Policy Anal. Manag. 29(2), 285–304 (2010) 11. De Muro, P., Mazziotta, M., Pareto, A.: Composite indices of development and poverty: an application to MDGs. Soc. Indic. Res. 104(1), 1–18 (2011) 12. Parekh, A., MacInnes, T., Kenway, P.: Monitoring Poverty and Social Exclusion 2010. Joseph Rowtree Foundation, pp. 1–115 (2010) 13. Income Statistics Division. Annual Income Estimates for Census Families and Individuals (T1 Family File)—Individual Data: User’s Guide (2016) 14. Government of Canada. Tackling Poverty Together (2017) 15. New Brunswick. The New Brunswick Economic and Social Inclusion Plan, October 2009, pp. 1–5 16. New Brunswick Economic and Social Inclusion Corporation. A Choir of Voices (2009) 17. New Brunswick Economic and Social Inclusion Corporation. Overcoming Poverty Together the New Brunswick Economic and Social Inclusion Plan (2014) 18. Human Development Council. The Face of Child Poverty in New Brunswick (2017) 19. Statistics Canada. Dictionary, Census of Population, 2016—Census metropolitan area (CMA) and census agglomeration (CA) [Internet]. 2016 [cited 2017 July 14]. http://www12.statcan. gc.ca/census-recensement/2016/ref/dict/geo009-eng.cfm 20. Ben, Letaifa S.: How to strategize smart cities: revealing the SMART model. J. Bus. Res. 68(7), 1414–1419 (2015) 21. Bakici, T., Almirall, E., Wareham, J.: A smart city initiative: the case of barcelona. J. Knowl. Econ. 4(2), 135–148 (2013) 22. Meijer, A., Bolívar, M.P.R.: Governing the smart city: a review of the literature on smart urban governance. Int. Rev. Adm. Sci. 82(2), 392–408 (2016) 23. Albino, V., Berardi, U., Dangelico, R.M.: Smart cities: definitions, dimensions, performance, and initiatives. J. Urban Technol. 22(February), 3–21 (2015) 24. Hasan, M., Hossain, E., Niyato, D.: Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches. IEEE Commun. Mag. 51(6), 86–93 (2013) 25. Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. Urban Technol. 18(2), 65–82 (2011) 26. Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., et al.: Understanding smart cities: An integrative framework. Proc. Annu Hawaii Int. Conf. Syst. Sci. 2289–2297 (2011) 27. Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., et al.: Smart cities of the future. Eur. Phys. J. Spec. Top. 214(1), 481–518 (2012)

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Information Integrity for Multi-sensors Data Fusion in Smart Mobility Doaa Mohey El-Din, Aboul Ella Hassanien and Ehab E. Hassanien

Abstract The security of smart environments is a very important issue for data and application. The smart city is considered that the automatic management for city relies on internet-of-things (IoT) technology. Internet-of-things refers to the interconnected set of sensors via Internet that targets to improve management and analytics. Smart City includes smart mobility, smart tourism, smart agriculture, smart water, smart energy, smart health, etc. According to Statista [1], there is an evolution of investment of smart cities a world that achieves to 81 billion dollars in 2018, and 95.8 billion dollars in 2019. The predicted investment of Smart city technology statistics reaches to 158 billion dollars in 2022. The data integrity is one of essential dimensions of secure the data in Internet-of-things domains. Multi sensor fusion is an essential process for making decisions automatically, remotely and concurrently. Sensor fusion is an integrated method for variant data and signals from multi-sources for managing the IoT devices. The Safety Internet-of-things Environment affected on Information protection and Integrity on Sensors Fusion network. The data protection depends on data integrity that targets reaching the data accuracy and data consistency (validity) over the internet-of-things fusion. Data integrity is very sensitive data so protecting data integrity is the main focus of many projects security solutions. This paper shows the evolution of smart mobility for variant smart cities and presents the Integrity challenges of multi-data fusion. It can facility to identify and classify challenges on big data. This paper provides measuring the factors of quality the data integrity. It proposes a taxonomy data fusion model for challenges on the data fusion from multi-source in smart domains. Keywords Information integrity · Multi-sensors data fusion · Smart environment · Internet-of-Things · Big data · Interoperability D. M. El-Din (B) · E. E. Hassanien Faculty of Computers and Information, Cairo University, Giza, Egypt e-mail: [email protected] A. E. Hassanien Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_6

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1 Introduction Smart Environments [2] deal with a big number of information whether offline or in the real-time stream [3] entitled big data [4]. Smart environments become a hot area of research because it touches various domains in physical life that can be controlled and analytics the data, such as smart tourism [5], and smart education [6]. According to Statista [7], there is an evolution of usage of the smart environment from 2013 till now. Recently, the statistics refer to the increasing confidant and reliable in smart environments. In 2018 [8], more than 90% respondents from the internet-of-things involve in the industry such as the technology, media, and telecommunications industry. 54% pronounced their reliability and confidence are raised in their company was building sufficient digital trust controls into their Internet-of-things (IoT) programs [9]. Smart city [10] refers to the automated management and remote monitoring for sensors in variant smart domains that is based on Internet-of-Things (IoT) [11]. Smart City includes smart mobility, smart tourism, smart agriculture, smart water, smart energy, smart health, etc. The main concept of Smart city depends on the information combining and sensors communication that is based on internet-of-things. Internetof-things [10, 11] refers to the interconnected set of sensors via internet that targets to improve management [12] and analytics [13]. An enormous data is extracted from various sensors for each domain. They have many resources producing different types of data [14]. Among these resources are systems that continuously produce fine-scaled and exclusive data. These systems require to fuse number of data types from multi-sensors. Multi-sensor data fusion [15] is defined by the useful integration process of data from multiple sources to reach data deduction into one source interpreting the meaning of the data to support users to visualize, monitor, and make a decision. The correct sensors fusion transaction affected on the guarantee and integrity of the data. Recently, Internet of Things (IoT) technology and Cyber-Physical mechanism systems [16] appear in everywhere with high influence daily activities. Cyber-physical system (as shown in Fig. 1) refers to manage and control systems and users through using computer-based algorithms, connected via internet. Smart mobility is based on physical system that usually controls by smartphones. The recent Cyber-physical is contrasting the traditional systems which rely on the standalone devices control that connects with physical input and output. So trust of the aggregated information has a great effect in variant systems in various contexts. The measures of data integrity should be high adaption risk. Data security [17] has not been the main concern of IoT product manufacturers. Data integrity [18] is a subset of data security. There are two types of sensors fusion [19] in IoT environment in centralized based on the data analysis process: (1) Centralized fusion and (2) Distributed fusion architecture. It is very significant to safe these data fusion and keep the meaning of them. So the security of the information in the fusion process is important for achieving the accuracy and consistency of fused data, which called Data integrity. Data integrity [19] should use standard conditions, and rules for preserving data by checking errors and validation routines. Data

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Fig. 1 A cyber physical system architecture

Integrity is the assertion that data records are proper, right, complete, and protected within their original context, containing their relationship to other data records. This paper proposes a new taxonomy of data security and presents challenges of data integrity. This study can support the assessment of these challenges that have a big effect on data fusion. Achieving sensor fusion is vital for interconnecting multiple things together across different communication networks. Recently, used billions of smart sensors communicate via the Internet. The rest of this paper is organized as the following: Sect. 2 presents outlines of related work in data integrity. Section 3, Smart Mobility. Section 4, the proposed taxonomy of smart mobility. Section 5, Open Research Challenges. Section 6: Discussion. Finally, Sect. 7 conclusion and proposes directions for future work.

2 The-State-of-Art Recently, the smart environment relies on the huge big data fusion that can extract from various features.

2.1 Internet-of-Things and Smart Environments The Internet of Things (IoT) [20, 21] provides a physical key of connecting several things devices through a physical network or cloud network. This network has big data of records from these devices which called sensors. Smart Environment is considered a small world that can simulate the real environment with different kinds of smart

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Fig. 2 Examples of several applications of smart environment that are based on the internet-of-things devices and sensors

devices or sensors based on Internet-of-things such as presented in Fig. 2. Smart Environment makes life easier and more comfortable to make decisions remotely and monitoring objects such as things or some people. Smart Environment targets dealing with big data, tracking objects, making a suitable Decision in emergency cases and stable cases. The main challenge of the smart environment is data fusion or multi sensor data fusion that requires to understand data features and combine between various data. There are fusion types: fusion feature level and fusion decision level. It also can Support a variant number of users requirements, and Predict actions in the same environment. Smart mobility [22, 23] is considered an interconnected group of vehicle sensor to support decisions on the traffic concurrently and control the problems of vehicles such as congestion and cars pollution. Smart mobility includes three types: smart vehicles, traffic infrastructure, and multimodal smart city as shown in Fig. 3. Smart vehicles refer to manage and control the sensors on several vehicles and cars that holds position information, number of passengers, and speed rate. Traffic congestion can be observed based on the percentage rate of vehicles on the roads and numbers of vehicles of each road, and time for each traffic following. A Multimodal smart city that refers to connect and integrate big data from various sensors for different objectives. Mainly, that can manage vehicles and predict a solution for several problems concurrently in any road in the city. These data should be safe and trust because they support users in decision making. For any IoT system that requires data security and data integrity. IoT combines between advanced networks and communication technologies for various applications which will impact many properties of people’s lives and bring about much

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Fig. 3 Smart mobility dimensions

suitability. However, given the huge number of connected sensors become high risk for attacks, virus and attacks so the data security and integrity are very significant for smart environments. Many researches investigate and discuss the security aspects of IoT but with an emphasis on just one level of the IoT architecture. These smart sensors reach [24] around 50 billion by 2020. Data integrity [25] is an essential significant parameter to reach the consistency of the data. Data protection [26] is considered the safeguarding process for data from corruption or loss. The importance of data protection increases as the amount of data created and stored continues to grow at unprecedented rates.

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2.2 Data Integrity Versus Data Security Data integrity is not to be confused with data security, the propriety of keeping data from unauthorized parties. Data security covers the data protection during sensors connection and storages. Data integrity repairs the data assurance, information accuracy and consistency of, data over its entire life-cycle. It is a crucial property to develop and implement the data. It is at times used as a proxy term for data quality, while data validation is a pre-requisite for data integrity. Data integrity and Data security are the two significant properties for ensuring extracted and transferred which control and manage from various social IoT users. Data integrity ensures the validity of data. Data security guarantees protected data contra loss and unauthorized access. Data security has three main layers each one has several units and features as Fig. 4. The essential differences between data security and data integrity appear in Table 1. But there are risks of data security that can be classified into five classes, as the following (as classified in Fig. 5). But there are threads of data security that can be classified into two classes: hacks or virus (as shown in Fig. 6). Data integrity is the opposite of data corruption. Data corruption is considered any error in computer data that happen during processing and any transaction on it. Information integrity has several techniques to guarantee data on communicating

Fig. 4 Three essential security layers for Internet-of-Things (IoT) architecture

Fig. 5 Data security risks

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Table 1 Differences between data security and data integrity Data security

Data integrity

Target

The same target of the usability of data is preserved all the time

Definition

Data security ensures the accessed data. It makes sure the privacy and protection of personal data

Data integrity makes sure the data is correct and not corrupt

Deal with

Security deals with protection of data

Integrity deals with the validity of data

Definition

Hardware depends on the security solutions prevent unauthorized read/write access to data

Data Integrity identifies a data quality, which ensures the data is complete and has a whole structure Data integrity is most often talked about with according to data residing in databases integrity as well

Based on

Hardware based two factor authorization schemes are highly secure because the attacker needs physical access to the equipment and site. But, the dongles can be stolen and be used by almost anybody else. Backing up data is also used as a mechanism against loss of data

If data integrity is preserved, the data can be considered consistent and can be given the assurance to be certified and reconciled

Relationship

Generalize data security has several aspects and properties

Is considered subset of data security and data quality

Required methods

It requires the data masking

In order to make sure the information integrity is preserved

Types

There are many ways of data security: Data asset, data recovery, firewalls, cryptography, anti-virus, human aspect, anti-spyware

Entity integrity, referential integrity and domain integrity are several popular types of integrity constraints used for preserving data integrity in databases

Features/properties

Sensitivity of personal data against unauthorized access

The usability of information to address specific needs in context of real-world circumstances and implications

Conditions

Backing up, designing suitable user interfaces and error detection/correction in data are some of the means to preserve integrity, while authentication/authorization, encryptions

It has to ensure the stored data in the database corresponds precisely to the real world details

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Fig. 6 Data security threads

information. Data integrity is not to be confused with data security, the refinement of protecting data from prohibited parties. There are some aspects of data security that are near in meaning but the difference between them is very important and valuable, which is shown in Table 2. There are several types of data integrity constraints [27]. Data integrity is normally enforced in a database system by a series of integrity conditions. Three kinds of data integrity conditions are an inherent part of the relational data model: the integrity of entity, referential, and domain. Entity integrity takes care of the idea of a primary key. Entity integrity is an integrity rule that every table should have a primary key. Referential integrity that takes care of a foreign key concept. These conditions that rely on foreign-key values. The usual state of affairs is that the foreign-key value refers to a primary key value of some table in the database. Occasionally, and this will depend on the rules of the data owner, a foreign-key value can be null. Domain integrity focuses on all columns in a relational database that should be declared a defined domain. The main unit of data in the relational data model is the data item. A domain is a group of values of the same type. User-defined integrity refers to a set of rules specified by a user, which do not belong to the entity, domain and referential integrity categories. Pervious researches mention here in Table 3, which discusses the types of integrity and used algorithms. Using a hash function, it can map a variable-length mission into constant length hash rate digest. A hash function relies on a function h that expresses a minimum and compression function. A hash function has several security characteristics: Preimage resistance (one-way), 2nd-preimage resistance (weak collision resistance), and Collision resistance (strong collision resistance). There is a tied relationship between data quality and data integrity: for reaching the data quality that requires to guarantee file integrity and system integrity. File/data integrity should be consistent, complete, and have suitable accuracy. The system integrity has main characteristics that are timeliness, validity, a consistency cross system (as shown in Fig. 7). Big Data [29, 30] is a huge number of data from specific or variant contexts that require to analyze, collect, fusion, and visualize. It has several main characteristics and challenges as value, volume, varsity, and variety. There are two distinct parts: securing the organization and its customers’ information in a Big Data context; and using Big Data techniques for analyzing, and even predict security.

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Table 2 Differences between sensor factors: data confidentiality, data integrity, and data availability Confidentiality

Integrity

Availability

Definition

Preserving data from detection the unauthorized parties

Keeping information by unauthorized parties

Ensuring that authorized parties are able to access the information when needed

Based on

Information has value, especially in today’s world. Bank account statements, personal information, credit card numbers, trade secrets, government documents. Everyone has information they wish to keep a secret

Data only has right value. Information that has been manipulated with could evidence costly

Information only has value if the right people can access it at the right times Denying access to information has become a very common attack nowadays The main goal of DDoS attacks is to deny users of the website access to the resources of the website

Encryption ensures that only the right people (people who knows the key) can read the information Encryption is very diffusion in smart environment

Data confidentiality, cryptography has a very main role in making sure data integrity. Mutual methods to keep data integrity contains hashing the information by comparing it with the hash of the mission

Includes the redundancy data

Types

Medical confidentiality Clinical and counseling psychology Commercial confidentiality Banking confidentiality Public policy concerns

Entity integrity, Referential integrity, Domain integrity, User-defined integrity

Internal, external

Factors

Trust the data

Completeness, soundness of Data

Correctness and reasonableness OF Data

Errors occurrence

Less errors

More errors

Caused by

While errors in data integrity are caused by bugs in computer programs

Errors in data validity are caused by human beings - usually data entry personnel

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Table 3 Differences between data quality and data integrity Data quality

Data integrity

Definition

Data Quality [28] refers to the characteristics that determine the reliability of information to serve an intended purpose including planning, decision making and operations. It is the state of complete features and attributes that define the usability of information to address specific needs in context of real-world circumstances and implications

Data Integrity refers to the characteristics that determine the reliability of the information in terms of its physical and logical validity It is based on accuracy, validity and consistency which are data parameters It is the absence of unintended change to the information between two successive updates or modification to data records

Based on

Criterion based evaluation of data Criterion based system of data management

Several factors (accuracy, availability, completeness and consistency)

Relationship

It is more generalize than integrity (includes system integrity and data integrity)

Data Integrity is a subset of Data Quality, which relates to characteristics beyond the validity of data

Dimensions

• • • •

Validity Reliability Timeliness Precision integrity

One of data quality

Threads

• • • •

Documentation and audit trails Outsourcing Technology Competence of personnel

• • • •

Time Technology Temptation Corruption, intentional or unintentional • Personal manipulations • Technological failures Lack of audit verification and validation

Big data security [31] is the collective tools measure utilized to ensure both the data and analytics processes from attacks and other malicious activities that could harm or negatively affect them. Much like other forms of cyber-security. Data integrity algorithms [32] utilized to save blocks of data.

2.3 IoT Data Fusion Interoperability The usage of sensors achieved more than 13 billion [33], a number that’s jumping every day, the IoT is generating huge data amounts with the projected to transfer business once it has been collected and analyzed. The capability to control the data based on age and relevancy produces the difference between a useful data lake and

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Fig. 7 Data quality types

a costly and inefficient one. Big data analytics [34] constructs massive value but it comes with a unique group of storage and data management. For any Information system, the interoperability returns to 1988 [35]. The classic definition of Interoperability is “the ability to make systems and organizations work together”. The IEEE discusses interoperability definition is considered “the power of two or more systems or elements to interchange information and to utilize the data that has been exchanged”. Interoperability [36] refers to the particular application’s requirements or needs. As a result, different categories of interoperability have been emerging. Interoperability has some types: Technical interoperability [37], Semantic interoperability [38], Syntactic interoperability [39], and Cross-domain interoperability [40] are examples of these categories. There are variant interoperability types (as known in Fig. 8) are required to help seamless and heterogeneous communications in the IoT. Reaching interoperability of data fusion that is a vital role to interconnect multiple sensors pass various communication networks. In fact, for the IoT to raise, sensors connecting via internet, that can be heterogeneous, require to connect to other sensors or applications.

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Fig. 8 Data interoperability types

3 Smart Mobility Smart mobility [41, 42] refers to smart vehicles, cars based on using the daily activities in a smart city (as Fig. 9). Urban data transportation (containing cars, taxis, buses, and trains), that requires to fuse the data in the transportation network and car sharing network. Smart Mobility is a useful tool to reach a more potential future. Smart mobility effects on the car services structure. More than 4.9 million people are utilizing freight in Europe. Two significant terms to inform Vehicle-to-vehicle and vehicle-to-infrastructure. Vehicle-to-vehicle (V2V) technology is communication between two or more vehicles. Vehicle-to-infrastructure (V2I) technology is a centralized connection between vehicles and the infrastructure that usage for send information simultaneously to the infrastructure. Smart Mobility is a very broad vision, and the research into the Smart Mobility is still in its infancy. The author [42] discusses that Smart Mobility is defined by “the usage of data and Communication Technology in new transport technologies to enhance urban traffic”. The researchers [43] describe that Smart Mobility “is a notion of comprehensive and smarter future traffic service in a set with smart internet-of-things technology. A Smart Mobility society is considered a simulation of reality management system for vehicles and their communication between them in the smart traffic following systems”. The researcher [43] refers to Smart Mobility as “local and supra-local accessibility, availability of ICTs, modern, sustainable and safe transport systems”. A smart transportation and mobility domain include info-mobility, and passenger’s mobility [31]; there is a classification taxonomy for smart city covering the description of the business model; and another taxonomy of smart cities [44]. There is a specific smart mobility taxonomy [45]. The proposed smart mobility for our research that keeps information integrity of multi-sensor fusion. The researchers [46] introduced an effective infrastructure for the cost to enable traffic lights system. The data reported are the cyclist’s mobile phone location, direction, and speed that is obtained via GPS. Previous research [47] presented an intelligent mobility framework, the SIMPLICITY framework, implemented in mobile sensors. It develops three essential functionalities: the essential context-based service, pushing data services to the final user. The researchers developed [48] the Smart Car that can help the traffic car control

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Fig. 9 A smart mobility architecture for connected vehicles

framework. It enables citizens to upload the smart traffic environment, using the smart car features through wireless sensors, and crowdsourcing. Smart car is considered an interconnected with the vehicle connection bus and utilizes hardware to return information from external sensors. The used platform entitled Arduino was configured to show a variety of wired and wireless communication interfaces. 1. Digital city: refers to the ICT for helping the construction of a wired, interconnected network of sensors for sharing information, such as e-government [49]. 2. Green city: refers to an ecological vision of the urban space, depends on the sustainable development idea. It uses for minimizing pollution and energy consumption and constructing general green areas [50]. 3. Knowledge city: refers to policies targeting enforcing information and knowledge available for cultural institutions [51]. There are two types of smart mobility: public and private. Car sharing is a service that allows you to reserve cars, picking park area. It enables minimization of urban traffic congestion, minimizing polluting (gas and noise), and reduction work of public space [50]. Our paper targets analyzing and classifying the actions of Smart Mobility. It also shows the importance and spread of integrity the data in the intelligent network on smart mobility network. So the proposed IoT based smart environment will offer enormous benefits to society.

4 The Proposed Taxonomy for Smart Mobility The basic architecture for data fusion in smart environment. There are three mining processes on data in multi-sensor fusion as the sequenced follow (as shown in Fig. 10):

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Fig. 10 The Basic architecture for multi-sensor data fusion in smart mobility environment

• Data Fusion [51, 52]: the fused data has to be prepared to combine from different data sources. These data require to clean from several outliers and make analytics on. • Data pre-processing: cleaning. • Data Classification and reduction: refers to the used algorithms for the data to extract and get the data patterns and measure these patterns of discovered knowledge. These patterns may be (video, audio, text, image, 3D images, maps, or data sources of sensor simulators). The Internet of Things (IoT) gets connectivity to about every sensors found in the physical space. This paper provides a review analysis of data fusion in smart mobility environment. This environment requires classification, fusion, and analytics for big data extracted from various sensors based on IoT. It focuses on the safety data challenge in data fusion for internet-of-things. That can improve data management, security and privacy issues. Fusion through IoT requires data security and integrity. Data integrity lost that make invalid data added to the database. Existing data modified to an incorrect value. Changes to database lost. Changes partially applied. Through data integrity constraints restrict data values that can be inserted or updated. Data integrity is the maintenance of trusting accuracy of data and trusting the data consistency. The term is broad in scope and may have widely variant meanings relying on the specific context. Data integrity is not to be disquieted with information security. This paper shows the proposed Taxonomy Data Integrity factors for Smart Environment. Smart mobility requires to apply one of these dimensions and some of the factors to reach the good smart automation system. No smart environment can avoid the integrity of data that requires to ensure the accuracy and consistency of stored data, indicated by an absence of any alteration in data between two modifications on the data record. Data integrity is important for database design that is based on conditions and procedures but that is required to check errors and clean data. Data integrity can also be a performance, gauge during these processes based on the detected error rate. Data should be preserved free from deterioration, modification or unauthorized disclosure to drive any number of mission-critical business processes with accuracy. Database security professionals employ any number of exercises to assure data integrity, containing: Data encryption, Data backup, Access controls, Input validation, and Data validation. Data integrity is a primary part of information security. Data integrity aims to the data accuracy and consistency for data in a database. The concept of Data Integrity utilized to discuss a state. Data values are standardized regards to a data model and type.

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Fig. 11 The Proposed taxonomy data integrity factors for smart environment

All data properties have to right, containing rules, relations, dates, and definitions. Data integrity is mandatory in a database when it is developed and authenticated in-progress for checking the errors and validation routines. Data-integrity system raises: Stability (one centralized system implements all data integrity operations), Performance (all data integrity operations are performed in the same tier as the consistency model), Re-usability (all applications benefit from a single centralized data integrity system), and Maintainability (one centralized system for all data integrity administration) (as shown Fig. 11). Smart mobility in smart city should require higher provision of public spaces, more compact form, greater heterogeneity of data, protection from risks, and control with social internet-of-things (SIoT). Smart mobility becomes one of the IoT revolution [53], the investment raises in it to connect 25 billion connected sensors in 2015. The projected statistic number of connected devices reaches to 50 billion, according to Cisco IBSG2011 [54]. Exploring Data Quality and Data Integrity. Essentially, Data Integrity is a subset of Data Quality [55], which relates to characteristics beyond the validity of data as described below.

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4.1 Completeness The measured percentage of completeness and is defined based on specific variables and business rules.

4.2 Uniqueness A discrete measure of duplication of identified data items within a data set or in comparison with its counterpart in another data set that complies with the same information specifications or business rules.

4.3 Timeliness The degree to which the data is up-to-date and available within an acceptable time frame, timeline and duration. The value of data-driven decisions not only depends on the correctness of the information but also on quick and timely answers. The time of occurrence of the associated real-world events is considered as a reference and the measure is assessed on a continuous basis. The value and accuracy of data may decay over time.

4.4 Validity A measure of conformity to the defined business requirements and syntax of its definition. The scope of syntax may include the allowable type, range, format and other attributes of preference. It is measured as a percentage proportion of valid data items compared to the available data sets. In the context of Data Integrity, the validity of data encompasses the relationships between data items that can be traced and connected to other data sources for validation purposes. Failure to establish links of valid data items to the appropriate real-world context may deem the information as inadequate in terms of its integrity. Data validity is one of the critical dimensions of Data Quality and is measured alongside the related parameters that define data completeness, accuracy and consistency.

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4.5 Accuracy The degree to which the data item correctly describes the object in context of appropriate real-world context and attributes. The real-world context may be identified as a single version of established truth and used as a reference to identify the deviation of data items from this reference. Specifications of the real-world references may be based on business requirements and all data items that accurately reflect the characteristics of real-world objects within allowed specifications may be regarded as an accurate piece of information. Data accuracy directly impacts the correctness of decisions and should be considered as a key component for data analysis practices.

4.6 Consistency This measure represents the absence of differences between the data items representing the same objects based on specific information requirements. The data may be compared for consistency within the same database or against other data sets of similar specifications. The discrete measurement can be used as an assessment of data quality and may be measured as a percentage of data that reflect the same information as intended for the entire data set. In contrast, inconsistent data may include the presence of attributes that are not expected for the intended information. The comparison of Data Quality vs Data Integrity largely centers on the dimension of validity associated with the data. In context of Data integrity, the attributes of data completeness accuracy and consistency are also closely related, followed by the completeness of the information. The timeliness and uniqueness of data are more useful to understand the overall quality of data instead of the integrity of information. In addition to these six key dimensions of Data Quality, every organization may use their own metrics and attributes to understand the true value that the available information holds for them.

5 Open Research Challenges There are four open research dimensions in data integrity that can accept new motivations which mentioned in Fig. 12. Anomaly detection [55] is identified the rare items, events or observations which raise suspicions by varying significantly from the majority of the data. This connection presents the selection data is based on data points anomalies as identifying these events are typically very interesting from a business perspective. Anomalies of data integrity is the first challenge of corrupted data or confused data such as error or events that effects on the data modification. Stability data is important that takes care of Big Data. There are several networks types requires to be safe the data such as the recent one namely, Blockchains [56].

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Fig. 12 Open research challenges of data integrity in smart environment

The second challenge is data in storage, which can be stolen or held hostage while resting on cloud or on-premise servers. These three attentions issues play a vital role in constructing a flexible end-to-end big data security philosophy for any communities. Many communities can develop security measures to keep some tools for big data analytics. One important tool of data security is encryption. Encrypted data [57] is useless to external factors such as hackers if they don’t have the key to unlock it. Constructing a firewall is one of big data security tool. Smart city acts the big data in real time stream in generating, running, and producing from multiple resources. These extracted data is big data with the factors of data. Several systems transfer small and traditional data into infrastructure datasets. Several systems depends on the machine learning systems that implement data analytics. All these urban data are used to run intelligent city technologies, so it is significant to save these huge amounts of data and information secure. Furthermore, it is necessary to safeguard the information privacy of locked and personal and to certain data accessed. Privacy is ensured by protecting five security factors: avoid identities and protect personnel and their data confidentiality. Prohibiting spatial tracking; communication protection which indicates not to eavesdrop any kind of conversations; and finally, transactions protection that protects every single purchase, exchange and query. • Hacks: grow the network tools for eavesdropping and spying [58] on several communication channels, catching the traffic behavior and fetching the traffic map. Eavesdropping is critical threat that drives the data integrity and confidentiality which reasons economy and failures. There are several thefts that affects urban infrastructure by stealing intangible stuff such as sensitive data, information, credentials, software and cryptographic keys; and by stealing tangible physical objects such as several sensors in machines (smartphones, laptops, and tablets, etc.) and technological equipment. • Attacks [58]: Denial of Service DoS: is to flood communication till services and sensors depending on this blocked connection. DoS attacks have a big impact the availability of systems or connections. Data Security, privacy and related issues are hot topics especially that smart city’s technologies and systems are becoming very important to optimize cities and enhance the quality of life (as shown in Fig. 13). Information system (IS) comprised of

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Fig. 13 The data security types of information systems

components working together to produce and generate accurate information, the making decision requires accurate and time information and data integrity. Data security is one of the most valuable assets for any organization. Its security contains several proceedings and measures taken to conserve data systems. Database security [59] is the mechanism that protects the database against intentional or accidental threads. Security policy describes the security measures enforced. The security mechanism of the underlying DBMS must be utilized to enforce the policy.

6 Discussion Data integrity relies on data quality with some essential factors (which mentioned in Fig. 14): accuracy, validity, completeness, etc. Data integrity is considered a subset of data security for making decisions. Data quality depends on the rules of a group of rates of qualitative or quantitative variables. There is no conflict between information integrity and security. Data security contains the conditions of data protection but the integrity includes the rules of trustworthiness of data. Data security targets reducing the risk of leaking intellectual property, industry documents, healthcare information. The strategies of data security contain permissions management, data classification, identity and access management, outlier detection, and big data analytics. Reducing risks management and maintain data integrity for smart mobility in smart city: 1. Validate Input: When your data set is supplied by a known or unknown source (an end-user, another application, a malicious user, or any number of other sources) you should require input validation. That data should be verified and validated to ensure that the input is accurate.

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Fig. 14 Information fusion integrity factors

2. Validate Data: It’s essential to believe that data processes haven’t been corrupted. Identify the specifications and key attributes that are important to your organization before you validate the data. 3. Remove Duplicate Data: the information sensitivity from a secure database to clean data and remove redundancy data. 4. Back up Data: In addition to taking off repeats to secure data, data backups are an essential part of the process. 5. Access Controls: there are several controls of validation: input, data, removing duplications, and backups. Only individuals who access should have an access key—ensuring that the keys to the kingdom are kept secure. 6. Always Auditing: saving data integrity from tacking down the root. Data integrity can be preserved by validating input, removing duplicating data, accessing controls, validating data, backup data, and always keeping an audit trail.

7 Conclusion The goal of this paper is that ensure the information integrity in multi-model sensor fusion for smart mobility. Smart mobility enables to control and manage the traffic congestion and vehicles rates on various roads in smart cities. Smart mobility takes

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care of the time dimension for each snapshot of traffics on the roads. Data integrity is considered a subset of data security for making decisions. Data integrity guarantees information quality or data quality that takes some factors for reaching good quality: validity, timeliness, accuracy, completeness, and consistency. Multi-sensor data fusion is a part of information systems integration. Multi sensor fusion refers to combining strategies and operators. It requires to cross several paths of data before fusing the information such as association, classification and aggregation. That can improve and manage the data.

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Legal Issues of Social IoT Services: The Effects of Using Clouds, Fogs and AI Sz. Varadi, G. Gultekin Varkonyi and A. Kertesz

Abstract Information and Communications Technology (ICT) is rapidly evolving, social network services and smart applications have started to dominate the world of the Internet spreading on interconnected smart devices. Cloud computing is a key enabler of Internet of Things (IoT) applications, while and Fog and Edge computing provide further methods for efficient data management. Beside such architectural enhancements, Artificial Intelligence (AI) solutions also spreading fast to provide smartness. The operation of such complex systems raises legal issues such as who owns or processes the data, who is liable in terms of security breach. In this chapter we aim to discuss the latest advances in ICT legislation in the European Union that affect these technology developments, as well as the service usage of end-users. First, we categorize IoT applications and summarize the EU GDPR guidelines affecting the design and operation of these applications. Then we present the results of a survey on legal awareness of potential users of these complex systems, discuss the relation of AI and the user consent, and conclude our work with recommendations for legal compliance of these applications.

1 Introduction The concept of Cloud Computing has been introduced to ensure that users can access their applications and data on demand, from anywhere in the world [1]. Services offered by clouds range from the infrastructure to the application-level, and they have Sz. Varadi · G. Gultekin Varkonyi Department of International and European Law, University of Szeged, Szeged, Hungary e-mail: [email protected] G. Gultekin Varkonyi e-mail: [email protected] A. Kertesz (B) Software Engineering Department, University of Szeged, Szeged, Hungary e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_7

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already opened new market opportunities by allowing organizations to focus on their core competencies. In recent years Information and Communication Technology (ICT) has enabled connecting more and more devices, even very small ones, to the Internet and to the Cloud, commonly called as the Internet of Things (IoT). Things or devices in this network can interact and communicate among themselves and with the environment by exchanging data and information sensed, and react autonomously to events, and influence them by triggering actions with or without direct human intervention [2]. IoT is the source of Big Data and Cloud is the means to manage it, as IoT itself has limited data storage and computation capacity. Data generated by IoT environments can be useful in many ways, particularly if analyzed for insights using data mining and Artificial Intelligence (AI) algorithms. Fog Computing provides the missing link in the cloud-to-thing continuum [3]. Fogs enable the sharing of resources at the edge of a network. By using fogs, data is possibly sent to multiple Fog node locations (depending on the volume of the data generated), closer to the user [4] for processing. By doing so, Fog reduces service latency, and improves QoS (Quality of Service), resulting in superior user-experience. The application of these technologies, however, moves functions, responsibilities, and management away from local ownership to third-party provided services. This raises legal issues such as who owns the data, who is liable in terms of data loss or security breach, and other privacy and security concerns, which is also important in social networking applications. The General Data Protection Regulation (GDPR) [5] came into force in May 2018 to strengthen the users’ influence on their personal data, and to reduce administrative formalities, as well as to improve the clarity and coherence of the EU rules for personal data protection. By responding to these advances, in this chapter we investigate Fog characteristics and identify use cases in so-called IoT-Fog-Cloud environments that will be later used to discuss possible legal issues. Our main contribution lies in the extract of corresponding legislation in the EU, and in the proposal of recommendations on how to govern data management in these systems. The remainder of this chapter is as follows: Sect. 2 introduces related works, the research background, and our proposed IoT use cases exploiting Fog and Cloud resources. In Sect. 3 we introduce the corresponding European legislation and the conducted survey. In Sect. 4 we perform role mappings for the identified cases, and state our recommendations for designing and operating IoT-Fog-Cloud environments, and in Sect. 5 we discuss implications of AI on the user consent. Finally, Sect. 6 concludes our chapter.

2 Related Work and IoT-Fog-Cloud Use Cases In the past decade we experienced how computing infrastructures evolved: the first single cloud providers entered the market than with cloud bursting techniques cloud federations were realized by inter-operating formerly separate cloud systems. In the case of IoT systems, data management operations are better placed close to their

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origins, thus close to the users, which resulted in better exploiting the edge devices of the network. The group of such edge nodes formed the Fog, which is a distributed paradigm [4], where cloud storage and computational services are performed at the network edge. This new paradigm enables the execution of data processing and analytic application in a distributed way, possibly utilizing both cloud and near-by resources. The main goal is to achieve low latency, but it also brings novel challenges in real-time analytic, stream processing, power consumption and security. Beside such architectural developments, Artificial Intelligence (AI) methods receive new interests by managing Big Data coming from these complex, heterogeneous systems. Both AI methods and Fog computing process a vast amount of personal data, therefore many of these applications face security challenges and legal issues. Security concerns for IoT was investigated by Escribano [6] by discussing the first opinion of the Article 29 [7] of the Data Protection Working Party in this regard. They stated that it was crucial to identify and realize which stakeholder was responsible for data protection. They also highlighted the main challenges in privacy and data protection, which are: lack of user control, low quality of user consent, secondary uses of data, intrusive user profiling, limitations for anonymous service usage, and communication- and infrastructure-related security risks. Yi et al. [8] further extended these concerns towards the Fog. They presented a survey, in which they argue that secure and private data computation methods are needed, and privacy need to be addressed in three dimensions: data, usage and location privacy. Mukherjee et al. [9] envisaged a three-tier Fog architecture, where communication is performed through three interfaces: Fog-cloud, Fog-Fog and Fog-things. They stated that secure communication is a key issue, and privacy-preserving data management schemes are needed. Even though they mention, they did not detail legislation challenges, which is the aim of our chapter with the proposed use cases. Some related work already addressed GDPR rules for emerging technologies. Urquhart et al. [10] published a report on the importance of accountability to data protection in IoT systems. They argue that distributed data flows, inadequate consent mechanisms and the lack of interfaces hinder user control on the behaviour of IoT devices. They argue that GDPR guidelines can regulate development processes for IoT systems, and they propose systems design recommendations to ensure accountability in such systems. Fernquist et al. [11] investigated how data from IoT devices can be used to identify a user. They concluded that data profiles extracted by monitoring from social networks of user communicates and devices usages can be used to identify individuals, which is a serious threat for privacy. Cath et al. presented a survey paper of three reports (both from the US and EU) on the design of policies leading towards the development of a so-called good AI society [12]. Their work concluded that the overviewed visions address adequately various ethical, social, and economic topics, but they fail to provide an overall political vision and long-term strategy. They also argued that solutions should rely on human dignity (GDPR also point to this direction) to develop a comprehensive vision on how responsibility, cooperation, and sharable values should be considered to arrive to a good AI society. They also highlighted that without paying attention, AI development and its application may easily distort human life, e.g. users may be profiled into false

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categories. Thus, we need to ensure that our new smart technologies will adhere to privacy by design principles. Villaronga et al. [13] investigated the legal background behind the Right to Be Forgotten principle introduced by the GDPR. They concluded that due to technical problems, it may be impossible to fulfill the legal goals of the Right to Be Forgotten in artificial intelligence environments. Concerning IoT application areas Want et al. [14] set up three categories to classify them: (i) Composable systems, built from a variety of nearby interconnected things; (ii) Smart cities, utilities of modern cities such as a traffic-light system capable of sensing the location and density of cars in the area; (iii) Resource conservation applications, used for monitoring and optimization of resources such as electricity and water. Atzori et al. [15] proposed a survey and identified six domains: transportation and logistics, health-care, smart environments (home, office, plant), personal and social, finally futuristic domains. In this paper we do not aim to classify all application fields, but to define certain architectures that fit most application cases involving cloud, IoT and Fog utilization, to enable further investigations concerning security and privacy issues within this environments. The OpenFog Consortium also categorized IoT and Fog use cases in [3]. They introduced four cases to represent hierarchical fog deployments. They argued that functional boundaries of Fog architectures are fluid and can be deployed in many combinations. Real world applications may use physical deployments involving multitenant fog and cloud systems owned by multiple entities. Based on the discussed related work and definitions we extend and revise this view and derive six use cases to be able to better highlight the corresponding legal aspects. We differentiate six architectural cases starting from a closed, local area to an open, distributed system. The different administrative domains are denoted by dashed lines in the figure. While in an earlier work we have already started to examine and define legal fog use cases [16], in this paper we continued this work, and arrived to the following cases: 1. Case 1—Fig. 1. Local, ad-hoc IoT environments can be formed from near-by things (e.g. smart watch, thermometer, smart phone and TV) through a router to perform a certain task. In this case no data is moved outside of the local environment. In other words, local services gather and process user data. Generally, these applications do not need compute-intensive fog devices. 2. Case 2—Fig. 2. IoT devices or sensors are connected to a mobile device (either directly e.g., through Bluetooth, or through a router) that gathers data and then forwards it to a cloud service. In this way, data can be stored and processed in a remote location in the cloud. Visualization of the processed data can usually be done in a web browser. In this way, some user preferences may be given by the user for data storage and processing. 3. Case 3—Fig. 3. Certain IoT devices (such as smart TVs or smart refrigerators) communicate with a cloud service directly. In this case data is both stored and processed in a remote cloud. Generally, commercial IoT products utilize this type of architecture.

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Fig. 1 IoT-Fog-Cloud deployment architecture of Case 1

Fig. 2 IoT-Fog-Cloud deployment architecture of Case 2

Fig. 3 IoT-Fog-Cloud deployment architecture of Case 3

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Fig. 4 IoT-Fog-Cloud deployment architecture of Case 4

Fig. 5 IoT-Fog-Cloud deployment architecture of Case 5

4. Case 4—Fig. 4. IoT devices and sensors are connected to a Fog device capable of storing and processing (e.g. aggregating) data, and the application using the data may be run in a smart phone (e.g. for instructing actuators) or in the cloud. In this way data can be stored and processed both locally at Fog devices, and in a remote location in the cloud, where a cloud service is running. The main difference compared to Case 3. is that the user has some influence on the data aggregation process (e.g. with a specific content or application setting). 5. Case 5—Fig. 5. IoT devices and sensors are connected to a local Fog device capable of storing data, and both a local, private cloud, and a remote public cloud are also available for performing data storage and computational tasks. The application running in the Fog may decide where to store and process the data based on certain conditions (e.g latency, software or application component availability). 6. Case 6—Fig. 6. In the most complex case, IoT devices can access and utilize multiple Fogs (residing in different administrative domains), and actual load conditions affect the selection of the appropriate one. In this architecture, IoT-sensed private data may be sent to different Fog devices or services, and through them they may reach different cloud destinations (in a remote administrative domain).

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Fig. 6 IoT-Fog-Cloud deployment architecture of Case 6

If the location of a device or the user changes, the fog node selected for storing or processing the data might change dynamically as well. From these case we can see that the collection, aggregation and processing of user data can be done is various ways. In the next section we summarize legislation affecting these tasks, data flow, storage and processing, and later we give guidelines and recommendations how to comply with such regulations in the identified cases.

3 GDPR and Its User Perceptions 3.1 The New European Regulation Rapid technological developments and globalisation has lead to significant increase in the scale of personal data collecting and sharing. The European Commission has put a big emphasis on introducing the digital single market within internal market with the free flow of personal data without any obstacles, ensuring a coherent and strong data protection at the same time. The General Data Protection Regulation [5] was created by this will, which entered into force on 25 May, 2018. From that time, the level of protection of the rights and freedoms of individuals with regard to the processing of such data is equivalent in all Member States, instead of the different rules set out by the formerly effective Data Protection Directive. The main objectives of GDPR are: to modernize the EU legal system for the protection of personal data to respond to the use of new technologies; to strengthen the influence of the users on processing their personal data; to reduce administrative formalities and to improve the clarity and coherence of the EU rules for personal data protection. We identified the following key changes affecting data stored and processed in IoT-Fog-Cloud systems.

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Definition of new terms: • Personal data: is any information related to a natural person (i.e. user or data subject). It can be the name, identification number, location data and online identifier of a user, or one or more indicators specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that person. • Controller: is a natural or legal person, public authority, agency or other body who determines the conditions of the processing of personal data. • Processor: is a natural or legal person processing personal data on behalf of the controller. Processing activities (that may be automated) include any operation performed on personal data, such as collection, recording, structuring, storage, adaptation, alteration, retrieval, use, etc. • Joint controller: a processor who processes data beyond the controller’s instructions is to be considered as a joint controller. • Pseudonymisation: means the processing of personal data in a way that personal data can no longer be connected to a specific data subject (e.g., user of an IoT device), without the use of additional, clarifying information, which should be kept separately. Principles governing the data process: For the principle of data limitation, the terms of the data collection purpose, data quality and storage duration, have to be declared by stakeholders keeping their necessity in mind. Transparency has a key importance: stakeholders should inform the user: of what data is collected, how it is stored, and for what purposes it will be processed. A comprehensive responsibility and liability framework for the controller should also be established. Beside these, the data processing should be lawful and to achieve that, the exact legal grounds are detailed in the Article 6 of GDPR. One of the legal grounds for processing is the so-called consent. Requirements of the data subject’s consent: The GDPR requires the data subject to agree with the processing of his/her personal data in the from of a consent, which should be freely given, specific, informed and unambiguous, and should be acquired for all purposes. It should be given by an explicit indication of the data subject’s wishes, inactivity should not create the consent. It has to cover all data processing activities, and the data subject has the right to withdraw it at any time. Concerning Fog environments, this means that each time a Fog node aims to collect data for a new activity or purpose (other than covered by the original consent), a new one have to be given by the data subject. Data protection by design: GDPR introduced a new term to enhance privacy called data protection by design. This means that the controller should implement appropriate technical and organizational measures and procedures to ensure data protection both by the time of developing a service, as well as by operating it. In most cases, such methods should target the minimizing of data processing, and the use of pseudonymisation of the personal data. These methods are usually performed by Fog nodes of IoT-Fog-Cloud systems. The data subject should also be enabled to monitor the data processing.

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Obligations of the controller: In accordance with the GDPR, controllers should provide detailed information to the data subject at the time of data collection: including its identity and contact details, the legal basis and purposes of the processing, the storing period, if there is automated decision-making or profiling, and if the controller intends to transfer personal data to a third country. Controllers or processors, not established in the territory of EU, should appoint a representative in the EU, who can act on behalf of them. The regulation requires that the controller should use only processors who apply appropriate technical and organizational measures to protect the rights of the data subject. When two or more controllers determines the purposes, conditions and means of the processing of personal data together (as in multiple fog nodes), these joint controllers should settle their respective responsibilities in an agreement between them. Rights of the data subject: The GDPR ensures the data subject’s right to require the erasure of her/his personal data, this is the so-called right to be forgotten, and it is applicable, when the data is no longer necessary in relation to the purposes for which they were collected or otherwise processed. The data subject also has a right to withdraw the consent on which the processing is based, or when the storage period has expired, and where there is no other legal ground for the processing of the data. Upon such an erasure or withdrawal request of the data subject, the corresponding controller (who made the personal data public) has the obligation to inform any third parties (having access to that data) to erase any links, or copy or replica of the personal data. In the case a third party publishes that data, the controller will be responsible to perform the erasure, if it has authorised the publication before. In some cases the retention of the personal data is necessary, e.g. reasons of public interest in public health, or statistical and scientific research purposes. Data portability is another right of a data subject, in which the controller should provide means for transferring data from an electronic processing system to another. Therefore the data subject has to be able to obtain his/her data from the controller in a structured and commonly used electronic format. Finally, the data subject can object to profiling, including the evaluation of certain personal aspects or analysis or prediction of a particular natural person’s performance at work, economic situation, behaviour, or other properties. Provisions to guarantee the data security: In general, appropriate measures should be implemented by the controller and the processor for security, by protecting personal data from accidental or unlawful destruction or loss, and by preventing unlawful forms of processing, access or alteration of data. Impact assessment is also a new element in GDPR to govern the obligation of controllers and processors for data protection. It should detail the planned processing operations before each risky processing operation by stating the necessity and proportionality of the processing, and the possible risks to the rights and freedoms of data subjects, as well as the means for addressing these risks. Such risks in IoT-Fog-Cloud systems may arise from: systematic evaluation of personal aspects of natural persons (e.g. in the Fog data may be gathered from personal devices), monitoring of a publicly accessible areas (e.g. smart homes). Furthermore, the GDPR clarifies the content and procedures of

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the codes of conduct and encourages the Member States to introduce data protection certification mechanisms and data protection seals and marks to allow data subjects to assess the level of data protection provided by controllers and processors. Data transfer to third countries: GDPR unifies data protection legislation within the EU, and also clarifies data handling related to third countries. A data transfer could only be carried out, when an adequate level of protection is ensured by a third country. Such situation may occur when the data of an IoT application is managed by multiple fog or cloud providers—as in some of our identified use cases. The GDPR states that the European Commission decides whether an adequate level of protection is provided or not. Such a decision should include the rule of law, respect for human rights and fundamental freedoms, relevant legislation and independent supervision. International commitments or other obligations arising from legally binding conventions or instruments, and participation in multilateral or regional systems may also affect this decision. When the decision found that the adequate level of protection is ensured, periodic reviews should be done at least every four years to monitor the relevant developments in the third country by the Commission. A list of those third countries, where an adequate level of protection is or is not ensured, is published in the Official Journal of the EU. Remedies, liability and sanctions: The new regulation provides right to a judicial remedy in case of a personal data breach against a controller or processor by turning to the national personal data protection authority, or to the national court, except for controllers or processors being public authorities of Member States exercising their public powers. One of the possible penalties are administrative fines, but the Member States are allowed to define other penalties. GDPR introduced unified rules to be applied in every Member State in the same way, leaving discrepancy amongst them. This means that all parties of operating and using a fog application related to a Member State of the EU should be aware of this regulation, and the correct identification of controller and processor roles are crucial. The fog use cases we revealed in Sect. 2 highlight that multi-tenancy is even more existent in IoT-Fog-Cloud environments than in pure cloud systems, and the number of participating stakeholders can also be higher (specially with multiple Fog providers).

3.2 Data Protection-Related Awareness of Users We conducted a survey to measure EU and non-EU students perception about data protection, their awareness of the GDPR, and their evaluation of data protection right in the era of IoT, Fog and AI based technologies. We prepared a questionnaire of 18 questions gathering information on age, nationality, field of study and general knowledge on data protection, GDPR and legal implications of emerging ICT technologies, such as AI, IoT, Fog and Cloud computing. Our aim was to measure the students perception are to create a descriptive study for future professionals on

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the current state of the GDPR and AI technologies, and to provide hints for future research directions. We seeked answers for the following main questions: 1. Is there any difference between EU students’ and non-EU students’ perception about the right to data protection? 2. Is there any difference between law students and other students understanding of the GDPR? 3. Is the GDPR sufficient enough to cover all aspects of the possible breaches caused by the use of emerging ICT technologies? 4. What is the level of knowledge of the students about these technologies, and their possible effects on data protection? We circulated the questionnaire in November 2018, among 200–300 international students studying at the University of Szeged, Hungary (within the EU), and at the Hacettepe University, Turkey (outside of the EU). In total, 110 active university students responded to our invitation. The participants’ age distribution was the following: 57% of the participants were between 18 and 21 years old, 33% of them was between 22 and 25 years old, and only 10% of them was over 25. Concerning sex distribution, 57% was male, and 43% was female. Most of the participants completed their second to fourth semester of their programs, while the rest are either in the first to third semester of their bachelor programs or in the beginning of a master program. The nationality of the participants varied widely, the responding students were originated from more than 20 countries. Nevertheless, most of the participants were Hungarian and Turkish students. According to the survey results, representation of EU students (with 62%) was more than non-EU students (with 38%). Their field of study also varied highly; most students studied law (40%) and computer science (34%) out of 20 different fields. Concerning general knowledge on data protection and privacy, most participants thought that their personal data could be used for serious crimes including monetary loss, and one third of the participants considered dangerous enough, if service providers use their personal data to create user profiles and use those profiles for targeted advertisement which could generate high profit. We expected that EU students are more informed about the GDPR than non-EU students, but the results showed that there is no real difference between the two groups, and both groups thought that the GDPR is not enough to answer the possible challenges arising from emerging technologies. One of the novelties of the GDPR was to extend the territorial scope of the data protection law of the EU outside of the physical territories. Only 9 participants among the 110 indicated that they were aware of the fact that the GDPR is a legally binding document for the non-EU countries, too. Concerning the fact that GDPR requires web services a detailed consent to be acquired from users, 65% of the participants knew about this issue, while 31% thought that notification is enough (what most web pages do by default). A significant result emerged with our survey which clearly shows that the participants thought that emerging technologies like IoT, Fog, Cloud and AI introduce

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dynamics changes related to data locality, data transfer, and data processing, which cause new possible ways of data breaches: 81% of the participants had this opinion, while 13% of the participants indicated that data protection would not be possible anymore, since technological developments further complicate data protection. Concerning AI, all of the participants knew at least one type of AI applications. From those AI types, autonomous cars are the best known, but military robots, personal assistants, chatbots, and social robots are also well-known without a big difference between the groups. The least known AI method is the crime prediction service, which was marked by only 29% of the participants. Finally, we asked the participants about what possible problems may arise of implementing GDPR rules for the use of AI technologies. 76% of the participants foresee that AI technologies could combine personal data and user behaviour in a way that the original user consent would be invalidated. Our survey also prevailed how consent could still not be enough to protect personal data in the era of emerging ICT technologies.

4 Role Mappings of the Use Cases and Recommendations Based on the use cases discussed in Sect. 2 there are four stakeholders in an IoTFog-Cloud ecosystem (as depicted in Table 1). The User has access to certain IoT devices and sensors, which generate possibly sensitive, private data. The data storage and processing may be done locally or close to the user in the fog, operated by a Fog provider (who may be the user itself). When necessary, parts or an aggregated form of these data may be further transfered to a cloud (either private or public) for storage or processing, operated by a Cloud provider. Finally, generally for commercial IoT devices, certain services are used to manage these devices (and their data) operated by a Service provider (who may also be any of the earlier stakeholders). In Table 1 we identified the corresponding roles for these stakeholders (DP as data processor, DC as data controller and JC as joint controller as introduced in the previous subsection) in terms of data protection defined by the GDPR.

Table 1 Identified roles in IoT-Fog-Cloud use cases Use case User Fog provider 1 2 3 4 5 6

DC DC – DC/JC – –

– – – DC/JC DC/JC DC/DP/JC

Cloud provider

Service provider

– DP DP DP/JC DP/JC DP/JC/DC

– DC DC DC DC DC

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As we can see from this table, as we broaden the scope and complexity of the managed systems, the user control of the sensed private data weakens, and the responsibility of data protection are shifting towards fog, cloud and service providers. Furthermore, in the most complex cases dynamic role changing is possible, which makes it hard to identify the appropriate data controller. For example the Cloud Provider in Cases 4–6 can act as a data controller or a data processor or a joint controller under different conditions. For example, in Case 5, let us imagine a scenario, when a user operates a camera surveillance application in her smart home. The application is capable of identifying foreigners coming close to the house, with a computation intensive algorithm using AI techniques for object tracking. The application is executed on fog devices composing a small datacenter owned by the user. In this case the user is also the fog provider playing the role of a data controller. During the weekends, gamer parties are held in the house exploiting a significant amount of computing power from the fog data center. In such situations, the video surveillance application notifies the loss of computational power, and bursts out the image processing algorithms to a public cloud (e.g. Amazon). If the user paid virtual machines are executing parts of the user application in the remote cloud, the public cloud provider will be a data processor (acting according to the instructions of the user). But, if the user utilizes a cloud service for the same purpose executing a different application for a certain image processing task, the (cloud or) service provider may processes data beyond the user’s (i.e. data controller’s) instructions, therefore the provider will become a joint controller. Concerning our recommendations, as seen in Table 1, the roles of different stakeholders in IoT-Fog-Cloud ecosystem keep on changing depending on the deployment architecture of applications. Therefore, organizations must employ data mapping to be aware of their data flows (where the data flows from, within and to). This will not only help to identify the interaction of data between different stakeholders, but will also make proper assessment of the privacy risks related to storage, processing, and transmission of data. Classification and data mapping are necessary to support data portability, right of access, and right of erasure. GDPR requires organizations to collect relevant and limited data for specified transparent, explicit, and legitimate purposes. Processors (which can be fog nodes) will need to ensure accuracy of data by keeping it up to date. This information also needs to be transparent to individuals, and consent for collecting the data will have be collected. This recommendation directly maps to use Case 4, where it was seen how users can affect the data aggregation process. In Fog computing, multiple nodes throughout the network can store and process the data—as exemplified by Use Case 6. This opens multiple vulnerable points to malicious attacks. Therefore organizations should implement decentralized and scalable secure infrastructure to ensure data protection, identity authentication, and detection of rogue Fog nodes and IoT devices. If some fog nodes happen to be controlled by a malicious user, it is difficult to ensure the integrity of the data, specifically if data is distributed to multiple fog nodes. Multiple data flows increase the attacking surfaces, hence making the system more vulnerable. Therefore, a central understanding of data semantics (metadata),

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authentication, and a place to manage a “single version of the truth” or data integration is needed, when it comes to dealing with massive amounts of distributed data within multiple Fog devices and cloud environment. Organizations should adopt a privacy by design approach in the design, development of any device that collects personal data or is the source of big data for analytical purposes. The technical challenge is, however, to enable devices with limited processing power and/or memory to receive and respect such privacy by design policies. In addition, privacy impact assessments should be performed and recorded by data processors, and a data protection officer should be appointed for accountability purposes. With Fog devices capable of storing and processing data, multiple copies of user data might exist in the network. Therefore, organizations must ensure that all copies of data is deleted (a) whenever the personal data are no longer necessary in relation to the purposes for which they were collected or otherwise processed; or (b) the data owner/user withdraws consent on which the processing is based and where there is no other legal ground for the processing; or (c) user objects to the processing to storing of the data. Such requirement may arise from use Cases 4, 5 and 6.

5 Discussion on AI and the User Consent Our analysis shows that data controllers are at the utmost position on designating the faith of their users’ data. Around 175 ZB of such data [17] is available today on different platforms such as cloud, smart phones, IoTs, or cell towers. Those data collected from social media, shopping sites or browsers with or without the users’ will, are boosting AI technologies since data is the most basic component of AI and Machine Learning. One may ask, which are the legal bases within GDPR that data controllers should consider to operate their AI services? Since data controllers who offer personalized services for individuals may not refer to the legal bases pursuant to the Article 6 of the GDPR, but consent is the most referred legal bases by them as for today and most probably in the future, too. Obtaining valid consent is based on the principle of the users’ free will, which indicates their intention to permit or not to permit a data controller to further process certain data regarding them [18]. Article 7 of the GDPR lays down several other conditions for consent and Article 29 Working Party has updated the guidelines on consent [19]. Transparency rule is one of the basic rules ensuring the users’ free will. However, data controllers often fail to present transparent data either to the data subjects or to the other data controllers or processors as a result of technical and practical implications arising from AI technologies together with legal uncertainties. From the technical point of view, well-known black-box algorithms prevent data controllers from ensuring data subjects’ right to explanation, which is not embedded in the GDPR explicitly [20]. Data controllers struggle with foreseeing all the possible purposes in the beginning of single data processing activities. As a result, they prepare such static privacy statements that are not proper for the dynamic nature of

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AI technologies. A study measuring Android-based applications’ behaviors and their potential non-compliance level with their own privacy statement proves that almost half of the some of the 18.000 app studies were found with one or two potential inconsistency [21]. Ensuring transparency for right to erasure and presenting meaningful information about the logic involved with automated decision making, including profiling activities that data controllers process data for is a must. The right to erasure is especially related with withdrawing a consent, which is the last step of giving or taking back user’s permission to process their data by data controllers. For AI-based services, which learn from user data to offer more and better services, it is a question whether erasing data from the algorithm is desirable. AI application offering personal health care services certainly needs its user’s medical history and current medical conditions to assist with accurate solutions. What the data controllers do, in the end, is to provide users such consent texts that are general or that are written only in direction with the legal obligations, which cannot be practically fulfilled. We can see from this discussion that the application of AI techniques further complicates the previously identified cases, and also has an impact on the role mapping processes. Bearing this in mind, both the IoT-Fog-Cloud application developers and the related service providers need to be very cautious and precise about data management in these complex systems.

6 Conclusions We have proved that recent advances in ICT, and IoT environments generate unprecedented amounts of data that should be stored, processed and analysed mostly using Artificial Intelligence methods. Cloud and Fog technologies can be used to aid these tasks, but their application give birth to complex systems, where data management raises legal issues to comply with. In this chapter we identified use cases in IoT-Fog-Cloud environments and gathered GDPR novelties affecting the design and operation of IoT applications exploiting novel ICT technologies. We also presented the results of a survey composed of questions related to GDPR awareness of users on privacy issues related to IoT-Fog-Cloud systems and AI solutions. Finally, we presented role mappings in these cases, and proposed recommendations on how to govern data management in these systems to adhere these legal regulations. Acknowledgements The research leading to these results was supported by the Hungarian Government and the European Regional Development Fund under the grant number GINOP-2.3.215-2016-00037 (“Internet of Living Things”). This paper is a revised and extended version of a conference paper [16].

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Social Internet of Things and New Generation Computing—A Survey Hamed Vahdat-Nejad, Zahra Mazhar-Farimani and Arezoo Tavakolifar

Abstract Social Internet of Things (SIoT) tries to overcome the challenges of Internet of Things (IoT) such as scalability, trust and discovery of resources, by inspiration from social computing. This survey aims to investigate the research done on SIoT from two perspectives including application domain and the integration to the new computing models. To this end, a two-dimensional framework is proposed and the projects are investigated, accordingly. The first dimension considers and classifies available research from the application domain perspective and the second dimension performs the same from the integration to new computing models standpoint. The aim is to technically describe SIoT, to classify related research, to foster the dissemination of state-of-the-art, and to discuss open research directions in this field. Keywords Social internet of things · Application domain · Computing model · Survey

1 Introduction We live in a digital world where the number and variety of smart devices are increasing, rapidly [1]. A smart object can be a sensor or any physical device that has the ability of sensing the environment, collecting data, connecting to the network, and processing [2]. Some examples of these devices are smart phones, smart watches, smart TVs, medical and health devices, and vehicles [3]. The integration of these devices through Internet connectivity introduces a new field called Internet of Things (IoT) [4]. In fact, the Internet of Things is composed of a global network of smart H. Vahdat-Nejad (B) · Z. Mazhar-Farimani · A. Tavakolifar PerLab, Department of Computer Engineering, University of Birjand, Birjand, Iran e-mail: [email protected] Z. Mazhar-Farimani e-mail: [email protected] A. Tavakolifar e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_8

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things that each of them has its own address and communicates with others based on the standard agreements [5]. The Internet of Things offers smart services to control the things and user’s environment by creating the connection between smart things [6]. Nevertheless, the fulfillment of the Internet of Things has faced some challenges such as scalability, trust and discovery of resources, information and services [7]. Hence, Social Internet of Things (SIoT) has been recently proposed, with an idea that has originated from the social network [8, 9]. The novel paradigm of SIoT has emerged to describe the world where everything around the humans can be cleverly comprehended and interacted [10]. In fact, SIoT attempts to solve the problems of scalability, trust and discovery of resources, information and services by modeling the IoT as a social network. Hence, it provides a platform for better interactions between people and things [11, 12, 13, 14, 15]. Furthermore, privacy protection technologies utilized in social networks can be employed to improve the IoT security [16–19]. In the past years, SIoT has attracted the attention of many researchers. In this regard, several survey studies have examined the Internet of Things from various aspects such as architecture [20]. Similarly, the field of SIoT has been investigated in terms of the operating system in 2018 [21]. However, the need for a research that investigates the existing SIoT systems in terms of the application domain and the way of integrating to the new computing models is felt. Available research studies on SIoT have been conducted in different domains and also they have exploited specific computing paradigms such as cloud or edge. This chapter proposes a framework with two dimensions of application domain and computing paradigm for investigating the SIoT systems. Afterwards, it systematically reviews and classifies the related research with respect to these dimensions. Finally, the conclusion remarks and future research directions are discussed. After this introduction, the proposed framework and the specifications of the considered projects are investigated in Sect. 2. In Sect. 3, related projects are explored in terms of the application domain. Section 4 explains the computing paradigm used in these projects, and finally the conclusion remarks and future research directions are discussed in Sect. 5.

2 The Proposed Framework In this research, the SIoT papers are considered from two main perspectives including the type of application domain and the utilized computing paradigm. The proposed framework is illustrated in Fig. 1. Domain type depicts the main application of the considered SIoT system. IoT has previously entered almost all domains of daily lives. SIoT has limitations to be applied in all these domains, because of the social nature of it. In general, SIoT systems have been integrated into a few fields such as Healthcare [22, 23] and Intel-

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Healthcare Domain

ITS Smart place

SIoT Computing paradigm

Cloud Edge

Fig. 1 The proposed framework

ligent Transportation Systems [24, 25] to facilitate the process of automation and intelligence. In recent decades, different computing paradigms have been introduced such as cluster [26], grid [27], pervasive [28], cloud [29], edge [30] and fog [31] computing. Owing to the fact that the Internet of Things is arisen from these computing paradigms, SIoT solutions sometimes have employed these computing paradigms to fulfil the SIoT idea. In the proposed framework, the existing articles are also investigated based on the type of their computing paradigm. The advantage of such an investigation is to forecast the future integrated computing paradigm. Table 1 demonstrates the overall specifications of the investigated papers. Since this survey is carried out in the beginning of 2018 and due to the required time for reviewing and publishing a paper, most of the considered papers are published in 2017. It shows the novelty of the SIoT paradigm. Although numerous papers have been published either in IoT or social computing, the number of outstanding works done on SIoT is limited. In the following sections, these projects are investigated and classified from the perspectives of domain and computing paradigm.

3 Project Review: Domain Internet of Things has entered into various fields of human life [32]. Comparing to IoT, SIoT uses the social structure of things and has limitations in the application domain. After reviewing previous research papers on SIoT from the perspective of application domain, they can be classified into the groups of Healthcare, Intelligent Transportation System and Smart place. In the following, these domains are discussed:

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Table 1 Projects overview Project

Reference

Country

Year

Publication Conference

2017

Publisher Journal

EMS

[60]

Italy

*

IEEE

V-doctor

[61]

Italy

PHY

[62]

Canada

2017

*

IEEE

2017

*

IEEE

VSNP

[63]

India

2018

tNote

[64]

Canada

2014

*

Springer

SIoV

[65]

Italy

2014

*

IEEE

LSE

[66]

Italy

2017

*

IEEE

HVAC

[67]

Italy

2017

*

IEEE

iSapiens

[68]

Italy

2016

*

IEEE

COSMOS

[69]

Greece

2014

*

IEEE

I-Painting

[70]

China

2017

*

Springer

*

Elsevier

Healthcare: Healthcare domain has long been one of the most momentous concerns of humanity. Today, SIoT has gained much significance in this domain [33]. It is necessary to provide a system for elderly care as senility abruptly affects the health and many of the elderly suffer from at least one chronic illness. Among the research studies, EMS project introduces an SIoT-based elderly monitoring system, which collects and analyzes environmental and physical data and sends them to the caregivers as needed. In the v-doctor project, a framework for the integration of E-health and SIoT has been proposed to develop the monitoring system of elderly. This system can provide special services for elderly through the issuance of medical guidelines and by discovering nearby objects and choosing the people who can help them. The PHY-Aided project introduces a security technique for protecting the healthcare system of SIoT. In this system, social networks are employed as a trustworthy platform for sharing the data of users and healthcare providers. Intelligent Transportation System (ITS): Transportation is another domain that IoT plays an important role in [34]. Nowadays, the use of vehicles has increased dramatically as regards to the ongoing expansion of cities and population growth. It leads to the increase of traffic, accident and air pollution. Accordingly, transportation is one of the most important domains that benefits from the advantages of IoT to solve the mentioned problems [35]. In this regard, IoV has been proposed earlier [36, 37] and its various social structures and types of communication have been investigated [38]. The importance of transportation domain is to the extent that it has also been penetrated by the paradigm of SIoT and several studies have been conducted in this field [39]. Indeed, SIoT is introduced to omit the main difficulties of traffic management [40]. Among the investigated projects, SIoV consists of three components including management for collecting and analyzing data, security for managing the trust, and facilities to discover desired services for Intelligent Transportation Systems.

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Vehicular Social Networking (VSN) is a type of social networks built by users on the roads. SIoV is applied in the VSNP project for the interaction of vehicles and raising the level of driving knowledge. Afterwards, an algorithm is proposed to control the traffic and road safety. In tNote, a Social Networking Vehicle Architecture (SIoV) and an infrastructure for data storage on VANET are proposed, which allow users to share their information with other vehicles. Smart Place: Smart services and applications can affect and improve the quality of our daily lives. Smart Place is a concept that allows the use of advanced technologies for urban environments and buildings. It results in welfare in the quality of life by providing a variety of services [41]. Smart building is a solution for monitoring and managing a building. It has various capabilities such as temperature, lighting and gate control as well as safety [42]. Among the observed projects, the LSE introduces an SIoT-based approach to address the security issues such as trust, entity discovery, and interoperability in large-scale smart environments. In this system, an intermediate layer is utilized in order to hide the heterogeneity and mobility of devices in the smart place. The HVAC project proposes a smart system for efficient consumption of energy in smart buildings and employs the SIoT paradigm to reduce the cost of consuming electricity and energy. Similarly, to diminish the energy consumption, the COSMOS project provides a platform for handling limited resources in the SIoT paradigm. In this system, users are able to receive an energy plan based on their personal needs and budget, which is obtained by observing the past energy consumption patterns on the COSMOS platform. Finally, a Java-based platform for designing and implementing the smart environment is introduced by the iSapiens, which uses SIoT to address the constraints of scalability and computing capacity. Table 2 categorizes the considered projects regarding the domain type.

Table 2 Project review according to domain type

Project

Reference Domain Healthcare

ITS

Smart place

EMS

[60]

*

V-doctor

[61]

*

PHY

[62]

*

VSNP

[63]

*

t-Note

[64]

*

SIoV

[65]

*

LSE

[66]

*

HVAC

[67]

*

iSapiens

[68]

*

COSMOS

[69]

*

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4 Project Review: Computing Paradigm Several computing paradigms have been proposed in recent decades and evolved over time [43, 44]. In this section, the latest computing paradigms that are used in SIoT are investigated. In general, considered research projects are divided in two categories based on the employed computing paradigm: cloud and edge computing. In the following, these projects are investigated. Cloud Computing: The goal of cloud computing is to provide diverse computing services via the Internet. In fact, computing services are supplied to the users as web services [45, 46, 47]. According to the NIST definition “cloud computing is a model for enabling ubiquitous, convenient, and on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [48]. Cloud computing users are not commonly the owners of the cloud infrastructure; however they lease it out from a third-party supplier to avoid high costs [49]. Indeed, resources are used by consumers in the form of a service and users only pay the renting cost of the resources they have utilized [50]. Some of other advantages of cloud computing include cost effectiveness and enhanced efficiency, reliability, security and scalability [51, 52, 53, 54]. Generally, the investigated projects employ cloud due to the storage, processing and concurrent service provisioning capabilities. Owing to the limited storage of mobile devices of users, cloud is employed as a storage resource in most of the projects [53, 55]. Regarding the investigated projects, a cloud-based SIoT system is provided in the V-doctor system. Cloud is used in this project to improve scalability since computational load is increased by unlimited requests for simultaneous searches from different devices. In the VSNP project, vehicle data is uploaded, stored and processed on the cloud to control the traffic. Likewise, to manage and process the data of vehicles and road side units, cloud is applied in tNote project. I-painting system, which provides a smart painting service for children utilizes cloud for storing and sharing the paintings. Furthermore, a cloud-based system to process and store data in smart buildings is introduced in HVAC project. To diminish the energy consumption, the COSMOS project provides a method commensurate with limited resources of SIoT, which uses cloud to store data. Edge Computing: In cloud computing, the complexity of processing, storing and network configuration is hidden in data centers. Nevertheless, cloud is not proper for real-time applications due to the fact that it is far away from the user’s devices and has a WAN delay [56, 57]. On the other hand, regarding the ever-increasing use of IoT as well as mobile devices and the huge volume of data that sensors and devices produce, real-time processing of this information has become a challenge [58]. Consequently, a novel paradigm is recently introduced, which is called edge computing. It accomplishes storing and processing on the edge of the network [59]. Wide smart environments are inherently open and dynamic and include a large number of interactions. In these environments some factors such as trust, processing

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Table 3 Project review according to computing paradigm Project

Reference

V-doctor

[61]

VSNP

[63]

t-Note

[64]

HVAC IPainting

Cloud computing

Edge computing

Storage

Reducing energy

Processing Serving simultaneously

*

[67]

*

*

[70]

*

COSMOS [69]

*

[66]

iSapiens

[68]

Reducing latency

* *

LSE

Reducing bandwidth consumption

*

*

* *

*

*

and managing data, interoperability and scalability are momentous. To manage and process data, LSE project employs cloud computing paradigm integrated with edge computing. The edge computing paradigm along with the cloud provides a great computational power for processing tasks. Likewise, the iSapiens system improves the storing and distributed computing by the help of edge computing and also eliminates the side-effects such as transmission delay and bandwidth shortage. Table 3 summarizes these research papers in terms of the type of utilized computing paradigm.

5 Conclusion In this survey, the SIoT research studies have been investigated from two main perspectives, including the type of application domain and the utilized computing paradigm. Despite the fact that a few research studies have been conducted in the SIoT, their growth rate is strikingly high that makes it a hot research topic. Although SIoT has entered into various specialized domains such as Healthcare and Intelligent Transportation Systems, the number of projects in each of these fields is significantly low. Regarding the investigated projects, it has been specified that in the majority of SIoT systems, cloud is employed for storage as well as simultaneous service provisioning platform due to limited resources of users’ mobile devices. Besides, despite the novelty of edge computing, some research studies have exploited it to solve the SIoT realization challenges, which demonstrates the necessity of introducing a new architecture for SIoT based on new computing models. Indeed, SIoT is a new paradigm, still in its infancy, that tries to overcome the issues of scalability, reliabil-

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ity, and discovery of resources and information through inspiring from human social networks and provides a platform for better interactions of humans and things. It is expected to see a large number and more specialized research in this emerging field in the future.

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Security Threats of Social Internet of Things in the Higher Education Environment Ahmed A. Mawgoud, Mohamed Hamed N. Taha and Nour Eldeen M. Khalifa

Abstract Within the trials of the utilization of technology for the society, efforts have proven advantages of the new wave of rapid-growth change that began and is anticipating to proliferate with more potent connectivity and interoperability of diverse devices, named as the Social internet of things (SIoT). It is a rising paradigm of IoT wherein distinctive IoT devices interact and set up relationships with each in the academic field nowadays. Objects are establishing their social relationships in an unbiased way. The main issue is to understand how the objects in Social IoT can interact in the higher education systems to implement a secure system. Consequently, focus on the trustworthiness models. Because of the billions of linked devices, there may be a huge risk of identity and information leakage, device manipulation, records falsification, server/network attack and subsequent effect to application platforms. Whilst the variety of these interconnected devices keeps to grow each day in the academic learning field, So, does the wide variety of security threats and vulnerabilities posed to these devices at universities. Security is one of the most paramount technological studies troubles that exist nowadays for IoT. Security has many aspects; security built in the device, security of data transmission and data storage inside the systems and its applications. There may be an intensive quantity of literature that exists on the problem with endless issues as well as proposed solutions; however, maximum of the existing paintings does no longer provide a holistic view of protection and data privacy issues in the IoT. The primary aim of this research work is to state the risks and threats that faces SIoT by identifying (a) The essential domains in which SIoT is highly used in higher education, (b) The security necessities and challenges that SIoT is currently dealing with, (c) the existing security solutions which have been proposed or applied with their barriers. Keywords Internet of Things · Social networks · Higher education · Security challenges

A. A. Mawgoud (B) · M. H. N. Taha · N. E. M. Khalifa Faculty of Computers and Information, Cairo University, Giza, Egypt e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_9

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1 Introduction Due to the rapid growth in connectivity field nowadays, it opened the route for a new level of technologies that are going to reshape how network devices are interacting with the physical world, one of those technologies is the internet of things [1]. Internet of Things (IoT) describes the connection of different types of smart devices with unique access [2]. IoT is reshaping the interaction methodology between users and devices with the novel capabilities of networking of the intermediary devices. As a result, it will transform our lives into a hyper-connected social environment [3]. There are three generations of IoT, the first generation is the tagged things while the second generation is web services and internetworking and the third generation is social and cloud [4]. Figure 1 presents the three generations of IoT and the raise of the third generation of IoT which include SIoT. The idea of using IoT with the concept of social technology to establish social networking relationships is gaining popularity; because of the effect of social-oriented elements in boosting both composition and discovery of the provided information by the objects that interact with the physical world [5]. The need for combining the concepts of both (Internet of Things) and (Social Networks) worlds became advisable; this is due to the developing recognition that a “Social internet of things” (SIoT) paradigm could bring many ideal implications right into a future international populated through sensible objects permeating the normal life of human beings. In fact, applying the social networking ideas to the IoT can lead to numerous benefits: (a) The SIoT structure may be formed as required to assure the network navigability, so as that the invention of objects and services is achieved efficiently, and the scalability is assured like within the human social networks. (b) Models designed to study

Fig. 1 Generations of IoT

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Fig. 2 A simple concept design for SIoT

the social networks may be reused to deal with SIoT related issues (intrinsically associated with huge size of networks of interconnected devices). In the SIoT [6], devices will have social relationships among human beings-andthings and between things-and-things that behave like social circles. It builds profiles on the idea of numerous IoT applications’ statistics. Such profiles are exchanged inside a SIoT network that is accessible to various IoT applications. On this way, SIoT networks offer recommendation services for enhancing the performance of IoT applications through sharing and the usage of different IoT applications’ records. Moreover, the profiles built via SIoT networks also can assist a single IoT application through searching out comparable situations that have been addressed in the past for the same IoT application. Figure 2 shows a simple concept design for SIoT. The SIoT differs from social networks and from the IoT in three major factors: • The SIoT establishes and exploits social relationships among things, instead of only amongst owners or human beings. human beings can be concerned for mediation; however, the key roles are achieved through things. • Things can discover assets and services themselves via social relationships to the IoT, which presents an allotted solution and decreases human efforts. • The SIoT does no longer depend on internet technologies; alternatively, it is a platform for social networking services (SNSs) which deals with objects in place of coping with human beings. Software for the SIoT is already being leveraged in sectors like higher education, healthcare, and telecommunication. Now, universities and faculties are becoming a member of the circle. a number of the methods the SIoT can gain education can be manifest, while others are not as

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apparent [7]. So, this chapter will present the most important implications for linked devices in higher education and the way they might shape the learning for the new generation. The future of universities isn’t about using them as a technology. it’s far about how universities will adapt to the converting needs of the destiny information employee, the future of work, and the economic system. This chapter provides an outline of IoT in better education establishments, specifically in universities and looks at numerous emerging developments which can be evolving better schooling and discover the capability impact and the future of SIoT in higher education. similarly, exploring a number of IoT challenges concerning higher education zone. Most of the devices and applications aren’t designed to deal with the security and privacy attacks and it will increase a variety of security and protection problems within the IoT networks like confidentiality, authentication, information integrity, access manipulations, secrecy, and many others. On each day, the IoT devices are targeted through attackers and intruders [8]. Many previous studies stated that 70% of the IoT devices are having huge vulnerabilities. Consequently, a dynamic mechanism is extremely had to be set in order to secure the devices linked to the internet against hackers and intruders. The SIoT had to secure the data privacy, academic universities secrets, and critical e-learning infrastructure.

2 Background and Motivation It is vital to examine the potential advantage and effect of the SIoT evolution concept in each the physical and the virtual environments. It is similarly essential to investigate the use of the internet of things in virtual educational environments and e-learning systems in a smart campus. Moreover, the utilization in the smart universities paradigm is obvious in which extended connectivity and interoperability growth the services given to the citizen [9]. The IoT and the social networks are worlds not truly that a long way apart from each other as one would possibly assume, has started to appear inside the literature. Things involved into the network together with human beings, the social internetworks may be constructed based on the internet of things and are significant to analyze the family members and evolution of the devices inside the SIoT. Subsequently, the convergence of IoT and social networks has been taken into consideration. In that work, a person can share the academic services provided via the universities smart objects with college students or academic stuff. Gartner estimates that by 2020, more than 25% of all organization attackers will use the IoT. The challenge of detecting attacks can be compounded via IoT deployments in settings wherein there is a lack of technical specialists, consisting of homes and small organizations. From an operational technology angle, the Social IoT makes social IoT devices more automated and connected. Cyber-physical systems have an effect on the physical world and, whilst compromised, protection can be jeopardized, and the environment can be harmed. Therefore, a successful hacking on a SIoT system has the

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ability to be as critical as the worst industrial accidents so far. Hacking attacks are increasingly executed through experts with tremendous sources and an excessive level of technical knowledge, and because the IoT affects humans’ each day lives and business operations, there can be lots of incentives to hack IoT systems. Many current IoT devices are extraordinarily easy to hack, and the IoT has speedy turn out to be a famous enabler for huge disbursed Denial of service (DDoS) attacks [10]. Mitigating DDoS is complicated as neither the proprietors nor the sellers of the devices endure the expenses of the attacks, and IoT-based DDoS has the capability to come to be the main trouble for society [11]. Consequently, vital infrastructure should not only be capable of face up to direct hacking, but it should also be resilient to attacks including DDoS and jamming. Privacy is regulated and structured in distinctive methods throughout nations and jurisdictions. The media interest in cybersecurity has raised public awareness [12]. Even seemingly innocuous data regarding energy consumption or room temperature, as an example, may expose a lot about someone’s behavior. However, with billions of sensors everywhere, the SIoT will significantly increase the amount of probably sensitive records being generated regarding human beings’ activities. Compounding the hassle, in most instances, people will not be privy to the sensors around them, or how the mixed facts from diverse resources can be misused. The wealth of data in clouds and devices, occasionally in exposed locations, increases the risks of business espionage and the surveillance and monitoring of users. Inside the SIoT, the individual segment of data won’t reveal a lot, however, the importance of data may make it conceivable to determine organization strategies via the use of analytics. Despite the fact that the traffic is encrypted, significant patterns can be exposed via the traffic analysis.

3 Social IoT in Higher Education The usage of the SIoT to facilitate the educational environment for the student, college members, and other staff members yields capability new educational services and situations. Examples of the service to be provided consist of: 1. Smart Classroom: That’s prepared with the SIoT managed objects to provide the capability for remote features. Those features include the capability to set the classroom ahead of lecture time. The teacher can set the overhead projector, set the lights, and set the room temperature, making an allowance for time and energy saving. The device to be managed should be able to receive orders to begin a certain action as such they have to be equipped with sensors as illustrated in Fig. 3. 2. Smart Parking Areas: that is geared up with sensors to govern the occupancy of the parking spaces and gives the indicators about the available potential of the location, once more saving time and power.

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Fig. 3 An example of IoT environment inside classroom

3. Smart Secure Environment: which is equipped with digital sensors that give signals to a security control room to increase campus protection and keep away from undesirable incidents. 4. Smart Student Feedback: which permits students to participate in academic environment development via a sensing mobile application. To obtain a smart educational environment, there is a need for SIoT infrastructure to be in location; composed of sensing devices, communication hyperlinks, and applications (usually, the usage of cloud computing). The splendor of this infrastructure is that it could be brought regularly; including smartness as it grows, and now not requiring major capital investment to start with. An organization can construct the smartness step by step, assessing the output of the system as long as it grows. The reviews to be generated through the system applications could be used to signify which location of the infrastructure have priority over others.

3.1 SIoT-Enabled Campus Requirements To utilize the new technology into the educational system, many applications will be introduced. These applications will deal with the needs of the students in addition to the lecturers and also will serve the institution’s administration. those applications can offer the ordinary e-learning abilities in a greater pervasive nature. The distributed computing energy can have an outstanding impact on system design and utilization.

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The students’ mobile devices will play an important role in applications execution and data access. The vast use of mobile cellphone computing is predicted to grow the share of the SIoT applications together with educational packages [13]. The SIoT communication is primarily based on interoperable protocols operating in heterogeneous environments and systems. Interoperability of various SIoT devices should be allowed through the use of various protocols on the sensor layer (Bluetooth, Zigbee, etc.) and extraordinary networks connectivity (LANs, WANs). Cloud Computing is used so as to allow SIoT infrastructure. It permits the connection if the billions of devices and sensors which might be used in developing the applications. The motivation behind cloud computing is its capability to be used on demand and as according to the necessities of the particular application.

3.2 Impact of SIoT in Better Education There is no doubt of the effect of ICT utilization on learning and the education environment nowadays. The incorporation of such technology was not necessarily planned however the rapidity of proliferation within the society has caused adoption in the education environment. That led to numerous drawbacks educational establishments be afflicted by because of lack of readiness; take dealing with plagiarism for example and smart smartphone use on campuses. With the advent of SIoT, it turns into vital to recall its capacity proliferation and adapt accurate techniques consequently whilst investigating its splendor from the diverse perspectives. Those views encompass, at first hand, the learner who is interested in extra adoption of technology with intelligence; the staff who are interested in increasing educating effectiveness and decrease administrative efforts; and the administrator who has management responsibilities in dealing with the procedure, and certainly from the learning procedure perspective [14]. Assuming the appearance of the SIoT to campus entails investigating the unique perspectives in needs, readiness, or posing threats. The Learner attitude. For learnertargeted education, the learner is a primary class entity of interest. His preferences, consolation and motivation are essential for learning engagement. With the accelerated use of technology, the younger generations are becoming not only inquisitive about the smart use of technology however they become well-familiar with its utilization. From the student perspective, proper SIoT implementations can open the door for academic environment usability in order to support the studying outcomes. Providing flexible education activities can attract and encourage students particularly with social advice. The Educator perspective. What are the results on the educator, and how he/she can benefit from the plethora of intelligence inside the environment college students are enjoying? Educators are interested in extra students’ engagement, higher education effectiveness that the environment is anticipated to help. Several new automated capabilities can support the lecturer. Examples are automated attendance capturing and reporting of academic task completion.

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The gaining knowledge of administration attitude. Schooling governance is expected to reveal effects of such change given the subsequent challenges: create education opportunities, focus on scholar orientation, allow innovation scenarios, control user privacy. In order to determine the value of all of the above perspectives, it is essential to investigate the educational situations that could come to light with the application of the new technologies based totally on SIoT [15]. The educational scenarios. One student has imaginative and prescient impairment to be able to watch lecture material. His mobile device keeps particular problems while he has to attend a flexible gaining knowledge of pastime. Based on the social advice, fellows advocate the learning interest and provide him the comfort of the capability to complete it. The smart room recognizes distinctive sorts of problems and the IoT devices suggest available accessibility functions. The activity facilitator is knowledgeable about such difficulties and consequently, the material is customized to the student; the printer gives the educational material enlarged enough.

3.3 SIoT System Architecture The variety of IoT applications has led to numerous IoT structure models. We begin with a 3-layer structure: • Perception layer • Network layer • Application layer. The perception layer, additionally called the recognition layer, is the lowest layer of the traditional structure of SIoT. This layer is chargeable for gathering information from “things” or the environment (like Wi-Fi Sensor Networks, heterogeneous devices, sensors, and many others) and processing them [16]. A few different models consist of one extra layer: assisting layer that lies between the application layer and the network layer. As an example, the ITU-T (International Telecommunications Union—Telecommunication Standardization sector) indicates a layered SIoT structure that is composed of four layers. The SIoT application layer containing the utility person interface is the top layer. The offerings and application aid layer is the second layer from the top. The third layer is the network layer which includes networking and transport abilities. In the end, the lowest layer is the device layer, which incorporates gateways, sensors, RFID tags, and many others. The safety abilities categorized into regular and particular, are distributed alongside all four layers. The three fundamental factors of the proposed system are the SIoT Server, the Gateway, and the Objects.

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SIoT Server

The SIoT Server does not encompass the sensing layer however only the network and the application Layers. The Application Layer includes three sublayers. the base Sublayer consists of the database for the storage and the control of the data and the applicable descriptors. These document the social member profiles and their relationships, in addition to the activities, accomplished through the objects in the actual and virtual worlds. Data about humans (object owners in addition to visitors) also are controlled [5]. The relevant ontologies are stored in a separate database and are used to symbolize a semantic view of the social activities. Such a view is extracted through suitable semantic engines. Certainly, ontology and semantic services are essential to provide a machine interpretable framework for representing practical and non-practical attributes and operations of the SIoT devices. In this context, several works have been already performed, which can be a start point for the definition of an ontology for use within the SIoT system.

3.3.2

Gateway and Objects

As to the Gateway and objects systems, the mixture of layers may additionally vary specifically relying on the device characteristics. the subsequent three situations can be foreseen. In a simple one, a dummy object (e.g., both an RFID tag or a presence sensing device) this is geared up with a capability of the lowest layer, is enabled to send signals to another gateway. The Gateway is prepared with the entire set of functionalities of the three layers [17]. In another scenario, a device (e.g., a digital camera) is capable of sensing the physical global data and to send the associated data over an IP network. The object could then be set with the capability of the network Layer aside from that of the application one. Consequently, there is no need for a Gateway with application Layer capability. An application Layer in a server, somewhere inside the internet, with the gateway application layer capability, could be enough.

4 Security Analysis Some IoT devices are positioned in untrusted areas and attackers can gain physical access to them or even get control of the device. Many IoT devices do not meet security best practices requirements including least-privileged or role-based access [18]. IoT attack vectors can target devices, gateways, SIM/mobile, transceivers, and wearables and may take gain of vulnerable passwords, lack of encryption, backdoors, and so on. The extensive style of IoT-unique operating systems, firmware versions and custom configurations makes the development of popular IoT security solutions. Track-

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ing and patching the numerous IoT OSes is a tremendous challenge. Moreover, IoT security solutions have to be extremely scalable to have an ability to be applied to an exponentially increasing quantity of diverse IoT gadgets. A developing sort of IoT applications creates new security challenges. Further to standard security domains including cryptography and secure communication, IoT security additionally focuses on trust/identity management, information confidentiality, data safety, and so forth [19].

4.1 IoT Security 4.1.1

IoT Security Challenges

Three classes of IoT dangers encompass: (1) risks which are regular in any internet system (2) risks which are particular to IoT devices (3) safety to ensure no damage is caused by misusing actuators. Traditional security practices which include locking down open ports on devices belong to the first category (for instance, a refrigerator connected to the internet which will send alerts about the product inventory and temperature can also use an unsecured SMTP server and may be compromised via a botnet). The second category consists of problems particularly associated with IoT hardware, e.g. the device can also have its secure data compromised. As an example, some IoT devices are too small to support proper asymmetric encryption. Moreover, any device that can connect to the internet has an embedded operating system deployed in its firmware and a lot of those embedded operating systems are not designed with security as their primary consideration [12]. In order to make IoT services available at low price with a huge range of devices communicating securely to every different, there are numerous protection challenges to conquer [20]. • Scalability: Dealing with a massive number of IoT nodes requires scalable security solutions. • Connectivity: In IoT communications, connecting diverse devices of various capabilities in a secure way is another assignment. • End-to-End Security: End-to-end security features among IoT devices and internet hosts are equally essential. • Authentication and Trust: Proper identification and authentication capabilities and their orchestration within a complicated IoT environment are not yet mature. This prevents the establishment of trust relationships among IoT components, that is a prerequisite for IoT applications requiring ad hoc connectivity among IoT components, which include smart university scenarios. Trust control for IoT is

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needed to make sure that information analytics engines are fed with valid information. Without authentication it is not feasible to make sure that the information flow produced by using an entity consists of what it is meant to contain. • Identity Management: Identity management is a problem as bad security practices are often applied. As an example, the use of clean textual content/Base64 encoded IDs/passwords with gadgets and machine-to-machine (M2M) is a common mistake. This must get replaced with controlled tokens such as JSON web Tokens (JWT) utilized by OAuth/OAuth2 authentication and authorization framework (the Open Authorization). • Attack-Resistant Security Solutions: Variety in IoT devices effects in a need for attack- resistant and lightweight protection solutions. As IoT devices have restricted compute resources, they are susceptible to resource enervation attacks.

4.1.2

IoT Security Attacks

To emphasis security risks in IoT, its acronym has been provided as Interconnection of Threats (IoT). Certainly, IoT devices are specifically susceptible to physical attacks, software attacks, side-channel attacks [21]. Figure 4 shows Security Threats to IoT Devices. Current IoT systems are constructed the usage of technology solutions from a extensive sort of providers. Some of these systems are an eclectic mix of additives repurposed from present solutions for use in specifically designed systems with the desire that the additives will paintings collectively in a secure way. Security measures inside the IoT components, if any, have not been designed to consider the dependencies as a consequence of the IoT connectivity abilities. For instance, industrial devices frequently do not have right authentication mechanisms due to the fact they have been designed for use in physically protected and remoted environments. Some other example is the assignment of offering software program updates or security patches in a scheduled way to end nodes without impairing functional protection [22]. Comprehensive risk and risk evaluation techniques in addition to management equipment for IoT systems are required. Developing mitigation plans for IoT assaults

Fig. 4 Security threats to IoT devices

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Table 1 Attack classifications per IoT process phases Phase

Attack/Threat

Description

Data perception

Data leakage or breach, data sovereignty, data loss, data authentication

Data leakage can be internal or external, intentional or unintentional, involving hardware or software

Storage

Attack on availability, access control, integrity, denial of service, impersonation

Availability is one of the primary security concerns. Distributed denial of service

Intelligent processing

Attack on authentication

An IoT solution provides data analysis

Data transmission

Channel security, session hijack. Routing

Threats in transmission, such as interrupting, blocking, data manipulation, forgery, etc.

End-to-End delivery

Man or machine. Maker or hacker

Delivery of processed data on time without errors or alteration

requires understanding attack types and the collection of movements taking place when the attacks are occurring. Let us begin with considering IoT attack categorization. Evaluation of security attacks helps to recognize an actual view of the IoT networks and allows us to put a mitigation plan [23].

Attack Categorization for IoT Process Phases In general, an IoT process can be taken into consideration, from data collection to data transport to the end users. Table 1 below demonstrates the type of attacks classified for the five phases of IoT: data perception, storage, smart processing, data transmission, and end- to-end delivery [24].

Security Threats at the Sensing/Perception Layer To implement IoT security, it has to be designed and constructed into the devices themselves. which means SIoT devices should be capable of proving their identification, maintain authenticity, signal and encrypt their data to keep integrity, and restriction locally stored data to protect privacy. The security model for devices has to be strict enough to prevent unauthorized use but sufficient to assist secure ad hoc interactions with people and other devices on a temporary basis. For example, whilst unauthorized changing of the toll charge on a connected parking meter need to be avoided, the meter should have a secure interface to reserve and pay for the parking spot for a restricted duration [25]. In Physical damage, some attackers can also lack technical knowledge and their assaults are limited through destroying devices. As device enclosures are often not

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tamperproof, the devices can be opened and their hardware can be accessed through probes and pin headers. Physical protection requires designing tamper resistance into devices so that it is hard to extract sensitive data such as personal data, cryptographic keys, or credentials. Many devices cannot defend their code and data from external access. As a result, an attacker can clone entire devices or manipulate their software and information: for example, to control a glucometer as a way to provide incorrect readings. Any other example is damage to loads of smart traffic light devices by thieves who stole the devices’ SIM cards. The stolen cards have been then used to make mobile phone calls in South Africa. The damage to the traffic mild system resulted in many car crashes and an excessive cost to repair the whole system.

Security Threats at the Network and Service Support Layers The provider support layer represents the IoT control system and is chargeable for onboarding devices and users, applying regulations and policies, and orchestrating automation throughout devices. Role-based access control to control user and device identity and the actions they are authorized to take are essential at this layer. To obtain nonrepudiation, it is also vital to maintain an audit trail of adjustments made through each user and device in order that it’s far impossible to refute actions taken inside the system. This monitoring information can also be used to become aware of doubtlessly compromised devices when unusual behavior is detected. Some usual attacks on the network and service support layer [26]. • Node Capture: An expert attacker can extract the data that the devices contain rather than destroying them. • Sinkhole attack: If sensors are left unattended within the network for long durations, they become vulnerable to sinkhole attack. this attack, the compromised node extracts the data from all of the surrounding nodes. • Selective Forwarding Attack: Malicious nodes may additionally choose packets and drop them out, thereby selectively filtering certain packets and permitting the rest. Dropped packets can also bring essential sensitive information for similar processing. • Witch Attack: This attack occurs while a malicious IoT node takes benefit of failure of a legitimate node. While the legitimate node fails, the actual hyperlink takes a diversion via the malicious node for all its future communication, leading to information loss. • Hello Flood Attacks: A malicious node initiates a hello flood attack by sending hello messages to all of the neighbors that are available at its frequency level. Consequently, it turns into a neighbor to all of the nodes within the network. As the following step, this malicious node will broadcast a hello message to all its neighbors, affecting their availability. Flooding attacks purpose non- availability of sources to valid users through distributing a massive number of nonsense requests to a provider.

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• Man-in-the-Middle (MITM) Attack: man-in-the-middle attack is an example of the eavesdropping viable within the IoT. As device authentication involves a change of device identities, identification theft is feasible due to a man-in-the-middle attack. • Replay Attack: during the exchange of identity-related data or different credentials in IoT, this data can be spoofed, altered or replaced. A replay attack is basically a shape of an active man-in-the-middle attack. • Denial of Service (DoS) Attack: because the IoT devices in IoT are resource restrained, they are susceptible to resource utilization attack. Attackers can send messages or requests to a particular device to consume its resources.

4.2 IoT Security Requirements Security needs to be addressed during the IoT lifecycle from the initial design to the services running. For example, implementation of security functions should begin throughout device production. Code signing and code obfuscation are a few steps that producers can observe to make sure their device is not hacked or undesirable code are not inserted by using a malicious user [27].

4.2.1

Security in Short-Range Low Power IoT Networks

Security in RPL IPv6 Routing Protocol for LLNs (RPL) is designed for routing IPv6 traffic in lowpower networks implemented over 6LoWPAN with high or unpredictable amounts of packet loss [28]. The RPL security utilizes a “Security” field after the 4-byte ICMPv6 message header. Information in this field indicates the level of security and the cryptography algorithm used to encrypt the message. RPL offers support for data authenticity, semantic security, protection against replay attacks, and confidentiality and key management [29]. RPL attacks include selective forwarding, sinkhole, Sybil, Hello flooding, wormhole, black hole and denial of service attacks.

Security in Bluetooth Low Energy (BLE) BLE Protocol BLE is a low-power version of the Bluetooth 2.4 GHz wireless communication protocol [30]. While the BLE data rate and radio range are lower than the same metrics in classic Bluetooth, BLE is designed for very low-power applications running off a coin battery (for example, the popular CR2032). The low-power and long battery life make it possible for BLE sensor devices to operate for many years without needing a new battery. To enhance security, the BLE version 4.2 introduces the new BLE

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Secure Connections pairing model [31]. Let us briefly review the main BLE security challenges: passive eavesdropping, MITM attack and identity tracking [32]. Eavesdropping The protection against passive eavesdropping can be based on encrypting communication with a key. While earlier versions of BLE (Bluetooth 4.1 or older) devices used easy-to-guess temporary keys to encrypt the link for the first time, BLE 4.2 uses the Federal Information Processing Standard (FIPS) compliant Elliptic Curve DiffieHellman (ECDH) algorithm for key generation (Diffie-Hellman Key—DHKey). Man-in-the-Middle (MITM) Attacks Protection against MITM attacks is to ensure that the device the communication started with is indeed the intended device rather than an unauthorized device presenting as the intended one. LE Secure Connections pairing provides MITM protection by using the numeric comparison method [33]. Privacy/Identity Tracking As most of the BLE advertisement and data packets contain the source addresses of the devices that send the data, third-party devices could associate these addresses to the user identity and track the users. A frequent change of the private addresses so only the trusted parties could resolve them can serve as protection against this thread [34]. Zigbee Security Zigbee Protocol. Zigbee is a wireless technology based on the IEEE 802.15.4 standard and used in various application areas, including home automation, smart energy, remote control and academic field. It has a longer range than BLE and a lower over the air data rate than BLE. Zigbee Security Features. As with other IoT protocols, Zigbee has unavoidable trade-offs made to keep the devices low-cost, low-energy and highly compatible. To simplify the interoperability of devices, Zigbee establishes the same security level for all devices on a given network and all layers of a device. In addition, it assumes that “the layer that originates a frame is responsible for initially securing it”. Zigbee supports 128-bit AES encryption [35]. Security in NFC Near-Field Communication (NFC) is a subtype of RFID technology — HighFrequency (HF) RFID and is based on 13.56 MHz, HF passive RFID/contactless card technology. As NFC devices must be in close proximity to each other (no further than a few centimeters in most cases), it makes NFC a popular choice for secure peer-to-peer communication between consumer devices [36]. Table 2 shows security risks and mitigation in NFC.

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Table 2 Security risks and mitigation in NFC Threats

Security need

Phishing attacks

Interfaces authentication

User tracking

Random UIDs

Relay attacks

Synchronization

Data corruption and manipulation

Use of secure channels

Eavesdropping

Use of secure channels

Interception attacks

Devices should be in an active-passive pairing

Malicious host

Interfaces authentication

4.2.2

Managed IoT Security Services: IoT Security-as-a-Service

Managed IoT security services are provided as a part of the IoT managed service or as a separate service. Managed IoT security solutions have to provide protection to every layer of the IoT ecosystem. As the article scope and size obstacles do no longer make an in depth IoT manager security services provider (MSSP) discussion feasible, we point out only some vendors as examples of the MSSP offerings [37]. Verizon provides IoT protection Credentialing service that provides an “over-thetop” layer of security, above the consumer’s current security. According to Verizon, IoT security Credentialing gives trusted authentication (the potential to provide pick out students and/or devices login to apps or IoT devices) and data privacy to assist maintaining information secure through encryption. It makes use of cryptography strategies to secure communications at the community part. Trustwave gives a controlled IoT security service to secure and monitor IoT infrastructure and services. The service permits developers and vendors of IoT services and products to perform security scanning of embedded devices, interface applications, back-end services, and APIs. According to Paladion, it gives the controlled security service with cyber protection capabilities beyond traditional MSSP services because it combines machine learning, artificial intelligence, and automation. CyFlare, in partnership solution Synergy 24 × 7 MSSP services, has evolved a controlled IoT safety answer primarily based on ZingBox IoT guardian for academic agencies [38].

Next Generation IoT Security: Information Confidentiality Homomorphic Encryption Homomorphic encryption schemes make it viable to carry out mathematical operations on ciphertexts. As a result, using fully homomorphic encryption (FHE) information analytics on encrypted statistics or looking on encrypted information can be accomplished without revealing seek patterns and without genuinely seeing the

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unique data. An instance of the use case for FHE is an evaluation of private healthcare IoT statistics to examine the disaster in order that the information proprietors can be assured of information privacy [39]. Searchable Encryption Searchable encryption schemes permit a storage provider to look for keywords or patterns in encrypted records. Whilst keyword searches may be accomplished, the saved records cannot be decrypted, and it is not possible to gain any information of the underlying plaintext [40].

Next Generation IoT Security: Trust Trust Establishment In most IoT situations trust need to be set up ad hoc with formerly unregistered and unknown peers, and without person interaction [41]. This requires new and lightweight trust establishment algorithms. current trust establishment solutions mainly focus on setting up trust in public keys and their challenge to users. Future IoT solutions will even need trust in transactions and agreements, in addition to trust in the integrity of devices and systems [42]. Blockchain and IoT: Trust in Transactions Blockchain-primarily based protocols that are gaining reputation can address the challenge of setting up trust. one of the key constructing blocks of destiny IoT agree with infrastructures can be smart contracts based totally on blockchains, as they may be a prerequisite for business-critical interaction among devices without direct human interaction [43]. But, blockchains require computational assets and feature high bandwidth overhead. This boundary their use in IoT and new lightweight blockchain-based technologies are needed [44]. Trust in Platforms Strategies on automated establishment of trust in remote systems exist: hardware and software remote attestation. hardware remote attestation has high costs because it uses particular hardware modules such as HSMs which can be prohibitive for lowprice sensor hardware [3]. Moreover, additional resource consumption such hardware isn’t always appropriate for many battery-powered devices. Software remote attestation can offer an acceptable protection level for most applications, but it cannot conceptually assure trust in the overall platform. Similarly, development of code obfuscation, white-box cryptography, and control-flow integrity technology can offer holistic software- remote attestations in the future [45]. Identity Management The existing identity and access management systems provide secure, incorporated management of information from different devices and platforms. In the future, autonomous information exchanges amongst different entities are expected to be managed based on superior security and trust management technologies [46].

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4.3 Privacy 4.3.1

Privacy Through Data Usage Manage

Information usage control is an extension of conventional access control concepts. Future data usage control technologies will increase traditional access control concepts to track and label data as it is processed through various systems. They will define high-quality-granular usage regulations as a way to enforce privacy properties over massive data sets even as still taking into consideration running learning algorithms and analytics over them. The key benefit of data usage control is that it presents users with the capacity to control the use of their information even when it is controlled through others. this will assist to meet legal requirements in lots of jurisdictions (for example, general data protection regulation in the European Union) [47]. Future SIoT machine implementations will want so that it will domestically manage facts exposure and to interface with a diffusion of other systems.

4.3.2

Privacy in Multifaceted and Dynamic Contexts

When services from a software enterprise, the device manufacturer or a software provider access the records, it results in additional attack surfaces for breaching confidentiality of the consumer data. From the records owner’s point of view, services with consensual access to user records are still all potential adversaries. As more data is being stored, transmitted and processed through shared infrastructure, future SIoT platforms would require new advanced technologies and services to implement adequate access controls [48].

5 Conclusion Nowadays the rapid growth development of the internet of things nowadays IoT makes it impossible to ignore a serious debate about its future role in higher education and what type of choices universities will make in regard to this issue. The fast pace of technology innovation and the associated job acknowledged widely by professional in the field, implies that both studying and teaching in higher education requires a reconsideration of lecturers’ role. The current use of technological solutions such as ‘learning management systems’ already raises the question of who sets the agenda for teaching and learning. Moreover, many sets of tasks that are currently placed at the core of teaching practice in higher education will be replaced by SIoT based on sophisticated devices designed by developers. In effect, now is the time for universities to rethink their function and pedagogical models and their future relation with social internet of

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things, most of the devices and applications aren’t designed to deal with the security and privacy attacks and it will increase a variety of security and protection problems within the IoT networks like confidentiality, authentication, information integrity, access manipulations, secrecy, and many others. On each day, the IoT devices are targeted through attackers and intruders. We consider that there is a need for studies on the implications of the current security models in SIoT. We also believe that it is important to focus further research on the new roles of lecturers on new learning techniques for universities students on imagination, creativity, and innovation in a secure environment with a humanmachine interaction using social internet of things.

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Social Networking with Internet of Things Aid Bahraini Medical Professionals’ Decisions Through Their Knowledge Sharing Anjum Razzaque and Allam Hamdan

Abstract The rise in the demand to improve healthcare (HC) service quality led research to focus on cost effective initiatives like the social networks facilitated by the social internet of things (SIoT). Social networks and SIoT support HC professionals, physicians, the case of this study, for better decision-making to reduce the highly reported diagnostic errors. This research in progress critiqued current literature to propose the need to assess the effect of physicians’ leadership on their decision making (DM) style, mediating by their social capital (SC). Such two relations are proposed in a viable framework worthy of future empirical assessment. Keywords Social internet of things · Physicians · Leadership · Medical decision making · Social capital theory · Healthcare social networks

1 Introduction Scholarly and practical demand asks for improved healthcare (HC) service quality, considering the volume of long-term diseases [1]. The HC service quality suffers from the highly rates of physicians’ diagnostic errors from poor medical decision making (DM) [2, 3]. Certain initiatives were taken by the HC sector to improve HC service quality, e.g. electronic health record (EHR). Such initiates are expensive and promising but were reported a failure due to the slow adaption rate. Such an initiative cannot aid in reducing physicians’ diagnostic errors [4]. There is still hope if this study can propose the context of social networks aided by the Social Internet of Things (SIoT). With the advent of the Web 2.0 and Health 2.0’s social networks have been further shaped up by the SIoTs to facilitate Knowledge Management (KM) tools such as A. Razzaque · A. Hamdan (B) Ahlia University, Manama, Bahrain e-mail: [email protected] A. Razzaque e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_10

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virtual communities (VCs) to gain ever more attention as effective tools within the HC sector [5, 6]. The applications and systems on the Web 2.0 are effective intelligent systems to aid clinicians to monitor patient progression during delivery of their care and aid patients as intelligent reminder systems. Such systems continue depending further on accuracy of data where clinical data warehousing [7]. Such tools are incorporated by various HC professionals for experience and knowledge sharing in order to support one another through the social capital of resources embedded within such platforms [8, 9]. Considering that, the social networking initiative is cost effective, hence can aid the HC sector to achieve lower costs [10]. Such an initiative is the key. Moreover, the collective intelligence that is getting stored within the social networks is a phenomenon that has attained attractive attention by many scholars (e.g. [8, 11]; add 3 more). Websites like Facebook and Twitter (current examples of social networking site) have made data and information so conveniently available that many scholars have performed various empirical investigations on such platforms [12]. In such a scenarios schools have proposed to use social networks as trust bonding bridges, where on mobile platforms naught knowledge sharers and knowledge seekers even closer than ever before. During this era social networking research got directed towards the area of SIoTs such that the future of the Internet is characterized as ubiquitous [13] where participating objects in conversations reserved for human beings. Here objects are aware of the communication structures, hence such objects will be able to develop a spontaneous infrastructure within the social networks based on what knowledge needs to be attained or shared [14]. Now the question is how social networks can aid the HC sector’s physicians to facilitate their DM quality so that this could help improve their medical DM quality. DM is a managerial activity determined through leadership and its outcomes reflects the success of such an individual and his/her organizational faith [15]. The demand to empower physicians’ DM and leadership has been a long time demand, e.g. as reviewed in publications in i.e. 1967, 1970 and then the 1980. Physicians require leadership skills to make effective decisions. Leadership occurs anytime, e.g. carrying out tasks during physicians’ routine care. While the NHS outlines clinical service efficiency, service attain through value of money, safety, effectiveness and patient experience as benchmarks for HC quality, such a change requires effective leadership [16]. Even though previous research has assessed the role of social capital on knowledge sharing (e.g. [8], 11), the effect of the role of SC on DM is yet to be assess; as per the observation made within the review of the published literature. Furthermore, the role of leadership for DM is also a new research area currently gaining attention [17]. There is need to also consider the mediating role of physicians’ SC, derived from the SC theory (SCT) when proposing the direct role of their leadership on the style of their DM, since physicians make sense and DM through models and portray their work through storytelling where ties relational leadership with accomplishment of tasks to express that DM is a practice, which is relational for a leader to possess [17]. The rationale behind critiquing reviewed literature to integrate physicians’ leadership, their medical DM quality and SCT is since physicians view themselves as independent consultants whose medical DM quality [16] is reliant of their own

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Social Capital Theory

Physicians’ Leadership capability

Physicians’ Medical Decision Making Quality

Fig. 1 Mediating role of social capital theory between physicians’ leadership and their decision making in a social network environment

leadership, which can be further enhanced when they are able to participate with social network of ties in order to facilitate their medical DM [18]. Physicians SC mediated between their leadership that facilitates their DM quality within a VC environment since social media is playing a changing role in Web 2.0 to create virtual environments, which can remove culture, language and geographical barriers and allow collaborator of common interests, such as so clinicians to participate to recommend during online problem solving [7]. The social media environment becomes especially important since physicians have shifted towards the adaption of practices are influenced through face-to-face social interactions, within HC based social networks of social capital of resources within participants relationships, most important tools, in the HC sector [18]. Based upon the above introduction, in the next section—i.e. the literature review section, the authors of this paper move forward to critique review of literature to further propose the direct role of physicians’ leadership on their medical DM quality and the mediating role of SCT between physicians’ leadership and their medical DM quality, as depicted in Fig. 1.

2 Literature Review Social media is a KM tool where varying types of stakeholders are able to share their experience and knowledge with an aim to improve their decisions outcomes (*). Further on this section is utilized to describe an in depth critique of reviewed literature to understand the direct relationship between physicians’ leadership and their medical DM quality and the mediating role of SCT between their leadership and their medical DM quality, as depicted in Fig. 1.

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2.1 Relationship Between Physicians’ Leadership and Medical Decision Making There is rising interest in HC based leadership, i.e. clinical leadership in particular in the NHS, especially since scholarly publications of 2008 and changing NHS resolutions in 2010. Hence, there is a rising demand for empowering physicians for DM over organizational budgets and policies [16]. In the HC sector, leadership is also pushing employees in the right direction based on vision and strategy. Various types of leaderships exist: coercive, collaborative, despotic and democratic leadership. Due to shifting policies in the UK HC sector, medical leadership has become a new buzz word, i.e. migration from management to clinical leadership [19]. Fulop and Mark [17] discussed the link between leadership and DM using the Cynefin framework. This study was also within the case of the HC sector. This is in addition to past studies, which have described the link between leadership and DM using the social constructionist and the social constitutive approaches. This study is in line with Riaz and Khalili [15], where it was also reported that leadership plays a central role for KM in HC institutions through their behavioral and interpersonal skills to link KM with DM in three levels: individual level, organizational level and group level. As depicted in Fig. 1, and as reported by Fulop and Mark [17], the Cynefin framework is a cognitive approach that refers to the action of sense-making through the utilization of narration and language, considering that physicians perform DM using models and like to express their achievements or work through storytelling. As depicted in Fig. 2, the Cynefin framework is composed of 4 contexts that show the relationship between cause and effect: simple and complicated, which is more orderly, in contrast with complex and chaos. It is possible to move from one domain to another. All domains require situational diagnostic analysis through the leadership qualities of a decision maker. The center of Fig. 2’s model depicts the fifth domain, which is called disorder. This domain aims to express that a decision maker does not know what domain his/her situation is in. A domain of best practice is the simple domain, while in the complex domain there is disturbance, which needs to be bought to order. The chaos domain is where there lays an opportunity asking for a change. This is the domain where there is a problem, which is in need of a solution. While the simple and complicated domains fall under the cause and effect of an order, i.e. where cause and effects are known, the complex and chaos domains fall under un-order, i.e. cause and effects are unknown and past actions will not help to provide current or future solutions. There are five types of DM styles, i.e. (1) rationale DM, (2) intuitive DM (3) dependent DM, (4) avoidant DM and (5) spontaneous DM. Rationale DM is performed through logical and reasoning. Intuitive DM is performed through gut feelings. Dependent DM is performed through others’ support. Avoidant DM is performed by postponing and disproving, time consuming and spontaneous DM is a quickly performing an impulsive decision [20]. As per analysis of the author, when bearing in mind the Cynefin framework, the Rationale style of DM is appropriate for all the five contexts. Intuitive style of DM could be more applicable within the

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COMPLICATED •

Non-repeating cause & effect relations Patens should be managed Perspectives should get filtered Complex system should be adapted

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Time required to detect cause & effect relations Analysis required Scenario should be planned System should be thought out

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SIMPLE

No cause and effect relation Intervention and stability focused Use of enactment tools Zone for crises management

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UNORDER

Perceivable cause & effect relation Best practices are legitimate Standard procedures for operation Process re-engineering

ORDER

Fig. 2 Cynefin framework [17]

disorder, complex and chaos context of the Cynefin framework. The dependent style of DM could be applicable disorder, complicated, complex and chaos based contexts of the Cynefin framework. The spontaneous style of DM is most applicable simple and complicated contexts since there is already a form of order established. Furthermore, it is transformational leadership which would be most applicable in unordered forms of contexts of the Cynefin framework since transformational leader act as role models and use trust to charismatically harbors motivation and encouragement of creativity, by stimulating intellect and power, to sustain and attain good group performance. When transformational leader take their time to perform rationale DM, they comprehensively choose between various alternatives, coordination and knowledge [20]. Still, it is important to also bear in mind that leadership is more motivational, flexible, encouraging, where a leader is willing to develop others. Leaders structure and restructure to modify motivations, to drive differing personalities, based on their leadership personality, cognitively diagnostic capabilities. However, leaders’ intelligence, confidence, dominance or assertiveness plays a week role towards their leadership quality. Leadership characteristics are based on emotional intelligence. In other words, a leader should be aware of his/her strengths and weaknesses and should be sensitive of the culture in order to create group identification through group norms to improve productivity and performance [19].

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There are varying theories describing leadership: great man theory—stating leaders are born but not made and thus not application in the HC sector, trait theory—where possessed traits like intelligence, adaptive, dependable, selfconfident, dominant, creative and assertive form good leading skills (not applicable in the HC sector), behavioral theory—focuses on leaders do, i.e. effective behaviors, contingency theory—focus on how leaders work in situations and how they work with their followers, transactional leadership—practices in HC where such a leader who forms hierarchy where he/she either punishes or rewards depending on performance outcomes and transformational leadership—an inspirer through vision preferred in the HC sector [16]. It is the leader’s ability to perform daily or long term DM and leadership is a value each manager should possess. There are four types of leadership: transformational, transactional, charismatic and team leadership [20]. Furthermore, [15] also mentioned that transformational and transactional leadership aid leaders to perform DM, which is based on KM processes and rationality/logic to perform problem-solving. Here, choices are weighed, and these choices are based on KM processes. KM processes means the utilization of knowledge creation, modification, application, transference, storing, accessing and disposing of organizational knowledge is the organizational intellectual capital, i.e. structural, human and consumer capital. Pertaining to leadership of physicians, it is interesting to consider what Rehman and Waheed [20] also reported that there are five leadership styles: (1) to decide, (2) to individually consult him or herself, (3) consult with the group, (4) facilitate or (5) delegate. Leadership style is then based on DM future events or circumstances, i.e. significance of the decision, expertise of the leader, leadership commitment, commitment likelihood, support of the group, group expertise and the ability/efficiency of the group. Based on the just-mentioned argument, the first proposition of this study: Proposition 1: Physicians leadership has a positive and significant influence on their medical DM quality, within a social networking platform within a SIoT environment.

2.2 Mediating Role of Social Capital Theory Currently, leaders are more unstable and virtual. Leaders are unstable due to changing organizational policies and global competition, etc. Such leaders are also virtual since work now is more online, hence spans across time and space. Hence, to develop today’s organizational leaders, the focus shifts towards the needs of the organizational SC [21]. The context of organizational SC was also discussed in another study, which focused on the cost cuttings IT management changes that initiated from 2007’s economic downturn where IT investments costs got lowered and in 2009 the focus was to find a way to sustain cost cuts while maintaining business growth. Superior IT management skills are the key for convincing certain IT investment. Such skills come from human capital, i.e. individual organizational capabilities and skills but they too fall short if not accompanied with communications skills, i.e. SC of opportunities.

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The SC of a social structure is what leadership creates to help human capital become more productive and effective. This is since leaders are part of a social structure where certain actions facilitate individual in such a network of relations. Here, individuals within such a social unity expect something in return for the resources they initial share with one another, through goodwill. SC can be written as a formula, i.e. SC = physical capital to facilitate organizational production + human capital to aid individual effectiveness through individual capabilities and skills set. Through such SC of relations, new intellectual capital is created for organizational benefit [22]. From the perspective of both concepts, social network and SC, it is important to take notice that while a social network is a social structure, composed of connections representing social ties where some are weak, strong, indirect or direct, SC is the content, structure and insight of a participant’s social relationship within a network. Group, organizational and individual performance requires both social network and SC in order to attain success. Also, both, social network and SC are key determinants for leadership success [23]. In conclusion, when bearing in mind the relation between social network and SC, it is the social capital of relationships between a leader and other leaders, as well as other organizational stakeholders, makes a leader successfully achieve goals. E.g. a chief information officer (CIO) would collaborate with heads of other organizational department as well as customers and the employees under his/her leadership and department [22]. Now, when considering the relationship between SC and DM, it is first important to consider that decisions transform ideas to business opportunities requiring knowledge [24] where since a long while research has stressed that interpersonal relations intervene to improve the communication process for affecting DM. Individuals make decision not really due to the influence of media but due to face-to-face with individuals whose interactions influence one DM [25]. Organizations encourage a information management environment so this environment of intelligence gathering and sharing harbors DM based on a well-informed knowledge foundation [26]. To understand the important of SC for DM, it was interesting how [18] reported that physicians have shifted from individual clinical DM to evidence based medicine (EBM). Here, DM is based on an individual but backed up and driven by medical review of literature. This is important to consider since this study assesses the impact of social networks on physicians’ attitudes on adapting EBM since not all physicians prefer to utilize EBM for medical DM. SC plays a positive role in improving physicians’ attitude to adapt the practice of using EMB. This is not the case in those institutions where there is no encouragement to adapt using EBM. The empirical findings of this study suggested that older physicians had a higher rate to opt for adapting EBM. Furthermore, physicians who would perceive that gaining evidence was achievable were more willing to adapt EBM than those who would feel that their access to scientific evidence was limited. Also it was reported that those clinics that supported EBM were more effective in medical DM than those clinics, which utilized a snore traditional models of DM [18]. In conclusion, leadership and SC go hand in hand considering the definitions of leadership, in addition to the definitions portrayed in other studies. A leader encourages followers’ participation to achieve a common goal through interactions and interpersonal skills. Such ties form a success-

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ful relationship between a leader and followers so one can identify with another and, hence, deploy mutual resources to reach a particular goal. This shows that leadership is a SC, which surrounds his/her followers through perceptions and relational ties within and outside the organizations where follows are [21]. Based on the above argument, the second proposition is: Proposition 2: Physicians SC plays a mediating role between their leadership and their medical DM quality, within a social network platform within a SIoT environment. The next section is utilized to discuss how literature was reviewed to propose the conceptual framework, depicted in Fig. 1, of this paper.

3 Research Methodology This is a deductive research initiated by attained a broader understanding of the landscape of the HC research with interest as to why the HC sector suffers in service quality. To make the initial research initiative possible, a broad review of literature primarily spanned over a review of current journal and conference articles, in the following research areas: SCT, Healthcare 2.0, social network, social media, virtual community, knowledge management\s knowledge sharing, leadership and DM. Furthermore, the review of literature’s primary focus was to investigate how physicians would be at aid in order to facilitate HC service quality. However, the review of literature was not limited to only HC related articles, but articles with case studies also from other sectors like managerial related research, which was possibly derived out of the banking sector, etc. The authors critiqued the reviewed literature to pinpoint gaps in research, which had a direct affiliation with the problem/s in the HC sector, i.e. investigating how HC service quality could be improved by integrating those theories, which are recently gaining attention in the HC research, e.g. leadership, physician DM and SCT, which were reported as promising towards current HC cost effective initiatives, e.g. Web 2.0’s social networking platform’s VCs for physicians’ knowledge sharing. As a result, the authors of this paper were able to propose a conceptual framework based on two propositions: (1) the direct role between physicians’ leadership and their DM quality and (2) the mediating role of physicians’ SC between their leadership and their DM quality.

4 Discussion and Conclusion This is a research in progress. The aim of this research was to propose a literature driven conceptual framework. The aim of the framework was to depict two propositions: (1) direct role of the physicians’ leadership on their medical DM and (2) the mediating role of physicians’ social capital between their leadership and medical DM. The literature review section of this paper systematically portrays a critique of

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reviewed literature before suggesting the above-mentioned two propositions. There are theoretical, as well as, practical implications pertaining to this conceptual framework. From a theoretical implication point of view, this paper’s proposed conceptual framework is promising if empirically assessed. This framework is Underpinned by the integration of social networks concepts in facilitation with the IoT, leading to what is referred as the SIoT paradigm. An empirical assessment of this framework will finally prove the critiqued theories in the review of literature of this paper. Hence, future research can empirically assess the proposed conceptual framework of this paper, with a preference of applying quantitative data analysis, considering that there are existing instrument pertaining to leadership, SCT and DM quality. Such empirical assessment will pose as a contribution to knowledge since such an empirical assessment has not been conducted before, and particularly in the HC sector, bearing in mind the critiqued review of literature presented in this paper. It would be feasible to also empirically assess this study’s conceptual framework, not only within the context of physicians as a target population but also patients, as well as other HC professionals such as nurses, etc. One reason for this is since the Web 2.0 has got popular with patients who are now willing to share their own data to assist their own care [7]. Furthermore, future research could also consider culture as an intervening factor, within this paper’s conceptual framework. This is beginning to become an important aspect for consideration since Bhugra [19] suggested that future research could assess whether leadership styles varies across cultures. This is even more important when Bhugra [19] also reported that until now the purpose of leadership is unclear while leadership can be forward thinking whether successful or not and only depends on what they are working on. Leadership can, or in some cases cannot be a formal authority and is functional, i.e. process oriented leader rather than the one who is oriented to personal traits of his/her subordinates. This is important since in HC, HC professionals work within a system. Considering that the empirical assessment of this paper’s conceptual framework will furbish reality to the critiqued theories and if supported by the future empirical analysis; HC institutions will have a cleared reason and incentive to setup or strengthen their ICT infrastructure/s and policies to encourage and sustain physicians’ leadership for medical DM within the SC of relations within a social network environment. Future research should also consider this study’s framework which should be empirically assessed within the context of Bahrain’s HC sector since struggling service quality of the HC sector is a global epidemic, as well as a concern within the Kingdom of Bahrain.

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An Artificial Intelligence Approach for Enhancing Trust Between Social IoT Devices in a Network J. Senthil Kumar, G. Sivasankar and S. Selva Nidhyananthan

Abstract In the present communication era, physical objects or things begin to communicate with each other and share information using the Internet as backbone. This enables the things to be smarter and capable of communicating with authenticated devices and it has evolved the term Internet of Things (IoT). Social IoT (SIoT) devices engage and adopt social networking paradigms between the smart devices. Enabling socialization requires trusted linkage between the smart devices. Privacy and Security enhancements between SIoT devices are serious concern and are highly mandatory in the current communication era. By solving the challenges in establishing the trust between SIoT devices, in turn enhances their security aspects. Cyber profiteers may make use of those feebleness of the SIoT devices to gain profit out of it unethically. This chapter deals with DeepChain framework, Artificial Intelligence (AI) technique for imparting robust trust between SIoT devices considering Quora, Facebook and Twitter social networks. Here, trustworthiness is evaluated in a bilateral manner between the trustor and trustee SIoT devices for making authenticated devices to survive and be safer in the network and environment. SimpleLink Wi-Fi modules are configured to form mesh network of SIoT device for experimentation along with set of simulated nodes. Trust transitivity calculations are made using traditional, conservative and aggressive. Performance of the trust evaluation scheme is analyzed for dynamic environment for distinguishing malicious behavior of SIoT nodes using AI algorithms for two different cases. The case with delegated success rates, gain, damage and cost to the trustee SIoT devices performs better than the case with success rates alone. Quora social network has better success rate while undergoing aggressive mode of transitivity calculations. Keywords SIoT · Trust · Social networks · Transitivity · Artificial intelligence J. Senthil Kumar (B) · G. Sivasankar · S. Selva Nidhyananthan Mepco Schlenk Engineering College, Sivakasi, India e-mail: [email protected] G. Sivasankar e-mail: [email protected] S. Selva Nidhyananthan e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_11

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1 Introduction The Internet of Things is spreading faster than any other technology. It promises a world of interconnected devices, ranging from everyday appliances to enterprise infrastructure. Thousands of companies are being investing billions of dollars into the next IoT smart widgets. Researchers are visualization something greater, such that what if all your devices track your social graph and all of your content were compiled in a way that you could link and manage [1]. We call this as social IoT, a complete ecosystem for our connected life. It forms an internet of devices that is centered on persons, building a network beyond IoT into a world of untapped potential at its core. SIoT and its interfaces gives us access to every part of our connected life. We can then add few intuitive rules engine for customizing social content and device interaction through easy-to-use modules [2]. An user can create custom rules, such as if an early morning meeting is scheduled and traffic is slow, start brewing the coffee sound the alarm twenty five minutes early and send the best route to my vehicle. If we are truly caring for our loved one, and if a loud noise is detected and we are away, start recording by activating webcam and send a notification to our phone. If no action is taken in 30 s, then it can be configured to flash our office lights, make a call and then send a live stream of the video. At the enterprise level, we could automatically restrict access to certain areas using a camera, a facial recognition module and a database of employees. If a visitor is recognized, open the door for 10 s and if a visitor is not recognized door remains locked. Meantime, record a short video and send file to cloud storage and take highresolution photo of the subjects face and send notification to nearest security officer. With complete SIoT platform, the potential for more complex developer applications is endless. For example, if a retail store wants to attract more shoppers, a developer could create an application that gives customers rewards for promoting the brand. Something like, if a user posts an in-store photo to social media and tags the brand, print a customized coupon in the store and send a notification to their phone. SIoT applications can also be built for the real world, in entertainment, fitness, travel and many more. Developers can use those marketplace, to distribute their applications by making it available to large group of users. SIoT devices builds a complete ecosystem for our connected life a world, where everything is tailored to meet user demands based on their personal preferences with a network centered around them. It is mandatory to address the challenges involved in establishing the trust futures between SIoT devices. Imparting novel solutions using artificial intelligence technique for trust enhancement between devices has become ultimate choice of designers. In order to increase the robustness and trust between SIoT devices of the system, artificial intelligence algorithms impart positive trust among smart SIoT devices in the network. Also, decentralized trust evaluation strategies are in great demand for various social networks and varying number of SIoT devices to safeguard themselves in the network [3, 4]. This chapter is arranged as follows. Section 2 highlights background and motivation of trust enhancement in SIoT devices and network. Detailed description of using

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Artificial intelligence techniques for SIoT is presented in Sect. 3. Trust enhancement solutions for SIoT devices are explained in Sect. 4. Analysis and discussion of results is elaborated with illustrations in Sect. 5. Finally, conclusion and future directions are presented.

2 Background and Motivation SIoT networks allow people and smart devices to interact with each other and support social navigation [5]. Those people in every country have their own culture and history. But for everyone in the world, security is the key concern in their devices as well private life. Most importantly, their devices collects and transfers large volume of data throughout the data. Imparting physical security and cyber security to SIoT devices are major challenges, still need to be addressed with utmost care. Enhancing trustworthiness between those devices for leveraging higher degree of interaction between SIoT devices are in huge demand [6]. The notion of trust relationship involves a trustee and a trustor engaged in an environment to characterize the security aspects of SIoT devices and network [7]. Trust management in SIoT environment has several constraints depending on direct, indirect, local or global trust between the trustee and trustor. Such trust can also be subjective or objective based on weights or QoS properties employed in SIoT devices. Trust between SIoT devices are expected to be dynamic and context dependent between successive nodes [8]. At a particular level, trust between SIoT devices also depends on past experience of events and it’s a composition of various attributes such as dependability, competence, reliability for trust enhancement [9]. Malicious SIoT nodes may perform certain attacks affecting the trust between the devices. Few of the malicious trust affects are Self-promoting, Ballot stuffing, Bad mouthing, Discriminatory, Whitewashing and Opportunistic service attacks [10]. Because of large constraints and huge requirements, SIoT network of devices hugely differs from social networks. Few of the major constraints and challenges of SIoT networks are involvement of large amount of devices, limited resources, limited storage, power efficiency, and adaptive dynamic configuration. Execution of trust establishment algorithms on such minimum constraint devices are highly challenging. SIoT devices supports various trust modes for computation of trust metrics in the network. Composition, Propagation, Segregation and Update of trust are four categories of design consideration for SIoT trust enhancement [11]. Trust composition can be established using QoS trust and Social Trust between devices, which ensures quality services and improves the degree of trust to the owners. Through trust propagation, observations between SIoT devices can be propagated either using distributed or centralized scheme. Aggregation of the observed trust can be done either using static, dynamic, Bayesian or Fuzzy based models. Finally, trust updates are made either by time driven or event driven techniques.

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3 Artificial Intelligence for Social IoT Social Internet of Things (SIoT) plays an important role in enhancing several real-life smart applications. In SIoT, smart things may join the network and few objects may leave the system based on mutual trust among them. Due to this rapidly expansion of SIoT environment, there is a chance for explosion of the data, also securing the data is highly challenging. In order to understand and handle the SIoT modifications, frequent updating of security models is essential. Hence implementing security in SIoT using traditional approaches is ineffective. So Artificial Intelligence and Machine Learning techniques are proposed to address the security problem in SIoT. Machine Learning and Deep Learning algorithms can be used for analysing normal or abnormal behaviour of the SIoT interactions. These methods can predict new attacks by simple derivations and mutations of previous attacks. Deep Learning and Machine Learning are concerned with training machines to learn from real-world samples to act intelligently and autonomously, which allow the machines to become smart machines. But, still various investigations show that threats include poisoning, evasion, impersonation and inversion attacks can be thrown against Machine Learning and Deep Learning algorithms. Due to these attacks, discrimination power of the classifier in distinguishing normal and abnormal behaviour of the system, accuracy and performance ultimately decreases. Meanwhile blockchain systems are relatively more secure and transparent than centralised systems, where Blocks are chained together through each block containing the hash of the previous block’s header [12, 13]. So Deep Learning or Machine Learning can integrate with Blockchain to overcome the security and trust issues in social networks [14–17]. One real time application example of the use of AI/ML algorithms in blockchain structures is Singularity NET, the firm which created the brain of Sophia the Robot.

3.1 DeepChain DeepChain combines Blockchain techniques and cryptographic primitives [18] to deep learning to attain a fully functional secured SIoT system. Blockchain is concerned with keeping accurate records, authentication, and execution, while AI helps to make decisions, and engendering autonomous interaction. Framework of DeepChain is shown in Fig. 1. Let assume N parties Pi , i = 1 . . . N and those parties agree to have some predefined information of the collaborative model. The information attached to a transaction at the initial iteration is denoted by T0 . After jth iteration, the updated model transaction is referred as T j . All the transaction {T j Pi } at round is securely aggregated from parties by a trading contract through launching transactions and it uploads the transactions to DeepChain. While workers update training parameter through processing contract. Workers update the weights as given in Eq. 1.

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Fig. 1 DeepChain framework

N     1   C W j+1 = C W j CPi C Wi, j N i=1

(1)

  where CPi C W  i, j are local weights, C is a cipher to encrypt the weights used by party Pi and C W j is weight at round j in T j . Major building blocks of DeepChain are bootstrapping, incentive methods, asset statement, cooperative training and consensus. DeepCoin distribution and genesis block generation includes DeepChain bootstrapping. An incentive mechanism act as a driving force for the participants to participate in a collaborative training task honestly and to produce and distribute rewards or penalties to the participant based on their contribution. Asset statements describe the asset states of the party, which does not reveal the content of asset instead which enables the participants to find co-operators and accomplish the deep learning task. A collaborative group can be constituted based on asset statement of the parties who have similar deep learning task. Consensus protocol is enables all the participants to make an agreement upon some event in a decentralized environment.

3.2 DeepChain Security Analysis Security and Trust management plays an important role in SIoT for reliable data fusion and mining, enhanced user privacy and information security and it helps the people to overcome the perceptions of uncertainty and risk. Threshold Paillier algorithm is employed in DeepChain to acquire confidentiality guarantee for gradients, which is based on the properties of Carmichael function in Z n2 [19–21]. Let assume there exists a trusted setup with t participants are honest and local gradients of each

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party should not be exposed to anyone. And participants should not disclose the downloaded parameters from the collaborative model at any circumstance. Let n = pq where p and q are large prime integers and g is an arbitrary element from Z n2 such that its order is a multiple of n. Consider m is a message to be encrypted in Deepchain model; then each participant encrypts cipher text as given in Eq. 2. C(W) = gm × kin

(2)

where ki ∈ Z n 2 . With threshold Paillier algorithm, cipher text can be correctly decrypted even with t corrupted parties. Enhancement in auditability and fairness can also be done to handle the malicious disturbance in the collaborative training process. Also trusted time clock and secure monetary penalty security mechanisms in Blockchain are used to enhance fairness during collaborative training. Trusted time clock mechanism force the operation to finish before respective time point and monetary penalty functions are used for gradient collecting and collaborative decryption. To equip ourselves against evolving threats, the implications of AI must become an integral part of SIoT system security. It is the time to overcome the lessons already learned from SIoT security issues by applying AI techniques. If these are considered when designing the infrastructure and devices of the future, then Artificial Social Internet of Things (ASIoT) will holds the power to transform our lives. Thus Artificial Intelligence plays an important role in SIoT Security and thrust implementation. Moreover Artificial Intelligence and BlockChain combination provides better security, throughput and training accuracy when compare to standalone machine learning and deep learning techniques.

4 Trust Enhancement Solutions for Social IoT Devices An authorized trustor has a valid goal towards providing access to trustees, allotting tasks to them and evaluating their outcomes. Based on the trust relationship, behavior of authorized truster and trustees must be consistent in the SIoT network. Trustors are uncertain and not have complete control of the trustees. They just rely on the actions of trustees to attain the goals. Deviated outcomes of fixed goals affects the trust relationship between trustees and trustor devices. Actual outcomes may also be subset of desired goals, which can be modified to meet the requirements by tuning the gain and cost factors between the devices [3]. Trust evaluation on trustee Social IoT devices are done by trustor with valid scheme. Context dependent trustworthiness is unique to every set of tasks handled by the IoT devices. Decisions made by the trustors vary based on changes encountered in the context. Figure 2 shows the ingredients of trust evaluation mechanism encountered while mutually evaluating between SIoT devices. This dynamic process

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Fig. 2 SIoT devices mutually evaluating their trust using an authorized trustor

of trust evaluation involves pre-evaluation, reverse evaluation, decision, result and post-evaluation stages [4]. In SIoT, trustworthiness is evaluated both sides to safeguard the trust confidence between trustor and trustee. This ensures non malicious resource exploitation and SIoT devices accepts delegation of tasks based on request. Pre-evaluation and postevaluation stages are performed mutually in SIoT based on the outcomes of the trust decisions. The trustor runs the Paillier algorithm employed with DeepChain for acquiring guarantees of confidentiality between the devices. Trustworthiness among SIoT devices are unique to specific category of tasks delegated to them. Past history based evaluation of trust relationship was one of the popular category of trust evaluation. In SIoT network, the task say t may include various characteristics. c(t) denotes one of the characterizes of task t. In this model of trust evaluation for SIoT devices, tasks operates based on set of characteristics. Trustworthiness of the devices based on different characteristics can be evaluated through various previous tasks. Between the SIoT devices, trustworthiness parameters can be transferred. This leads to transitivity of trust relationship among the SIoT devices and enables to trust the devices without any restrictions. Model based on context and transitivity with restrictions adds more authentication for trust relationship between SIoT devices. Figure 3 shows the transitivity between similar categories of tasks in a SIoT network of devices. Trust transitivity can be calculated using aggressive, conservative and traditional approaches. In aggressive approach, assessing the characteristics of tasks are performed along multiple paths. This approach unearths many potentially authenticated

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Fig. 3 Similar category of tasks during trust transitivity Fig. 4 The conservative trust transitivity mechanism for the tasks with multiple characteristics

trustee SIoT nodes, but at the cost of complex search strategies and larger overhead in communication between the devices. In the conservative approach, if intermediate SIoT devices or trustees includes all characteristics of recent tasks in the experienced tasks then trustworthiness is ensured. Figure 4 shows how the intermediate SIoT nodes are engaged in ensuring trustworthiness in conservative transitivity approach. If SIoT device 1 trusts device 2 with task t and the device 2 trust SIoT device 3 with task t , then device 1 can infer trustworthiness towards device 3 with task t . If new task characteristics is included in the experienced tasks of middle nodes along the path from source, then all characteristics of experienced tasks can be inferred for trustworthiness evaluation in aggressive approach. Figure 5 shows how the SIoT nodes are involved in ensuring aggressive trustworthiness. Here SIoT device 1 trusts device 2 and device 2 trusts device 4 with the same task t. device 1 trusts device 3 and device 3 trusts device 4 with the same task t . This ensures that device 1 can ensure trustworthiness towards device 4 with task t .

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Fig. 5 The aggressive trust transitivity mechanism for the tasks with multiple characteristics

5 Analysis and Discussion of Results For evaluating the performance of the proposed AI based trust enhancement strategy, three different social networks are considered. For network connectivity and tasks sharing between the SIoT devices Facebook, Quora and Twitter networks are considered. Data are collected from various users and included in the network. Real world networks were formed with 10 number of SIoT nodes and appended along with simulated sub networks for including more set of SIoT nodes. The experimental setup uses the IoT framework deployed with the support of Texas Instrument’s CC3200 SimpleLink Wi-Fi module for framing a set of 10 number of SIoT nodes. Remaining set of nodes are simulated to replicate the characteristics of CC3200 module in the MATLAB environment. Also, task sharing is ensured in a similar fashion as real network of SIoT devices. The set of nodes includes two trustors, and three unauthorized trustees. With the mesh network formed linking the SIoT devices, the devices are engaged in sharing the tasks with the authorization from the trustor. Deploying the AI based Paillier algorithm employed with DeepChain in the trustor, the trustworthiness between the trustees are ensured positively. Trust transitivity using the tasks provided from the social network platforms are calculated using aggressive, conservative and traditional approaches. They are evaluated considering the average potential number of trustees in the network as well as number of characteristics considered for each social network. Figure 6 shows the performance of trust transitivity observed for Facebook social network for various potential trustees. It is obvious from the figure that, for minimum set of characteristics the trust levels are higher and as the number of characteristics increases the trust level as decreases. Also, it can be noted that the aggressive way of transitivity calculation has better trust establishment over conservative and traditional approaches. Similar such observations are reported from the Fig. 7 and Fig. 8 for Quora and Twitter social networks respectively. All such social network enabled SIoT devices

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Fig. 6 The aggressive, conservative and traditional trust transitivity mechanism for potential trustees using Facebook social network

Fig. 7 The aggressive, conservative and traditional trust transitivity mechanism for potential trustees using Quora social network

have better transitivity among them, when they consider minimum characteristics, while engaged in aggressive way of transitivity calculations. Performance of the SIoT devices in the network is also monitored by considering the net profit while handling the social network tasks in their transitivity. This was performed for all the three considered social network under two scenarios for around 3000 iterations. In the first scenario, the social networks are considered with trustor delegating high success rates to the trustee SIoT devices. Figure 9 show the observed net profit for the social networks, with Quora network gaining reasonably better profit with more iterations. But collective net profit maximum is just around 0.1 in this case.

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Fig. 8 The aggressive, conservative and traditional trust transitivity mechanism for potential trustees using Twitter social network

Fig. 9 Monitored net profit for different social networks with trustor delegating high success rate tasks to the trustee

In the second scenario, the social networks are considered with trustor along with high success rates also delegates the gain, damage and cost to the trustee SIoT devices. Figure 10 show the observed net profit for the social networks. In this case even though Quora network tops the collective net profit maximum of all the networks are around 0.58 in this case. This improves the performance of the system around 48% compared to the first scenario. So, the social network tasks engaged in transitivity demanding high net profit need to include all the features of the networks. With collective set of SIoT nodes, performance was evaluated for the three social networks and the trust transitivity was computed for every networks using different calculation schemes. Consolidated performance measures of trust between SIoT devices in the network are tabulated in Table 1. From the table, it is evident that

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Fig. 10 Monitored net profit for different social networks with trustor delegating tasks considering success rate, gain, damage, and cost to the trustee

Table 1 Performance comparison of the SIoT node properties for various social networks Aggressive

Conservative

Traditional

Metric

Quora (%)

Facebook (%)

Twitter (%)

Success rate

68

60

53

Unavailable rate

27

27

36

Number of potential trustees

12

7

7

Success rate

58

54

49

Unavailable rate

38

33

46

Number of potential trustees

11

6

6

Success rate

28

29

23

Unavailable rate

67

61

74

5

3

3

Number of potential trustees

Quora social network has better success rate while undergoing aggressive mode of transitivity calculations. Also, it can be observed such that unavailable rate is much higher while performing traditional way of transitivity among tasks. It ensures that only authenticated turstor can avail the services and also completely blocks malicious nodes to break into the network. Using this context and characteristics based model for trust worthiness evaluation malicious behavior of SIoT nodes can be detected effectively.

6 Conclusion SIoT is exploding across the world in this digital era. At the same time, cybercriminals also expanding their capabilities to launch a faster and sophisticated attacks against Social Network. In order to make the digital transformation a great success,

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security personnel needs to overcome this challenge by providing AI assisted security solutions, which can detect the security threats and trust issues in SIoT and response to the issues faster. Discussed framework for enhancing thrust between SIoT devices provides new features and benefits for the developers of SIoT systems. Existing research in security, privacy in SIoT devices and networks tend to focus on providing suitable solutions for overcoming them. However, maintaining trust relationship between SIoT devices is the major issue the developers of smart things infrastructure encounter nowadays. This chapter deals with the issues involved in enhancing trust relationship between SIoT devices considering Quora, Facebook and Twitter social networks. Here, trustworthiness is evaluated in a bilateral manner between the trustor and trustee SIoT devices using traditional, conservative and aggressive trust transitivity calculations. It ensures that only authenticated turstor can avail the services and also completely blocks malicious nodes to break into the network. Using this context and characteristics based model for trust worthiness evaluation malicious behavior of SIoT nodes can be detected effectively. Performance of the trust evaluation scheme is analyzed for dynamic environment for distinguishing malicious behavior of SIoT nodes using AI algorithms for two different cases. The case with delegated success rates, gain, damage and cost to the trustee SIoT devices performs better than the case with success rates alone. Quora social network has better success rate while undergoing aggressive mode of transitivity calculations.

References 1. Stergiou, C., Psannis, K.E., Kim, B.-G., Gupta, B.: Secure integration of IoT and cloud computing. Futur. Gener. Comput. Syst. 78(3), 964–975 (2018) 2. Soro, A., Brereton, M., Roe, P.: Social Internet of Things, 1st edn. Springer (2018) 3. Abdelghani, W., Zayani, C.A., Amous, I., Sedes, F.: Trust management in social Internet of Things: a survey. In: Conference on e-Business, e-Services, and e-Society, I3E 2016, Swansea, UK, September 13–15, Proceedings, pp. 430–441 (2016) 4. Lin, Z., Dong, L.: Clarifying trust in social Internet of Things. IEEE Trans. Knowl. Data Eng. 30(2), 234–248 (2018) 5. Kim, J.E.: Architecting social internet of things. Ph.D. thesis. University of Pittsburgh (2016) 6. Geetha, S.: Social Internet of Things. World Sci. News 41, 76 (2016). Avinashilingam 7. Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001) 8. Grandison, T., Sloman, M.: A survey of trust in internet applications. IEEE Commun. Surv. Tutorials 3(4), 2–16 (2000) 9. Yan, Z., Holtmanns, S.: Trust Modeling and Management: From Social Trust to Digital Trust, pp. 290–323. IGI Global (2008) 10. Bao, F., Chen, I.R., Guo, J.: Scalable, adaptive and survivable trust management for community of interest based Internet of Things systems. In: 2013 IEEE Eleventh International Symposium on Autonomous De-centralized Systems (ISADS), pp. 1–7. IEEE (2013) 11. Guo, J., Chen, R.: A classification of trust computation models for service-oriented internet of things systems. In: 2015 IEEE International Conference on Services Computing (SCC), pp. 324–331. IEEE (2015) 12. Yaga, D., Mell, P., Roby, N., Scarfone, K.: Blockchain technology overview. National Institute of Standards and Technology (NIST), U.S. Department of Commerce, Draft NISTIR 820 (2018)

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13. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media (2015) 14. Al-Garadi, A.M., Al-Ali, A., Du, X., Guizani, M.: A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018). arXiv: 1807.11023 15. Yan, Z., Zhang, P., Vasilakos, A.V.: A survey on trust management for Internet of Things. J. Netw. Comput. Appl. 42, 120–134 (2014) 16. Saied, A., Overill, R.E., Radzik, T.: Detection of known and unknown DDoS attacks using artificial neural networks. Neurocomputing 172(8) (2016) 17. Cui, H., Zhang, H., Ganger, G.R., Gibbons, P.B., Xing E.P.: GeePS: scalable deep learning on distributed GPUs with a GPU specialized parameter server. In: Proceedings of EuroSys (2016) 18. Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., Luo, W.: DeepChain: Auditable and PrivacyPreserving Deep Learning with Blockchain-based Incentive. IACR Cryptology ePrint Archive (2018) 19. Nishide, T., Sakurai, K.: Distributed paillier cryptosystem without trusted dealer: In: International Workshop on Information Security Applications. Springer (2010) 20. Schoenmakers, B., Veeningen, M.: Universally verifiable multiparty computation from threshold homomorphic cryptosystems. In: International Conference on Applied Cryptography and Network Security. Springer (2015) 21. Hazay, C., Mikkelsen, G.L., Rabin, T., Toft, T.: Efficient RSA key generation and threshold paillier in the two-party setting. In: Dunkelman, O. (ed.) Topics in Cryptology—CT-RSA 2012, CT-RSA 2012. Lecture Notes in Computer Science, vol. 7178. Springer (2012)

A Survey of Internet of Things (IoT) in Education: Opportunities and Challenges Mostafa Al-Emran, Sohail Iqbal Malik and Mohammed N. Al-Kabi

Abstract Internet of Things (IoT) is the most challenging platform that designates the correlation of physical objects in the near future. A wide spectrum of review studies has been conducted to analyze and synthesize the use of IoT and its applications in various domains. Nonetheless, research neglects providing a comprehensive review study regarding the application of IoT in education. Therefore, the main purpose of this study is to highlight the recent progress of employing IoT applications in education and provide various opportunities and challenges for future trials. More specifically, this review study summarizes the prospects of adopting IoT in education, medical education and training, vocational education and training, Green IoT in education, and wearable technologies in education. It is concluded that the adoption of IoT and its applications in developing countries is still in its early stages and further research is highly encouraged. Keywords Internet of things · IoT · Education · Opportunities · Challenges

1 Introduction The term “Internet of Things (IoT)” was coined for the first time by Kevin Ashton in 1999. He uses IoT to outline a system where the ubiquitous sensors are used to connect the physical world with the Internet. The term IoT is considered by many experts as it represents the next step to the digital society. IoT helps the employability and M. Al-Emran (B) Applied Computational Civil and Structural Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam e-mail: [email protected] S. I. Malik · M. N. Al-Kabi Information Technology Department, Al Buraimi University College, Al Buraimi, Oman e-mail: [email protected] M. N. Al-Kabi e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_12

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nurture the competitiveness of academic and commercial establishments. The digital transformation, current trend of automation, and data exchange by different vendors refer to “Industry 4.0” which are based on IoT [11, 39, 42]. A recent study conducted by Assante et al. [10] shows the importance of IoT to Small and medium-sized enterprises (SMEs) in Europe to survive and be sustainable to non-European enterprises. They emphasize the importance of providing training to different employees to use IoT technologies. Nowadays, many cities around the world start a transformation process to be smart cities. Furthermore, many academic establishments in the world start to use IoT. Therefore, such transformations need to teach IoT technology to engineers. A number of higher education establishments start to offer IoT related elective courses to Computer Science & Engineering Under Graduate students [51]. Smart cities have smart waste management, smart parking, smart traffic management, smart lighting, environment monitoring, smart irrigation, smart intrusion detection systems of homes, banks, etc. In addition, social networks are one of the most rapid technologies that effectively penetrated a large number of sectors [8, 37, 56, 57], and have been successfully integrated with several technologies, where IoT is not an exception. In keeping with this, the IoT, social networks, and the three worlds of the Internet are consolidated together in order to transform the physical real world into the virtual one. The resulted paradigm refers to the “Social Internet of Things (SIoT)”, which has the ability to support new applications and networking services for the IoT effectively and efficiently [12]. The essential aim of SIoT is to incorporate devices into users’ daily life by taking the benefits of social networks like user-friendliness and interconnectivity. To maintain the aim of SIoT and ensure its successfulness, new appealing services need to be provided in order to encourage users to socialize their devices [15]. The innovations in the IoT will increase due to the continuous advances in cloud computing, nanoelectronics, communications, sensors, big data, and smart objects. IoT is one of the parts of the Internet. In that, IoT allows humans to connect to each other, human and things to be connected, things to be connected to other things. Therefore, the emergence of IoT helps to establish giant intelligent systems. The adoption of IoT in different fields helps to revolutionize these fields. The higher education is one of those fields that started to adopt IoT to enhance the learning, training, management, experimentation, etc. [63]. A number of higher education establishments around the world adopt IoT to yield profound changes in their performance (teaching, learning, management, training, buildings, etc.). IoT extends across a number of disciplines that include computer and information science, engineering, social and mathematical sciences. The world witnessed a growing interest in Science, Technology, Engineering, and Mathematics (STEM) in many countries after realizing the importance of STEM to enhance the economy. The fourth industrial revolution (4IR) was introduced for the first time in Germany, in which the technology is embedded within societies. IoT is one of the main components of 4IR. There is an increasing demand for experienced professionals in the IoT, but there are few higher education establishments that offer IoT related courses in the fields of STEM. Research shows that STEM students have

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limited experience or no experience to design and implement IoT applications [27]. Therefore, those students join their jobs later with no or limited IoT experience. The current STEM curriculum in higher education establishments has not enough room to add many IoT related courses. Most of the higher education institutions around the world do not offer a curriculum to empower skills and knowledge in IoT. This urge educators to offer IoT technology in their courses. Experts predict that IoT technology will be the main influential technology in the next 5–10 years. Many types of digital learning can be categorized under the umbrella of IoT. For instance, E-Learning (Electronic-Learning) refers to the learning where electronic devices like the computers and channels like the networks (Internet/Intranet/Extranet) are used [55]. M-Learning (Mobile-Learning) is any form of knowledge that is presented using hand-held and portable devices [3, 4]. U-learning (Ubiquitous learning) represents some form of simple mobile learning. The leap to the pervasiveness of knowledge is represented by the cast of U-learning, where the learner can access additional learning content and use the facilities of collaborative learning environments [26]. Concerning the adoption of IoT, Marquez et al. [35] proposed a model to integrate objects to Virtual Academic Communities (VAC). They tested their model and discovered that the adoption of IoT yields a more engaging learning environment for learners, and the instructors got more information about the learning process, which in turn enhances the pedagogical process. Further, Njeru et al. [44] conducted a research study to test whether the adoption of IoT in education helps online learning and teaching. To achieve this goal, a system has been proposed to help the management of the educational institution to make informed decisions. The proposed system used to collect the data from IoT devices and analyze them in such a way to improve the information delivered by the instructors and accessed by learners. It has been concluded that the adoption of IoT in education helps online learning and teaching. Bagheri and Movahed [13] showed that the use of IoT in higher education is not limited to teaching and learning. Instead, the study listed several applications of IoT in higher education. First, campus energy management and eco-system monitoring, where IoT is used to manage energy and monitor eco-system. Second, secure campus and classroom access control, where IoT is used to make universities safe and secure places for instructors and students. Third, student’s health monitoring, where IoT is used to monitor patients and prevent diseases. Fourth, improving teaching and learning, where IoT is used to enhance the pedagogical process. In the same vein, Aldowah et al. [5] conducted another study to examine the impact of IoT on higher education. It has been concluded that the use of IoT in education has a positive impact on the learning process. A number of review studies were carried out to examine the employment of IoT in various domains. Nonetheless, research neglects providing a comprehensive review study regarding the application of IoT in education [24]. Thus, this study is an attempt to review the main opportunities and challenges that IoT brought to education, and provide various implications for further research. More specifically, this survey tackles some of the IoT-based review studies (Sect. 2), the general use of IoT in education (Sect. 3), IoT in medical education and training (Sect. 4), IoT in

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vocational education and training (Sect. 5), Green IoT in education (Sect. 6), IoT and wearable technologies in education (Sect. 7), discussion and implications (Sect. 8), and conclusion and future research directions (Sect. 9).

2 Review Studies in IoT A number of studies were carried out to review the concept of IoT across various domains. In general, Ammar et al. [9] conducted a study to review the security of IoT frameworks and provide a comprehensive analysis of the hardware, smart apps, architecture, and security features of the frameworks under observation. Additionally, ˇ Colakovi´ c and Hadžiali´c [22] conducted a review study to provide a detailed overview of open issues and challenges that need to be considered while conducting future IoT research with more emphasis on the technological perspective. Further, Bertin et al. [16] provides a detailed review study by reporting several access control models, architectures, and protocols for the IoT. Moreover, Cui et al. [23] conducted a study to review the employment of machine learning in the IoT domain, in which the recent advances in machine learning methods across several IoT applications were reported. Besides, Carcary et al. [19] conducted a systematic review study to understand the IoT adoption by examining the factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) at the organizational level. In addition, Wang et al. [65] carried out a study to review the Blockchain technologies by focusing on IoT applications. Concerning education, Zhamanov et al. [66] conducted a study to review the IoT smart campus model and applications. The study describes the implementation of these applications within the university campus, IoT-based flipped classroom, IoT-based entrance system, student’s feedback, IoT-based orangery, and IoT heating system. In the same vein, Dominguez and Ochoa [24] conducted a survey regarding the use of IoT and smart objects in education. The study attempted to explore the opportunities of adopting IoT in education and the key challenges that curb its adoption. A considerable amount of review studies has been published on the application of IoT in different domains. Nonetheless, there is an ambiguous relationship between IoT applications and education. Although the review studies conducted by Zhamanov et al. [66] and Dominguez and Ochoa [24] provide an overview of employing IoT in the education sector, these studies were limited in terms of breadth, focus, and scope. Thus, this study is an attempt to review the IoT-based education studies and provide an overview of the key challenges and open issues for the employment of IoT in education.

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3 IoT in Education Information and communication technologies have transformed the learning fashion from traditional-based learning into digital-based learning [34, 54, 7]. IoT is one of such paradigms that contributes to such initiatives. This section exhibits a review of the studies related to the general use of IoT in education. Pei et al. [48] conducted an analysis of the benefits of IoT in education. More specifically, the study presents the pros and cons of using mobile education. The study proposes a mobile education architecture which uses the cloud to offer flexibility and expansibility. In addition, an IoT-based educational mobile learning tool is designed and developed for the primary schools in northern Thailand [50]. The main goal was to provide an effective teaching and utilize a large number of tablet computers. Besides, Sarıta¸s [59] presents in his detailed study the relationships between IoT, cloud computing, and the emerging learning theory—connectivism. He concludes that “educational institutions need to develop a comprehensive strategy including curricula, professional development of teachers, educational philosophy, data security, legal and political issues, and transformation of resources and infrastructure to be able to address the many unique challenges that lie ahead”. To overcome the limitation of adding many IoT related courses to STEM students, He et al. [27] proposed a transformation to STEM core courses by blending IoTbased learning framework with their corresponding lab projects. The study presents the design of the proposed framework and suggests effective learning approaches to address the observed challenges. Following that, a case study is presented by incorporating the proposed IoT-based learning framework into a software engineering with system analysis and design course. The study concluded that most of the students have a positive impression towards the employed framework. In addition, Burd et al. [17] presented four main approaches used by computer science instructors to integrate the concepts of IoT and computer science courses into their curricula. They present briefly the challenges and choices to teach IoT and list a number of tools that help fresh IoT instructors to get started. Another study conducted by Maenpaa et al. [31] to tackle the practical and problem-based project course. The study presents a framework for assessing the students’ learning outcomes regardless of their different educational backgrounds. These findings were updated and improved by the conclusions drawn by Burd et al. [18] while conducting interviews with IoT mentors. In that, two research questions were addressed. The first question they tried to answer is what are the relevant contents for a curriculum that dedicated to those who intended to be IoT specialists? They suggested a transdisciplinary curriculum that includes threads from several disciplines on one campus, which is related to the ACM/IEEE 2013 Computing Curricula Knowledge Areas. The second question they tried to answer is what are the pedagogical practices that have to be adapted to teach the suggested IoT curriculum? They present the teaching methods they used, the challenges of each method, and a number of IoT teaching approaches. IoT project-based courses are designed for

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a team of students not for a single student, and the problems they face in their IoT projects involve physical and logical knowledge. Concerning personalized learning, Moreira et al. [40] conducted a study to provide personalized education to learners by using the data collected through IoT, cloud computing, and learning analytics tools. It is indicated that this approach is able to provide personalized curricula that depend on the abilities of each student. Another study proposed an educational curriculum to construct an IoT prototype system which was related to the field of engineering education and to be constructed by students of the liberal arts [2]. The study pointed out that students were able to construct IoT prototype systems in a stepwise manner, and the students were able to create ideas for the fields that are familiar with them. Moreover, Kamal et al. [28] designed and implemented a learning kit to teach the basic concepts of IoT technology. In that, blockchain technology was used to distribute digital information. Both the IoT and blockchain in the learning kit work together to present the concepts related to blockchain technology and IoT to the learners in a simple and attractive way. Majeed and Ali [32] proposed a model for making a university campus smart through the adoption of IoT technology. The study tried to embed IoT in education and use the Internet to connect physical objects, sensors, and controllers. The proposed model aims to make smart classrooms, smart car parking area, and deliver smart education to the university students. Furthermore, a study carried out by Kassab et al. [29] to present the 3-D scheme for IoT in education. First, the study presented the ethical, technical, economical, and physical challenges of adopting IoT in education. Second, the study presents the 3-D scheme that consists of three dimensions, namely delivery mode, perception, and learning principles. The study also showed how the adoption of IoT can help instructors, students, and staff in affecting these academic establishments. It seems strange that these days we are trying to make our schools and universities smart, while it supposed for these establishments to make us smarter.

4 IoT in Medical Education and Training Gómez et al. [26] proposed a system to enhance the learning of students by allowing the student to interact with the physical surrounding objects which are virtually associated with a subject of learning. Many of the academic programs need students to interact with objects, such as in medicine, computer engineering, and mechanical engineering programs. The tests conducted on the working prototype proved that the proposed system helps the instructors in their teaching process and enhances the student academic performance. During the last two decades, there were relatively few medical schools that use case-based learning (CBL), but today CBL is considered as an effective pedagogy to teach medicine through the use of flipped learning and IoT. The use of these three techniques (i.e., CBL, IoT, and flipped learning) improves the learning abilities of medical students [6].

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5 IoT in Vocational Education and Training The collection of sensors in IoT is beneficial to students and teachers in vocational establishments. It is argued that IoT should be used in vocational education and training to enhance the learning-teaching process. The adoption of IoT in vocational training yields qualified students, a safer educational environment, self-directed learning, and economical use of educational resources and enhanced learning [64].

6 Green IoT in Education Nowadays, there is a growing interest in the concept of Green IT. Green IT aims to reduce the electrical power consumption of different computers, peripherals, and devices, and this move has positive economics and environment impacts. Murugesan [43] defines Green IT as environmentally sound IT. In that, Green IT involves studying the design, manufacturing, use, and disposing of computers, servers, and associated subsystems such as monitors, printers, storage devices, and networking and communications systems efficiently and effectively with minimal or no impact on the environment. Murugesan [43] presents the strategies used for transformation to Green IT through the use of screensavers, eco-friendly design, enabling power management, turning off systems when not in use, green data centers, energy conservation, and virtualization. Murugesan concludes that the adoption of Green IT leads to reduce power consumption, lower costs, lower carbon emissions, improved systems performance and use, and space savings. In education, Ozturk et al. [46] conducted a review study of Green IT from the practitioners and academicians point of views, and found a high overlap between them. Greening IT becomes more meaningful and powerful technique. Both of these two parties have realized the importance of Green IT to make our environment sustainable. Moreover, Suryawanshi and Narkhede [60] studied the essential factors that could affect the successful implementation of Green ICT in educational establishments. It is concluded that the factors that significantly affect the success of IoT include optimum utilization of resources, stakeholder’s involvement, renewable energy, energy conservation, institutional policy, Green ICT committee activities, and legislation. Additionally, Maksimovic [33] explores the possibilities of using Green IoT in engineering education. Maksimovic also tackles the advantages and challenges for using Green IoT in smart classrooms. It is argued that Green IoT is based on Green communication technology, Green computing technology, smart grid, and applications.

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7 IoT and Wearable Technologies in Education Wearable technologies are a subset of IoT [62]. Wearable technologies are getting popularity day by day and in the near future, these technologies will be an indispensable part of our daily life [20]. Wearable technologies are defined as “the technological devices that are worn on a user’s body” [45]. Further, these devices may be worn as eyeglasses, wristwatch, wristband, shoes, or clothing [61]. The literature reports three main categories of wearable technologies such as wearable health technologies, wearable textile technologies, and wearable consumer electronics [20]. Among wearable devices, smart glasses are leading a paradigm shift in user’s everyday life [20]. Google Glass is the best and respectable example of smart glasses. The Google Glass comprises of central processing unit, high-definition camera, integrated display screen, microphone and wireless connectivity [41, 47]. For educators, it is stated that Google Glass can help in “providing enhanced flexibility to information, opportunities for seamless collaboration, and a potential for sharing and ultimately learning” [21]. The use of Google Glass technology as a teaching tool was reported in different studies related to the medical domain. For instance, Knight, Gajendragadkar, and Bokhari [30] broadcasted surgical procedures onto a mobile phone for others to view using Google Glass. Moreover, some studies reported telemonitoring in which trainers used the Google Glass to instruct trainees wearing Google Glass in medical procedures [49, 53]. Dunn [25] discussed that the recording facility provided by Google Glass is the key feature which can facilitate teaching and learning. Students can record their interaction with other students or their actions while on field trips. Later, students can utilize this recorded information to analyze their own and other responses. Teachers can record their lectures and use these resources in flipped learning.

8 Discussion and Implications This review study attempted to provide an overview of the existing trends in employing IoT in education. In general, IoT provides several opportunities for educational institutions, instructors, and students. For instance, IoT enables the students to access the learning contents from any tool or device that is connected to the Internet [14]. In addition, IoT allows the educational institutions to monitor and track the learners’ achievements and learning capabilities by analyzing the collected students’ information through sensors and wearable devices [14]. Further, IoT enables both students and instructors to be recognized, and attendance can be recorded automatically through the usage of RFID tags or face-recognition algorithms [1]. Besides, IoT enables the process of finding free places to study and offer additional lessons on demand for students through the use of occupancy detection and tracking function [1].

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In medical education, IoT along with flipped learning enable medical students to take effective decisions and enhance their collaboration in learning practices [6]. In vocational education, IoT can serve as an efficient pedagogical tool for building training skills in students who attempt to find answers to unexplored problems using electronic devices [64]. In addition, wearable technology allows students to track and record their learning behavior and improve their interactive experience [38]. On the contrary, there are several challenges that are associated with the application of IoT in education. First, computer science and engineering departments need to develop their curriculums to incorporate IoT courses in order to qualify their fresh graduates with sufficient capabilities to manage and work on different IoT projects. Second, educational institutions need to improve their strategies by including IoT in their developed curricula, preparing and organizing orientation sessions for their staff regarding the pros of IoT, offering professional developments plans for instructors, and making their students aware of the different IoT applications. Third, IoT is still in its early stages and many issues are still unsolved such as wireless coverage, sensors high costs, and battery life among others [36]. Accordingly, IoT engineers and developers need to take these issues into consideration in order to facilitate the employment of IoT applications. Fourth, although m-learning applications like augmented reality and learning analytics are studied and discussed in many IoT-based studies, their adoption is still scarce and requires further examination. Fifth, many scholars believed that security and privacy are among the main challenges that hinder the deployment of IoT in education [52]. Thus, future endeavors in applying IoT in education need to consider these factors in order to reduce the obstacles observed in previous research and sustain its effective use. Sixth, the use of Green ICT in education has been thoroughly studied in many developed countries [60]; however, its adoption in developing countries is still neglected. Scholars need to examine the factors affecting its adoption in order to help the decision-makers to take effective procedures for its deployment. Seventh, it is argued that wearable technologies like Google Glass are much used in medical education [58]. Thus, scholars need to explore the use and adoption of these technologies in other domains.

9 Conclusion and Future Work The education sector is regarded as an effective candidate for the deployment of IoT applications. The main purpose of this review study is to provide a holistic view of the employment of IoT applications in education and to discuss the opportunities and challenges associated with this initiative. A number of studies proposed and showed the effectiveness of establishing IoT-based learning frameworks. Such a move helps to create a new paradigm of learning. This innovative paradigm of learning helps to enhance learning and teaching processes. Despite the fact that several educational institutes have used the IoT in their campuses, the adoption of IoT and its potential use is still scarce, especially in

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developing countries. The stakeholders in these institutes need to develop the skill for analyzing the students’ needs, suggesting and designing the required sensors and software for IoT applications, and setting the guidelines for its effective use. In addition, through the analysis of the reviewed studies, it has been observed that there were no empirical studies concerned to examine the impact of IoT applications on students’ learning performance. Hence, there is an abundant room for scholars to examine the factors affecting the deployment of IoT applications in education in general, and the developing countries in particular.

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Assessing the Performance of Container Technologies for the Internet of Things Based Application Ruchika Vyas, Kathiravan Srinivasan, Aswani Kumar Cherukuri and Karan Singh Jodha

Abstract Our planet is a weave of inter-connected devices that are generating data tremendously and continuously communicating. The virtual and physical objects (devices) are connected by a global network (internet) with self-configuring capability. One of the many emerging technologies in present times is Internet of Things (IoT) where everyday objects are not only connected and communicating with each other but also capable of taking real-time decision with minimal human interaction (smart devices), Hence IoT is the future of the internet and can be referred as Internet of Everything or Web of Things. A large amount of data (structured/unstructured) is generated by IoT applications. The existing architecture of IoT application’s devices doesn’t have enough capability to store and process the data. The cloud computing technology centralizes the data in one place to analyze, process and provides ondemand access. So the new style of IoT with Cloud Architecture would provide a solution to data storage, real-time analysis and make IoT application smart, scalable, reliable and flexible. However, developers are facing multiple barriers while developing, creating and scaling IoT application. The technology of Docker which is open-sourced, is useful for container-based solution of virtualization, which can help to develop and scale its application easy and fast. The result of benchmark tools for performance measurement comparison between host and docker shows the positive impact of using the container. The test results have indicated that Docker meets central IoT requirements through a rich set of features.

R. Vyas National Centre for Medium Range Weather Forecasting, Noida, India e-mail: [email protected] K. Srinivasan (B) · A. K. Cherukuri · K. S. Jodha School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India e-mail: [email protected] A. K. Cherukuri e-mail: [email protected] K. S. Jodha e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_13

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Keywords Internet of Things · Container technologies · Docker

1 Introduction “Internet of Things”, a new hype in the web world owes its origin to the creation of IPv6 and further with extension of wireless networking with 6LoWPAN in the years 2000 and 2007 respectively leading to real-world applications being integrated with our physical environment by vitally utilizing remote sensor like devices. The trading of information for useful time-sensitive analytics by intelligent systems is automated by IoT which is primarily the essence of IoT i.e. to make information sharing intelligent objects. The IoT is a very big concept to understand its potential of large scaled application and business model and challenges. The IoT provides benefits to industries by improving health care, enabling intelligent transportation system and modernizing manufacturing, which tends to empower business all over the world. According to CISCO Internet Business Solutions Group (IBSG) [1], survey more than 50 billion devices will be connected to the internet. The position of IoT in Gartner Hype cycle in 2018 was at the peak of inflated expectation, which shows the upcoming business market value of this technology. Intelligence and Connectivity are moving into the sensors, actuators and devices and applications across, energy (oil and gas), healthcare, transportation, and more combined with internet protocols for computing and the cloud. We now have the industrial internet of things unlike the regular internet the industrial internet must meet higher standards of security and safety. It must also stand the test of time the industrial internet needs a common architecture to connect the sensor to the cloud, power to the factory, and cities to medical services. Industrial IoT is indeed transformative, it will rise the industries boundary and recreate the important feature of the intelligent system. The IoT is a very big concept—and part of the challenge in understanding its meaning and potential rests in the huge spectrum of applications that may benefit from an IoT business model [2]. Connecting devices may sound simple in our digital world, but the IoT means different things to different users, and has extensively varied development requirements depending on the application. Although early IoT deployments have been limited to powerhouse firms with enough internal resources to develop their own proprietary systems, today’s vendors are creating off-the-shelf, standards-based IoT solutions. Integrating these as building blocks, developers can more easily create high performance IoT frameworks for mainstream users [3]. The research evaluates container technology as a promising solution for the deployment of IoT applications. Container technology offers multiple features that address the presented challenges, including: isolation of applications from their surrounding area, resource restrictions against applications, and a standardized method to package, distribute, and deploy applications onto devices [4]. IoT being one of the greatest developing innovation will be another guidance and direction provider to the web world and will be primarily responsible for capricious

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business improvement and profits. For instance, the appending of advanced sensors to many objects for sending and accepting data for the automation in the Manufacturing and Industrial sector is leading to the generation of vast quantities of useful information. The IOT maximize the efficiency and throughput of the entire application/product development, application operation and service delivery, etc. The Industrial application includes machine to machine communication, operation/motion control, big data analytics, smart energy and smart grid, decision making or prediction, risk analysis. Hence we can say that IoT is a new wave of revolution for research and business market. A lightweight approach for creators requiring IoT applications at a large scale is containers as the virtualization makes the development, deployment and testing process highly rapid. As every individually isolated container runs an instance of user-space over an operating system that are common among them, it is credited to the lightweight virtualization which is container-based. Independent virtual network interfaces, separate file systems and independent process spaces are all a part of particular containers despite the fact that operating system is shared between them. Considering a hypervisor-based virtualization in which an operating system runs for every Virtual Machine leading to increased system resources usage, very less memory resources are used by containers as resource isolation can be implemented using control groups via which system resources can be allocated to the containers. Docker, an open technology platform is a way on Linux systems to achieve container-based virtualization. Just about anywhere, these containers can be deployed quickly and build rapidly regardless on a private or public cloud, or on IoT devices that include physical hardware or within a local Virtual Machine. • Lightweight • Open Source • Secure Few of the key driving factors like accessibility of sensors, microcontrollers and commoditization of wireless modules has resulted in exponential growth of Internet of Things (IoT). Further, with the ever increasing production of data by the diverse inter-connected devices, the demand of systems for its processing and interfacing mechanism with these devices is at a rise. Industry standard, security, plan of action and whole IT ecosystem are bound to be majorly affected due to the daily increase in IoT devices connected through the internet network. A solution for developing scalable IoT applications is Docker that is easy, fast and container-based virtualization technology and can answer the problems being faced by developers. In this work, Sect. 2 is about the background of IoT and Cloud, in this chapter history of IoT, IoT characteristics complementary to Cloud, and architecture information flow in IoT with Cloud is discussed. Cloud-Centric IoT architecture has been discussed which describes four type of communication pattern between IoT components. Section 3 is explaining the various virtualization technologies and selection of container technology for IoT application. In this section, Docker is explained for IoT

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application requirement. Section 4 is explaining the implementation and evaluation analysis done for this work and the performance result is given. Section 5 is the conclusion of this work and giving some future direction.

2 Background This technology has been developed gradually after the invention of The INTERNET in the late 1960s. In 1969, the U.S. Defense Department’s Advanced Research Projects Agency Network (ARPANET) researchers developed many of the protocols used for Internet communication today. The invention of TCP/IP and Domain Name System (DNS) have led the internet in a new direction, and in the year 1989, World Wide Web (WWW) is proposed by Tim Berners-Lee. Figure 1 shows the IoT evolution, the idea to connect the physical world (objects) to the internet was first given by a group of Auto-ID center at Massachusetts Institute of technology (MIT). The executive director of Auto-ID center Kevin Ashton introduced an idea of linking physical objects using Radio Frequency Identification (RFID) and sensor technology [5]. In the year 2000 commercial company LG announces the internet refrigerator plans. In the years 2000–2004 many attempts have been done in order to invent more ideas for IoT. In the year 2005, International Telecommunication Union (ITU) has published the first IoT report. In this report, a new definition is given for IoT, “The terms anytime, anyplace connectivity for anyone will be changed to connectivity for anything. The connection will multiply and create an entirely new dynamic network of networks” [5].

Fig. 1 Internet of Things (IoT)—evolution

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Fig. 2 Gartner’s hype cycle for emerging technologies, 2018

According to CISCO Internet Business Solutions Group (IBSG), the IoT was born in between 2008 and 2009 as the number of connected devices over the internet was more than the people. The survey says that till the year 2020, 50 Billion devices will be connected to the internet [1]. In the year 2011, the new protocol, IPv6 is invented. IPv6 uses a 128bit address approximately 3.4 * 1038 addresses. IPv6 is simple to manage it has capabilities like auto configuration, simple operating model and NAT are not required. The protocol, IPv6 is invented to solve the problems in IPv4 like address space. In the same year 2011, The IoT was included in the Gartner Hype cycle. This cycle gives the information about upcoming technologies and their life cycle from “technology trigger” to “plateau of productivity”. The IoT was managed to take “peak of inflated expectation” [2]. Figure 2 is Gartner Hype Cycle [6] in the year 2018 and we can see that IoT is still at the peak of inflated expectation. This cycle helps Industries to make business strategies and R&D over emerging technology.

2.1 IoT Characteristics IoT has three important components such as Instrumented objects, backbone network, and smart services. IoT refers physical objects to monitor, access or communicates using the Internet. IoT allows physical Things (including humans) to be connected Anytime with Any network providing Any services that can be Anything accessible by Anyone at Any place (6A) with using key elements such as Consumption, Connec-

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tion, Conversion, Centralization, Cognition, Configuration, and Coordination (7C). IoT is composed of seven essential characteristics, they also are known as 7C’s of IoT [6]. These essential characteristics of IoT are explained below. • Consumption: Operational data produced by devices (sensor/physical objects) to consume into IT structure where data can be generated by any physical things and consumed over any path or any network. • Connection: Connectivity between IoT devices should be compatible and smart as the network accessibility depends on connection. The devices are connected using the internet (wired/wireless) and standard protocols are used for secure, and reliable connection. • Conversion: The collected raw data should be converted into meaningful information. At device to gateway layer using local intelligence over the gateway, the collected sensor data are converted to metadata for further transmission. • Centralization: In IoT, the collection of data happens from distributed environments, but the data thus collected should be centralized at a single place for applying analysis. • Cognition: In this part using algorithms (software/hardware) the knowledge and wisdom is applied to collect data information in order to take appropriate decision. • Configuration: This is used for the safety of data when data transmission (bidirectional) from the cyber world to the physical world and! Or physical to the cyber world, the configuration of hardware/software should be compatible such as the automatic update can be done and security configuration must be used for authorization and privacy of data. • Coordination: The coordination between the devices is a better approach for business logistics and scheduling. At Business layer, the output of each service is compared for better service and better coordination between software/hardware and application component enable a cost effective IoT application.

2.2 Architecture and Information Flow in the Internet of Things with Cloud The IoT provides foresight functionality that can be realized in two ways (a) Objectcentric (b) Cloud-centric (Internet-centric) [7]. The object-centric architecture of IoT is about the smart object framework. Smart objects have a unique identification, using RFID and Sensor technologies “Objects” can be traced and monitored their condition. The main functionality focus over ability to perform object-centric decision making, embedded processing, and ad hoc networking. In this work, I have explained the Cloud-centric architecture of IoT. The Cloud-centric architecture, mainly focuses on the internet services, which are highly dynamic and these different services runs over distributed networks [8]. This kind of architecture of IoT with cloud enables industries to use their on-premises architecture to enhance the IoT application environment. A high-performance cloud

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Fig. 3 IoT cloud-centric architecture

structure can give better resource utilization for IoT application. The architecture includes Big Data analytics, machine learning tools, data mining and artificial intelligence tools for enabling the better approach of converting information into knowledge (wisdom). The features like load-balancing, firewalls, and network segmentation make easy to scale multi-tier IoT application. Figure 3 shows the IoT-Cloud-Centric architecture. The functionality of communication (workflow) between IoT components can be divided according to their connectivity topology. The Internet Architecture Board has described four type of communication pattern: Device-to-Cloud, Device-to-Device, Back-End Data-Sharing and Device-to-Gateway, where a model can be applied for the same application more than once [9]. • Device-to-Device communication: In this type of communication pattern two devices communicate and interoperate directly with each other. The devices support peer to peer connection where Bluetooth, Z-wave, or ZigBee like protocols are often used. The devices exchange their messages using different networks like the Internet or IP network, example like if someone opens the door, the door sensor sends a message to the light sensor to turn it on. This type of communication pattern is used when the data packets are small and required low data rate. • Device-to-Cloud communication: In the following type of pattern for communication, the devices gets directly connected to internet cloud service. The provider of application service here will send/receive data using cloud service which helps to control message traffic. The connection between devices, service provider and cloud service can be done using Ethernet/Wi-Fi. This pattern is an IP-based end to end communication, various standard protocols such as CoAP, DTLS, UDP, TLS etc. are used. A large amount of data transmission may lead to data storage problems which can be solved by third party vendors by providing their server infrastructure. Microsoft Azure IoT Hub is an example of this pattern where secure and reliable bi-directional communication between IoT devices and cloud are man-

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aged. The device-to-cloud model enables various SDKs to simulate device and extend capability beyond its existing features to provide value to consumers. • Device-to-Gateway-to-Cloud communication: The earlier described pattern Device-to-Cloud is good enough to use if the same vendor is the provider of device and cloud service since this works well if they use RFID technology. In the case where non-IP based devices connecting with each other, the use of gateway technology bridges the different technology and provides more secure functionality. The gateway is a pass-through component which gives bi-directional communication from device to gateway and gateway to the cloud. The IoT device gateway sends/receive messages with different communication mechanisms such as one-toone and one-to-many using publication/subscription model. The device gateway communicates with device and cloud using standard protocols like MQTT, WebSockets, HTTP 1.1, REST APIs etc. [9]. Gateways are known as an intelligent device which provides compatibility with both IPv6 and IPv4 protocols. Gateways support both types of connectivity, wireless and wired. In this type of pattern at first layer device to gateway sensors and actuators connect and transmit the data using various protocols like Bluetooth, ZigBee, and 6LoWPAN. At the second layer incoming data are translated into JSON format and transferred to the cloud (smart engine/Backend service) using MQTT protocol. • Back-End Data Service: The usage of smart engine is done to collect, process, analyze, and act on the device generated data in the following service. These services are provided using customized SDKs and APIs. The back end service contains load balancing technology which handles user request. The load balance uses a round robin algorithm to distribute request among available instances. However, some applications like games require heavy internal caching so Session affinity sends all requests from the same client to the same virtual machine instance. The machine learning tool, Big Data Analytics helps to provide Data as a Service to IoT application, which turns data into a useful information. NoSQL (non-relational database) helps to store and retrieve the various types of large amount data. The enhancement of technical innovation occurs with possibility of commercial rise if the effective IoT communication pattern that is illustrated above are implemented in an open community. In [7], a discussion has been presented on the viability of key application domains being driven in the near future by IoT based research in which a cloud centric approach [26] presents a solution of achieving the implementation of IoT on a worldwide scale. It also present few of the taxonomy and trends related to IoT. A relatively newer approach for IoT domain has been discussed that is cloud centric [7]. In a shared environment, visualization and networking are allowed by the framework that promotes independent growth in each sector. It also throws some light on challenges being faced and upcoming trends in Aneka/Azure cloud platforms using a data analytics case study. With cloud at its center, it is highly unlikely that standardization underway will be affected. In [12], Cloud computing concepts and virtualization technology concepts are described. The benefits and types of virtualization are explained. The paper has

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mainly focused over hardware type virtualization. The utilization of more resources by CPU and memory is the the foremost goal of virtualization so different type of hypervisor comparison are given based on virtual machines performance monitoring factors [12]. In [13], Docker as the container based innovation approach is utilized for the proving ground for High-Performance Computing (HPC) applications. Besides, [13] executes autodock3, a modular visualization software for the most part, utilized for Protein-ligand docking being done in Docker compartments and Virtual machines made utilizing a correlation of the execution times of the docking procedure in both Docker holders and in VMs is investigated. The [13] gives a concise presentation of containers being used by hypervisor put together virtualization frameworks with features with respect to certain distinctions. The subtleties of Docker, OpenStack and Auto docking are clarified for the proving of test net conditions [13]. The outcomes demonstrate that the container based frameworks are progressively proficient in lessening the general execution times for HPC applications and have a better memory conservation for running in parallel [13]. In [15], Docker based container technology is explained as a solution for container management in the case of multiple hosts for SaaS providers. The goal in [15] is to use SaaS offering cloud systems for them to move from existing open source systems. A highly complex application is formed due to the numerous existing dependencies. Hence, to manage the running systems dependencies specification, an infrastructure solution is needed. In [15] the case study analysis and requirements of docker for SaaS is elaborated. Docker has many existing solutions, which fulfils the SaaS requirements like Cluster scheduler, Control management, etc. total 10 types of solutions are explained. In [19], the constrained devices execution evaluation of container advances is outlined, for this situation, on Raspberry Pi has been described. Docker keeps running on a Single Board Computer gadget, for example, Raspberry Pi 2 (RPi2). For the source of finding correlation, we utilize the native execution, like running the benchmark apparatus without including any layer that is virtualized. The outcomes are found by averaging of more than 15 runs. Results demonstrate a practically insignificant effect of the container virtualization layer as far as execution and performance, whenever contrasted with native execution and performance [19].

3 IoT—Container Technologies 3.1 IoT Challenges for Migrating Application to the Cloud Cloud offers incredible comfort when IoT application contained information is moved onto them, for example, a decrease of expense and multifaceted nature identified with direct hardware management. In like manner, the complex situation present with CloudIoT incorporates numerous angles identified with a few heterogeneous

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points, where while requiring specific abilities, every one of them faces difficulties. For example, security, privacy, reliability, availability, portability, and semantic interoperability are the accompanying capacities required to ensure trusted and productive administrations [10]. In the scenario of an IoT application when moved onto a cloud service, new concerns emerge because of the absence of some fundamental properties which can include trust in the services of a specialist organization, data stockpiling knowledge of their physical area and service level agreements (SLAs) knowledge. As needs are, new difficulties like session riding, SQL injection and vulnerabilities like session hijacking and virtual machine escape require explicit consideration. In this scenario, multi-occupancy could bargain security and may prompt delicate data spillage. Further, public key cryptography can’t be connected at all layer, because of the processing power imperatives forced by the things. • Security: The focus of malwares are on the ever rising decentralized entry points due to the exponential rise in IoT associating more gadgets together. More affordable gadgets are progressively subject to altering as they are present in physically compromised districts. The rise in new security dangers and multifaceted nature can be accounted to machine-to-machine correspondence, middleware introduction as well as multilayers of softwares. To deal with security and its provisioning, [11] a wide range of procedures and sellers are tending to these issues with policy driven ways. • Service delivery: All framework parts of an IoT service are firmly coupled to the particular application and are normally given as context-specific disconnected vertical solution. In this, suppliers are required to review target application situations, select equipment devices, coordinate heterogeneous subsystems, break down its prerequisites and give service maintenance [10] for each sent application service. • Complexity, confusion and integration issues: Various protocols and IoT frameworks and substantial quantities of APIs are a test without a doubt due to various stages of them. The perplexity around developing norms is practically certain to moderate selection. It is very likely that unexpected improvement resources are bound to be consumed due to the fast advancement of APIs which will cut down the ability to add new functionalities by team members of the project. Extra subsidizing for IoT ventures and longer “runways” for new companies will also be the case due to the [11] unforeseen improvement resources needs that will increase the time to revenues and will cause a slip of schedules. • Big data: Highly specific consideration must be paid to transportation, stockpiling, and preparing of the enormous measure of information that will be delivered with an expected number of 50 billion gadgets that will be organized by 2020. IoT will be one of the fundamental wellsprings of enormous information on account of the ongoing improvement and advancements especially one like Cloud technology that enables the system to perform complex queries on the data which in-turn empower it to store data for quite a while and do examinations as per the requirements [10]. • Evolving architectures, protocol and standards: There will undoubtedly be continuous turf wars as heritage organizations look to secure their restrictive frameworks

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preferences and significant number of players required with the IoT whereas open framework defenders attempt to set new principles. There might be numerous principles that develop based on various necessities controlled by device classes, capacities, control prerequisites, and employment. To contribute and impact future guidelines [11], this presents open doors for platform merchants and open source supporters.

3.2 Problem Definition The invention of IPv6 in the year 2000 and later in 2007 wireless networking extension 6LoWPAN invention which empowers diverse gadgets to interface with the physical circumstance of normal applications. Consider remote sensor, such advances provided any other publicized idea inside the net world, referred to as “Web of Things” additionally called “Internet of Things”. The Internet of Things (IoT) is a plan to make physical gadgets or objects smart, which proportion information and take a choice steadily in a real-time scenario. A computerized interplay between smart systems for changing information for significant time-sensitive analytics is provided by IoT. Internet of Things (IoT) is in one of the biggest developing era, IoT will give a brand new path to the world of internet and development and profit in the unpredictable enterprise. For instance, to lead to a huge amount of beneficial information [7], superior sensor are being attached to several objects for sending and receiving facts in the Manufacturing and Industrial automation sectors. Before absolutely adopting this technology a few crucial questions need to be taken into consideration, for example, what are the maximum particular barriers for industries to undertake this era of technology? How to make use of the massive quantity of information by usage of standard utility of corporation? What is the usual architecture to observe? What may be a good chance or a bad effects with Industrial-IoT? To share a common set of assets while the workloads fulfil the property of isolation from each other, an abstraction layer can be used for hardware where the use of virtualization technology is done to obtain abstraction. Further tremendous operational efficiencies can be obtained via virtualization technology in IoT applications also dynamic resource allocation and management, load balancing and resource utilization can be achieved. A way to drive IoT industry to profitable business can be driven by on premise or off premise or on demand scaling. To develop and scale IoT applications, virtualization technology Docker provides an easy and rapid open source framework.

3.3 Selection of Container Technology Improved load balancing, higher system availability, much less management/management time and cost, and less fail over time are few of the benefits of

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Fig. 4 Hardware (Full) virtualization versus Container (OS) based virtualization

virtualization. Based on the type of resources, virtualization can further be classified as operating system and hardware level virtualization which are depicted in Fig. 4. With usage of own kernel, guest OS runs over host OS in hardware level virtualization which is a virtualization technique based on hypervisor. Using an abstract layer of the hypervisor, the actual hardware is interacted with the guest system. This form of virtualization is effective in offering security and isolation but faces the issue of high overhead due to Hardware emulation (visitor OS to host’s hardware interaction overhead). OS level virtualization is one other sort of virtualization useful for reducing overhead. It is also known as container based virtualization [12]. Better resource computing performance and zero performance overhead are obtained by containers as they utilize the same kernel of the host system. Further, they are able to act as host to their containers and isolate resources due to their virtual light weighted environment approach. On one hand where the scope within one environment of a VM is restricted by hardware virtualization, sharing of binaries is done easily by containers with its peer containers [12, 13]. Sharing the binaries and libraries acts helpful for saving storage space with the case of utilization of container technology as IoT devices are generally resource poor. Also, there exist certain IoT application requirements that are unable to be fulfilled using containers. A comparison between different container vendors has been provided in Table 1 [12, 14].

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Table 1 Container technologies and properties Property

LXC

OpenVZ

Free BSD Jails

Solaries

Docker

Rocket(rkt)

Development lead

Canonical Ltd.

OpenVz Org.

Free BSD Org

Oracle and illumos

Docker Inc.

CoreOs Inc.

Platform (OS) support

Linux

Linux

FreeB SD

Illumos (open solaries)

Linux

Linux

Resources/IO reduction limitation

Partial

Yes

Yes

Partial

Yes

Partially

File system isolation

Yes

Yes

Yes

Yes

Yes

Yes

Portability and migration

Restricted

Yes

Yes

Yes

Yes

Yes

Interoperability No and standardization

No

No

No

Yes

No

Manageability remote access

No

Yes

Yes

Partial

Yes

No

Root privilege security

Yes

Yes

Yes

Yes

Yes

Yes

Nested virtualization

Yes

Partial

Yes

Partial

Yes

Partial

3.4 Docker for IoT Application Requirements An astute blend of git-like way to deal with bundling softwares and Linux containers (useful for operations) is provided by an exciting new technology Docker that offers an alternate way to deal with structuring and running applications. Hence, to keep running without any conditions or dependencies, your Docker containers have all that they require. Due to the very own containers of every application, seclusion is obtained in docker for each of them. Also, a DockerFile is set up that acts as a blueprint for the container, containing a set of steps for container creation. The standard binaries and libraries of softwares like Python, Redis, Postgres, etc. are built by the steps present for application installation (Fig. 5). To disconnect system calls and resource utilization (CPU, memory, disk I/O, etc.) legitimately on your server, namespaces and cgroups are utilized to create the real container to run an application utilizing Docker engine. An overhead-free associated multiple containers is the final product. Here individual application considers itself in its own machine performing independently from anyone else. Further, it is with completely virtualized machines on the server (Fig. 6).

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Fig. 5 Docker image

Fig. 6 Understanding of Docker data management

The service oriented architecture (SOA) provided by Docker container technology is sufficiently capable to handle composite applications. The isolation and independent deployment of each container makes it easy to use with each other [15]. An application becomes scalable and easily developable with the use of light weighted open sourced technology like Docker [17]. But the primary question that remains unanswered till now is how rapid development of IoT applications can be achieved using Docker container? Interoperability, power, community connectivity, safety and standard protocols are few of the challenges that still exists due to the complex nature of development

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of IoT applications. The solution of these may lie in usage of Platform as a service or even Software as a service that is provided by Docker [16]. For using Docker for IoT applications requirements, below mentioned are some of the points explaining it. • Easy and faster deployment for applications: Whether be it windows or Linux based systems, Docker containers are supported by both of them. Also for the development, testing and deploying of any application, the process’s time duration is reduced due to the very less launch time and negligible run time overhead for it [15]. • Cluster scheduler and service discovery: In case of a bottleneck being identified in a container, the automatic migration and failover is provided by the cluster scheduling feature that permits the control of a Docker cluster. To run the application in a distributed manner as a key-value pair mechanism, the service discovery makes the storage of ports and IP addresses storage possible for running applications. Also, passing of environment variables and IP addresses can be done by its container linking mechanism to only linking containers [15, 17]. • Security and privacy: Security becomes one of the primary challenges in an IoT application as in the case of a distributed and decentralized surrounding, machine to machine interaction takes place. The mechanism of Docker proves efficient to face this issue. To protect from the incoming threats, the host OS and kernel level security are enabled by it. TLS/SSL certification provided by it adds on to it in the case of server/client verification. And for an application inside a Docker container, root access to it is the only permitted action [15, 17]. • Interoperability: With the integration of container technology with IoT device, a better solution can be obtained for the problem of interoperability which is currently a big challenge. Portability among various devices and remote control functionality are few of the features offered by Docker. This makes the handling of huge data volumes easy and provides scalability [16]. • Management solution: High costing for an IoT application exist when planning to develop, manage or monitor one such application. Thus for these IoT applications, reducing the cost in terms of money or manpower is a primary concern for IoT industry. Here, Docker can prove beneficial for IoT application development for admins and developers for obtaining low maintenance and low cost services [15].

4 Implementation: Experiments and Results 4.1 Hardware and Software Selection 4.1.1

Hardware

Hardware requirements of devices used in the IoT predominantly arise from their tiny size, a high number of connected devices, and their distributed locations. The first

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important aspect is energy efficiency. “In many cases, the sensing nodes are batteryoperated satellite nodes, so a low-power spec is a basic requirement. Integrated circuit designers have many ways to reduce power consumption”. In general, hardware with energy-efficient ICs are referred to as embedded systems (ES). Since ES support a high level of energy efficiency and can be constructed at small sizes, ES is the optimal hardware type for IoT devices [23]. Embedded devices are specified for a special purpose, a generic, programmable, embedded device is required for developmental purposes of this thesis. These kinds of devices are generally referred to as development boards or a single board computer (SBC) used to develop embedded software on the target platform. Out of available SBCs, the most popular one is chosen for the test environment i.e. Raspberry Pi 3. To identify the most popular SBC, the most significant surveys will be analyzed. A comprehensive survey was conducted by the Linux Foundation in 2016. Readers selected the Raspberry Pi 3 as their favorite among 81 Linux/Android hacker boards in our 2016 SBC Survey, followed by the Odroid-C2 and BeagleBone [24]. Monitoring and analyzing temperature and humidity throughout the evaluation project the Sht11x sensor is used. This is a single-chip temperature and humidity sensor-driven temperature and humidity sensors. This type of sensor has advantages like Small size, Low cost, Easy to integrate, Factory calibrated in wide temperature range: 0 −40…. +125 °C for sensor temperature and 0 −70… +380 °C for object temperature, High accuracy of 0.5 °C over a wide temperature range (0…. +50 °C for both Ta and To), Available in 3 and 5 V versions, Sleep mode for reduced power consumption, Different package options for applications and measurement versatility. For network connection, LAN cable is used which provided the Ethernet connection for SBC [24]. The sensor is connected to Pi using the sensor node board (Fig. 7).

4.1.2

Software

Interconnectivity between devices in the communication network is a precondition, as the ITU states. In order to fulfil the requirement for interconnecting devices, supporting standards for interoperability are important. Standards for interconnecting devices are central for addressing the heterogeneity of devices equipped with differing software as well as hardware. Standards in interoperability comprise the standards for network interconnectivity and software interaction, which is referred to as middleware [27]. Middleware is a software layer between the OS and applications on a device that offers standardized interfaces towards the application as well as towards the OS of IoT devices to support interaction between them. In This Experiment container technologies is used, which function as standardized middleware. Self-healing, selfoptimizing, self-management, self-configuring and self-protecting are few of the high level automation techniques required by a large number of devices, including in networks as well as for applications.

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Fig. 7 Top 10 SBC, result survey done by Linux Foundation

High level of security is a central requirement for software in the IoT. “Confidentiality, authenticity and integrity of both data and services are prone to significant security threats as in the IoT, everything is connected” [28]. Based on the requirements given above, the appropriate software will be selected. In general, the software, which is needed can be differentiated into four categories: • An operating system (OS) for devices i.e. Raspbian Jessie and Hypriot OS for installing Docker. • Software to establish a distributed network, i.e. Ethernet connection between devices and putty for remote connection through SSH. • Middleware on every device to be deployed between the OS software and the IoT application, i.e. Docker, container based virtualization. • IoT applications that offer services on devices and security parameter used in container. Other than these some libraries and performance measurement tools are used which are explained in next part.

4.2 Experimental Setup The equipment setup has been presented in Fig. 8. For the experiment. For the host, Raspbian Jessie OS latest version has been used where the appropriate IoT gateway and LAN having static IP is utilized to establish network connection [28].

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Fig. 8 Experimental setup

For an IoT application capable of collecting and examining data from sensors, the experiment was performed. The host machine (raspberry pi in our case) contains the installed Docker engine.

4.3 The Proposed System and Algorithmic Steps In this section proposed system (flowchart) of the experiment is explained, in Fig. 9 the flow is clearly described. The easy installation of Docker over pi in a Raspberry pi makes it a good choice for connecting devices. Below mentioned are algorithmic steps for performing the experiment. Step 1. Docker installation over Pi: Due to the ARM processor usage by Pi and x86 architecture build, containers can’t work directly as Linux. An open source Hypriot is used to do work on Pi easily as manual compilation is required by it for this software [18]. Step 2. GPIO access library installation: To allow the setting up and accessing of GPIO pins, we utilize Wiring Pi which is used for RaspberryPi [20]. Thus to perform controlling, reading and writing using scripts, we use this library. After this, we use the IoT gateways that are correct to achieve the connection between a host and the sensor nodes. Step 3. Collecting and reading the sensor data by running an IoT application over a host. The program for reading and collecting sensor and humidity data is written

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Fig. 9 Proposed system: flowchart

in C. Python script is used for connecting the serial port and showing the result over Pi and later the collected data is saved in CV file for further data analysis use [25, 26]. Step 4. Performing sensor data analysis using R. The R script is written for analyzing the sensor data and showing the result in graph or showing the max and min value. Step 5. All requisite library packages and software layers being contained in a generated DockerFile. This Dockerfile contain all library packages including GPIO library, Git, Linux base image, Performance tool installation packages, and R server and Volume registry for saving the data. Step 6. Dockerfile being used to build a Docker image. A container is created that is run over the host OS with docker programs that receives the command from Docker. Step 7. Analysis of Data by an IoT application that is running inside the container. Memory, Disk I/O and CPU performance are measured when the application runs inside the docker on a host. We further perform analysis of load balance of web service by running another container. Step 8. For security docker provide only root access and if someone want to use container using network connection then using TLS and SSL certification the container can be assign with a private key which provide full security. Docker also provide kernel level security to containers. Tests were ran to determine, whether files on host devices were able to be changed when running an unprivileged container [24]. The test has shown that this was not possible. This is in contrast to a privileged container, which allowed the host system’s

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CPU benchmark Total time (s)

Execution time (s)

Host

5.5831

5.5796

Docker

2.9436

2.9385

MEMORY benchmark (average speed (MiB/s) memcpy

Dumb

mcblock

Host

1035.0826

1235.0402

1682.8682

Docker

1021.812

1233.7074

1680.1522

DISK IO benchmark (operation performed in KB) Read

Write

Total transferred time (KB/sec)

Host

960 KB

640 KB

43.9425

Docker

960 KB

640 KB

33.1966

files to be changed. Docker provides a script that checks for several common security issues. To summarize, Docker’s isolation of applications “is not as strong as that of virtual machines, which run independent. OS instances on top of a hypervisor without sharing the kernel with the underlying OS”. However, Docker offers various options to provide a high level of security. Because this high level can only be achieved if all security features are applied appropriately, experience with security in Docker is crucial.

4.4 Performance Measurement: Results We utilize few of the benchmark tools to evaluate Docker for IoT applications that is the primary objective of this experiment. The results of the benchmark are presented in Table 2 by averaging the results obtained over 5 times run. • Central Processing Unit: Sysbench tool is utilized to test Central Processing Unit performance. The difference between the host case and docker case are presented in the table, where an insignificant impact is observed being done by the container engine on the Central Processing Unit performance [22]. • Memory Input/Output: The available memory bandwidth calculation is done by using a large array of data into the memory, we are able to test memory performance using mbw tool [21], which provide better configurability by the usage of three different test that are mcblock, memcpy and dumb respectively. The table presents the average compared result. • Disk Input/Output: To evaluate Disk Input/output sysbench tool is used [22]. To obtain the system’s Input/output performance, we utilize this benchmark. A ran-

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dom file that is larger than RAM is created by this benchmark and is RUN over the system.

5 Conclusion An overview of the concept of IoT (Internet of Things) is presented in this work which also explained that how complementary characteristics of IoT and Cloud would give a new architecture. The benefits of cloud for IoT Application is discussed. IoT components connectivity topology is divided into four types that are Device-to-Gateway, Back-End Data Service, Device-to-Device and Device-to-Cloud respectively. Developing an application for IoT with the cloud is difficult because of the inherently distributed complexity and heterogeneous platforms, communication protocols and interoperability. The objective of this work was to analyze and evaluate, whether container technology satisfies the requirement for IoT application. In order to test interoperability, Docker images were successfully started on different nodes in the test environment. It showed that Docker offers a container format that is portable amongst Docker engines on different devices. Security was addressed by isolating containers against their environment. Docker offers capabilities to define detailed access permissions for an application to reach location outside the container environment. By default, applications inside a container do not have root privileges (i.e. containers run in unprivileged mode) [29]. Root privileges are the highest permissions a user can have. If an application finds an option to run outside a container, it has very limited in its permissions and cannot cause serious problems on the host devices. The successful performing of IoT application development inside a Docker container was done. A positive impact can be observed between Docker and host from the results of benchmark tools for performance measurement. The test results have indicated that Docker meets central IoT requirements through a rich set of features. Through such features, it is capable of handling all major challenges of the IoT including high levels of automation and manageability, a wide range of hardware and software platforms being supported, and maintaining of safety of multiple IoT devices. The future work includes the further exploration of Docker for real time IoT applications like cluster scheduling and real-time decision making for IoT applications. The future work also includes to concentrate on the IoT challenges and various kinds of threats and maintaining the privacy and security in real time smart IoT Application. Acknowledgements Aswani Kumar Cherukuri sincerely acknowledge the financial support from Vellore Institute of Technology under VIT SEED Grant. Also, Aswani Kumar Cherukuri sincerely acknowledges the research grant: SPARC/2018-2019/P616/SL under the SPARC scheme of MHRD, Govt. of India. Conflicts of Interest The authors declare no conflict of interest.

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23. López, Tomás Sánchez, Ranasinghe, Damith C., Harrison, Mark, McFarlane, Duncan: Adding sense to the Internet of Things—an architecture framework for smart object systems. Pers. Ubiquit. Comput. 16(3), 291–308 (2012) 24. Raspberry Pi 3 takes the cake in 2016 Hacker SBC Survey. (2017, July 06). Accessed from http://linuxgizmos.com/raspberry-pi-3-takes-the-cake-in-2016-hacker-sbc-survey/ 25. Tschofenig, H., Arkko, J., Thaler, D., McPherson, D.: Architectural Considerations in Smart Object Networking, Internet Architecture Board (IAB), March 2015, ISSN: 2070-1721 26. Tao, Fei, et al.: Internet of Things in product life-cycle energy management. J. Indust. Informat. Integr. 1, 26–39 (2016) 27. Chamberlain, Ryan, Schommer, Jennifer: Using Docker To support reproducible research. ACM SIGOPS Operating Systems Review Special Issue on Repeatability and Sharing of Experimental Artifacts archive 49(1), 71–79 (2015) 28. Mathias, R.: Evaluation of Container Technology as a Model for the Infrastructure of the Internet of Things, Thesis, 2015, Otto-Friedrich-Universität Bamberg (2015) 29. Fraga-Lamas, Paula, et al.: A review on internet of things for defense and public safety. Sensors 16(10), 1644 (2016)

Peak-End Rule Promotes Social Capital for Knowledge Management in Thru Social Internet of Things Anjum Razzaque and Allam Hamdan

Abstract Psychologically speaking, the mind is unique for judging experiences. A moment during a film or a meal can influence the overall judgment of the thinker wither towards a positive manner or towards a negative manner. Empirical research reported evidence of the role of peak-end rule applicable during circumstantial experiences. This literature review study proposes a conceptual model, viable for future empirical assessment, integrating three schools of thoughts: (1) Social Capital Theory and (2) the Peak and End Moment mediated by (3) Experience—harnessed by the knowledge sharing behavior in social networks which are facilitated by the Internet of Things infrastructure and architecture. Social capital is an intangible resource, like shared knowledge between participants in a virtual community. This keeps a virtual community alive provided the social capital of resources are not depleted when members’ participation begins to drop. Past research assessed the role of Social Capital Theory on knowledge sharing motivated via the Social Cognitive Theory; reporting situations where virtual communities died out once there was decreasing participation. Current literature has led us to pin-point a need for an integration of peak-end rule, to facilitate experience, which therefore can promote social capital in a virtual community: to understand why participants decided to virtually make or break their social ties. Also, this time this study considers how social internet of things facilitate this whole phenomenon. Such research is advantageous to practitioners, working on virtual community and social networking and internet of things environment. Keywords Social capital theory · Experience · Knowledge sharing · Peak and end moment · Virtual community · Social internet of things

A. Razzaque · A. Hamdan (B) Ahlia University, Manama, Bahrain e-mail: [email protected] A. Razzaque e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_14

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1 Introduction Peak-end rule researchers have looked for a comprehension of summary assessments of a variety of experiences, for various reasons. Firstly, general assessments of pain and joy are connected with a differing quality of experiences, and constitute vital information for future decisions. Secondly, the course in which an individual reviews an experience is a key determinant in the matter of whether they will repeat it later on, and regardless of whether they will recommend it to others. Thirdly, retrospective summary assessments can decide the way that individuals review recollections of a specific experience, later on. For instance, a short occasion can incite affectionate memories, which a person could appreciate long after that experience is over [3]. Future assessments as well thought to be essential. They can bring out feelings, for example, doubt and fear, before the experience even happens, which could impact the choice in respect to whether to follow an experience [3]. Taking the lottery ticket as a sample, Ariely and Carmon [3] recommend that frequently tickets are purchased, less in light of the fact that individuals hope to win a prize, but since they offer the purchaser an opportunity to fantasize for a few days on how it may feel to win the cash. As per the views of the authors of this study, the just mentioned argument above expresses the role of Peak-end rule and the importance of such a role can be better understood if one begins to comprehend the limitations of the social capital of resources within a virtual environment.

1.1 Limits to Social Capital and the Motivation Behind Sharing Knowledge Since past couple of years the Internet and specifically its Web 2.0 is migrated from a simple means to search for information but a source for interaction. This is when social groups like virtual communities (VCs) have become the buzzword since the past few years now in published research. VCs are not only used by participants to help each other solve problems but businesses also use VCs to get closer to their customer, e.g. by asking for customer feedback through this VCs where their customers are also members of such VCs. It is participation that has made VCs successful. Customers get motivated to participate through the good experiences they begin to attain through their virtual interactions with other members of their VC/s. Hence making experience and motivation are expectations and perceptions where expectations are when a user asks why other users utilize a VC and perceptions is a perception rated after they have indulged in an interaction [8]. It is pure motivation that inspires participants in VCs and social networks to meet friends online to acquire information for entertaining and enjoyment, amongst other things. While there are two types of VCs, i.e. virtual community and social community, participants from social networks know one another, which is unlike the case from online VCs. This is where one should realize that motivations vary

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as to how VC participants are encouraged differently than those from the social network platforms. Such participations formulate the specifying characteristics of such community types [37]. As notes from Zhu and Chang [37] previous studies have online considered the singly types of online communities while ignoring the perspective that online Cs are not online VCs but also the social networks that also are characterized as online communities. It was also interested how such a gap in research was further limiting the validity and reliability of the respective instrumentations within the online community research context. Our paper, further extend to Zhu’s and Chang’s study by integrating the values of the peak-end rule, so to apprehend a deeper understanding of role of Peak-end rule when assessing the motivation of participants of VC and social networking. The reason why such an understanding is important is due to the fact that motivations reflect the amount of participation. It is because of motivation that a participant could adapt a social network platform over a VC since social networking platform facilitate participants to make new acquaintances and virtually connect with old friends as well as look for entertainment. On the other hand the motivation that would make a participant indulge in a VC would be to attain his/her identification when part of such an online group and to voluntarily exchange resources [37]. Chiu and Hsu [7] empirically assessed motivation of VC participants’ knowledge sharing behavior via the Social Cognitive theory, i.e. (also correlated with social network platforms and the resources with such networks [33], where stimulation to share knowledge is the key towards attaining ‘social influence’ through motivation, which is the personal beneficiary expectations or the mere enjoyment one would attain from assisting their VC peer members. Motivation is currently a research area of interest by the academic scholars [19, 33] and, thus, an inspiration in this study. Such an area has particularly been under focus in operations management research especially via the ‘motivation-opportunity-ability’ (MOA) framework and the operation of MOA is the knowledge sharing behavior amongst peer or employees or team players and therefore MOA is a facilitator of knowledge sharing [34].

2 Literature Review A few scientists contend that peak (the most extraordinary) real-time emotion decides recall-based evaluations [14] other discoveries propose that the average of real-time emotions encounters is more essential [27]. Still other findings recommend that the end (last) minute has a little yet critical association with the retrospective assessments of emotion, [27]. The field of psychology is various, and extensively more mature and significantly established than that of computing [12]. The next segment introduces the peak-end rule from a psychology point of view, as proposed by Daniel Kahneman. Giving an outline of the theory, it examines different experiments, and takes a gander at the conclusions drawn from them. Peak-end rule was proposed by Kahneman et al. [21] on retrospective assessment. The theory is a case of the principle of weighted averaging. It accepts a zero (or almost zero) impact of all moments, with

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the exception of the most extreme and last minutes [21]. Kahneman and his associates performed a few experiments to test the peak-end rule: on the therapeutic techniques of colonoscopy and lithotripsy [30]; cold pressure (cold-water continuance study) [21]; on aversive sounds [31]; and on pleasant and unpleasant short films. These experiments will be illustrated. In their work on patients’ memories of painful medical treatments, Redelmeier and Kahneman [30] recorded real-time and retrospective assessments. They recorded progressively the pain experienced by 154 patients experiencing colonoscopy, and 133 having lithotripsy. Also, they recorded the patients’ retrospective ratings of the total pain, at the end of each procedure. The findings showed that a patient’s judgment of total pain correlated firmly with the peak pain intensity, and the real-time ratings in the last 3 min (end ratings). They suggested that a patient’s memory of a painful procedure reflects the force of pain at the worse part and the last part of the experience [30]. Ariely and Carmon [3] specified that for practical reasons, examination had a tendency to research brief unpleasant experiences, as opposed to pleasant-, or a blend of pleasant and unpleasant experiences. Unquestionably, it is not clear how peak-end rule could be practical to a combination of different experiences. In their work on ‘determinants of the remembered utility of aversive sounds’ Schreiber and Kahneman [31] collected data from 36 students from the University of California, about their real-time evaluations, and overall evaluations of sets of aversive sound clasps. Results found by Schreiber and Kahneman [31] demonstrate a solid correlation between peak and end ratings, and the overall level of unpleasantness. The investigations of Kahneman and his associates demonstrate that wide assessments of the impact on a single sense in various diverse sorts of episodes were sensibly anticipated by peak and end moments. Nonetheless, it is very sure that the peak-end rule does not take out different factors that decide a general assessment. Different variables could be imperative in impacting retrospective assessment; for instance: the velocity of change for better or - worse, or the possibility of anticipatory emotions, for example, fear or hope. The work on retrospective assessment had just started. Understanding the procedure of review evaluations is crucial, since what individuals think about the past, frequently figures out what they do future. The techniques utilized by Daniel Kahneman and his partners in their examination of peak and moments, varied. Those utilized as a part of three of the studies are explained in the next section: In the water pressure study, members were asked to submerge both their hands (on two events) in cold water. The members were given what was known as a “discomfort meter“, to report their continuous distress evaluations. The meter comprised of a potentiometer and a direct exhibit of 15 light-discharging diodes (LEDs). A single green LED stayed lit toward one side of the showcase at all times. Members were requested that modify the potentiometer, the estimation of which was recorded. The computer kept a record of the water temperature and the distress values reported by each of the subjects, which could go somewhere around 0 and 14 [21]. In the study that was directed on aversive sounds, members were requested to use computerized software to report their real-time experience of pleasantness or unpleasantness, while listening to different sounds. Three experiments were performed. The members gave

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their real-time ratings while listening to every stimulus, by utilizing the mouse to modify the length and direction of a horizontal bar on a computer screen. The bar was inside a box which was marked “extremely unpleasant” on the left, and “neutral” at the middle, and “extremely pleasant” on the right. The computer kept a record of the time evaluated in seconds, and the real-time ratings of pain of each patient. The used bar ranged from: −250 (extremely pleasant) to 250 (extremely unpleasant) [31]. Patients’ real-time ratings during the painful treatment events were recorded utilizing the Gottman Levenson system for measuring emotional response. A computer screen had a marker that was controlled by a handheld gadget. The screen had a 19-cm visual simple scale, with “no pain” toward one side, and “extreme pain” at the other. The patients were asked to rate every 60 s. The computer kept a record of the real-time evaluations on a scale of 0 and 10; the lower the number, the less the pain. In this experiment, the 53 colonoscopy patients were not required to record their own particular evaluations: this was finished by an exploration partner [30]. As has been recommended, the strategies used to assemble continuous reactions from subjects fluctuated because of the way of the every analysis, except they all made utilization of an instrument to monitor the real-time experiences. In the water pressure experiment, the subjects were given the LED lights. The investigation of the pain medical treatment technique made use of a hand-held gadget. In the study of aversive sounds, members could rate their experience utilizing the sliding bar which showed up on a screen. Any reasonable person would agree that the arrangement of tests is exceptionally well thoroughly considered, in that it had utilized the diverse faculties of hearing and feeling (touch): of course, on account of the film-clips… Up till now, it is obvious from hedonic integrations research discoveries, that when individuals summarize experiences, they don’t integrate or condense the transient states experienced as the events develop. Various scientists have directed experiments testing peak-end rule in different domains. The following section discusses this work. One study that exhibited peakend rule in another field was directed by Kemp and Burt et al. [22] Forty-nine students, who traveled for a period of seven days, were requested to report their day by day (over the past 24 h) happiness level, through text messages. Along these lines, they were requested to report their overall happiness. Also, they were asked to recall the daily record of their reported happiness. Results demonstrated that the duration of the holiday had no impact on overall assessments and that the members were not able to recall the details of the daily change in their level of happiness [22]. Their discoveries inferred that peak-end rule was not a remarkably decent indicator of retrospective assessments. Another study that tried to test the theory in another domain was directed by Shatz [32] examining the way that individuals assess their days. The experiment, in light of peak-end rule, looked at whether people’s evaluations were an aggregation of feelings, or in light of peaks and feelings towards the end of the day. Results demonstrated that the retrospective assessment of a composite flow of events (which for this situation was daily routine) relies on the averaging of feeling evaluations. The feelings toward the end did not have such a prevailing party, nor did the vicinity of low peaks influence the assessment. Shatz [32] inspires to further stretching

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peak-end rule to other elaborate situations, towards content goals and emotionality [32]. Hassenzahl and Sandweg [17] conducted a study that explored: “how the intensity of experience relates to summary assessments of software product quality. Their experiment made use of a software application. This paper concentrates on peak-end rule in virtual community. This experiment is the closest in nature of virtual community since it is using software application. This makes their discoveries extremely significant for this exploration [17]. They found that the end of the past experience appears to decide how individuals build their overall assessment of product. They related their findings to memory impact, or what they called recency impact. They added that people’s summary assessment has a tendency to be founded on what they recollect from an occasion they simply experienced. It implies that it is less demanding for a person to review the later detail than to recollect an occurrence that happened in the past. Thusly, intense intellectual effort towards the end of an experience is by all accounts extremely huge for summary assessment [17]. These findings bring up the issue of the handiness of the normal routine of collecting retrospective assessment in view of experiential scenes; particularly the surveys that are adapted to assembling a subjective evaluation of emotion, (for example, enjoyment) in web assessment. This matter will be further analyzed in the segment on ease of use assessment. The experiments displayed (above) demonstrate that the peak-end rule is not generally repeated in different circles of human experience. This could be because of the difference in the nature of the experiences. The study conducted on individuals” evaluation of days showed that once in after a while the retrospective assessments can be more identified better using the average of moments instead of focusing on peak and end emotions. The study conducted on the software application, which may be thought to be near the virtual community research as far as the nature of experience, has demonstrated that the end minute emphatically impacted review evaluation [17]. In the holiday study, the peak-end rule was not observed to be a decent indicator of assessments; taught it is worth mentioning duration had little or no effect on people’s retrospective assessments [22].

2.1 Task or Content Request Ariely [2] performed two experiments in which direct levels of pain were delivered on people, one using a heat probe, and the other, squeezing their fingers using a vice [2]. The experiences differed as far as the length of time and level of pain (pain intensity expanded then reduced, or vice versa). Towards the end of the experiment, members gave their overall assessment of pain they encountered. The outcomes demonstrated that members indicated extensive affectability in the way that they experienced changes in force. The situation of expanding pain intensity was perceived as being more excruciating than that of diminishing pain force, despite the fact that the total momentary pain was identical. Moreover, the members favored the change to occur later as opposed to before, in the succession. Viewing a film or

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listening to sound clasps is not the same as to interacting with a system. The setting of virtual community interaction is more unpredictable, and includes more variables. For example, the level of physical collaboration in a web-browsing experience is far more than that when watching a film. An individual that searches fiends on a social website needs to perform certain activities to keep up the stream of his web route. Along the way, the person’s emotion would change for better or worse, depending on the situation. The multifaceted nature of the web social interaction makes it hard to test for the apparent change in force regardless. In the virtual world it is impossible to heat or squeeze web users’ fingers. This makes the testing of the task order effect hard; and not as direct as in the two experiments led by Ariely. In any case, it is known from the literature of emotion that individuals have reported moments of enjoyment on the web. The trouble at this stage is in figuring out how to test the impact of progress in intensity (regardless) on retrospective assessments of a social interaction web experience; accurately, how to control the intensity of enjoyment and which web experience to utilize. It is unrealistic to physically invert web experiences. For example, it is not possible to request that some individual invert his email-or online-chat experience. Using web tasks errands or virtual interaction groups appears to be a more sensible methodology. As stated by Marsico and Leviald [9] usability assessment procedures are as per the following: “The first generally consider task-oriented (high-level) characteristics, the second exploit results from behavioural research, the last are mostly based on style and context-free features” [9]. Web closed tasks are portrayed by the use of particular information that outcomes in a specific result [28]. An altered or closed task could be utilized to test the change of intensity regardless; in spite of the fact that there is a need to distinguish a method for increasing and decreasing the flow of pleasure in a virtual social interaction. A thought became exposed which includes using the number of links required to finish a particular virtual interaction assignment as a method for controlling undertaking trouble. It would then be imaginable to test the impact of switching the same settled tasks, retrospective assessments of enjoyment in virtual social interaction. It is not ensured that the utilization of the quantity of links would succeed in testing Ariely’s proposition regarding the change in intensity for better or worse. Subsequently, in late 2009 the Gwizdka paper was available. It proposed that task difficulty inside of a web search is identified with cognitive loads, and should be used carefully, particularly in a double task approach [13]. Another thought that is to be considered in the configuration procedure of the experiment is to test the content order; utilizing web areas, as opposed to closed web tasks. An open task has a low level of goal-specificity [28]. One of the advantages of the web is the browsers’ freedom of choice and the adaptability of their movement [6]. Free-, however circumscribed tasks give individuals more flexibility. Permitting individuals to search uninhibitedly the different contents of a website, the broad brows example is associated with the interest of every browser. Ping et al. [29] characterized the way that users consider the objects of the website as satisfiers or dissatisfiers as per the effect they have on the user [29]. This encourage considering utilizing website content or segments, as opposed to the setting of tasks, to take note of the impact of increasing and decreasing the intensity of enjoyment, on the users’ retrospective

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assessments of a website. Since, clients consider the objects of websites as satisfiers or dissatisfiers, it possible to assume that they may appreciate one section more than another. What experience do individuals appreciate on the web that can be utilized to test the impact of content order on retrospective assessments in virtual community? There must be an environment that promotes for need and personal motivation in order for something to be personally relevant within their sphere of involvement. Internet shopping is fit for giving such involvement, and also can provide a high level of enjoyment [18]. Website usability plays a critical part in business picture, and can impact client shopping behavior. Good usability is important to accomplish consumer loyalty [11]. Choosing which restaurant to go to when on vacation can be exceptionally concerning; settling on the correct decision may extremely influence the level of dining experience. In order to increase a user’s pleasure, it is vital to create him personally involved and motivated setting, in the context of the web. Imagine a group of participants involved in web-usability test. The first group is asked to perform a settled set from tasks in pairs; sharing one device related to online-shopping for digital watch; while the second group member was given more opportunity to browse the same selected online shopping website, A member of the second group is permitted to use her own personal device and communicate with her partners online as indicated by her wishes, inside of the same site. The level of association of the second member is higher than of the to start with, on the grounds that she has the opportunity to communicate through social media, and is not compelled to perform particular task limited to one shared device; in this way it is accepted that her level of enjoyment would be higher. According to Airely [3], there are two sorts of experiments. The primary, he called goal-directed, for example, waiting for some sort of administration, or games occasion. These experiences for the most part get their implications from their result. The second kind of experience, which Ariely called experience based, principally gets its importance from the occasion itself, and the result. Such experiences include: getting a message, viewing a motion picture, or eating. Ariely [3] included that different genuine experiences fall some place in the middle of these two sorts. For example, riding a motor bike to work, or playing a session of squash or football, are objective coordinated. Concurrently, the progressing knowledge itself adds toor contributes to the retrospective assessment, as far as the nature of the game or the riding experience. The experience of internet shopping or arranging a holiday is at first in the experienced based class, however when the web browsers purchases something or book a holiday, it changes to become goal-oriented. In the proposed experiment design (above), members won’t be requested to purchase an item or book an occasion. The research plans to understand the relationship between the events of the experience and retrospective assessments. As specified some time recently, experienced based procedures get their significance from events themselves. This implies it is ideal to approach individuals to browse for a holiday and not book one; and shop for things and not purchase any. Furthermore, requesting that every member book a holiday or purchase something is exorbitant, and thought to be unlikely.

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2.2 Duration Neglect The length of time of an event does not necessarily affect its summary assessment [20]. The main specialists to attract regard for this phenomenon were Varey and Kahneman [35]. They directed an experiment in which members gave a summary assessment of various speculative experiences, which changed both in their length of time and in their intensity after some time. Varey and Kahneman [35] and additionally Fredrickson and Kahneman found that retrospective assessments were affected by the most extreme and the last intensities of experiences. They likewise found that duration had no impact on the overall assessment (or what we allude to in this theory as the retrospective assessments of enjoyment). Fredrickson and Kahneman named this phenomenon duration neglect. According to Fredrickson and Kahneman duration neglect is part of the peak-end rule: that time has little-or no effect on a members’ retrospective assessments. In their work on sound clips, Schreiber and Kahneman [31] further tried this thought, by contrasting long-and short film clips, regarding time and retrospective assessments. The wonder of term disregard was likewise found to hold in their analysis on the medicinal procedure of colonoscopy, where global retrospective assessment did not associate with the duration of the medical technique [30, 31]. Earlier, Fredrickson and Kahneman conducted two experiments on aversive-and pleasant film clips. In the first experiment, 32 members saw aversive-and pleasant film clips, which differed in length of time and intensity. The real-time ratings of members were recorded, alongside the overall assessment of every clip. In the second experiment, 96 members saw the same clips, and later assessed the clips, as far as the apparent levels of a pleasant or unpleasantness of every section of film. The discoveries in light of the weighted previews, demonstrated that the length of time of a film clips had little impact on its review assessment. Kahneman and Fredrickson et al. [21] performed an experiment on 32 male students from the University of California. Members were required to put their hand in a tub of cold water. In the primary trial, the one hand was immersed for 60 s, in water at 14 °C. In the second trial, the other hand was immersed for a further 30 s (90 s) during which time the temperature of the water was gradually raised to 15 °C. They used one hand as a part of the main trial (immersed for 60 s) and the other hand in the second trial (immersed for 90 s). The experimenters found that the members liked to put their hand in water for a more drawn out time period, when the last part of the time included a change in the experience [(60 s at 14 °C vs. 90 s—during the most recent 30 s of which the temperature was being raised to 15 °C)] [21]. This phenomenon is described as violation of monotonicity: including a time of reduced discomfort to unpleasant events will result in better overall evaluation, in light of the fact that the average of pea-end is decreased. Watching a film at a cinema, or going by a theme park, is not equal to using an interactive device. In this way, designers might concentrate on making the connection for an emotion rather than the emotion itself. What are the outcomes of distinguishing affect and creating affective responses, on people’sjudgments? [16]. For example, is it conceivable to

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comprehend the way beauty makes emotions? At the end of the day, can individuals come to realize the way their emotions impact their judgment and choice making both in the instant and reflectively? More often than not, when there is a positive user experience, it is normal that users will come back to a website, and will advantage a business, regarding exposure and income. Interestingly, a negative user experience will raise the overheads, decrease user loyalty, lose the ‘word of mouth’ in publicizing, and wreck the brand identity [15]. The inquiries now are: How do individuals judge web experiences in social context? What guidelines would they follow in framing their overall assessments? Which moment would rule or identify with their retrospective assessments? Would the order of the task or procedure affect their retrospective assessments? The responses to these inquiries would be helpful to usability experts, and also web designers. Likewise, such information may aid web user’s customer retention, to urge users to visit a website, focusing on those moments that may build the web user general enjoyment.

3 Integrating Social Capital and Peak-End Rule The concept model depicted in Fig. 1 is the authors’ contribution expressing how social capital integrates with knowledge sharing behavior in knowledge management tools like virtual communities. This model is based on argument expressed in this paper. This model makes two propositions: a holistically integration of the role of peak-end rule with social capital theory for improving participation within online virtual communities, and a social networking platform. The two propositions are [1] expressing motivation through the peak-end rule for sharing knowledge, to therefore share experiences, in order to [2] be motivated to add towards the social capital of resources within a virtual community environment.

4 Facilitation of Social Internet of Things on Peach-End Role for Knowledge Sharing First in order to understand how social internet of things (SIoTs) facilitated this whole phenomenon, it is best to first introduce the concept of SIoTs. Ample scholars who researched social network provided evidence to suggest than a participant or a group of knowledgeable participants in a social network require participation for sharing knowledge in social networks like VCs, and this requires effective policies to effectively discriminate knowledge in SIoT facilitated VCs. This way a VC which hosts participants can be promoted and fed with enriching knowledge through the facilitation of IoT (i.e. an integration of a couple of technologies through a unique set of communication protocols and addressing schemes [4, 36], so that IoTs get

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Moments of interaction within an experience Knowledge sharing Experience

Peak-End Moments can make or break a node

Social Capital

Experience based Interaction Fig. 1 Concept model

converged with social networks. In the case of this study the social networks are referred as VCs. In such a scenario VCs gain momentum due to the growing awareness of SIoT’s trustworthy networks. This is the collective intelligence within social networks. This is the new era of science and this is nothing new. Social networking platforms like Facebook and Twitter have used such a phenomenon [24] integrated with internet search capabilities [26] or the optimization of the peer to peer networks [10]. In such a scheme social capital of relations end up establishing a higher level of trust and as a result resolve to greater efficient and effective collaborative solutions [25, 36]. Research lacks to understand basic aspects that will be able to achieve an actual social network, or in the case of this study—VC, effectively facilitated by intelligent objects/technologies. Hence there is a need for future research to investigate a stronger comprehension of the notion of the social relations amongst such objects/technologies, a holistic design of am acritude that expresses how SIoTs can be implemented based on the inter-relationships between objects/technologies [23].

5 Conclusion This is a literature review paper motivated through the inspiration to integrate of peak-end rule and social capital in addition to previous research, which utilized social cognitive theory to assess motivation to examine the behavior of knowledge sharing in an online environment. This paper initiated with journal review of literature to propose a conceptual framework ready for further empirical assessment in future research. It can be comfortably concluded that peak-end rule plays a positive role to sharing experiences and knowledge through the note of positive emotions in order

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to increase social capital of resources within an online environment. Through the expression of multiple scenarios, the authors of this study were able to lay out a story that expressed the significance and the future impact of theories like peakend rule and social capital theory to further extend research explorations. Also, it is important to consider that the theoretical model is driven by facts reported in current literature and is viable for empirical assessment followed by a tool for policy improvement in cross cultural and industrial contexts. After expressing the Fig. 1 model, this study also expressed the importance of underpinning the concept of SIoTs as underpinning platform architecture for facilitating peak-end rule to promote the social capital of resources of participants who share knowledge within VCs. SIoT are vital not only due to the trend of the current research but considering that the pace of technology is progressing in the current industry, it is essential to harness the power of the computing power can be used even in our bodies that in turn can connect to the Internet [5]. Moreover, by using sensors that can monitor the conditions of equipment connected to form IoTs all sectors, healthcare, retail, construction, manufacturing, academic, or transportation, can share knowledge for smarter decision making for achieving better results [1].

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Towards Smart Cities: Challenges, Components, and Architectures Djamel Saba, Youcef Sahli, Brahim Berbaoui and Rachid Maouedj

Abstract Smart City is a new technology that is based on the concept of the Internet of Things (IoT). The rapid increase in population and urbanization worldwide have increased the basic ways to manage urbanization, with maximum impact on the environment and lifestyle of the occupants. The prior integration of new information and communication technologies (ICTs) into urban operations has promoted the concepts of digital cities. The design of the Internet of Things for Smart Cities resulted in ensuring smart cities’ goals with minimum human interactions. The smart city is provided as the best solution to face the challenges associated with the exponential increase in population and urbanization. However, in recent years, the concept of a smart city is still evolving and not being integrated into the world due to financial and technological hurdles. This document aims to provide a knowledge platform on the concept of the smart city. The work presents a brief overview of this concept, followed by its characteristics, architecture, composition, and implementation in the real world. Finally, we expose some challenges and opportunities during a literature review on smart cities. Keywords Smart cities · Internet of Things · Decision-making · Ambient intelligence · Interoperability · Smart city architectures · Smart cities communication · Cyber physical system

D. Saba (B) · Y. Sahli · B. Berbaoui · R. Maouedj Unité de Recherche En Energies Renouvelables En Milieu Saharien, URER-MS, Centre de Développement Des Energies Renouvelables, CDER, 01000 Bouzaréah, Adrar, Algeria e-mail: [email protected] Y. Sahli e-mail: [email protected] B. Berbaoui e-mail: [email protected] R. Maouedj e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_15

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Abbreviations ICT ISO LEB HEQ GDP IoT UC WSN M2 M UN ISO LEB BEPOS HEQ HST LP-WAN ADC ASC BEMS CEMS HEMS ITS

Information Communication Technologies International Standards Organization Low Energy Building High Environmental Quality Gross Domestic Product Internet of Things Ubiquitous Computing Wireless Sensor Networks Machine-to-Machine United Nations International Standards Organization Low Energy Building Positive Energy Building High Environmental Quality High-Speed Transition Low-Power Wide Area Networks Amsterdam Digital City Amsterdam Smart City Building Energy Management System Community Energy Management System Home Energy Management System Intelligent Transport System

1 Introduction For the past ten years or so, the term “smart city” has been used to designate an urban development process based on the integration of communicating computer technologies, in order to provide its occupants with services to meet their needs, automatically identified by their behavior and sustainable economic development. In other words, it’s a matter of deploying a kind of transparent cover, made of sensors, cables and servers of “Clouds”, on the city considered as a whole in network and which serves him of interface with everyone who interacts with it. In the current state of the experiments carried out, the domains of predilection of these networks are the networks of fluids (Water, Electricity, Gas) and flows (Transport of people, Goods and information—Radio networks, Bandwidth, Wifi) [1]. In other words, when the urban public transport waiting areas reveal the estimated time of arrival of the next vehicle, the approach is that of the smart city [2]. It is only a question of descending information, but one could in the same example add the information, taken to the rise of the passengers on the sensors of the expected vehicle, according to which there remain seated places and it would be then a rising

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Fig. 1 IoT connected devices installed base worldwide from 2015 to 2025 (in billions)

information reused. The same logic could be applied to energy consumption, for example, if a resident triggers household electrical equipment while the collective consumption is lower than that requiring the startup of an additional central by the supplier, it will be charged to normal service [3]. On the other hand, if it increases its consumption while the start-up had to be decided, it will be charged at a progressive rate. The IoT is a technology that has been much talked about for several years, and rightly so because it could well upset modern life as we know it. The planet today has more connected objects than people. In 2016, the global population reached 17.68 billion people, for 30.73 billion connected objects by 2020 (Fig. 1) [4], between 30 and 60 billion devices will be connected worldwide, then the number of smartphone users is forecast to reach 2.1 billion (Fig. 2) [5, 6]. The IoT economic impact already represents an annual total of about 2 trillion of dollars, and by 2025, the global IoT market could be anywhere between 4 trillion of dollars and 12.8 trillion of dollars a year [7, 8]. The IoT is a technology where objects become intelligent every day, each process of treatment becomes intelligent and each communication becomes informative. While the Internet of Things is still looking for its own form, its effects have already begun to make incredible advances as a universal solution for the connected scenario [9]. This technology has become very appropriate with the emergence of intelligent equipment. In addition, technological advances in Machine-To-Machine communication (M2 M), Ubiquitous Computing (UC), and Wireless Sensor Networks (WSN) have further consolidated the technology of IoT [10–12]. Simplifying the communication between intelligent objects without or with the intervention of the person is considered the main objective of the IoT [13, 14]. Likewise, smart objects that are

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Fig. 2 Number of smartphone users worldwide from 2014 to 2020 (in billions)

connected share their information and have the opportunity for authorized access to other objects to facilitate contextual decision-making [15]. Due to the importance of this technology, various IoT-based applications have been developed: Smart City, Smart Home, Smart Grid, Smart Health [16–21]. The smart city has become the star of the last decades, because of the extraordinary urbanization in the world (Fig. 3). Conducting urban actions with the help of ICT has made cities effective. But, the incorporation of ICTs to perform actions in the city does not make it possible to understand a smart city [22–24]. The smart city has been chosen among the other urban models, such as the digital city, telicity, and the city of information because it draws the abstraction of all other models [25], it inherits the effective mechanisms of IoT [16]. Then, it provides realization elements for smart cities, namely data management and application processing. In general, the smart city is an urban environment that is based on ICT and other technologies to improve the performance of urban operations and the quality of services provided to occupants. Formally, a smart city gathering different disciplines to improve the city intelligence [26]. Other studies present the smart city as a modern city that uses ICT and other techniques to advance the quality of occupants’ lives, competitiveness, and efficiency of urban services. Secondly, the economic and environmental aspects of smart cities were aimed at improving the quality of life of the occupants by reducing the contradiction between supply and demand. [27]. To meet the demands of everyday occupants, the smart city focuses on optimal and sustainable solutions for governance, transportation, health care, and energy management [3, 28, 29]. The United Nations (UN) guesses that almost 66% of the world’s population will be urban by 2050 [30] (Fig. 3). Indeed, in recent years, almost 75% of the energy produced is consumed by cities, this consumption produces almost 80% of

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Fig. 3 The world rural and urban populations (1950–2050)

greenhouse gases that are dangerous to nature [25]. Experts and researchers agreed that the smart city is the best solution to meet the challenges of population growth, urbanization, good energy management and nature pollution, etc. As a result, the International Standards Organization (ISO) introduces standards to ensure the safety and quality and performance of a wide range of smart cities. As a result, we can ensure that compliance with smart city standards offers countless prerogatives for smart city management while supporting real-time monitoring. The community of experts and researchers has proposed a set of experimental solutions for smart cities, because of its opportunity and the high attention paid to sustainable development in recent years [31]. However, most of the work presented belongs to the category of experimental test benches. Transforming a test bench experience into a real world is a very difficult task due to the lack of user environment and other causes. While smart city architecture provides services and an experimental benchmark, these architectures do not correct the scalability, the mobility support, and the heterogeneity of IoT devices [32]. Some experiments support the heterogeneity of IoT equipment, although the deployed environment is extremely distinct from the actual urban environment [33].

2 Definition of Intelligent City In recent years, the concept of “Smart City” has recognized considerable importance. If the term smart city is used around the world, however, no clear and precise definition could be formulated for it. Through the different articles, the authors try to provide more and more details given the number of areas of application that covers the smart city but no vision and universal definition could be developed.

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2.1 “Smart City”: A Vague Concept The difficulties encountered in defining this concept are multiple and present different causes. Indeed, they relate as much to the understanding of the term “Intelligent” as to the identification of the needs of different cities, which are generally not the same. First, the adjective “Smart” depends on the meaning attributed to it. In the various articles, different names are used to designate this concept: smart city, knowledge city, digital city, etc. This is why it is important to distinguish the “Smart City” from the “Digital City”. These two terms are often confused or used as synonyms, although there is no real question of the same thing [34]. • Digital city: the digital city is based on ICT. This notion becomes interesting from the moment when digital is put at the service of the smart city and the population [35]. • Smart city: the smart city is based on digital tools to improve the quality of life of people. Indeed, technology is used for the intelligent development of the urban area both in terms of mobility and environment, citizen participation, etc. It may, therefore, seem obvious that the smart city often stems from the digital city for better urban management [36]. Secondly, the “Smart City” label remains rather vague. To date we distinguish several definitions for the concept of smart home. These definitions generally cover only a few aspects of what is found in all articles. Finally, another difficulty in defining this notion of the smart city comes from the fact that each city is specific and does not face the same local problems. Therefore, the answers to be given are also not the same. The city depends on the decisions made by the public authorities but not only: the citizens also have their say; they play an important role. A city is considered intelligent when it is able to meet the needs of its population in an automatic way [37]. However, there are common characteristics in each smart city: • • • • • •

Internal processes of the city and management of the citizen relationship; Digital development of the territory and reprogramming of the public space; Living environment, mobility, safety and environment; Digital solidarities; Education and culture; Citizen participation.

2.2 Other Definitions Now, let’s take a look at some definitions, commonly found in the different articles, to understand the different aspects covered by the smart city concept. The smart city is defined by Albino et al. [38] as “a prospectively performing city in terms of environment and life, mobility, economy, population, and governance,

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designed on the intelligent combination of endowments and activities”. Then Gann et al. [39], Smart City “is a city connecting the elements of informatics infrastructure, social infrastructure, physical infrastructure, and commercial infrastructure to achieve a collective intelligence of the city”. Then Leanza et al. [40] presents the smart city as an interconnected, instrumented, and intelligent city. It includes devices and data capture tools such as smart meters, smartphones, the Internet, social networks and a host of other data collection systems. The interconnection between all the objects makes it possible to integrate this data into the computing platforms and to broadcast them in the various terminals of the city. Finally, intelligence includes visualization, management, optimization, analysis and modeling services for better decision-making [41]. Thus, the researchers of the smart city Liege Institute define the smart city concept as follows [42] “The smart city is an ecosystem of stakeholders or terminals (occupiers, companies, institutions, universities, and others) committed to a sustainable strategy (called” 3P—People, planet, profits), while relying on technologies as facilitators To achieve its objectives and carry out related actions, this approach involves the development of a common strategic vision and the implementation of concrete initiatives in various fields (economy, intelligent mobility, environment, housing, people and governance) in order to: generate sustainable economic development and provide a better quality of daily life with rational management of natural resources. In addition, smart cities require the development and dissemination of new business models that will effectively contribute to their dissemination to financial instruments and a good understanding of stakeholder dynamics. Finally, the academic developments of these problems must be achieved by “integrating sound managerial and financial approaches to discussions on engineering, the environment, and urban planning”. In conclusion, in determining what a smart city is, it is important to consider the role of human capital, education, the environment, mobility and social and relational capital in urban development and rural, and ICTs.

3 The Smart City Characteristics The smart city includes attributes, themes, and infrastructure. The attributes draw the characteristics of the smart city. The themes are the pillars of the smart city. Infrastructures that offer the operational platforms of the smart city. The smart city is made up of a set of attributes. Most smart city proposals based on four important attributes: sustainability, comfort and quality of life, urbanization, and intelligence (Fig. 4) [25]. Sustainability is about pollution, energy, climate change, and ecological systems. The quality of everyday life attribute of citizens seeks to improve the well-being of citizens. The attribute of urbanization and intelligence concerns the technological, infrastructural and governing domains of the transformation of the rural environment towards an urban environment. Intelligence is defined as the desire to advance the social, environmental and economic criteria of the city and its citizens.

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Fig. 4 Characteristics of a smart city [34]

In recent years, sustainability has been examined as a primary paradigm of urban development. In fact, sustainability has played a key role in the emergence of smart cities [22]. Modern cities develop through natural means. It is therefore important to examine the scarcity of non-renewable energy sources. As a result, the conservation of natural heritage and energy sources has become a requirement for the sustainability of smart cities [43]. In the first years of the smart city concept appearance, was presented with the objective of improving the quality of the comfort of the citizens. This is achieved by integrating innovative solutions that reduce restrictions on social learning and barriers to social participation. As a result, the quality of urban service provision has improved while ensuring a good quality of daily living comfort and a better financial situation for qualified employees [25]. In this context, experiments have been conducted in different cities around the world to improve the quality of life. For example, the project “Algiers, the smart city”, based on the development of technologies, aims to make Algiers a hub for African technology companies by actively assisting entrepreneurs in terms of logistics, research funding and infrastructure development [44]. Smart Cities are connected cities aim to reconcile technological innovation with the economic, social and ecological challenges. In “Oslo, Norway”, the focus is on intelligent lighting: 10,000 streetlights have been equipped with sensors to adjust the brightness according to the lighting needs. The goal is to reduce electricity consumption by 70% [45]. The Norwegian example has inspired other European cities that have adopted the e-street project: 11 countries are committing to reduce their electricity consumption thanks to connected streetlights. “San Francisco, USA” is one of the smartest cities in the world, it is characterized by a range of connected solutions [45]. Highly committed to sustainable development, including waste recycling, the city is also committed to 100% renewable electricity for all municipal services. In the same context, With the growing population, the city of “London” has been involved for several years in a “smart” and sustainable [45]. To engage

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the citizens, the city has set up a “Talk London” platform that brings together all the information needed to live, work, and circulate in London. The city has also developed a system to encourage walking: pedestrians are accompanied throughout their journey through interactive terminals. In another example, “Barcelona” is an intelligent and avant-garde city. The city has created the Urban Lab, an experimental laboratory for companies, allowing them to test a project in the field with the support of the city services [45]. The city is well connected and has sensors to manage lighting, green spaces, traffic lights, the intelligent waste collection, mobility, and traffic. In another example, “Singapore” where 85 percent of the population owns a smartphone, this city, has launched a smart nation program to address issues of mobility, energy management, and green innovation. The city hosts the “CleanTechPark” is an eco-business park in “Singapore”, which includes various disciplines as industry and green buildings [45]. In terms of mobility, the aim is to reduce the use of the car as much as possible, electricity savings. He smart city model developed by Giffinger et al. [46] assesses mid-sized European smart cities based on six characteristics that appeal to the economy, mobility, the citizen, the environment, governance and quality of life (Table 1). It is, therefore, a classification tool. Thanks to this, the current state of the city can be examined and areas requiring special attention for its development can be identified [47]. • Smart Economy: this includes factors related to the city’s economic competitiveness, i.e.: the city economic importance on the national and international market, entrepreneurship, labor and production flexibility, innovation, and entrepreneurship [47]. • Smart People: this characteristic is the consequence of various factors related to the development of human and social capital. It understands the level of qualification and education of citizens, social diversity and the quality of social interaction with regard to integration and public participation as well as openness to the world [47]. For some authors such as Toppeta [48, 49], some e-education projects, such as online courses and distance learning, for example, offered by the cities, provide favorable results for the development of this dimension of Smart People [50]. • Smart Governance: it is the aspects related to political participation, citizen services, and government transparency that will be taken into account in defining smart governance [47]. • Smart Mobility: mobility includes not only modern and sustainable transport systems, but also aspects related to the availability of ICT as well as local and international accessibility that is very important today with globalization [47]. According to Letaifa [50], the most efficient way to realize smart mobility is urban planning, which focuses on collective rather than individual modes of transport using ICT. Promoting more efficient and smarter transport such as carpooling, carbike combinations and the use of public transport leads to more efficient traffic [51]. • Smart Environment: is the aspects that concern natural and climatic conditions, pollution, resource management and environmental protection becoming increasingly important, cities are trying to minimize their ecological and energy footprint,

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Table 1 Characteristics and features of the smart city [47] Characteristic

Description

Smart economy (competition)

Economic image and brand Productivity Innovative spirit Anchorage International Entrepreneurship Flexibility of the labor market Ability to transform

Smart people (human and social capital)

Qualification level Learning throughout life Participation in public life Social and ethnic pluralism Flexibility Creativity Cosmopolitism

Smart governance (participation)

Public and social services Participation in decision making Strategies and political perspectives Government transparency

Smart mobility (transport and ICT)

Accessibility (inter) national Availability of ICT infrastructure Sustainable, innovative and secure transport systems Local accessibility

Smart environment (natural resources)

Environmental protection Sustainable Resource Management Attractiveness of natural conditions Pollution

Smart Living (Quality of life)

Social cohesion Cultural facilities Quality and safety housing Tourist attraction Health conditions Education equipment

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resource management for example the new technological innovations such as clean energy (solar, wind, etc.) that also improve efficiency [47]. • Smart Living: according to Giffinger et al. [47], this characteristic covers various aspects related to the improvement of the quality of life in terms of services like health, tourism, security, culture, etc.

4 The Smart City Pillars and Trends The city’s ecosystem includes a set of four companies that can intelligently meet ten urban needs.

4.1 The Four Pillars of the Smart Home The intelligent ecosystem that is the city, resulting from individual intelligences, is a set of four pillars which are the four main professions of the city (Fig. 5). • Build: these are less and less realized individually, and more and more configured in overall urban projects. The smart logic is already at work in this area but called to intensify. Buildings can be LEB (low energy building), BEPOS (positive energy building), HEQ (high environmental quality), etc. They depend first

Fig. 5 The four pillars of the smart city

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of all on the uses, it’s always the people, and their intelligence, that makes the qualities of buildings. They also depend, and increasingly, on the neighborhoods in which they find themselves. An intelligent metropolis is a territory where the optimization of traffic and consumption, with life-cycle calculations, is managed at the neighborhood level and not just at the building level. • Develop: the city, the buildings, the public spaces, the parks and gardens, the equipment. All these objects are becoming more intelligent themselves. They are connected to each other. They address information. For all these objects to work together optimally, certain conditions are the responsibility of the metropolis, WiFi broadband. • Treat: many companies now have products to make information systems work together better and, by analyzing all these data, produce better and cheaper services. The city, increasingly digitized, can release new services. • Exploit: the city is a daily exploitation of networks. Be it water, energy, transport, waste or telecommunications, an urban system is primarily a set of networks. Companies know how to invest and know how to maintain them. Increasingly, these networks are, in terms of interconnected information systems. The city is thus an overall infrastructure serving the inhabitants and their activities.

4.2 Trends and Needs Ten trends can be listed around which better dataflow management can lead to cost optimization and improved performance. These trends reveal needs that—without waiting for magical results—smart logic can better respond. • In energy, we live in a HST period: high-speed transition. A first dimension of smart cities is the development of smart grids. An intelligent metropolis optimizes energy expenditure on its territory. • In financial terms, beyond the global crisis, local finances are caught in the grip of the electoral cycle and in the perspective of a probable future deterioration. A smart metropolis is making efficiency efforts. • In the social sphere, metropolises are experiencing increased competition, externally, and inequalities that are progressing internally. An intelligent metropolis increases its attractiveness while being concerned about social cohesion (especially by fighting the digital divide). • In terms of settlement, metropolitan areas are experiencing both a diversification of populations and a transformation of families. An intelligent metropolis provides information and services adapted to contemporary demands, whether it is security or childcare. • In demographic terms, the basic situation is aging. Urban planning can facilitate generational diversity, hence solidarity between generations. An intelligent metropolis manages the necessary adaptation of the city for the older ones, robotization and home automation can greatly help.

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• An intelligent metropolis is not an institutional revolution but a set of modalities for more efficient management of more complex territories. • An intelligent metropolis can limit unnecessary travel and ensure more enjoyable mobility. • In terms of urban form, density and compactness will not solve all the difficulties of urban sprawl, but they will limit them. An intelligent metropolis makes it possible, through the functional mix of buildings and neighborhoods, to limit problematic fragmentation. • In innovation, the world is full of examples and achievements; dependent on knowing how to capture, dissect and digest them. An intelligent metropolis takes care of what others do, without being oversold by solutions that do not suit it. • In geopolitical matters, the centers of the world moves and so-called smart innovations and achievements are presented and sold to emerging countries. There is no smart city offer per se, but components of the smart city.

5 Architecture of Smart City In general, the architecture of the smart home comprises four layers, namely the detection layer, the transmission layer, the data management layer and an application layer (Fig. 6). Each layer includes security modules. However, the detection layer is at the bottom of the architecture and is responsible for collecting the data. Then, the transmission layer is responsible for transporting the data from the lower layer to the upper layer. The third is the data management layer which is responsible for processing and storing the useful data by the application layer, this layer is the most superior of the architecture of the smart home. • Detection layer: Practitioners have argued that smart city implementations rely on different types of data and calculations because of their importance in decisionmaking. On the one hand, data collection is considered the most important role because it controls the rest of the operations of a smart city. On the other hand, data collection is considered the most difficult task because of the great heterogeneity of the data. The smart city is composed of diverse data from different urban operations: load balancing in a smart grid, community waste management, device control in a smart home, personal health monitoring, disease management epidemics, disaster management, etc. • Transmission layer: The transmission layer is a meeting of various communication networks. It includes various types of wired, wireless and satellite technologies. It is divided into two sub-layers, namely access transmission and network transmission. However, Bluetooth, Zigbee, M2M, RFID and Zwave are access network technologies with relatively short range coverage. Similarly, technologies with wider coverage, namely long-term LTE, 3G, 4G, 5G and low-power wide area networks (LP-WAN) are referred to as transmission network technologies.

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Fig. 6 The smart city layered architecture

• Data management layer: The smart city characterized by a large volume of information, complex calculations, information storage, and intelligent decisionmaking. However, the data management layer is considered the brain of the smart city, it is located between the transmission layer and the application layer. This layer performs various tasks of analysis, organization, manipulation, storage and decision making of the data. However, the effectiveness of the data management layer is vital for a sustainable smart city, because the service performance of smart city operations relies on data management. The basic task of the data management layer is to maintain the vitality of the data, with a focus on data evolution, cleansing, linking, and maintenance. This layer can also be classified as data processing, data analysis, data fusion, data storage, and event and decision management. • Application layer: the application layer is the most superior layer of smart city architecture it mediates between urban citizens and the data management layer. Application layer performance greatly influences the perspective and satisfaction of smart city users as they interact directly with citizens. In fact, citizens are concerned about the intelligent behavior of the city, which offers them intelligent services. Finally, the application layer is composed of various components belonging to several domains.

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This layer includes a few services, namely intelligent transportation, weather forecasting, community development, network distribution, etc. in addition this layer increases the performance of the city through many applications using data processed and stored. Secondly, information sharing between different applications has emerged as a promising approach to the evolution of smart cities. Experts say the smart city is the perfect solution for managing the challenges of basic urbanization, namely, adverse effects on human health, scarcity of resources and aging infrastructure, waste management, pollution of air, and congestion of roads. Finally, urbanization is arranged by objective, namely, urbanization guided by industrialization and urbanization led by the company, in order to allow resilient management. Economic growth and interdependence between policy formulation and implementation are at the center of entrepreneurial urbanization. Technological advances have converted the conventional concept of urbanization into a more sophisticated vision. Various studies have been conducted to understand urbanization and the smart city. Urban development and smart city, smart city culture, smart city science and technology, urban policies, and social and economic development of the city of Melbourne, Australia, were evaluated by Yigitcanlar et al. [52]. Then, Caragliu et al. [53] have addressed the relationship between urbanization and smart cities in Europe in a number of ways, namely, attention to the urban environment, education level, accessibility to ICTs. All of these factors have a positive influence on urban wealth. At the end of these studies, the experts said that urbanization was one of the essential attributes of the concept of a smart city. According to Caragliu et al. [53], the intelligence of the city is concerned with improving the standard of living of the urban community in social, economic and environmental terms. Analyzed the partial correlations between human capital, public administration, the length of the transport network, per capita gross domestic product (GDP) and employment in the entertainment sector to measure the intelligence of smart cities in Europe. In addition, conducted research to find out the correlation between ICT infrastructure and economic growth over the past two decades [54]. Finally, Alawadhi et al. [55] have conducted a study of four cities in North America to understand the intelligence of these cities they started several aspects of the life of the citizens (social, economic, technological, informational and others). The results of this work are really interesting in the sense of proposing improvements and meeting the daily needs of citizens.

6 The Birth and Spread of Smart City Concept In the 1990s, with the emergence of digital and Internet in the urban environment, publications on the concept of the digital city or digital city began to emerge. But despite research and work are previously done on the concept of smart cities, the first work published on this concept was in 1994. However, it was not until the 2000s and, more particularly, 2010 that the writings multiplied and experienced some success. This particular focus on this topic began when ICT multinationals like IBM, Cisco,…

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as well as international bodies like the European Commission or the OECD started to show interest and when the European Union has recognized the smart city as one of its main axes of development [56, 57]. The main difference between the digital city and the smart city comes from the importance given to ICT. In general, the digital city is seen as “an information system that gathers data about the actual city and puts it in a virtual public space, where citizens can interact with the system and with other users”. Then, the smart city is an extension and a development of the digital city, it is based on various material tools and programs, in addition, it focuses on the improvement of the living conditions and the comfort of the citizens [58]. However, the presence of smart cities is stronger in countries where economic and scientific development is more advanced. In addition, the size of the city plays a key role in the realization of the smart city. Indeed, the bigger the city, the more impact it has on the environment that needs to be positively resolved by the smart city solution, but also we have the opportunity to share data and knowledge and e-services [59]. Finally, at present, the concept of the smart city spreads very strongly across the different continents in both theoretical research and empirical implementation. Finally, improving the quality of life of citizens is considered among the main objectives in urban areas.

7 Smart City Key Factors The smart city main components are [60, 61]: • The territory: the smart city geographical location. • Infrastructure: the city physical components, i.e. means of transport, buildings, streets, etc. • People: all the population of the city, includes the inhabitants of the city but also the people who work there, the students, the tourists, etc. • The government: the local government that governs the city. To make a city smarter, its components need to consider the three factors, as follows [62]: • Efficiency: it shows the ability of service either public or privately provided for different members of society, namely, citizens, companies, etc. • Environment consideration: it is a question of taking into account the effects produced by big cities in urban environments. The smart city must make sure to minimize damage to preserve the quality of the environment. However, the important aspects to which particular attention must be paid are: energy consumption, water, and air pollution and road traffic. • Innovation: the use of new technologies in smart city projects is a key element as they improve the city components. They provide better services and reduce their environmental impacts. [63]. Indeed, the ultimate goal of a smart city should be

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the creation of public values, but that would mean that all projects developed are aimed at citizens [62].

8 Open Data: Definition and Impact on Smart Cities The concept analysis of the smart city is a key element in the development of this concept, it highlights the importance of open data. The analysis operation also helps citizens to feel more integrated with the initiatives of their municipality.

8.1 What Is Open Data? First of all, we have to define the concept of “open data”, that is to say, to know exactly what it is?

8.1.1

Definition of Open Data

According to Open Data Hand Book, open data is defined as follows: “are data that can be freely used (reused) and redistributed by anyone (shared)…” [64]. Based on the combination of the definitions of Domingo et al. [65] and B˘at˘agan et al. [66–68], the open data represents the notion that “some data from public or private companies can be processed by anyone is published without copyright restrictions or other control processes”. The notion of “openness” also refers to the idea that anyone can have free access to the data but can also use, modify and share it for a variety of purposes, provided that they maintain their provenance and guarantee their free access. Internet platforms are then used to facilitate access to all private or public data in order to offer certain transparency but also the relation of new business opportunities [65].

8.1.2

The Nature of Open Data

Il est essentiel que les données soient disponibles dans leur intégralité et que leurs coûts de reproduction soient acceptables. En outre, l’accessibilité via Internet est recommandée et de préférence sous une forme modifiable et pratique. Then, the terms must be concluded to allow the reuse and redistribution of information. Also, the use, re-use, and distribution of information must be within the reach of any person and any group. There can be no discrimination, even against specific fields of application such as the use of certain data only for purposes related to education.

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Domains of Open Data Applications

The open data concept applies to several domains. Indeed, the information available to the general public comes from various sectors such as: • Cultural: information generally held by museums, archives, galleries, and libraries about cultural objects or works. • Environment: data relating to the natural environment (the quality of the air, the seas, the level of pollution, etc.). • Finance: financial market information (stocks, bonds, etc.) and government accounts (income and expenses). • Weather: data to understand and predict climate and weather. • Science: data relating to the framework of scientific research. • Statistics: information produced by statistical offices such as socio-economic indicators and censuses. • Transport: the data transmitted in connection with this area are, for example, public transport timetables, itineraries, etc.

8.1.4

Why We Use Open Data?

The purpose of open data is to facilitate access to local information, regional and national data in an easily manipulated format using software tools. These public data projects have very specific intentions: • Transparency: in daily life, citizens need to be informed about the actions of the government. To do this, government data and information must be freely and easily accessible to the public. In addition, citizens must be able to share them with each other. Transparency is not just about access to data, but about reuse and sharing of that information. • Participation and commitment: generally, citizens do not commit to their government on a regular basis, they only do so during election periods. This is why, by allowing data to be open, people can be informed much more directly, which encourages them to become more involved in decision-making. So it’s no longer just a question of transparency because, in addition to being aware of what is going on at the governance level, citizens can take part in it. • The liberation of social and commercial values and innovation: at present, digital data are essential resources for social and commercial activities. For better access to data, it is necessary to create new services and businesses that bring commercial and social value.

8.1.5

The Data Efficient Use and Access Limits

Everyone has the opportunity to use open data. However, digital resources and infrastructure, financial and educational resources, or software and digital materials that

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allow the efficient use of data is not accessible to everyone [69]. Gurstein et al. [69] have proposed an approach for the use of open data, making it possible to transform open data into meaningful results for the largest number of people. In addition, cost, the language of dissemination, Internet accessibility, professional or technical requirements and training in their use must be taken into account for the effective use of open data.

8.2 The Role and Impact of Open Data in Smart Cities 8.2.1

The Open Data: Means for Urban Development

The role and objectives of open data in smart cities are the same as those explained in the previous point. According to B˘at˘agan [66], the smart city phenomenon is considered as a new stage of urban development. We can then ensure that open data in a smart city collaborates in the development of the city. With the population abundance in today’s urban areas, reducing expenses, finding new ways to manage the complexity of systems, increasing efficiency and improving quality of life are paramount [66]. That’s why one of the goals of smart city technology is to optimize all systems to reduce costs and increase efficiency, quality of life and work through the use of smart city technology. New technologies, production levels. In addition, implementing a smart city requires finding new ways to integrate and manage a vast amount of information in different domains, such as global markets, national infrastructures, but they may also concern the climate data (temperatures, humidity,…), geographical location, security, mobility, etc. [66]. In fact, according to B˘at˘agan et al. [66], knowledge can be defined as follows: “… the complete use of information and data associated with the ideas, commitments, motivations of individuals is new technological equipment, skills, achievements. In other words, knowledge reflects a deep use and understanding of information interconnected with the intelligent use of technologies.” Today, access to information is an essential element in transmitting the knowledge of society. Information is, therefore, an important resource that constitutes a public good, that is to say, that its consumption by one person does not prevent the availability of information for other individuals. In generally, focuses on four areas: • • • •

Transparency and accountability; Citizen participation and engagement; Innovation; Internal and external collaboration.

During the implementation of open data, designers and researchers encounter obstacles, among them [66]: • Data quality issues; • Cultures opposed to the opening of information;

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• Open data charges. In addition, B˘at˘agan [66] have cited four types of instruments used for open data: • Economic instruments: competitions, open data portals financing, etc. • Training and education: workshops, knowledge exchange platforms, conferences, etc. • Legislation and control: technical standards, public sector information laws, etc. • Voluntary approaches: programmers and global strategies, voluntary public schemes, etc.

8.2.2

The Types of Open Data Projects

Through articles and readings, it was found that the most common projects encountered for open data concern those relating to governments and therefore the public sector as they are the largest producers of information. a. The open government The concept of open government is fairly recent. The first initiative in this direction was put in place by the US government in mid-2009 with the site “www.data.gov”; monitored by the UK government in 2010 with “data.gov.uk” [66]. Other initiatives emerged in 2010 in various countries such as Australia with the declaration of the Open Government, Denmark which launched an open data innovation strategy, Spain which developed open data policies [66]. By adopting this initiative of open government, the governments or the communal administrations are able to offer services of better quality and to improve the democracy by collaborating with the companies, the non-profit organizations, and the committed citizens. Therefore, the benefits that flow from this project are: • Providing reliable information through integration: Consolidating all data makes finding reliable solutions easier. • Information security and information governance: how information is created, stored, used, archived and deleted. • Access to quality data: it is necessary to guarantee information clean, standardized, and not duplicative. • Real-time connection: governments provide access to diversified and distributed information in real time. in 2009, in the Visby declaration, EU member countries should use open data to make their data freely accessible to all and to encourage the public sector [66]. Through openness, transparency, better connectivity, and increased exchange of information and knowledge, new opportunities for public administrations are created making them more efficient and effective. In addition, it allows them to offer more user-friendly services while reducing costs and administrative burdens. This is what the European Commission is trying to do with its open government approach. Indeed, open data and public services support collaboration for the production, design, and

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delivery of public services. The aim is also to strengthen citizen engagement and participation by making government decisions and processes more accessible [70]. b. The Open Government Data Microsoft has developed the Open Government Data project which is a free and open source software solution based on the cloud. It allows governments to publicly make their data available for easy access [66]. This initiative still encourages the use of open data. Government data is then available on the Microsoft Cloud platform to facilitate access to citizens, communities, and developers for analytics and development. This type of initiative comprises: • • • •

Improves collaboration between government and private agencies; Increases government transparency; Promotes the participation of citizens and communities in the government; Shows a unique view of data analysis and trends.

c. Mobile applications Mobile applications are the most promising tools for real-time, sensor-generated open data mining. Then, according to Ericsson, the majority of mobile phones sold in 2014 are smartphones [71]. On the other hand, the creation of new applications for this type of device does not require specific technical knowledge. Application markets such as Google, Microsoft (Nokia) and Apple benefit from mobile GPS functions to integrate localization in most applications such as social networks, finding a bus station or events, etc. In Europe, one hundred and thirteen mobile applications including open data have been developed by developers across the continent. Two categories of domains were very successful following the development of applications: the transport sector had twenty-six and the tourism sector twenty-two. In addition, these applications can be used in more than one country. Generally, an application using open data to offer new services works best when combining multiple data sources.

9 Measuring of Smart Cities From loT to IoT “significant”. Today, several indicators aim to quantify the intelligence of cities. From the level of pollution to the number of traffic accidents to a safer public space or more efficient heating in a building. However, indicators such as smartphone penetration rate, renewable energy production, and household Internet access will simply remain interesting but insufficient figures. We distinguished different organizations and researchers trying to identify the best clues for urban intelligence. One of the main conclusions is that relevant and up-to-date statistics are usually missing. In this context, [72] Philippe Compère, who works with Remourban, explain a project on the development of a reproducible urban regeneration model for medium-sized cities. It has become apparent that there

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are surprisingly more gaps in the energy sector than in mobility or information technology, despite the fact that energy supply remains a strategic priority in all countries. In addition, there has been an attempt to collect data associated with different elements of cities across the 34 countries of the Organization for Economic Cooperation and Development (OECD) [72]. Since 2014, comparisons have been made with nine criteria, open data available to researchers and citizens. They include access to services, civic engagement, environment, individual income, employment and education. It is difficult to make meaningful international comparisons to produce a standard of living satisfaction index. “This is a sign of the richness and diversity of the different places,” says Paolo Veneri, an OECD economist [72], who explains that their goal is “to set priorities” in order to effectively improve people’s standard of living. In addition, Miimu Airaksinen [72], develops performance indicators and data collection procedures to monitor and compare smart city solutions across Europe. However, it is emphasized that today we talk a lot about the Internet of Things, but we should focus more on the Internet of significant objects. “In this sense,” the values of the indicators need to be flexible because it is very important to understand the overall vision rather than focus on optimizing the indicators. According to Masiero [72], intelligence must be both a citizen and a public administration that must build a real dialogue with the people. A smart city is above all-inclusive, which means that it must give everyone the ability and opportunity to be an active citizen. Airaksinen [72] adds another point: the perceived quality of life depends on family relationships and context. This is not included in most schemas, which only take into account quantifiable elements. Finally, what should we expect from this stimulating work on indicators? If researchers succeed in improving them and offering new tools for policymakers, then the quality of life in our cities will probably be significantly improved. In addition, the concept is found in all the operating procedures of the city, appearing as a tool for decision support in a policy of urban transformation. Several cuttings have been made by urban planners to delineate the contours of the smart city, including that proposed by researchers Rudolf Giffinger and Haindlmaier Gudrun [72] who have developed a smart city ranking divided into different areas. They classify the “Smart City” into six factors broken down into various indicators, which themselves include several criteria (see Table 2). Finally, the phenomenon of a smart city depends enormously on the size of the agglomeration. Although no size limit is required for smart city operation, the ability to bring together the maximum resources is more important for larger cities, more inclined to achieve economies of scale in setting up new ones. Heavy infrastructure and financing services with a greater number of end-users in sight. In this sense, the smart city phenomenon closely follows that of metropolises (urban agglomerations populated by several million inhabitants) and which by centralization of functions exert an influence on the territory. More than elsewhere, urban agglomerations are the ideal breeding ground for the implementation of smart models, where solutions to the challenges of high urbanization are the most critical.

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Table 2 Factors to measure smart homes Factor

Criteria

Smart economy

Spirit of innovation, entrepreneurship, economic image and brand, productivity, labor market flexibility, international integration, transformation capacity

Smart governance

Public and social services, transparency of governance, citizen participation in decision making, strategies and policy perspectives

Smart environment

Attractiveness of natural predispositions, Protection of the environment, effective management of renewable resources.

Smart citizen

Level of qualification, affinity for lifelong learning, flexibility, creativity, social and ethnic pluralism, cosmopolitanism and open, mindedness, participation in public life

Smart lifestyle

Cultural spaces, state of health, individual security, quality of housing, educational institution, tourism attractiveness, social cohesion

Smart mobility

Local accessibility, (inter) national accessibility, sustainable, innovative and safe transport system

10 The Top Intelligent Cities A smart city stands out for its ecological, technological or human and futuristic vision. These cities show that they have the interests of their citizens at heart and put in place projects and ingenious solutions to meet the energy, environmental or social challenges that any metropolis must or will face [73–75].

10.1 The Smartest and Most Inspiring Cities in the World • Tokyo: in recent years, Tokyo has made an ecological shift. The megacity continues its drive towards a greener city with Fujisawa; an eco-suburb that uses only energy from renewable sources. • Tallinn: capital of Estonia, Tallinn stands out for its free public transport system and its digital turn. From parking to the postal service, through the ability to vote or receive a prescription, everything now passes by a smart identity card. • Vancouver: with 97% of its energy coming from renewable sources, Canada’s metropolis aims to become the greenest city in the world by 2020. To achieve this, Vancouver plans to increase it’s already many green spaces and reduce maximum wasted water and energy. • Munich: Munich stands out particularly in terms of energy, fueled by several small power plants with varied resources rather than from a single mega-power plant. Thus, it manages its consumption better and reduces the energy losses of its electricity network. By 2025, the German city will only use renewable energy sources.

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• Vienna: recognized internationally for the high quality of life offered to its citizens, this city is considered a smart city for many other reasons. For 2020, Vienna plans to equip more than 300,000 m2 of solar panels, leaving no room for green energy. • Barcelona: equipped with a free public Wi-Fi network, available in 704 locations throughout the city, Barcelona is without a doubt a trendy city. Working to become self-sufficient in energy and to launch initiatives to support its aging population, the capital of Catalonia does not skimp on the means to become more sustainable. • Copenhagen: Copenhagen is known for its ingenuity. Its smart streetlights, able to analyze the quality of the air and go out to save energy when the street is deserted, are an example. With 40% of its population using bicycles as a means of transportation, it is likely to reach its goal of becoming carbon neutral by 2025. • Singapore: it is not surprising to find the famous city-state in this list. An ultramodern and organized city, Singapore has gone digital for all of its government services. Its efforts to minimize car transport and its high number of green certified buildings have earned it the title of Smart City. • Amsterdam: with its pilot project called Climate Street, an ecologically redesigned commercial street, Amsterdam is embarking on the race to become one of the most sustainable cities by 2040. This street is brimming with innovations such as green maintenance vehicles, streetlights energy-efficient as well as waste bins-compactors working with solar energy. The popularity of this project is so great that others are already inspired by it. • Paris: it is with its many social projects that the city of lights stands out from the others. With the creation of Exapad, a service aimed at finding solutions to ensure the independence of elderly people at home, as well as many urban garden projects and a bike rental service and free autos, Paris innovates constantly to improve the urban experience. • Guangzhou (China): while many Chinese cities are busy building monolithic apartments, Liuyun District in Guangzhou has taken a different direction. City planners have come up with a big neighborhood where people can walk freely, while being close to schools, shops and restaurants. The city gives priority to bicycles and pedestrians, reducing traffic and ensuring the safety of residents. • Tel Aviv (Israel): known for its culture of start-ups, the city of Tel Aviv is now making great efforts to move towards digital as a “smart and sustainable” tool. Approximately 100,000 people now have access to a “DigiTel Card”, a smart card offering benefits on certain cultural events, as well as access to a personalized account through which they can pay their bills directly, be informed of future road closures or future work. The application “DigiTel” allows them to report to the community any broken banks or potholes formed on the road, for example. The app can also be used to search for bike lanes or parking spaces. Tel Aviv is also working on the first means of transport by magnetic levitation, an aerial train (and very futuristic) with several individual capsules and named SkyTran. These small shuttles should see the light at the beginning of 2016.

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• Bogota (Colombia): Bogota is undoubtedly the cycling capital of Latin America. The city has no less than 376 km of bike lanes and focuses its urban development to encourage motorists to leave their cars in the garage for the benefit of cycling or walking. And now, Bogota also has the fastest buses thanks to the TransMilenio, a jewel of technology that has recently replaced all other forms of public transport in the capital and conquered the most refractory drivers to public transport. • Montevideo (Uruguay): the Uruguayan capital is the South American city with no doubt the best quality of life. With half of the city’s energy resources coming from renewable sources, it deserves this title. In addition, the state-owned energy company has been using attractive rates for two years to increase the share of solar panels in energy production. Another good point: almost 80% of the waste in the city is retired. • Montreal (Canada): among the “smart cities” of North America, Montreal appears as one with the most developed initiative. The city proves it for example by trying to create constantly the link between its inhabitants. During the year 2014, it has an electronic box of ideas to collect the proposals of the inhabitants to improve the functioning of the city, which was filled last year with 357 suggestions studied by the town hall from Montreal. Some of these proposals will soon be under construction, from the best use of transport to the protection of the environment. • Hamburg (Germany): the port of Hamburg aims to become the first “smart port” in the world. This challenge is on track since it has already been equipped with sensors in order to overcome one of its major problems: traffic jams on the platforms. The city also has sensors in the car parks informing truck drivers, via a tablet, the arrival time of their delivery and allows them to see if a location is free to load the goods. Many smart applications have also been developed in Hamburg, as well as sites launched by responsible citizens. This is the case of Leerst and medler, allowing residents to report and locate abandoned places in order to rehabilitate them. In addition, the municipality is also working on a “Digital City Science Lab”, a laboratory to promote research and innovation in the field of technology, particularly related to the environment. • Montpellier (France): to relieve congestion, Montpellier recently launched “Velomagg”, a bicycle sharing system based on the “Velib” model in Paris. Nearly 60 stations located throughout the municipality thus allow non-polluting and silent journeys. Montpellier has also developed water leak detectors which, linked to an application, make it possible to inform residents in real time about a possible leak in their home. But the sensors are mainly used to monitor the pipeline network as a whole, to avoid overconsumption in the city. • Nantes (France): If Nantes deserves its title of “smart” city, it is above all for its particular affection for open data. More than 500 different data are indeed open to citizens, through three websites. The citizens make an intensive use of it as evidenced by the 40,000 downloads of data made each month by the inhabitants of the municipality. Equipped with an application gathering all the information useful to the inhabitants of the city (bus schedules, opening of swimming pools, available parking spaces, menus of canteens, etc.), it also allows them to follow

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the efforts made by the city in terms of the environment and to get involved in concrete actions to help the city become more “green”.

10.2 The Top7 Intelligent Communities of the Year In [73] uses an analytical method to select the top 7 cities in the world and for each year separately. Each community is invited to answer a detailed questionnaire based on ICI indicators (Intelligent Community Indicators). A university team then examines the questionnaires and establishes a ranking of the candidates. The Top 7 represent models of economic and social transformation in the 21st century. They are not the most advanced technology centers, the most connected cities, nor the fastest growing economies in the world. Instead, each one illustrates best practices in broadband deployment and utilization, workforce development, innovation, digital inclusion and advocacy that deliver lessons to the regions, towns and villages around the world. They open new paths to sustainable prosperity for their citizens, their businesses and their institutions. Table 3 shows the top 7 smart cities in the last five years (2015, 2016, 2017, 2018, 2019).

11 Case Study: The City of Amsterdam Considered as one of the first smart cities not only at the European level but also at the global level, the city of Amsterdam began its development with a strategy focused more on the digital city. The first work concerning the digital city of Amsterdam were written around 1995 after the birth of the concept of Amsterdam Digital City (ADC) in 1994 [76, 77]. The appearance of this concept is linked to the use of ICT that has enabled the establishment of an exchange platform for citizens of the city. One of the main reasons for its introduction was related to the municipal elections approach. This platform is therefore considered as a political and social instrument. Indeed, thanks to this platform the citizens of Amsterdam are interconnected and can communicate with each other, exchange opinions, ask questions,… about political elections but not only. This digital city project was basically an experimental project that was successful for a long time. It was a temporary initiative that later became a permanent project. However, at a certain point, the public funds to finance this platform and ensure its functioning were no longer sufficient. That is why this experiment which was of the public sphere was transformed into a private project. From then on, Amsterdam Digital City became a trading company using e-commerce to finance the social aspect of this initiative. Unfortunately, the financial returns of this project were insufficient to support digital cities initiatives. However, thanks to this project, urban development has evolved in new directions [76]. At the same time, the city of Amsterdam is deeply concerned about environmental issues and, more specifically, about its environmental footprint. Pollution and energy consumption

Towards Smart Cities: Challenges, Components, and Architectures Table 3 The top 7 smart cities in the last five years

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Year

City

Country

Ranking

2019

Abbotsford, British Columbia

Canada

1

Chicago, Illinois

USA

2

Hudson, Ohio

USA

3

Sarnia-Lambton County, Ontario

Canada

4

Sunshine Coast, Queensland

Australia

5

2018

2017

2016

2015

Taoyuan

Taiwan

6

Westerville, Ohio

USA

7

Chiayi City

Taiwan

1

Espoo

Finland

2

Hamilton, Ontario

Canada

3

Ipswich, Queensland

Australia

4

Tainan City

Taiwan

5

Taoyuan

Taiwan

6

Winnipeg, Manitoba

Canada

7

Chiayi City

Taiwan

1

Edmonton, Alberta

Canada

2

Grey County, Ontario

Canada

3

Ipswich, Queensland

Australia

4

Melbourne, Victoria

Australia

5

Moscow

Russia

6

Taoyua

Taiwan

7

Hsinchu County

Taiwan

1

Montreal, Quebec

Canada

2

Mülheim an der Ruhr

Germany

3

New Taipei City

Taiwan

4

Surrey, British Columbia

Canada

5

Whanganui

New Zealand

6

Winnipeg, Manitoba

Canada

7

Arlington County, Virginia

USA

1

Columbus, Ohio

USA

2

Ipswich, Queensland

Australia

3

Mitchell, South Dakota

USA

4

New Taipei City

Taiwan

5

Rio de Janeiro

Brazil

6

Surrey, British Columbia

Canada

7

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then become important issues for the city [78]. In 2009, the Amsterdam Smart City Program (ASC) was set up by the municipality of Amsterdam with the support of the private sector, research centers and citizens of the city. First, Amsterdam defines the smart city as: “A city is smart when investing in capital and communication infrastructure fuel sustainable economic growth and high quality of life, in combination with efficient use of natural resources” [79]. The main objective of this program is the implementation of pilot projects in collaboration with IBM and Cisco. This collaborative project aims to test new technologies to achieve energy savings and better use of energy resources as well as reducing pollution and CO2 emissions in the city [80]. Finally, the Amsterdam smart city strategy aims at better energy management between public and private infrastructure and citizen participation.

11.1 The Different Types of Smart Projects For starters, according to the Amsterdam Smart City website, Amsterdam Smart City (ASC) is a partnership between governments, knowledge institutions (e.g. universities, research centers, etc.), private companies and citizens from the city of Amsterdam. This site defines the concept of “Smart City” as: “A city where infrastructure and social and technological solutions facilitate and accelerate sustainable economic growth. This improves the quality of life in the city for everyone” [81]. The first smart city projects in Amsterdam date back to 2009 with the transformation of the Utrechtsestraat public area into a commercial street where sustainability aspects through cooperation between local businesses and citizens are a major concern [79]. Indeed, this project aims to find sustainable solutions in three areas: public space, entrepreneurs and logistics. The first two areas are committed to environmental control and the implementation of more efficient energy devices for lighting and heating. As for the third area, it concerns the use of electric transport for the collection of waste from a single supplier in order to reduce CO2 emissions [78]. An example through which the city of Amsterdam is considered the world leader is the promotion of electric mobility. Indeed, a strong presence of public refill points can be observed in this city. This is one of the most important initiatives in Amsterdam. In addition, the city of Amsterdam, in collaboration with IBM, installed a device in a large number of households that monitors real-time energy consumption in private buildings connected to a smart grid [81]. This project is supported by several local private companies. The aim of this system is to achieve a 40% reduction in CO2 emissions by 2025 compared to 1990 data. The city hopes to produce 30% of its energy from sustainable, clean energy sources [82]. In six years, the ASC program has experienced strong growth and currently includes over one hundred partners. These are involved in more than eighty-four innovative projects. The ASC program includes seven priority areas in which the various projects are developed and developed.

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Smart Mobility

This area deals with projects related to mobility and transport. The aim is to create a more efficient, more comfortable transport network with more security. It is linked to ICTs and has an open data system. It also aims for better accessibility in the city for citizens but also for visitors and travelers. At present, ten projects are under development in this category and four others have already been established. Example of a project under development: Vehicle2Grid [81]: In the face of an inefficient use of the solar power grid: more energy demands in the mornings and evenings when solar installations have low efficiency and fewer power applications during the day when the contribution to electricity is higher, the way of managing the supply and storage of energy had to be redesigned to avoid this waste of electricity. In order to use energy resources more efficiently, a pilot project “Vehicule2Grid” was conceived in Amsterdam in early 2014. It allows citizens to use the battery of their electric car as a means of storing excess fuel. The energy in a smart grid. In this way, citizens will be able to decide for themselves how and when this energy is used. In other words, the energy from the solar installations can be either transferred to an energy grid, used directly or also stored in the battery of an electric car for later use both for driving the car and for home appliances. The expected result of this project is a change in people’s habits by encouraging them to use more solar energy, electric vehicles and allowing them greater energy autonomy.

11.1.2

Smart Living

The projects related to this category aim to make the city a pleasant place to live, where citizens but also visitors feel good, where it is pleasant to work and spend time. The areas taken into consideration that affect the quality of life are safety, tourism, culture, and health. In order to make the Dutch city more enjoyable, twelve projects are underway and eight more have already been developed. Example of an already experimented project: Energy Management Haarlem [81]: Two hundred and fifty Liander customers in Harlem had the opportunity to test a free energy management system for four months. This system informs customers about the energy consumption of each household appliance plugged into their home. It allows users to control their consumption online but also to turn the devices on or off. This helps them save money on their energy bills. The main reason participants took part in this experiment was that they were primarily looking for a reduction in their energy costs.

11.1.3

Smart Society

This is an important aspect of the smart city: it includes human and social capital. A city cannot function without its citizens and visitors. The projects allow the development of the citizens but also that of the level of creativity and the quality of

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the social interactions between the people. To improve its human and social capital, the municipality of Amsterdam has implemented a series of projects, seven of which are under development and four have already been completed. Example of a project under development: Beautiful, Smart, Sustainable Wildeman [81]: This project involves improving the Wildeman estate by making it more beautiful, smart and sustainable with the help of local residents. The goal is to find smart solutions for improving the social and environmental quality of the neighborhood. This project brings together participants by themes to make them aware of the resources of their region and to work together on concrete initiatives to improve the quality of life of the neighborhood.

11.1.4

Smart Areas

A common approach for the development of the urban area, for sustainability and for the efficient use of raw materials is a key element for the development of a smart city [81]. For the moment, the city of Amsterdam is working on eighteen projects related to the development of the urban area. In addition to the projects in progress, eight others have already emerged. Example of a project under development: Smart Students: Students from the Amsterdam University of Applied Sciences get involved in the Smart Cities program by looking for smart solutions in the New West area in different areas: mobility, social aspects, sustainability, children and games, safety and the elderly, sport, exercise and health. Their solutions will then be offered to citizens in the neighborhood to gather their opinions on the issues and smart solutions.

11.1.5

Smart Economy

This area includes projects related to the attractiveness and competitiveness of the city in relation to entrepreneurship, productivity, innovation and openness to the international. For the Smart Economy theme, two projects have already been developed and a project is under development. Example of a project under development: Budget Monitoring [81]: This project makes it easier for citizens to participate in public policy and spending decisions. This budget monitoring gives citizens the opportunity to take more effective measures to live in a better environment.

11.1.6

Big and Open Data

Open Data is one of the core activities of Amsterdam Smart City. This mainly involves creating a sample of applications, decrypting data and organizing a platform for data management. These publicly available data make it possible to offer Amsterdam residents new opportunities and make decisions based on the actual data and facts observed. According to the Amsterdam Smart City website, the government defines

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Open Data as follows: “Open data sources should be raw data from the public sector [81]: • Which are public; • Not subject to copyright or other rights of a third party; • Financed by public bodies, in particular, made available to carry out this specific task; • Which preferably respond to “open standards” (no restrictions on use by ICT users or ICT providers) and are preferably computer-readable so that search engines can find information in the documents.” It is very important to note that the Dutch-speaking government insists that Open Data does not include data from individuals subject to state secrets or against commercial confidentiality. Three projects are underway for this category and two have already been designed. Example of a project under development: PICO—Tool Project for Innovative Communication and Design [81]: The PICO project is a design tool that will make it easy to evaluate the feasibility of a sustainable energy project. It allows the creation of energy scenarios, according to various sustainable applications used (district heating, biomass, solar energy, etc.), to find the most optimal. It will help identify opportunities in residential areas to save energy and produce sustainable energy. For the authors of sustainable energy projects (private companies, local councils, citizens), PICO identifies, upstream, all the aspects related to the projects. The information detected relates to savings and sustainable production options, the implications of operations, implementation time, costs, earnings, etc. Its purpose is therefore to facilitate decision-making regarding investments. It will act as a communication tool between the various stakeholders. This tool will be connected to a large number of recent data from various sources.

11.1.7

Infrastructure

These are city infrastructures related to ICT, energy, roads or even water. For this type of domain, three projects are in progress and three others have already been completed. Example of a project under development: Amsterdam Free Wifi [81]: The municipality of Amsterdam and the bars of the port of IJburg wanted to allow free access to WIFI by installing antenna connectors WIFI. This project was carried out by the city of Amsterdam in collaboration with contractors, ASC and KPN. The city of Amsterdam then thinks of extending this project to other public places in the city.

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11.2 Key Actors for the Development of the City With regard to the Dutch experience, from the Amsterdam Digital City project to the Amsterdam Smart city project, several actors are involved and play vital roles in the evolution and development of the city.

11.2.1

Amsterdam Digital City (ADC)

Initially, the main actors at the heart of the Amsterdam Digital City project were the citizens represented by the cultural and political center “De Balie” and the group of computer activists Hacktic. Their goal was to set up a ten-week experiment. This involved the development of an electronic democratic forum for Amsterdammers. This project, which was very successful, was extended until [81]. This information platform can be defined as a virtual city in which sites of private and public institutions but also citizens could share data with users of the platform. The topics were diverse (health, education, politics, etc.) and targeted citizens for both business and social purposes. This initiative never had specific governance, which is likely one of the reasons for its failure. Indeed, no significant investment has been made to maintain and update this project. Due to this lack of funding, the initiative has failed. The main stakeholders in this social platform for sharing information about life in the urban area were the citizens of the city. Other visitors and external users were also interested in this platform although their interests were more in the area of innovative communication rather than the content itself. As time went on, commercial actors became more and more involved. They provided free information to highlight their products and services in order to attract customers. This is why Amsterdam digital city acquired a more public-private character and thus lost its social profile. As a result, businesses and economic actors in the city of Amsterdam were also part of the stakeholders. In addition, public institutions also played an important role: hospitals, schools, etc. In conclusion, this Amsterdam Digital City project was a bottom-up initiative, with information sharing among city citizens without any defined management structure. It is now interesting to compare this first initiative which has failed for lack of funding and real governance to the Amsterdam Smart City (ASC) project which is currently in progress and which is very successful.

11.2.2

Amsterdam Smart City (ASC)

The city of Amsterdam began to focus more on issues related to environmental quality, energy consumption, and pollution. This is why she has sought to set up projects that take into account these issues. According to this author, this is how the smart city initiative was set up in Amsterdam. The main actor of this project is, therefore, a public body. In this case, it is no longer a “bottom-up” approach, as for the

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Amsterdam Digital City project explained above, but rather a “top-down” approach [83]. The founders of the Amsterdam Smart City program founded an association of actors working on the development of smart goals. It is a closed governance platform including associated partners and a hierarchical body. Compared to the Amsterdam Digital City project, it has a precise control mechanism allowing to have a look at the projects and the different stakeholders. The main actors of this initiative are the founders, namely: the Economic Council of Amsterdam, the municipality of Amsterdam, KPN (Dutch communication operator) and Liander (largest energy network management company in the Netherlands). The difference between the ADC project and ASC are: in the first, the launch engine of this one was a private topic. While in the case of the ASC project, the initiative comes from a public initiative and the shareholders who participate are both public and private. Unlike the ADC project, in which participants did not all interact with each other and the partners worked alone in the ASC project, there is significant cooperation and a strong link between all project shareholders. The aim is to create a four-propeller model (four important institutions): public agencies, social agencies, universities, and research centers and private companies to build a knowledge network at the city level and in this way enable it to develop intelligently [81]. In addition, citizens are the final stakeholders of the ASC project, although their role is not well perceived. Although they are the final actors in the project, they benefit from the benefits of the projects immediately but in another way, notably through the improvement of the environment and the quality of life in the city. Unfortunately, they are not always aware of what they earn through these initiatives. Finally, the involvement of citizens is essential to build smart strategies. Citizens and civil society are then seen not only as stakeholders but also as active and indispensable actors for the success of such projects. Finally, the citizens of the city represent not only the main shareholders but also the stakeholders of the Amsterdam Smart City project.

12 Conclusion and Perspectives Over the last ten years, the concept of “smart city” has extended the exploitation of technologies to provide citizens with services that are better adapted to their needs and more participatory, based on criteria of efficiency and effectiveness and on cooperation between the private and public sectors. There is not a true and only definition to describe a city as intelligent. However, to make a classic city as smart city; it logically needs to have an economy, a mobility, an environment, a government, an administration, a population and a way of life of smart city dwellers. In the smart city system, the use of ICTs in their infrastructure, services, and lifestyle is dominating to meet the needs of its citizens, but also to improve their quality of life. As well as achieving the economic growth of the countries. The concept of smart city is only an evolution and adaptation of sustainable urban development to the reality of our current societies, in which the criteria of compact city, citizen

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city and recyclable city are present, but also the dimension of innovative city like country’s economic development engine. Although this concept is more and more used by urban planners, it can also be described as a marketing label used by large international companies that have the technological and economic capacity to offer and implement costly projects in cities. to make them more efficient, safer, more attractive and more “intelligent”. Strengthening the public-private partnership is also important in this type of project. This work focuses on the topic of smart cities and is interested in understanding how to implement an open data platform that plays a key role in the development of cities and especially smart cities. This area is absolutely new and is fast developing in the world. The concrete objectives that open data allow to achieve the following objectives: • • • • •

Transparency, participation and collaboration of citizens; Reuse and retrieval of public data; The use of collective intelligence; The strengthening of society through a prudent opening of the city; The creation of new services and business models that meet the expectations of citizens.

At the end of this study, this work will have generated many questions and some points could be deepened in the future because of the limits of this work. We will complete this work by encouraging cities to embark on this type of smart development. We are confident that smart city initiatives will improve the living conditions of citizens. Although everything does not change overnight, the public process can serve as a model for the private sector to also raise awareness of societal, environmental, economic, governmental, and other issues.

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Smart Cities: The Next Urban Evolution in Delivering a Better Quality of Life Abdulrahman Sharida, Allam Hamdan and Mukhtar AL-Hashimi

Abstract This study aims to elucidate the impact of implementing the latest stateof-the-art of technologies on well beings of nations and its people who live in cities generally, and in Bahrain specifically. Technology became a part of our daily activities such as paying bills, surfing the internet, social media apps, news, weather forecast and even shopping (e-commerce). Researchers and Information & Communication Technology (ICT) experts expecting most cities will turn to be smarter in the coming few years which will occur positive returns. This study will illustrate the primary role of smart cities in improving the standard of living and quality of life, aiming to serve the rapid population growth in addition to improve the quality of environment. The main definition of smart cities is the automation of managing transportation, water, energy, security and safety, traffic, services, wastage, communications and other resources. Smart Cities play a major role in economic growth in terms of production, resources efficiency, job opportunities, new investors, and tourism. However, this part won’t be part of this study because smart cities is not fully implemented yet which makes quantitative analysis difficult. Keywords Smart cities · Sustainability · Internet of things (IoT) · Information & Communication Technology (ICT) · Quality of life

A. Sharida King Fahd Causeway Authority, Manama, Bahrain e-mail: [email protected] A. Hamdan (B) · M. AL-Hashimi Ahlia University, Manama, Bahrain e-mail: [email protected] M. AL-Hashimi e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_16

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1 Introduction In the past few years people start to use the term “Smart” to their gadgets such as phone, TV, watch and even home. First of all, we need to understand what “smart” means in terms of technology and physical machines. When a machine takes decisions/actions without human intervention based on processing data via intelligent computing systems, we call it a “Smart Machine”. Our smart gadgets became part of our daily life, but did we imagine that we would live in a smart environment where all physical machines surround us? We have read and heard endless news and articles about smart phones and smart cities and how to convert our current cities to smart ones. Thus, many of us asked what a smart city means. Smart city is a term of using technologies that process different types of data to create efficiencies, sustainability, economic growth, and enhance quality of life factors for peoples’ living and working in the city. They are built of Internet of Things (IoT) and Information and Communication Technology (ICT) which will facilitate controlling overall devices and monitoring them. Therefore, a strong platform and foundation of Information and Communication technology structure will be implemented. Nevertheless, all of the features above will be used also in Building Management Systems (BMS) that will use all smart facilities and gadgets in terms of energy, lights, sound and visual systems, Water Managements systems and so on. When it comes to outdoor environment, smart cities also include surveillance cameras which will give 24/7 security with face recognition in addition to WiFi hotspot (Public internet access via WiFi) will give internet access at anywhere in the city, in addition of finding a way to charge mobile phones while connecting to the hotspot. Smart cities have also smarter energy infrastructure in addition to preserving, optimizing and organizing resources (Including natural ones). Some cities suffer from lack of communication specially internet and poor cellular signals, where internet access became a need in our daily activities such as making calls, paying bills, emails, social network, etc. on the other hand, generating power and energy consume natural resources such as oil, gas and coal which are vulnerable to depletion. Moreover, it is commonly known that cities are having high pollution and the environment are kind of unhealthy and reduce the quality of life. Nevertheless, lack of Security surveillance in some cities cause a nightmare of its inhabitants.

2 Literature Review Smart cities became the new definition of urbanization where most facilities became automated and services became faster and more efficient. The implementation of smart cities needs to be well studied and reviewed especially that the whole project is not yet implemented. Some cities took the initiative to become smarter by implementing smart solutions partially. For example, smart building that includes sensors to control lights, tap water, fire alarm and power. Nevertheless, smart solutions reached

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street level by creating a transportation system that interconnecting all automotive, trains and buses, including e-vehicles (EV). It includes also implementing Smart lights, outdoor Wi-Fi hotspot and CCTV cameras in the streets aiming to build an integrated and synchronized systems and devices, which we call those days Internet of Things. Increasing environment quality will depend on using green/environmental technologies that mitigate the effects of human activities on environment. This research will discuss the alternatives to reduce pollution and maintain sustainability, such as using solar and wind energies and waste management system. Moreover, the safety and security of people’s lives will be within the scope of smart cities. Many researchers mentioned that the definition of Smart Cities is lacking and broad. Some researchers already mentioned that in their articles. “The concept of “smart city” is lacking. Therefore, finding a good definition of a smart city could be as considering innovations, in technology to be at the core of the smart city concept and in a broad sense, so including ecological, economic and also social sustainability” [1]. In the last few years, a large number of smart city initiatives have been implemented in Europe. However, these initiatives have not referred to a common framework in terms of language, objectives and recommended actions [2].

As long as is there is no flagship of smart cities, they are trying to be smarter by implementing smart solutions. “The most frequently cited examples are cities that are simply trying to become smarter” [3]. Researchers defined smart cities as a place where different type of physical devices and hardware are installed, programed and integrated with each other’s in order to transfer data and process them to take better operational decisions and actions automatically [4]. Others defined it as computing platform that is controlled via a smart grid which will control the functionality of data sharing and processing from physical devices in additions to electrical efficiency [5]. Some cities developed some smart projects to be smarter gradually. For instance, Utrecht (Netherlands) invested several projects in human, transportation, Information Technology (IT) infrastructure and wise management of resources [1]. Another example is the city of Aarhus (Denmark) which is working on improving the quality of life of its residents by integrating and synchronizing systems of energy, waste, water, healthcare and transportation [3]. Helsinki (Finland) focused on data communication and open data that could be used by public. Where in Vienna (Austria), approached for mobility concepts and solutions such as “SMILE” (Smart Mobility Info and Ticketing System) and “eMorail” which focused on integrating transport service and an intermodal e-car and e-bike service. Vienna was the world’s number one Smart City in 2011, then in 2012 it was ranked fourth in the European list of Smart Cities. Not only Vienna, Copenhagen (Demark) are looking for mobility projects. Barcelona (Spain) has the ambition to be the model of smart cities for the whole world due to its vision in information integration that will be achieved by building a solid ICT infrastructure in addition to smart parking. Moreover, support business

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innovation to achieve its vision. “The 22@Barcelona district created a Living Lab to support business innovation and to enable better usage of public spaces” [6] With the rapid growth of population, it is expected to increase to 6 billion people in 2050. As long as the cities are the hubs of global economy, most of the largest cities in the world will be accountable for more than 60% of Gross Domestic Product (GDP) by ensuring the smooth flow of goods and services around the world. Smart cities initiatives already have been started in Europe and United Arab Emirates. In this study, most researches were conducted in Europe by the lead of European Union (EU) [6], which created a set of regulation and innovation partnership in 2012 called Smart Cities and Communities Initiative [3]. Scarcity of natural resources that are used to generate energy and cities pollution that will affect majorly on inhabitants’ quality of life. On one hand, investments and economy will find a fruitful ground to increase production and efficiency. Smart energy is one of the major pillars of smart cities which is implemented by some countries where others are planning to be implement it. In Denmark, there is a coordination between business sector and environmental and energy sector in order to “Go Green” (Go Green with Aarhus www.gogreenwithaarhus.dk) where many of energy-efficiency system components were developed in Danish businesses, universities, and research/educational centers who are considered leaders in their fields [3]. One of the main infrastructure components is Smart grid, which is a centralized unit that control energy and power distribution and utilization. It is also play a primary role in occurring energy-efficiency based on devices and equipment operational need. Furthermore, smart grids help to make a better use or transportation system that aim to optimize the use of roads and apps by sharing and communicating the needful information with public with making streets smarter not by only enhancing safety, also to apply technologies on the grown on larger scale. “smart street: a smart street is then not only a means to enhance safety and livability but also a test ground for new technologies to be applied at a larger scale” [5]. When infrastructure is designed and launched, the second phase is to install all required hardware on the ground such as sensors, cameras and servers. Installing them is not the issue, the tough part is how to integrate all of them and make those devices communicate and share data between each other’s. This is what we call Internet of Things (IoT). Connecting all devices via internet by creating certain protocol which we called Internet Protocol (IP). The collected data from those devices will be processed to provide useful information that will be exchanged with different organizations, businesses and some other stakeholders. However, in this literature review, data security and complexity of urban systems are the main challenges. While data is shared among different devices and systems, many people still afraid to share their information via different types of apps and GPS (Global Position System) as they are seeking for privacy. Computing large volume of data and relying on cloud computing as it became the latest trend in providing data availability 24/7 are one of big ICT companies such as IBM, Microsoft and Oracle. Beside of Internet of Things there is Cloud of Things where both of those terms are combined [1].

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3 Research Methodology 3.1 Research Design The purpose of this study is to gain people’s insight about smart cities and getting their feedback about the conventional cities’ lifestyle and how effective smart cities will be, including the evaluation of people’s feedback about alternative solutions in smart cities.

3.2 Data Types and Sources There are several tools and methodologies to do a research. However, each research and study have its own design(s). For this case, the descriptive design will be selected because the study doesn’t need any experiments or test as it is about improving quality of life. Nevertheless, such project is not implemented “completely” yet. Therefore, a quantitative method by using a questionnaire form will be uses in this study.

3.3 Data Collection Techniques The research data will be collected from a questionnaire form which is designed by google forms which is an electronic form (eForm) that will be distributed as a link via e-mails, whatsApp and other social media applications in order to reach larger population than conventional method (distributing papers). Datta collection will be using Questionnaire instrument that will include demographic questions such as gender, age and living location. Also, a closed-ended question to valuate population background about smart cities. Moreover, there are rate scale questions (1–5) categorized based on research objectives criteria which are distributed as following, environment, Sustainable and smart energy, Smart Security & Safety, Smart Services and Overall perspective of smart city. We surveyed 148 people in Bahrain and other Gulf countries.

4 Data Analysis and Interpretation of Results 4.1 Demographic Analysis After collecting 148 responses, the above table contains statics of demographic data. Most of respondents are male with 73%. Moreover, different age range participated

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in the questionnaire. The majority were between 31 and 40 (48%) then who are between 18 and 30 (28%) (Table 1). Since smart cities related to state-of-art of technologies, usually mentioned age ranges use technologies more than elders’ people who are using technology more than 6 h per day with 55.4% out of 62.2% from all participants who use technology more than 6 h per day. Few who are using technology less than 1 h per day (2.7%). These statics shows that technology became a part of our daily activities where 97% Table 1 Demographic analysis

Variables Gender

Age

City

Climate

Smart cities background

Technology use

Frequency

Percent

Male

108

73

Female

40

27

Total

148

100

Below 18

1

1

18–30

42

28

31–40

68

46

41–50

27

18

Above 50

10

7

Total

148

100

Bahrain

86

58

KSA

28

19

UAE

10

7

Other GCC

3

2

Lebanon

9

6

USA

2

1

Other

10

7

Total

148

100

Sunny

136

92

Cloudy

5

3

Rainy

2

1

Windy

5

3

Total

148

100

No

50

34

Yes

98

66

Total

148

100

More than 6 h a day

92

62.2

4–6 h a day

37

25

1–3 h a day

15

10.1

Less than one hour a day

4

2.7

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Table 2 Quality of life Questions

SA

A

N

DA

SDAa

Mean

SD

4.300

0.723

4.150

0.786

Smart cities will improve Quality of Life (e.g. health, environment, safety and security and lifestyle)

Frequency %

64

67

14

3

0

43

45

10

2

0

If I got the opportunity, I am willing to live in a smart city

Frequency %

52

71

21

3

1

35

48

14

2

1

a SA Strongly Agree, A Agree, N Neutral, DA Disagree, SDA Strongly Disagree and SD Standard Deviation

of respondents are using technology between 1 to more than 6 h daily. This research aimed to be conducted in Bahrain. However, since smart cities became today’s discussion and interest of some countries, the questionnaire is distributed globally to test the acceptance of smarties. Especially in GCC where they have a vision to implement smart cities which will have a significant impact on economy. Based on above statics, 58% of respondents are living in Bahrain, 28% in other GCC countries and 14% in countries others than GCC. Therefore, most responses to climate type were sunny with 92% which is logical according to Bahrain. This question was added to determine type of energy is suitable in each city, where cloudy and rainy weather comprise 3% each. Respondents’ awareness and background about smart cities considered as one of major input in this study. Statics show that 66% of respondents are aware about smart cities. Which means that answering the questionnaire more reliable as they are aware of smart cities components and functionality as well. Moreover, it is aligned with reliability testing (Alpha Cronbach) which is equal to 79.1%. Eventually, most of respondents (Table 2) agreed that smart cities will improve quality of life according to alternatives, solutions and services provided by smart cities. Moreover, the majority agreed to live in smart cities if the opportunity is available. Those result will encourage investors and government to study and look for best practices and solution to implement smart cities believing that it will improve quality of live and economy as well.

4.2 Discussion of Findings Environment plays a major role in smart cities where the aim of smart cities is to improve quality of life. Therefore, a set of questions related were allocated to environment. Nevertheless, some other sets of questions are related to environment indirectly which will be discussed after in this chapter. According to Urban cities pollution, the majority agreed that cities have high pollution which will affect the

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quality of life in cities (Table 3). On one hand, participants agreed that water management should be implemented which could be by using Water Management Systems as there is a water wastage. Furthermore, keeping cities clean by using automated waste collection had been accepted and agreed by majority of participants. However, opinions were different according to implementing smart homes where some participants strongly disagreed. The reason could be that some people still may find some difficulties in using technologies or the cost of its maintenance. Energy has a major role in smart cities sustainability. It will be operated by renewable and clean energy resources such as solar and wind energies which are most common. According to solar energy, the majority strongly agreed on its effectiveness in their cities (Table 4). However, different opinions according wind energy effectiveness which result a neutral response as an average which is logical for the time being. Because most of participants are living in Bahrain which is well-know with its sunny climate, where windy weather is barely occurred. In spite of that, participants agreed and expecting that wind energy will be a good alternative, beside to solar energy, to gas, coal and fuel resources of energy which. Safety and Security is one of the main concerns of smart cities. Violence, accidents, hazards, etc. will affect greatly on quality of life and people’s peace of mind. However, participants have different opinions according safety and security. According to the below results, the majority agreed on having smart fire and gas leak detection (Table 5). Moreover, participants believe that installing surveillance cameras will mitigate violence and aggression, in spite they have different point views of covering cities with those cameras. As we all know about traffic congestion and high probability of accident takes a place, smart cities are looking to provide autonomous Table 3 Environment Questions

SA

A

N

DA

SDAa

Mean

SD

4.030

0.755

4.030

0.755

4.320

0.652

3.970

0.947

Urban cities are well-know with high pollution

Frequency %

39

79

25

5

0

26

53

17

3

0

In smart cities, wastes should be collected automatically instead of conventional collection

Frequency %

39

79

25

5

0

26

53

17

3

0

Water management and irrigation should be implemented in your city

Frequency %

63

70

15

0

0

43

47

10

0

0

Smart homes will make your home-living easier

Frequency %

49

58

32

6

3

33

39

22

4

2

a SA Strongly Agree, A Agree, N Neutral, DA Disagree, SDA Strongly Disagree and SD Standard Deviation

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Table 4 Energy Questions

SDAa

Mean

SD

5

0

4.190

0.811

3.280

1.069

4.090

0.864

SA

A

N

DA

Solar energy will be effective in your city

Frequency %

80

49

14

54

33

10

3

0

Wind energy will be effective in your city

Frequency %

20

45

46

31

6

14

30

31

21

4

Solar and wind energy will be good alternatives of fuel, gas and coal as energy resources

Frequency %

52

66

22

7

1

35

45

15

5

1

a SA Strongly Agree, A Agree, N Neutral, DA Disagree, SDA Strongly Disagree and SD Standard Deviation

Table 5 Safety and security Questions

SA

A

N

DA

SDAa

Mean

SD

4.000

1.069

4.100

0.917

4.390

0.685

3.340

0.937

Surveillance cameras should cover all areas in your city

Frequency %

58

52

24

8

6

39

35

16

5

4

Surveillance cameras will mitigate violence and aggression in your city

Frequency %

55

66

16

9

2

37

45

11

6

1

Having smart fire and gas leak detectors will give an advantage to the competent authorities in your city

Frequency %

71

66

8

3

0

48

45

5

2

0

Autonomous Vehicles Technology (Self-Driving Vehicles) will increase safety

Frequency %

60

74

12

2

0

41

50

8

1

0

a SA Strongly Agree, A Agree, N Neutral, DA Disagree, SDA Strongly Disagree and SD Standard Deviation

vehicles (Auto-Drive). Participants are closely to have neutral responses and slightly tend to agree on increasing safety by using those vehicles. Smart Services represent the current and future life styles where most people accomplish their daily tasks via phones, tablets and Personal Computer. Getting real-time information regarding traffic, transits, trains route, available parking and smart road assistance services are agreed by participants, as it will minimize traffic congestions which will lead to mitigate accidents.

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According to having public Wi-Fi (Hotspot), the majority agreed that it is needful in order to improve connectivity (Table 6). As we discussed previously, environment plays a major role in smart cities, therefore, using electronic vehicles will have a positive impact on environment which was strongly agreed that those vehicles will be a preferable alternative of fuel vehicles. Nevertheless, when we talk about smart services, we always remember e-commerce and online shopping. But what if we implement smart shopping in malls and super markets where all products details and Table 6 Smart services Questions

SA

A

N

DA

SDAa

Mean

SD

4.390

0.685

4.030

0.989

3.910

0.880

4.300

0.675

4.190

0.811

Real-time information about traffic and parking conditions and transit options to minimize traffic issues associated with major events or incidents in your city

Frequency %

71

66

8

3

0

48

45

5

2

0

In your city, there is a need to implement public Wi-Fi (Hotspot) to improve internet connectivity

Frequency %

56

55

25

9

3

38

37

17

6

2

Smart shopping that is purchased through smart devices and using mobile phone applications to search for available products and display their information will improve shopping behavior

Frequency %

38

70

31

7

2

26

47

21

5

1

Smart road assistance which detects any vehicle breakdown and afford a quick assistance response will be useful in your city

Frequency %

60

74

12

2

0

41

50

8

1

0

Electronic vehicles will be a good alternative of fuel cars

Frequency %

60

61

22

5

0

41

41

15

3

0

a SA Strongly Agree, A Agree, N Neutral, DA Disagree, SDA Strongly Disagree and SD Standard Deviation

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availability will be available on smart phone apps. This is what most participants agreed on, knowing that these services will save time and efforts in searching for products.

5 Conclusion After reviewing and analyzing participants’ responses, they have agreed that cities have high pollutions and smart cities will have a good impact on pollution mitigation which includes waste and water management. People are willing to implement latest technologies to keep environment clean. On the other hand, some participants strongly disagreed to implement Smart Homes. In spite of their advantages, but some people still hesitating to live in them. The reason could be that people may expect high costs of maintenance, complexity or side effects of using some sensors and signals in your home. Implementing new and renewable energy resources considered as one of the high priorities of the world’s advanced countries. The climate plays major role in determining what kind of energy resource(s) could be used. Referring to above results, the preferable energy resource is solar because most of participants are living in Bahrain, which is known with its sunny weather. On the other hand, wind energy has different opinions whether to strongly agree to implement wind energy or strongly disagreed. For those who strongly disagreed, from their point of view, it won’t be more useful that solar energy. In spite that surveillance cameras mitigate violence and aggression, results showed that some participants strongly disagreed to cover their cities with those cameras. The reason could be that people will feel discomfort while there are cameras monitor them and interfere their freedom. Implementing smart gas and fire detectors are primary backbone of smart cities security and safety. When it comes to Autonomous Vehicles, people still unaware about them because they are not implemented broadly. Therefore, they don’t have a full image of their features and whether they will increase safety or not. Outdoor connectivity needs to be improved via implementing public Wi-Fi (Hot spot) as still many cities are facing poor signal coverage. Connectivity is the platform of operating smart cities in terms of transferring data and connecting different types of devices. Strong connectivity will facilitate information transition among thousands or maybe millions of devices. Therefore, providing real-time information will affect inhabitants’ behavior which will also mitigate congestions, reduce waiting times and take the right decision. For example, weather forecast, flights delay, taxis availability and estimated time of arrival. Moreover, electronic vehicles will be more economic and time-saving especially when you re-charge your car at home instead of waiting in petrol station cues (There will be electronic fuel as an option also) this is what most of participants.

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References 1. de Wijs, L., Witte, P., Geertman, S.: How smart is smart? Theoretical and empirical considerations on implementing smart city objectives—a case study of Dutch railway station areas, Routledge. Innovat. European J. Soc. Sci. 29, 422–439 (2016). Accessed from http://10.0.4.56/13511610. 2016.1201758 2. Russo, F., Rindone, C., Panuccio, P.: European plans for the smart city: from theories and rules to logistics test case. Eur. Plann. Stud. 24(9), 1709–1726. Accessed from http://10.0.4.56/ 09654313.2016.1182120 3. Snow, C.C., Håkonsson, D.D., Obel, B.: A smart city is a collaborative community: lessons from smart Aarhus. California Manag. Rev. 59(1), 92–108. Accessed from http://10.0.4.153/ 0008125616683954 4. Viale Pereira, G., Cunha, M.A., Lampoltshammer, T.J., Parycek, P., Testa, M.G.: Increasing collaboration and participation in smart city governance: a cross-case analysis of smart city initiatives. Informat. Technology for Development 23(3), 526–553 (2017). Retrieved from http:// 10.0.4.56/02681102.2017.1353946 5. Meijer, A., Thaens, M.: Quantified street: smart governance of urban safety. Informat. Polity Int. J. Governm. Democracy Informat. Age 23(1), 29–41. Accessed from http://10.0.12.161/IP170422 6. Alaverdyan, D., Kuˇcera, F., Horák, M. Implementation of the smart city concept in the Eu: importance of cluster initiatives and best practice cases. Int. J. Entrepreneur. Knowl. 6(1), 30–51. Retrieved from http://10.0.9.174/ijek-2018-0003

Convergence of Blockchain and IoT: An Edge Over Technologies T. Choudhary, C. Virmani and D. Juneja

Abstract The term Internet of Things (IoT) defines the continuous growth of the always-online, data-collecting devices. Blockchain is a shared ledger or a database, distributed across an open or private processing system that expedites the procedure of recording business and data management in a business network. It empowers the design of decentralized transactions, smart contracts, and intelligent assets that can be managed over internet. It formulates the revolutionary decision-making governance systems with more egalitarian users and autonomous organizations that can control over Internet without any third-party involved. Various researchers have proposed the merging of the two technologies to ensure a secure and permanent solution to record data which is also processed by “smart” machines in the IoT. The IoT applications are also distributed in nature and the blockchain technology could be an imminent factor in deciding how the IoT connected devices communicate with each other. With the aid of blockchain, the security of IoT devices can be decentralized to a great extent thereby, eliminating the possibility of threats that hover around it at present. This chapter outlines an introduction to the blockchain technologies and its decentralized architecture, especially from the perspective of challenges and limitations. The main objective is to explore the trending research topics, benefits and drawbacks of blockchain towards making in a smart environment. The study explores its potential applications for business and future directions that are all set to transfigure the smart world. Keywords Blockchain · Smart environment · IoT · Smart healthcare · Smart supply chain

T. Choudhary · C. Virmani (B) Manav Rachna International Institute of Research and Studies, Faridabad, India e-mail: [email protected] D. Juneja Poornima University, Vidhani, India © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_17

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1 Introduction Blockchain technology today is redefining the way secure transactions can take place. The internet, as known today is highly vulnerable in itself and blockchain could be the answer to long impending problem. It can be used to solve the IoT fault lines in security. IoT devices are generally connected with each other mostly through public networks which, needless to say are extremely vulnerable to cyber-attacks. With the help of permanent and linearly indexed records, blockchain can simply and efficiently solve the problem for good. The financial threats may also come to an end as blockchain technology could be used for smoothening of the commerce process by presenting a payment and secure communication channel to users globally. Also, the people would be the legal authority in this matter thus rubbishing the traditional centralized methods of banking. Any type of hacking and tampering of the data like gaining control of device and records becomes non-viable because of the manner in which the blocks are stored and protected in a database specifically designed for the purpose in the blockchain system. Each and every IoT device presents a point of vulnerability and the risks are even higher because even the AI technology is involved in decision making for the users. Hence, blockchain can be utilized to render a platform that is secure, scalable and verifiable and that also has invincible security implementations. Blockchain drives cost and risk reduction without any third-party organization in control of the transactions and thus enables the strategic value and revenue streams for all involved in smart environment. This disruptive technology has tremendous opportunities that open the doors to detract the power from centralized authorities of the smart environment in the sphere of communications, business, and even politics or law. A blockchain is a distributed ledger or database that registers an ordered list of records of transaction which are connected together through an ever-expanding chain of blocks. The blocks are sequentially arranged in a linear fashion wherein every block contains the reference to the hash of the previous block, thereby forming a chain of blocks and hence the name; Blockchain. The blockchain is maintained by a network of nodes with each of them executing and keeping records of the same transactions. A blockchain network owes its popularity to a number of features, some of them being: • Public: In the various advantages, this feature surfaces as numero uno as it allows for all the participating stakeholders to view the blocks and also the transactions stored therein. However, the actual content of a transaction is protected by a private key. • Decentralized: It eliminates the conventional techniques that involved single entity or authority to approve the transactions and also to set specific rules for transactions to be accepted. A great level of trust is involved in the blockchain technology as all the participants in the network need to reach the same consensus to undertake the transactions.

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Public blockchain

Consortium blockchain

Private blockchain

•open to the public •anyone can participate in transaction •review/audit transactions •permissionless ledger •Ex- Bitcoin, Litecoin

•Partially decentralized. •The private part is being controlled by a group of individuals whereas the public part is open for participation to anyone. •Not all data is open to all of its participants. •Selected members can participate in transaction and review/audit transaction •Ex - EWF, r3

•Major part is private and is open only to a consortium or group of individuals or organizations that have decided to share the ledger among themselves. •A central entity controls and manages the rights to access or modify the database. •Any one can't make and review/audit transaction •Ex - Bankchain

Fig. 1 Types of blockchain

• Security: The database in Blockchain can only be extended and previous records cannot be altered at any cost and in case if alteration is required, it involves a high cost for the purpose. It can be broadly categorized under three main types: Public Blockchain, Consortium Blockchain, and Private Blockchain [1, 2]. As clarified with the name itself, in a public blockchain, anyone in the network can participate in the transaction. Each node maintains its own copy of the ledger and can review/audit the transaction. The consortium Blockchain authorizes some of the nodes to be open and some kept as private whereas in case of private Blockchain, mainly nodes are private and only a few nodes will decide to distribute the ledger among them. The difference between the three types is shown in Fig. 1.

2 Blockchain Architecture To understand blockchain, it is imperative to have some knowledge about the logical components of its architecture. The system consists of four architectural components as described in Fig. 2. • Shared Ledger The term shared ledger implies that the shared content and databases are accessible to the participants of a particular Blockchain system. It is a data structure managed inside the node application. It is an immutable record in which the transaction can be recorded once and can be accessed by all the participants on the network [3, 4]. Once the node application is running, one can see the shared ledger for that system.

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Fig. 2 Architectural components of a blockchain

shared ledger consensus algorithm addresses block

There is no upper limit to have node application per participant but it is important to note that it doesn’t depend upon the number of the ecosystem of the participant, there is one ledger for each ecosystem. • Consensus Algorithm It describes how the ecosystem will arrive at an agreement of the distributed ledger and depicting the network status. An ecosystem may opt for various consensuses based on the requirements; the following two categories of consensus exist: 1. Proof-based, leader-based, or the Nakamoto consensus: A leader is chosen and proposes a final value [5] 2. Byzantine fault tolerance-based: It is based on rounds of votes. • Addresses To uniquely identify the sender and recipient of data during a transaction, an address is specified. For a block, the address is mostly a public key. A sender can use the same address a number of times. To prevent identification, users are expected to generate new addresses for each one of their transactions which can prevent the linking to a common owner of the proposed transaction. • Block It aids in the transaction process, i.e., transferring values among users along with keeping few other records such as the hash of the previous block, a nonce, time stamp. The block architecture consists of the block header, for identification of a block and the block body. The next section throws light on advantages and applications of Blockchain.

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3 Advantages of Blockchain The technology has many feats to its name which make it a compatible choice for wide ranged applications over the traditional databases. Firstly, being a virtual ledger, it eliminates the role of banks/facilitators and allows direct transactions between peers. There is no central server for communication and all the users are at the same level to communicate with each other. Also, the transaction time is reduced. This property also makes the technology much more transparent and secure as any change can be made to the ledger without the approval of all stakeholders. Secondly, it has the ability to enhance real time visibility in the functioning of supply chain that prevents leakages and increases efficiency [6]. The data can’t be removed as the data structure of blockchain can be appended only. It can be tampered if more than 50% of the computation power is controlled and all the transactions are rewritten. It uses protected cryptography that makes it immutable and more secure [7, 8]. It is a single point of trust as all the transactions and data can be verified. Figure 3 highlights the advantages of blockchain to the organization [9–11]. Blockchain could be of extreme significance in enhancing the security across three varied domains namely: In blocking identity theft, preventing the manipulation of data and against frauds along with mitigating threats from Denial of Service (DoS) attacks. (a) Blocking Identity Theft: Since blockchain is a distributed ledger that allows in transaction of data and has a structure of network miner proof of works, the technology could be regarded as the best in mitigating data theft possibilities. Along with this, the very characteristic feature of blockchain terms it as tamperproof, thus making it most comprehensive in preventing any data corruption. Amongst all features, this is of particular significance as user data is of utmost significance and forms the very foundation of any organization. (b) Protection against Data Manipulation and Fraud: The blockchain technology is a mixed combination of cryptography, hashing and also a decentralized structure

Fig. 3 Advantages of blockchain

Blocks Identity Theft Against Data Manipulation And fraud Protection Against DoS Attacks

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Fig. 4 Components comprising blockchain Cryptography

Blockchain technology Hashing

Decentralized Architecture

which renders it nearly impossible for an unauthorized entity to barge in and make changes to the available data. The technology is adept in recognizing any form of manipulations and allows the organization to retain the confidentiality and integrity of user data. Figure 4 depicts the landscape of blockchain. The KSI (Keyless Signature Infrastructure) is a major solution that ensures not only data protection but also network protection [12]. With the aid of KSI, the authenticity of all electronic data can be proven mathematically. This becomes possible because the digital signatures original files in Blockchain are stored in KSI which then does the task of verification of files copies by re-checking the signatures of copies against the ones stored in the blockchain. Detecting the manipulation of data becomes an easy task because of the hashes which are stored in Blockchain resides in a number of thousand of nodes. Aerospace and Defense industries are active users of the KSI technology as accuracy and speed matter in issues of national and international importance. The health sector is seeing an increased use of technology to ensure better control over the patient’s medical records. (c) Preventing DoS and DdoS Attacks: In the cyber age, everything from software to hardware is under threat from a multitude of attacks. Denial of service attacks are the most recent in the scenario. Blockchain can facilitate in DNS (Domain Name System) that allows the access to websites using the domain names rather than by the conventional technique of IP addresses [13]. The DNS system is toxically centralized in a few root servers that are under the control of ICANN (Internet Corporation for Assigned Names and Numbers) which is responsible of the IP protocol addresses, protocol identifiers, domain system management functions along with root server system management. Hence, the blockchain technology makes it virtually impossible for any single entity to corrupt or manipulate the records by building a more distributed and much more transparent Domain Name System (DNS).

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4 Convergence of IoT and Blockchain Technology As there is an exponential technological advancement in the fields of blockchain and IoT, it is only probable that these would be having a huge impact on the very way we would live and connect in the coming decade. The convergence of the two most thriving technologies today would be efficient in creating an ambient environment that would ensure the data security and privacy along with rendering protection to all connected devices from various possible attacks. Blockchain would ensure greater security of data in comparison to the centralized data storing techniques [14, 15]. Thus, in the storage and management of data, the damage from the attacks on the database can be prevented. Also, the openness aspect of blockchain ensures the greater transparency of data when applied to any field that involves the disclosure of data. Figure 5 depicts the evolution of technology and how it would get further revolutionized upon integration with blockchain technology. The evolution of the technological frame would remain in the stride of greater good for humanity if the objectives data privacy and user privacy are retained. Apart from the many advantages rendered to the world via the convergence of the two, there are a few disadvantages as well as depicted in the Fig. 6. • Legal Issues The technology is still developing and evolving. Since there is no legal framework for its operation or any law codes mandated in its name, this could pose a problem in the coming years for the service providers as well as the manufacturers. • Storage Issues The ledger itself has to be stored on the nodes which imply that with the passage of time, the size of the ledger will also keep on increasing.

Current

• Closed and Centralized IoT networks

• Open access to IoT • Open access to IoT networks

networks

• Distributed cloud

• Centralized clouds Before 2005

Fig. 5 Evolution in technologies over the decades

Beyond 2025

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Fig. 6 Concerns over the convergence of IoT and blockchain

Legal Issues

Time Issues

Storage

Lack of maturity and Standards

• Time Issues This involves the time required to encrypt all the IoT objects included in a blockchain network. Owing to different computational capabilities of varied devices, it would not be possible to operate them all at a desired speed with the same encryption algorithms. • Lack of Maturity This would invariably result in the compromise on interoperability amongst various competing ledgers and platforms. But the advantages of the convergence of the two technologies far supersede their disadvantages [16]. They are illustrated in the Fig. 7 given. • Security A private blockchain is capable of storing cryptographic hashes of the individual devices firmware. This record would be impertinent in proving that no manipulation has occurred in the data. After proving the same, it is then possible to connect to other devices or possibly services. The identity and access management systems based upon blockchain technology can successfully fight against attacks related to IP address forgery or IP spoofing. Since, blockchain makes it impossible to alter the approved data; no device with a fake signature can connect to a network Immutability and decentralized access play a role in preventing and detecting the malicious actions. The network is resilient to failures because of the decentralized peer-to-peer network that lacks any points of failure and prevents any transactions from being manipulated. • Architectural Strength The IoT architecture may be termed as vulnerable in every part of the system as it is prone to various attacks such as DDoS, hacking, remote hijacking, data theft and so on. Through the verification processes, transactions are signed and verified cryptographically to prove that the originator is the actual sender of data thereby providing more security and integrity against data vulnerabilities.

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Instantaneous transfer

Autonomous

Architectural strength

Security

307

Scalability

Advantages Of Blockchain And IoT integration

Solving capacity constraints

Fig. 7 Advantages of IoT and blockchain integration

• Instantaneous Transfer Since it works at all times, all seven days a week, it is very much possible to carry out the reconciliation and payment of transactions under ten minutes. • Autonomous Blockchain provides for communication between the various IoT devices and also to enable transactions in an autonomous way as each device has its very own Blockchain account that eliminates the involvement of a third-party in the whole process. • Scalability Since blockchain is maintained by a network of peers, it renders the technology scalable. As more and more peers keep getting involved in the network, the computing capability keeps on becoming more and more scalable. • Solving Capacity Constraints The growth of the connected devices must be managed properly so as to enable in adapting to the network capacity of all the devices. With the help of smart contracts, it is possible for devices to communicate with each other in a secure way and also to execute actions automatically, thereby solving the problem of centralized entities.

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5 IoT Applications Using Blockchain Blockchain is already in the field, transforming a number of IoT sectors that include some as illustrated below in Fig. 8. • Automotive The automotive industry is all set for transformation with big names in the field adopting measures based upon the convergence of the two technologies. The solutions so adopted aims at producing reliable information in the organization along with the allowance of effective transaction communication between business partners, insurers, manufacturers, financing companies, regulators, service providers and customers among other stakeholders. • Healthcare Blockchain may take this sector to a whole new level by providing a perfect ambient environment for storing the patient’s data that is collected from a host of IoT devices. The data is also maintained safely without any threat of it being corrupted or altered by unauthorized entities. • Supply Chain Optimization visibility and demand are among the many problems that may be addressed by blockchain in the supply industry. It facilitates in the creation of a reliable environment with secured access to the shared data across all the stakeholders involved in the supply chain. It can be effective in identification of contaminated food items in the chain or simply for the purpose of tracking the food items for achieving targeted goals that may be associated with packaging. • Home Automation IoT technologies are increasingly being deployed in smart cities; smart buildings to provide for enhanced security and tackle the problems of traffic congestion, environmental pollution and so on. The blockchain enhanced IoT devices would strengthen

Fig. 8 Application sectors of IoT and blockchain integration

Automotive

Healthcare

Supply chain

Home Automation

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the security systems along with improved reliability and the protection of devices in the network with the data communicated across them.

5.1 IoT Solutions Using Blockchain Researchers have suggested and implemented many solutions to provide security with the advent of Blockchain technology. This section summarizes few of the major contributions in the Table 1.

Table 1 Literature survey Author

Description

Achievement

Dorri et al. [17]

Proposed work coordinated transaction of information using overlay network and cloud storage coordinates information

Privacy and security of smart home environments

Zhang and Wen [18]

Propose an IoT electric business model using P2P blockchain and smart contract

IoT E-Business for smart property

Xia et al. [19]

Presented information sharing framework that can grant access control to sensitive data on cloud

Medical data security issues

Shafagh et al. [20]

Proposed IoT based data-centric design for end-to-end encrypted data storage system

Sharing, flexibility and auditable protection of data

Lee and Lee [21]

Proposed firmware update scheme for embedded devices in IoT

Avoids tampering attacks

Ouaddah et al. [22]

Proposed access control architecture in IoT using fully decentralized pseudonymous and privacy preserving authorization

Decentralized access control management

Rodrigues et al. [23]

Blockchain signaling system for cooperating network defence system to position hardware

Streamline signals of DdoS attacks

Outchakoucht et al. [24]

Proposed dynamic and fully decentralized security policy using distributed architecture and machine learning algorithms for the control of IoT devices

Dynamic, optimized and self-adjusted security policy

Dinh et al. [25]

Performance evaluation metrics for processing of workloads in case of private blockchain

Act as benchmark for future design implementations

Crain et al. [26]

A variant of Byzantine consensus algorithm that helps a set of peers to reach on consensus on some value

Avoids Sybil attacks

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5.2 IoT Scenarios This section discusses the five IoT scenarios which are Smart Homes, Smart Grids, Connected Industry, Connected Health and Smart Supply Chain as depicted in Fig. 9. • Smart Homes Smart home refers to the automation of day-to-day routine tasks with the help of electronic machines installed in the house. In recent years, there has been an increase in the demand of smart home devices. The whole concept basically revolves around a luxurious life along with enhanced security and privacy. A multitude of IoT devices and services are involved that present an opportunity to the users for gaining improved control over their devices and an enhanced knowledge about the status of their houses. The devices involved may be connected via wired or wireless or even wireless sensors such as Wi-Fi, Bluetooth and other controlling and optimizing functions. Possible security threats involved in smart homes are mentioned in the Table 2. • Smart Grids The smart grid technology aims at the optimization of the distribution network of electric power to sustainably and efficiently utilize the energy resources. Transactive energy is one of the latest trend observed in SmartGrids that eliminates the need of any centralised third party and enables prosumers to trade energy directly. [27] Significant cost-effectiveness would be an advantage of such aan IoT enabled system, indeed technical solutions are being investigated and large-scale deployment are planned by major utilities companies. Smart grids are vulnerable to a host of attacks possible through the networks, communications and also via physical entry points. Table 3 depicts attacks on smart grids. • Connected Industry Connected Industry is known also known as the next industry paradigm, it aims at bringing self-organizing, fully-connected and intelligent factories in the coming decades. The various attacks that render it vulnerable are mentioned in the Table 4 along with their impacts and countermeasures.

Fig. 9 IoT scenarios

Smart Homes Smart Grids Connected Industry Connected Health Smart Supply Chain

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Table 2 Smart Home attacks Attack

Impact

Countermeasures

DoS and DDoS attack

• Attackers send messages to devices as RTS/CTS • Malicious code embedded in the user system.

• Application of cryptographic techniques • Applying Authentication measures

Private information leakage

• Sensors hacked by attackers • Home personal information may be monitored by attackers.

• Authentication measures • Encryption techniques between the gateway and sensors

Falsification

• Routing table in the gateway may be changed to collect packets • Leakage of confidential information

• Application of secure socket layer (SSL) techniques

Trespass

• Physical access to the house

• Control and authentication measures • Use of randomly generated passwords

Table 3 Smart grid attacks and countermeasures Attack

Impact

Countermeasures

Data injection attack

• Manipulation of exchanged data • Interception of communication links • Compromise on hardware components

• Vulnerability assessment • Risk management • Prevention techniques

Time synchronization attack

• Fake visualization of the system conditions

• Security reinforcement • Mitigation techniques • Detection methods

Availability attack

• Compromise on accessibility and information transmitted and collected

• Vulnerability assessment • Prevention techniques

Dynamic systems attack

• Compromise on sensors • Interception of outputs by attacker

• Mitigation techniques • Detection methods

Physical attacks

• Attacks on generation line, transmission and substation

• Security reinforcement • Prevention techniques

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Table 4 Connected Industry attacks and countermeasures Attack

Impact

Countermeasures

Virus scanner constraints

• Most virus scanners fail to fight against unknown cyber threats

• Information security Management System (ISMS)

Smart tags manipulation

• Tags move forward in the supply chain while carrying malicious content all along

• End-to-end encryption • Penetration tests • IT-security audits

Remote Maintenance

• Maintenance task by subcontractors renders it most vulnerable

• Strong authentication techniques • Digital signatures

Lack of authentication

• Manipulation via access to control panels

• Separation of subsystems

Table 5 Attacks and countermeasures on connected health Attack

Impact

Countermeasures

Data theft for monetary gains

Personal information including addresses, names, financial information and social security details may be compromised

Strong anti-malware defense Filtering of malicious content

Data corruption

Alters the data or output results

Continuous monitoring of networks

Data theft for impact

Stealing data of high profile people

Control and limit on the user privileges

• Connected Health The healthcare field is the least armored against cyber risks due to persistent inherent weaknesses in its very security posture. Most common threats and mitigation techniques have been discussed in the Table 5. • Smart Supply Chain Deliberate cyber-attacks may be performed on the whole business chain with the help of malwares through vulnerable access points. The attackers are adept in identifying the weakest member of the supply chain in order to gain access to other members of the supply chain. Websites and waterholes are mainly used to distribute the malware and also via the storage locations and software providers. These threats are mitigated via the techniques mentioned in the Table 6.

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Table 6 Attacks and countermeasures for connected health sector Technique

Description

Encryption

All the business hard drives must be encrypted to ensure access from authorized entities only

Cyber-security training

Staff awareness should be mandated across all organizations

Back-up

A secure backup and disaster recovery service should be in place to ensure data access to all business stakeholders

Update

To reduce the risk of running on an unsupported and out of date system vulnerabilities, the business organizations should be made to run on automatic updates

Data security policies

Only the endpoints that have been checked and approved should be used for devices

6 Future Scenarios Various hypothetical scenarios have been discussed in this section that aims to offer an insight into the future application of the convergence of IoT and Blockchain technologies. This section does not concern itself with the project design and architecture of the proposed scenarios but only presents the important concepts associated with the ideas so proposed. The three scenarios are namely; sports centre, smart museum and football club. • Sports Centre The hypothetical sports centre is supposed to have paddle-courts, along with the facilities of a gym and a pool. The local private Blockchain is used to store the data and keeps track of all the transactions of all the stakeholders involved in the centre. It can also function as a miner, thereby processing the incoming and outgoing transactions to and from the sports centre. The technology could be applied through smart contracts in as mentioned in the Table 7.

Table 7 Sports centre application of Blockchain Blockchain application via smart cards in sports centre

Advantages

Digital identity

To ensure that the identity of the user is recorded on the shared ledger To ensure that no unauthorized entity has access to the data or information or even physical entrance to the premises

Reservation of paddle tracks

To reserve the tracks in accordance with the availability of not only the clients but also the centre Avoids mediations via calls or physical appearance

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Payment of employees salaries

• payments via cryptocurrency • ensures guarantee of employees

Supply chain

• Smart contracts may be used to close the assignment contacts • Counters additional costs • Eliminates the need for physical presence

Purchase of museum products

• Presents the possibilty of buying products physically as well as virtually. • Virtual auctions,i.e, facilitates negotiations across internet.

Fig. 10 Description of the application of smart cards in a smart museum

• Smart Museum In the hypothetical museum, a number of floors may be taken into consideration, say six wherein different products are at display at different levels of the building. The museum may be consisting of all types of sensors, controllers and actuators to monitor and alternatively managing the lights, temperature and security of the museum as depicted in Fig. 10. • Smart Football Club Blockchain technology could be applied to the field of sports in ways more than one, such as, to improve the club members and the various football fans across the globe. Cameras can be used for monitoring people’s behavior during a match, the security aspect in entry and also to detect the movements in garages and parking areas. Sound, parking and temperature can be detected via sensors to analyze the happenings around the football stadium. Smart contracts may be used for the various purposes as depicted in the Fig. 11.

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Purchasing tickets

•Ease in choosing location and seats •Eliminates the concept of queues

Voting

•Facilitation of virtual votes •Enhances participation •Much safer and relaible

Payment of salaries

315

•Received via cryptocurrency

Fig. 11 Use of smartcards in smart football clubs

7 Conclusion This chapter presented a detailed discussion over the convergence of blockchain and IoT. Various attacks are detailed with the countermeasures. The chapter also presented future scenarios where IoT can take advantage of Blockchain and become the change of the Industry revolution 4.0.

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Social Internet of Things in Agriculture: An Overview and Future Scope Chandan Kumar Panda and Roheet Bhatnagar

Abstract Worldwide it is a great challenge to farmers and agricultural specialists and scientists to feed the growing population, and the irony is that agricultural lands are diminishing and getting squeezed in between whimsical weather and unpredictable commodities market of farm produces. The Social Internet of Things (SIoT) which is the ability of gathering and exchange of data over a network without human-tohuman or human-to computer interface rather it makes use of social objects creating a social network. The fundamental characteristics of IoT are seamless connectivity, accuracy of delivery of services, dynamic response, enormous scale, safety and security, promptness, mutual support, sensing and expressing and these can be used in agriculture as well. SIoT can enormously contribute in weather forecast, insect pest management, humidity, rainfall, soil fertility status, identifying leaf dryness and wetness, temperature variation, wind flow and soil moisture conditions. SIoT can also control plant environmental factors viz. temperature, humidity, carbon dioxide concentration and illumination according to the condition of crop growth in real time and it can be used in vertical farming. IoT has huge potential in smart precision farming. Keywords Agricultural challenges · Farmers · Sensor · Precision farming

C. K. Panda (B) Department of Extension Education, Bihar Agricultural University, Sabour, India e-mail: [email protected] R. Bhatnagar Department of Computer Science & Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India e-mail: [email protected] © Springer Nature Switzerland AG 2020 A. E. Hassanien et al. (eds.), Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, Studies in Computational Intelligence 846, https://doi.org/10.1007/978-3-030-24513-9_18

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1 Introduction World agriculture faces the challenges to feed burgeoning world population, reducing rural poverty throughout the world, and sustainable management of the natural resource factors [33], global food security is matter of concern for the coming fifty years and ahead as productivity of the crop has dropped in many places due to climate change and water dearth [44], however irrigated agriculture faced the competition of reducing the cost of cultivation and scarcity of high-quality water [22] and nutritional security is another challenge, as 2030, hundreds of millions of poor people will remain to be malnourished, until regional food production receive priority [8]. Smallholder farmers are unable to produce meaningful surplus for commodity markets, and eventually hurt by higher grain prices and non-exploration of potential of crop production technology and input intensification [23] and agriculture in everywhere squeezing in between whimsical weather and unpredictable commodities market of farm produces. Major factors responsible in losses in farming were late sowing and poor quality seed, environmental pollutions, insect and disease infestation, excessive irrigation and water losses, improper harvesting, heavy use of organic fertilizer and insecticides, lack of agricultural implements and inappropriate handling of ripened fruits; however SIoT can efficiently contribute in successful crop husbandry, irrigation management, weather forecasting and best possible usage of fertilizers, herbicide, fungicide and insecticide. The advancement of Information and Communication Technology (ICT), artificial intelligence, deep learning, data mining, Social Internet of Things (SIoT) and cloud computing are wise means for safeguarding the millions of farmer’s interests. The use of Social Internet of Things (SIoT) in agriculture is still in its infancy for supporting farmers, although it has huge potentiality to increase the income of the farmers and judicious use of input resources for farming and sustainable agriculture. Internet of Things technologies has immense potential in the field of food and agriculture to handle societal and environmental challenges encountered by these sectors. From the farm to fork the SIoT could transform the sector in food safety, reduction of agricultural input loss and food wastage [9]. The Social Internet of Things (IoT) in agriculture can be using the RFID technology, wireless sensor network and recognition technology and other intelligent technology [7] to get huge amount of environmental and crop performance related data, ranging from time series data to spatial data from cameras, mobile phones, and other IoT devices. The data collected through IoT devices can be analysed, computed and personalised for crop recommendations for any specific farm [24]. The use of Social Internet of Things (SIoT) in agriculture will increase from $30 million in 2015 to $75 million in 2020, for a compound annual growth rate of 20%. With the use of IoT technology in smart agriculture the productivity of cereal viz. wheat, maize, rice, barely etc. had increased to 7,340 kg/ha as compared to global average of cereal productivity of 3,851 kg/ha [35]. In food supply chain process, the SIoT can act in self-adaptive way in which smart objects perform, decide and learn autonomously [53] and track traceability of goods in movement for efficient logistics management and farm business operation [57], even through the SIoT a single farmer can deliver

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the farm produce directly to the consumers in small and wide region like in direct marketing. This will change the marketing scenario of equitable power of big companies and also small farmers in selling of produce [50]. Integrated applications of Near-field communication (NFC) and internet of things can read and write farm output, distribution and sale product automatically and can keep record and stockpile all product information, which completely resolve the trouble of information distortion of traditional RFID technology [51].

2 Concept of Internet of Things (IoT) The Internet of things is a network to connect anything(laptop, mobile, sensors, car, camera, home appliances etc.) with the stipulated or defined protocols of the internet [41] for integration of multiple technologies in real-time analytics, machine learning, wireless sensors, and embedded systems [54] equipped with the ability to gather and exchange data over a network without support of human-to-human or human-to computer interaction [12] for the purpose of smart solution of any problem, smooth functioning and coordination of devices with desire outcome with economy of scale. IoT is the networks of smart objects, sensors, actuators, smart phone those purpose is to interconnect “all” things, including every day and industrial objects, in a way so as to make them programmable, intelligent, and more capable of interact with humans and each other. Based on aforesaid discussion, Internet of things (IoT) may be define as “the network of smart devices connected through internet for global information sharing, processing, intelligence, interacting with each other and finally self-decision making”. IoT is the global network of standard and interoperable communication protocols in which physical and virtual “Things” have their identities, specific attributes and virtual personalities, wherein the things use interface of intelligence and seamlessly integrated into information network for data set communication with the ambience of the user and their associates. The fundamental characteristics of IoT are seamless connectivity, accuracy of delivery of services, dynamic response, enormous scale, safety and security, promptness, mutual support, sensing and expressing, however, while evaluating IoT solutions, the features to be considered modular and future-proof, open and independent, supports rapid start, distributed architecture, rebrandable, free methodology and design-time tooling, market-leading and strategic partnerships [18] researchers and scholars of SIoT had well identified the problems, prospects and challenges; and the technology protocols used in IoT such as Radio Frequency Identification (RFID) tags, actuators, sensors and smart phones [42] and IoT becomes a basis for interconnecting things viz. actuators, sensors, and other smart devices [5] and exchange information among devices and human and devices, take automated decisions, invoke prompt actions and provide excellent services to the users [26]. Social Internet of Things (IoT) is going to raise a global system wherein physical objects/devices are seamlessly and flawlessly integrated into information networks for providing most advanced and smart services for human-beings [58]. Over the last 5 years, the IoT

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sector in general and SIoT sector in particular shown high expansion for Information and Communication Technology service providers, which yielding to 20–50% annual increases in device connections and increased new revenue clarity and job opportunity to IT sectors. IoT has gradually stepped out from its infancy and it is on the verge of transforming the social networking through the billions of interconnected physical objects [19].

3 Concept of Social Internet of Things (SIoT) According to Social Internet of Things (SIoT) the objects are enable to instrumental in establishing social connectivity and relationships in an autonomous way in relation to their owners with the advantages of improving the network scalability in information delivery and service discovery [37] and social network of objects can find novel resources for better implementation of the user services [2]. In social networks smart physical objects are interconnect to the network to bring the physical world into the virtual dimension of decision making. Physical objects are capable to communicate on social network sites are smart to enter into their owners’ social loop for autonomous publishing information for the interest of selected communities of users and to perform some related automatic actions for users ease to perform [3] and capable of establishing social relationships with configure to the rules set by their users [38], finally, with the inseparable relations among human and smart objects, the pattern of Social IoT (SIoT) is gaining attractiveness in recent years [13]. The adoption of the SIoT prototype presents several benefits [4]: – the proper alignment of things’ social network once formed as per requirement will ensure network navigability for efficient discovery of physical smart objects and provide services scalability in human social network; – a level of reliability is expected for leveraging the extent of contact and exchange of information among IoT things that are friends; – models framed to assess social networks can be reclaimed to address IoT related issues in other areas, SIoT prototype haves scalability.

3.1 Social Internet of Things (SIoT) in Agriculture Agriculture is influenced by number of weather parameters, soil physical and chemical properties, insect pest occurrence and infestation, havoc of climatic condition, irrigation facilities and so many things. Out of these factors and phenomenon; most of these can be assessed in advance and can be managed judiciously to reduce crop loss and maximize yield through the Social Internet of Things (SIoT). Weather forecasts for crop pest management, is done by using SIoT on the basis of factors viz. atmospheric humidity, rainfall, crop nature and its type, soil fertility condition, leaf

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wetness and its duration, environmental temperature, direction of winds and its speed and soil moisture level. Actually, data of these parameters were collected in micro level through wireless sensors and insect and disease life cycle is monitored and predict pest outbreaks more accurately and its control and thereby SIoT support in reducing cost of cultivation, in sequel contribute in poverty alleviation and the upliftment of the living condition of people in rural areas [15]. With the advancement of Social Internet of Things in modern science, agricultural management system brought new changes to crop husbandry and high efficiency of agricultural production [55]. It is presumed that SIoT will be a real game changer in farming sector and the overall food supply chain that considerably strengthen productivity and agricultural sustainability, because it allows for [20]. • Better sensing and monitoring of crop production system, in relation to farmers resources and its use, crop development, crop harvesting and food processing; • Better knowledge on particular farming conditions and its ambience condition, emergence of disease and pests, weeds and other biotic and abiotic factors; • More sophisticated and remote application, dealing out and logistics activities by the use of actuators and robots, e.g. precise use of fertilizers and pesticides and remote control of ambient conditions during transportation of farm produces; The Social Internet of Things (SIoT) and cloud computing improves the competence of agricultural information delivery system [60] and present a mean to support farmers in managing package of practices in crop husbandry to satisfy local biodiversity needs and natural resource base limitations in the demand for food, in such a way that consider natural constraints, and which delivers system-level advantages in terms of biodiversity at different levels viz. farm, field and landscape [28]. Smart farming allows the association of crop data (i.e., crop yield, environmental factors, soil condition, irrigation facilities, and fertilisation data) and relevant data analysis results in specific crop varieties (i.e. plant genes and phenotypes). The integration of information through SIoT will revolutionize the food production system globally [24].

3.2 SIoT in Weather Monitoring Social Internet of Things (SIoT) technology had successfully used in crop husbandry in greenhouse and net house complex system which proves that the IoT as intelligent systems having more controlling intricacy as compare to local system [62] accordingly, in recent times grape ripening process effect by climate change, however, Internet of Things, combining with wireless and distributed explicit sensor devices provide better understanding of all operating parameters and provides a predictive system to carry out precision farming in vineyard and the winery [34]. The environmental factor change beyond the set threshold, IoT will automatically deliver a warning information/message to the administrator to remove the hidden danger. It can also manage ecological factors such as humidity, temperature, carbon dioxide

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concentration and illumination according to the condition of crop growth in real time [40, 63]. In solar greenhouse for the management of cumber crop, the use of IoT is gaining popularity due to use of cryogenic disaster indicator and microenvironment data analysis on real-time basis. The system act automatically when greenhouse temperature fall to threshold level and deliver SMS to people concern and triggered to start heating equipment to save the crop from cold shock. Internet of Things can swiftly analyze functional data set combined with 3rd party information, such as weather information and services, expert advisory etc., to offer new insights and strengthen decision making by farmers and expert [39].

3.3 SIoT in Crop Monitoring SIoT based technology support farmers to electronically observe soil and crop status and assess treatment options in Precision Agriculture [25] and monitor growth morphology of plant [63]. Real-time data acquisitions along with communication framework provides great opportunity to farmers and researchers of farm science through Internet of Things [56] and the SIoT intelligent production management framework can manage the plant surroundings, irrigation and fertilizer management [6]. Use of Wireless Sensor Technologies (WST) in farming provides early information and warning of equipment fault and its failure and a predictive repairing of tools, improving its energy use efficiency, processing, sensing synergy and actuation [45], Unmanned Aerial Systems (UAS) give an advance platform design, standardization of image georeferencing and mosaicing, and information extraction workflow to the farmers [61], smartphone or android phone become a useful tool in agriculture due to mobility, comparatively low cost set availability, huge computability, variety of practical applicability, apps supporting [43]. Multi-point measurement, field information collection and monitoring, instruments mobility in relation to the farming is precisely performed through IoT [10]. An experiment on Internet of Things in the vegetable greenhouses with remote supervision and computerized control and scientist remote diagnosis and consultation had strengthen its management level [59]. The rise of IoT has brought more efficient and smarter solutions to crop disease and pest control. IoT sensors can collect information pest situation anywhere in real-time, which helps farmers keep track of crop pests and diseases [1].

3.4 SIoT in Soil Properties Monitoring The Social Internet of Things, in its wireless sensor networks, can monitor soil moisture level, soil temperature of a small farms and can send a report to the farmers [27] and it can remotely monitor crop growth and take anticipatory precaution to sense crop damages and insect threats; assist farmers to carry out Smart Agriculture [39]. The Internet of things that can monitor soil pH value and rhizosphere zone

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multi parameter from distance location and can take proactive measures [63]. IoT can monitor growth morphology of plant [63]. A unique Smart IoT based Agriculture Stick support farmers in receiving real time data on temperature, soil moisture for effective environment monitoring which help farmers to do smart farming and increase overall crop yield and improve agricultural product quality.

3.5 Wireless Sensor Network (WSN) The advent of RIFD technology, printed circuit and availability of low cost material facilitated to the production of different types of WSN, those can be used in agricultural production system. WSN has several usage and advantages over conventional data collection such as: • • • • • • • •

Real time data can be collected from remote location with accuracy. Due to closed loop system, automatic decision can be provided. Larger coverage area and high temporal and spatial resolution. Drudgery in on field data collection reduced. Automatic data storage Weather condition cannot effect on data collection Sensors are small in such, easy to carry and easy to install In all types of terrains the sensors can be installed and can also report data on harsh weather condition. • Any one, from any location data can be accessed through internet • Relatively less expensive • Friendly GUI. Wifi based IoT and smart WSN grid can provide intelligent data collection, improve data collection reliability and give accurate information. IoT can provide smart environment monitoring architect for collection air parameters viz. air relative humidity, temperature, air pollutants through wireless sensors and sent data to server for further processing and action [29], CO2 concentration, temperature of substrate, soil moisture content, etc. and data collected through WSN is processed in an expert’s knowledge architect, and finally derive an best possible decision to feedback to ground equipment/instruments to act upon through actuators [31]. The wireless sensor networks were used for assessing and monitoring the farm physio-chemical conditions and accordingly micro controllers used to manage and automatic farm processes viz. irrigation, pesticide application and other activities. IoT can remotely view the crop conditions in the form of crop image and its video and it can reduce the cost of cultivation and augment the productivity of subsistence farming [49].

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3.6 SIoT in Precision Agriculture Precision agriculture which is also known as smart agriculture, use minimum resources such as fertilizer, optimum seed dose, water with the objective of maximum yield. By the wise deployment of SIoT sensor/wireless sensors and mapping fields, the farmers can observe and monitor the crop in its micro level for resource conserve and minimize the environmental impact [48]. Precision Agriculture optimizes farm production efficiency, improve quality and quantity, reduce environmental shock and minimize the use of natural resources (energy and water) on crop; although, practicing of precision agriculture is minimum in field level due to number of reasons, however, internet technologies and smart devices and its communication patterns in combination encourages for expansion of Precision Agriculture as it showed additional benefits in terms of cost and energy use [17] in reducing the labor cost expenditure. SIoT can more precisely obtain data on crops status and the environment condition to accomplish the scientific crop cultivation and wise management of the crop production equipment by the ways of automation, artificial intelligence, and remote control and to press forward in the transformation of agricultural growth in modern era in precision farming [30]. Precision farming devices with wireless connection can collect data through remote satellites and sensor at ground level and it can take into account crop status and adjust the requirement of each individual part of the crop field-for instance, by spreading extra fertilizer on areas that need more nutrients [14]. In addition, cameras mounted in IoT system can capture crop diseases and insect pests related data set in real time basis, help concern farmers find field problems and take preventive measures timely in an automated in precision farming [32] and also the use of smart cameras in IoT for process control, crop and field mapping and advanced imaging in agriculture becomes an important element of precision agriculture/farming that help in conservation of pesticides, fertilizer, and machine use time. This method moreover reduces the amount of energy consumption in terms of fuel [16]. In precision agriculture optimum use of resources is the basic principle for profit maximization. There are numerous factors those individually and in complex association influence in crop growth and economic yield. These factors may be broadly categorize into followings: Atmospheric Factors: Temperature (maximum, minimum and average) sunlight intensity, day length, humidity, rainfall, cold wave, fog, dew, wind velocity and its direction, leaf wetness and its duration. Soil Factors: Soil moisture level, plant nutrient status in soil and its availability (major and micronutrient), soil temperature, soil pH. Plant Pests and Diseases: Insect pests, disease, nematodes and others factors, weeds. However, monitoring all those factors individually is time consuming, cumbersome and costly. Although with the help of wireless sensor, most of the aforesaid factors/parameters can be measured, digitized and in combination can be used for safe guarding the crops.

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Fig. 1 Factors influencing plant growth and corresponding sensors used

In the Fig. 1 a comprehensive diagram is presented on factors contributes in plant growth and sensors can be used for monitoring those factors.

3.7 Sensor in Smart Agriculture There are different types of sensor may be used in farming optical sensors (Hyperspectral, Multispectral, Fluorescence and thermal sensing),crop health monitoring sensor, Airborne sensors (UAV), Yield estimation and prediction sensor, crops and weeds detection sensor, Sensors for fruit quality determination, Sensors for positioning, navigation, and obstacle detection. Tegio [52] Sensor Applications in Agriculture. Some of the sensors used in agriculture are as follows: Optical sensors—these sensors measure the type and intensity of the reflected light wavelengths to evaluate crop and soil conditions. The reflected green light wavelength can be used to measure chlorophyll in leaves and evaluate conditions causing the reduction in green color such as nitrogen status, sulfur and iron deficiency. For predicting organic matter, clay particle and moisture content of the soil, the optical sensors are used. Mechanical sensors—these sensors measure mechanical resistance of soil, often associated to soil compaction level. Compacted soil, which can be caused by the

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heavy weight of field equipment or just the natural soil forming processes, can lead to soil degradation and affect crop production negatively. Electromagnetic sensors—due to low cost, high durability and rapid response, these sensors are commonly used for on-the-go soil mapping. Electromagnetic properties of soil are measured by its capability to conduct or accumulate electrical charge and it is influenced by soil texture, organic matter content or total carbon content, availability of moisture, salinity level, residual nitrate content and other soil attributes. Electrochemical sensors—these sensors were successfully used to analyse soil fertile, by measuring the soil’s chemistry through test such as nutrient content and pH level. Two commonly used electrochemical sensors are ion-selective electrodes (ISE) and ion- selective field effect transistor (ISFET). The sensors measure the selected ions reaction (K+ , H+ , Na+ , etc.) in the soil as well as the absorption of these ions by the plants/crops. By monitoring, the concentrations of ion in crops, helps farmers to design fertilization application strategies that optimize production and reduce use of fertilizers. By positioning sensors in different location of the crop field, farmers can assess crops condition in micro scale, sustain use of resources, and reduce detrimental environmental impact. Smart agriculture started its journey, since 1980s, when the use of GPS became open to civilian. Accordingly, the farmers were cable to precisely map their crops and fields, and also monitor and apply manure and fertilizer and treatments of weed accurately to areas that need it. New smart sensor and IoT technology let farmers to remotely monitor the fields’ pest infestation occurrence and its population growth in real-time basis and take instant action to guard the crops, and also utilizing online cloud services and a dashboard for managing the data set [11]. In IoT-oriented smart farming, the architect is developed for monitoring the crop conditions with the support of sensors (the parameters monitors are light density, humidity, environmental temperature, soil moisture content etc.) and automating the management system through actuators and associated devices/physical smart object. Smart farming based on IoT is very efficient as compared to conventional approach.

4 Overview of Technology Behind SIoT The important SIoT technologies are RFID technology, sensor technology, networking of sensor technology and networking of sensor for seamless communication. The SIoT technology worked on interrelated four links namely, identification, sensing, processing and information transfer [21]. Internet of Things (IoT) based Ubiquitous Sensor Network Platform in green house crop production in hydroponic showed that internet technologies in combination with Smart Object Communication Patterns can encourage to develop Precision Agriculture system wherein benefits in cost in terms of less use of energy and inputs, however, per unit productivity is high [17]. The embedded devices used in this project:

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(a) RaspberryPi (ARM Cortex-A7 CPU 900 MHz Memory 1 GB) with programming Linux C/C++ and Python APIs libraries (b) Photon IoT (ARM Cortex-M3 CPU 120 MHz 1 MB flash, 128 KB RAM) with C language APIs libraries (c) SmartPhone (SoC m Processor CPU _ 900 MHz Memory _ 1 GB) with Android/iOS Objective C Java libraries (d) WIFI router (SoC m Processor) (e) Cloud-server Shahzadi et al. [46] used IoT based Smart Agriculture Expert System and used XBee-802.15.4 for communication module. Lin et al. [30] used WSNs by means of Bluetooth, 2.4 GHz, Zigbee, GSM, and Wi-Fi in the development of a Precise Agricultural Information System Based on IoT and that can be used in farmlands, warehouses, and greenhouses. Nayyar and Puri [36] used Arduino Mega 2560, ESP8266 Wi-Fi Module, Temperature Sensor-DS18B20, Moisture Sensor for soil in live temperature and moisture monitoring. Jayaraman et al. [24] used SmartFarmNet gateway (X-GSN), (JAVA semantic sensor stream processor based on Java. Arduino and ArduCrop sensor wrappers to interface with IoT devices); Cloud Data Store (LSM-Light) (LSM-Light developed using JAVA and Open Virtuoso triple store); Sensor Explorer (Java applications deployed in JBOSS); Reasoner Service(Apache Jena supported by SmartFarmNet OWL ontology); Data Analytics(Redis) in for Smart Farming. Sakthipriya [47] used MPR2400CA radio platform which is based on the AtmelATmega128L, 2.4 GHz MICAz mote, MDA300CA sensor node and atmospheric pressure sensor MPX4115A in Crop Monitoring Using Wireless Sensor Network. Brewster et al. [9] in Europe-wide large-scale IoT pilot utilised objects/sensors include water, soil and air monitoring in traditional micro-meteorological stations; soil sensors, and near and remote field crop sensors (NDVI, near-infra-red spectroscopy, hyperspectral images); water supply sensors, soil moisture sensor; leaf wetness and nutrients sensors; delivery points sensor, hydrant remote controls, water pumping stations, raft sensors, and irrigation systems with hydrometers). Agri-machinery monitoring (e.g., RTK-DGPS for accurate vehicle guidance, onboard sensors, VIS-NIR spectrometer), pheromone traps monitor and pest detection based on sensor).

5 Social Internet of Things (SIoT) Architectural Framework for Agricultural Domain In this section we propose a suitable SIoT based system for Smart Agriculture—a revolution in the agriculture industry that helps to guide actions required to modify and reorient agricultural systems to effectively support the development and guarantee food security during an ever-changing climate. Use of technology is increasing in the agricultural sector and is much needed for solving global food supply problem as food is one of the basic necessity of every human being.

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Fig. 2 Block diagram of social Internet of Things (SIoT) in SMART agriculture

Figure 2 presents a block diagram of a typical modern agriculture system which makes use of various electronic devices such as mobile phones, smart phones, cameras, sensors, computers etc. for capturing & generating data regarding field, crop, climate etc. Farmer activity log is silently and effectively monitored by these devices and record keep on getting updated and stored in the Cloud-based storage systems. An expert system is also required for sorting out queries from the farmers and providing them with scientific remedial guidelines to increase their farm produce. They may query through mail, call or app or any other mechanism devised for the purpose and get timely remedy for their crop & field related problems. This is how we can have social objects in the form of individual farmer inputs in real time and they can help build a social network for SIoT. So, Social IoT is going to be a major player in solving food deficiency problem across the world by helping farmers to have more produce from lesser available agricultural land (include concepts of vertical farming, rooftop farming etc.) and we will see an exciting array of technological innovations to make all these a reality.

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Fig. 3 Three layered system architecture of SIoT enablers for SMART agriculture

Figure 3 is all about a three layered approach imbibing the SIoT enablers for the purpose of Smart Agriculture. In a SIoT based system for smart agriculture we can think of three separate layers for specific purpose. These layers are hidden from the normal end users and are an abstraction, but show clearly the close linkage among different components both inter and intra; within an individual layer or across the layers. Application layer is just the external interface for the users to interact with the system (select, insert, delete and update data) from the cloud storage which is a part of the next layer which is the Network Layer. Apart from this the application layer consists of smart farm management, network of smart devices etc. The basic functionality of this layer is to provide seamless interpretation & analysis of farm data after data capture and thorough data analysis to the farmers. It communicates with the data procurement and analysis team for the real time data. The second layer in hierarchy is the Network Layer consisting of the Cloud Infrastructure & Services, high speed Internet, Mobile Communication and GPS network for data storage, compute and transmission purpose. At the bottom sits the Sensing Layer which consists of various sensors, RFID readers and other components deployed on the field providing real time data related to field and the plant. All these layers work in total synchronization with each other to produce a system involving many devices & things. We conclude with a summarization of all the discussions throughout the chapter in our next section.

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6 Conclusion World agricultural sector although faces the challenges to feed a growing world population but with the help of modern technological development and use of Social IoT in agriculture the problem can be well tackled increasing farm produce utilizing the scientific means & methods. Even though the use of Internet of Things (SIoT) in farming is still in its infancy for supporting farmers, although it has huge potentiality to increase the income of the farmers and judicious use of input resources for farming and sustainable agriculture. The scope of SIoT in agriculture are as followsi. Social Internet of things can collect data, process data and exchange data over a designed network platform without mechanical interface of human-to-human or human-to computer interaction i.e. it is an automated system. ii. The fundamental characteristics of IoT are seamless connectivity, accuracy of delivery of services, dynamic response, enormous scale, safety and security, promptness, mutual support, sensing and expressing and those can be used in agriculture. iii. SIoT can immensely contribute in Agricultural extension i.e. technology transfer and social networks/medias. iv. Environmental parameters (temperature, humidity, precipitation, dew, wind flow and its direction), soil parameters (soil moisture, soil temperature, nutrient contents, EC, pH) and plant factors(canopy, leaf wetness, insect pest infestation) can be identify, capture, processed and necessary decision can be automated through the SIoT for increase income of farmers and reduce drudgery in farming. v. SIoT can monitor and control environmental parameters viz. temperature, humidity, carbon dioxide concentration in control condition specially greenhouse, net house, hydroponics and in vertical farming. vi. The use of Wireless Sensor Technologies (WST) in agriculture provides early warning in equipment fault and failure and their maintenance, improving efficiency of energy management, synergy of different sensing, processing of data, communication of automated decision and actuation. vii. Precision farming tools with wireless support can collect data through remote satellites and sense crops status at ground level and adjust the need of each individual part of the crop field and act on spreading extra fertilizer on areas that need more nutrients. viii. There are different types of sensor may be used in farming optical sensors(Hyperspectral, Multispectral, Fluorescence and thermal sensing),crop health monitoring sensor, Airborne sensors (UAV), Yield estimation and prediction sensor, crops and weeds detection sensor, sensors for fruit quality determination, Sensors for positioning, navigation, and obstacle detection. ix. By placing sensors, the crops at micro level can be monitored by farmers from remote location by using SIoT by sensing humidity, light, temperature, soil moisture, etc. and automating the irrigation and other functional aspects.

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x. With the Social Internet of Things (IoT), a single farmer can deliver his farm produce directly to the consumers. This chapter has presented a literary review of the use of SIoT in agricultural sector for improving the farm produce, using crowdsourcing in building social objects and network, a typical architectural framework on SIoT in agriculture and concludes with a summarization of the discussions made throughout the chapter, highlighting the future developmental trends in the domain.

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