Towards Cognitive IoT Networks (Internet of Things) [1 ed.] 303042572X, 9783030425722

Table of contents :
Acknowledgements
Contents
Introduction
1 Introduction
2 Outline of Contributions
References
Current Research Trends on Cognitive Radio Based Internet of Things (IoT)
1 Introduction
2 Research Trends on Cognitive Radio Based IoT (CR-IoT)
2.1 Cognitive Radio Based IoT Architecture
2.2 Spectrum Sensing and Sharing for CR-IoT
2.3 Adaptation of Transmission Parameter in CR-IoT
2.4 Energy Management in CR-IoT
2.5 Security and Reliability for CR-IoT
3 Research Challenges in CR-IoT
4 Recommendations for Future Research in CR-IoT
5 Conclusion
References
Cognition Radio Enabled IoT
1 Introduction
2 Major Functionalities and Challenges with IoT
3 Major Functionalities and Challenges with Cognitive Radio
4 IoT and CR Frameworks
5 CR Enabled IoT
6 Conclusion
References
SDN-Enabled IoT: Ensuring Reliability in IoT Networks Through Software Defined Networks
1 Introduction
2 Related Work
2.1 Failure Recovery Mechanisms
2.2 Failure Recovery in SDN
3 Proposed Solutions for Link and Node Recovery
4 Link Recovery Solution for SDN-Enabled IoT
4.1 CoD Recovery Approach
4.2 LIm Recovery Approach
4.3 ICoD Mechanism
4.4 Performance Evaluation
5 Rapid and Robust Node Recovery of Data Traffic in SDN-Enabled IoT Networks
5.1 Reactive Approach
5.2 Proactive Approach
5.3 Hybrid Approach
5.4 Performance Evaluation
6 Conclusion
References
QoS Aware Spectrum Selection for IoT
1 Introduction
2 Related Works
3 QoS Aware Spectrum Allocation
3.1 QoS Requirement Model
3.2 Spectrum Capability Model
3.3 QoS Aware Spectrum Selection
4 Evaluation
5 Conclusion
References
Cognitive M2M Communications: Enablers for IoT
1 Introduction
2 Overview of Cognitive M2M Communications
2.1 MAC Layer Protocols of Cognitive M2M
2.2 Advantages of Cognitive M2M
3 Cognitive M2M as Enabler for IoT
4 Applications of Cognitive M2M Communication
4.1 Home Multimedia Distribution and Sharing
4.2 Intelligent Transportation Systems
4.3 eHealthcare
4.4 Environmental Monitoring
4.5 Smart Grid
5 Conclusion
References
Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making
1 Introduction
2 Problem Formulation
2.1 Cognitive Radio Engine Objectives
2.2 Best Compromised Solution
3 Monarch Butterfly Optimization
3.1 Migration Operator
3.2 Butterfly Adjusting Operator
3.3 GCMBO
4 Numerical Results
4.1 Best Compromised Solutions
5 Conclusion
References
Physical Layer Security of Cognitive IoT Networks
1 Introduction
2 Evolution of Cognitive IoT Networks
3 Security Requirements
3.1 Secrecy
3.2 Authentication
3.3 Data Integrity Awareness
3.4 Robustness
4 Attack Types
4.1 Physical Attacks
4.2 Attacks on Compromise of Credentials
4.3 Networking Attacks
5 Attacks on the Cognitive Access
5.1 Impact of the Cognitive Access
5.2 Primary User Emulation Attacks
5.3 Sensing Data Falsification Attacks
5.4 Objective Function Attacks
5.5 Jamming Attacks
5.6 Eavesdropping Attacks in CIoT
6 Case Study
6.1 Network Model
6.2 Trust Calculation
6.3 Beamforming-Based Attack Prevention
7 Conclusions and Open Issues
References
Internet of Energy Harvesting Cognitive Radios
1 Introduction
2 Background
2.1 Requirements of Big Data-Enabled IoT
2.2 Cognitive Radio Communications for Big Data-Enabled IoT
2.3 Energy Harvesting Communications for Big Data-Enabled IoT
3 Motivations for Using Energy Harvesting Cognitive Radios in Big Data-Enabled IoT
3.1 Motivations for Spectrum-Aware Communications
3.2 Motivations for Self-sustaining Communications
4 Internet of Energy Harvesting Cognitive Radios
4.1 Energy Harvesting Methods in Big Data-Enabled IoT
4.2 Applications of Cognitive Radio in Big Data-Enabled IoT
4.3 Challenges Posed by Internet of Energy Harvesting Cognitive Radios
4.4 Integration of Energy Harvesting Cognitive Radios with the Internet
5 General Framework of IoEH-CRs for Big Data Applications
5.1 Operation Overview
5.2 Node Architecture
5.3 Network Architecture
5.4 Discussion on Existing and Future Applications of IoEH-CRs for Big Data
6 Conclusion
References
Cultural IoT Framework Focusing on Interactive and Personalized Museum Sightseeing
1 Introduction
2 IoT Interactive Museums
3 Related Work on Cognitive IoT Museum Services
4 Related Work on System Architecture of Smart Museums
5 IRME Framework Services and Protocols
6 IRME Communication Protocols and Services Interoperability
6.1 Data Exchange Protocol of the Thematic Tour and Interactive Services
6.2 Navigation Service Protocol
6.3 Cognitive Service Protocol
6.4 Enhanced, Augmented Projection Protocol
6.5 Haptic Protocol
6.6 3D Modeling and Printing Protocol
7 Conclusions
References

Citation preview

Internet of Things

Mohammad Abdul Matin   Editor

Towards Cognitive IoT Networks

Internet of Things Technology, Communications and Computing

Series Editors Giancarlo Fortino, Rende (CS), Italy Antonio Liotta, Edinburgh Napier University, School of Computing, Edinburgh, UK

The series Internet of Things - Technologies, Communications and Computing publishes new developments and advances in the various areas of the different facets of the Internet of Things. The intent is to cover technology (smart devices, wireless sensors, systems), communications (networks and protocols) and computing (theory, middleware and applications) of the Internet of Things, as embedded in the fields of engineering, computer science, life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in the Internet of Things research and development area, spanning the areas of wireless sensor networks, autonomic networking, network protocol, agent-based computing, artificial intelligence, self organizing systems, multi-sensor data fusion, smart objects, and hybrid intelligent systems. Internet of Things is covered by Scopus.

More information about this series at http://www.springer.com/series/11636

Mohammad Abdul Matin Editor

Towards Cognitive IoT Networks

123

Editor Mohammad Abdul Matin North South University Dhaka, Bangladesh

ISSN 2199-1073 ISSN 2199-1081 (electronic) Internet of Things ISBN 978-3-030-42572-2 ISBN 978-3-030-42573-9 (eBook) https://doi.org/10.1007/978-3-030-42573-9 © 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

Acknowledgements

First and foremost, I am very much grateful to my Lord Almighty ALLAH who helped and guided me throughout my life and made it possible. I should thank all authors including those whose manuscripts were not included due to rigorous review-based selection process. I would like to mention here that all the contributing authors were so cooperative and kept their patience during different stages of the book development process. I must thank Professor Giancarlo Fortino and Professor Antonio Liotta for their assistance. I am forever indebted to Dr. Christoph Baumann, Mary James and the Springer production team for facilitating its publication. I would also like to thank my wife Momtaz Begum and my children Zabeer Ahmed and Zawad Ahmed for their patience and support. My special thanks to Mrs. Sufia Khaton (dearest mother) for the motivation that has encouraged me to keep going.

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Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Abdul Matin Current Research Trends on Cognitive Radio Based Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Rezwanul Mahmood and Mohammad Abdul Matin Cognition Radio Enabled IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Md. Mahfuzur Rahman and Mohammad Abdul Matin SDN-Enabled IoT: Ensuring Reliability in IoT Networks Through Software Defined Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pankaj Thorat, Sukhdeep Singh, Avinash Bhat, V. Lakshmi Narasimhan and Gaurav Jain

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QoS Aware Spectrum Selection for IoT . . . . . . . . . . . . . . . . . . . . . . . . . Md. Mahfuzur Rahman and Mohammad Abdul Matin

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Cognitive M2M Communications: Enablers for IoT . . . . . . . . . . . . . . . Rezwana Ahmed and Mohammad Abdul Matin

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Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making . . . . . . . . . . . . . . . . . . . . . . . Sotirios K. Goudos

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Physical Layer Security of Cognitive IoT Networks . . . . . . . . . . . . . . . . 101 Güneş Karabulut Kurt and Özge Cepheli Internet of Energy Harvesting Cognitive Radios . . . . . . . . . . . . . . . . . . 125 O. Cetinkaya, M. Ozger and O. B. Akan Cultural IoT Framework Focusing on Interactive and Personalized Museum Sightseeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Sotirios Kontogiannis, George Kokkonis, Ioannis Kazanidis, Michael Dossis and Stavros Valsamidis

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Introduction Mohammad Abdul Matin

1 Introduction The Internet of Things (IoT) [1] is the rapid advancement of the current Internet that has been transformed from the tradition human interaction into a network of interconnected devices. Due to explosion in the IoT devices networked based on wireless transmission, spectral efficient solutions needs to be accommodated efficiently to enhance connectivity and operational efficiency among large number of heterogeneous devices intelligently and autonomously [2]. Cognitive Radio is the best candidate technology for such intelligent use of spectrum [3]. Therefore, current technology trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT to support devices ranging from manufacturing, to health care, to buildings, and so on. The Cognitive Internet of Things (CIoT) will empower the current IoT with a ‘brain’ for high level intelligence [4, 5]. CIoT can learn current network conditions, analyze the perceived knowledge, make intelligent decisions, and perform adaptive actions, which aim to maximize network performance [6]. This creates opportunities to the physical world as well as digital world with new, disruptive business models which motivates researchers to focus intense research on Cognitive Internet of Things (CIoT). To facilitate general readers, this book presents a brief summary of current research on Cognitive radio (CR) based IoT in tabular form, where the key research focus and the useful reference for each topic are given. In a manner suitable to a broad range of readers, the authors provide state-of-the-art information that enables readers to design CIoT systems focusing on CR enabled IoT framework, QoS requirements for IoT applications, existing and emerging solutions to the design for CR engine, network architecture, energy harvesting, M2M connectivity, network security and explores recent research outcomes relevant to the CIoT system developments. Although the M. A. Matin (B) North South University, Dhaka, Bangladesh e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_1

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existing technologies and systems can serve as good starting point for the research on CIoT networks, there are still many issues open for solutions. However, the emergence of this new technology depends on the maturity of several technologies including the internet, wireless communications, sensors, big data and machine learning algorithms. The focus of this book is to recognize and discuss on how to nurture and cultivate CIoT technologies and applications.

2 Outline of Contributions This book attempts to explore cutting-edge developments of IoT technology and offer perspective, insights and their impact on modern society. Chapter “Current Research Trends on Cognitive Radio Based Internet of Things (IoT)” surveys current research progress on cognitive radio based IoT networks. Some design issues and potential improvement for CR-IoT network are also been discussed. This chapter also provides current research challenges for further improvement of CR-IoT technology and probable future research direction in CR-IoT domain. Chapter “Cognition Radio Enabled IoT” provides the functional similarities between IoT and CR, and the challenges that are important to be addressed to integrate CR technology for IoT and outlines the related benefits of such integration. A framework for cognitive radio enabled IoT has also been provided. Chapter “SDN-enabled IoT: Ensuring Reliability in IoT Networks through Software Defined Networks” focuses the issues related to failure management in software defined IoT networks. It proposes for-warding table configuration in network that can autonomously recover an Open Flow-based IoT network from a link or a node failure to preserve the network. The presented work is based on the observation that preconfiguration of the network for local failure detection and rerouting is essential to accomplish the rapid recovery. This chapter adopts preconfiguration of the flow rules for reducing the recovery time. However, this puts the strain on the limited available switch memory and overburden the software defined networks controller (SDNC) for rerouting all the discontinued flow. Toward this, the chapter focuses on and proposes forwarding table designs for link recovery, multi-link recovery, and joint link and node recovery. To address the deficiencies of existing recovery schemes, the presented solutions employ flow aggregation and grouping strategies to accomplish faster and scalable failure handling in SDN-enabled IoT networks. Chapter “QoS aware Spectrum Selection for IoT” introduces a spectrum selection mechanism that can be employed by IoT devices to meet the QoS requirements of IoT applications. The proper understanding of the QoS requirements is required in selecting and allocating appropriate spectrum resources for IoT applications. Chapter “Cognitive M2M Communications: Enablers for IoT” focuses on cognitive M2M communications and its potential as an enabler for IoT. An overview of

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cognitive M2M communications has been discussed along with its advantages and possible applications as a means to identify ways that can be applied to existing IoT networks to tackle the current issue of increasing spectrum congestion due to the significant rise in the number of devices in wireless networks, thus fulfilling the vision of IoT. Chapter “Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making” suggests a new scheme for designing a CR engine for wireless multicarrier transceivers using monarch butterfly optimization (MBO). A modified MBO version is also applied for the optimization of CR engine which includes a Greedy strategy and a self-adaptive Crossover operator, called Greedy Crossover MBO (GCMBO) with a fuzzy decision maker. Simulation results show that the GCBMO algorithm outperforms the original MBO and other popular algorithms. Chapter “Physical Layer Security of Cognitive IoT Networks, Gunes Karabulut Kurt, Ozge Cepheli” provides an overview of the main security requirements and summarizes the main attack types on the CIoT nodes including primary user emulation attacks, sensing data falsification attacks, objective function attacks and eavesdropping attacks. A case study targeting primary user emulation and sensing data falsification attacks based on a trust metric is presented. Chapter “Internet of Energy Harvesting Cognitive Radios” highlights the combination of two promising technologies Energy Harvesting (EH) and Cognitive Radio (CR) in the IoT domain. It outlines a new vision, namely Internet of Energy Harvesting Cognitive Radios (IoEH-CRs), to take IoT-enabled Big Data paradigm a step further. It discusses the basics of the EH-assisted spectrum-aware communications and their implications for the IoT, in addition to the challenges posed by the potential unification of these techniques. The key issues realizing the IoEH-CRs are energy scavenging techniques with efficient spectrum management protocols in the context of the IoT. A general operational framework for the IoEH-CRs, together with node and network architectures, is also presented. Chapter “Cultural IoT framework focusing on interactive and personalized museum sightseeing” presents an open source IoT Interactive Museum Experience (IRME) framework. IRME provides a real-time, responsive and personalized navigation to museum visitors. It includes indoor positioning, IoT sensors and actuators, haptic devices orchestrated over cloud services. This framework offers automated guidance based on visitors preference, AI visitor—smart bot interaction, visitors cognitive sense analysis and augmented reality exhibition of the museum artifacts via actuators. It is hoped that this book will serve as a useful resource for readers who want to know the current challenges and opportunities for research and innovation in the era of cognitive IoT (CIoT), as well as applications development and deployment for various domains.

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References 1. Fortino, G., Trunfio, P.: Internet of things based on smart objects: technology, middleware and applications. Springer (2014) 2. Liotta, A.: The cognitive net is coming. IEEE Spectr. 50(8), 26–31 (2013) 3. Matin, M.A.: Spectrum Access and Management for Cognitive Radio Networks. Springer (2017) 4. Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., Long, K.: Cognitive internet of things: a new paradigm beyond connection. IEEE Internet Things J. 1(2), 129–143 (2014) 5. Zhang, Y., Chen, M., Leung, V.C.M., Xing, T., Fortino, G.: Guest editorial special issue on cognitive internet of things. IEEE Internet Things J. 5(4), 2259–2262 (2018) 6. Zhang, M., Zhao, H., Zheng, R., Wu, Q., Wei, W.: Cognitive internet of things: concepts and application example. Int. J. Comput. Sci. Issues (IJCSI) 9(6), 151 (2012)

Current Research Trends on Cognitive Radio Based Internet of Things (IoT) M. Rezwanul Mahmood and Mohammad Abdul Matin

Abstract The attractive features of Internet of Things (IoT) and the concept of cognitive radio have raised the opportunity of creating a smart world. The advancement of cost-effective technologies and protocols empower us to make practical implementation of IoT which impact on human lifestyle, business and industries. Research interest has thus been dragged into the IoT domain to exploit its potential. However, the increased number of devices have caused the spectrum crisis issue. To mitigate this crisis, Cognitive Radio (CR) technology is integrated with IoT that can search for the available spectrum and reuse it for communication. By using cognitive capabilities, cognitive radio can avoid collision among the network elements to ensure better connectivity, accessibility, scalability and reliability of the IoT system. Currently, the research on CR-IoT is at its early stage. This chapter attempts to focus on the recent research efforts related to spectrum sensing, sharing and allocation, cost-effective architectures, transmission parameter adaptation, energy efficient proposals and security provisioning problems for CR-IoT. Some design issues in CR-IoT system are also being discussed in this chapter.

1 Introduction The term “Internet of Things” (IoT) was first introduced as a title of a presentation made by Kevin Ashton in 1999 [1]. Any physical objects enabled with IoT can communicate with each other without human intervention and share information and coordinate decisions among themselves. The transformation of these objects or devices from being conventional to smart is possible by the converging technologies such as ubiquitous and pervasive computing, embedded devices, communication technologies, sensor networks, internet protocols and applications [2]. These features in IoT create significant opportunities for home, medical, industrial and infrastrucM. Rezwanul Mahmood (B) · M. A. Matin North South University, Dhaka 1229, Bangladesh e-mail: [email protected] M. A. Matin e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_2

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ture applications, for instance, home automation for safety and security purposes, intelligent traffic system, healthcare services for remote locations, smart grids, smart city etc. Thus numerous heterogeneous objects are expected to connect to the internet. The need for promising technologies and architectures will rise eventually with time and increased number of IoT devices. It will also be a requirement to merge different technologies into IoT for the exploitation of its potentials. The implementation of the concept of IoT is feasible in real world with the help of low priced and less power consumption devices such as sensors, actuators, Radio Frequency Identification (RFID) tags etc. [3]. Advanced enabling technologies and protocols such as Machine to Machine (M2M), Wireless Sensor Network (WSN), RFID, Cognitive Radio (CR), Internet Protocol version 6 (IPv6), IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), Routing Protocol for Low-Power and Lossy Networks (RPL) and Constrained Application Protocol (CoAP) are also responsible for IoT implementations and applications [3]. However, Wi-Fi, 3G/4G mobile networks, Bluetooth and other technologies uses the unlicensed Industrial, Scientific and Medical (ISM) radio bands that are on the way to be crowded due to ever increasing IoT objects. To address the shortage of spectrum resources, Cognitive Radio (CR) can be a potential technology. It was first termed and described by Joseph Mitola [4, 5]. Simon Haykin also defined cognitive radio in one of his research works [6]. It can be said that cognitive radio is a self-aware (of its surroundings) and intelligent communication system that can adaptively change its operating parameters (transmission power, carrier frequency, modulation energy etc.) and efficiently utilize radio spectrum and all this characteristics can be referred as cognitive capabilities [7]. Applying CR technology in IoT, the sensor nodes can reuse the spectrum and avoid collision. Besides, user IoT device(s) will have greater accessibility to many networks and services with the help of CR features and the system will be scalable. The switching within cellular network and Wi-Fi networks will make CR-IoT devices more user-friendly. The ability of cognitive radio to establish effective communication among multi-terminal or multi-frequency devices is expected to ensure better connectivity among the IoT objects. The self-configuration characteristic of CR network can make the IoT wireless system more robust and reliable [3]. In recent years, a number of research efforts have been carried out to explore the advantages of cognitive radio applications in IoT domain. This chapter focus on the research topics related to CR-IoT. The publications considered for this study are published by Springer, Elsevier and IEEE. The research trends provide a general idea of how the cost and energy consumption issues in spectrum sensing, increasing decision variables for system involving increasing number of transceivers, channel selection and allocation in spectrum congested condition, security and reliability for IoT system have been addressed. Table 1 maps the research publications for CRIoT published by the previously mentioned publishers and the research areas are also included in the same table. In this chapter, the current research efforts on CR-IoT have been identified for potential improvement and to guide future research directions.

Current Research Trends on Cognitive Radio …

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Table 1 Research publications on cognitive radio based Internet of Things Research focus Publishers Year and reference 2014 2015 2016 2017 CR-IoT architecture Spectrum sensing and sharing for CR-IoT

Adaptation of transmission parameter in CR-IoT Energy management in CR-IoT

Springer IEEE Elsevier

[9]

Springer IEEE Springer

[20]

[8] [2, 10]

[21]

[11] [12, 13] [18] [22]

IEEE Elsevier

[19] [23–25] [31]

2019

[14–16]

[17]

[26–29]

[30] [32]

[33, 34] [35]

IEEE Security and reliability for CR-IoT

2018

IEEE

[40]

Korea Society of Internet Information (KSII)

[45]

[41]

[36, 37] [38, 39] [42, 43] [44]

2 Research Trends on Cognitive Radio Based IoT (CR-IoT) There has been several research on CR-IoT in the last few years. The main focus of their research studies are highlighted in this section.

2.1 Cognitive Radio Based IoT Architecture A few research attempts have been made to survey IoT architecture employing cognitive radio. A CR-IoT framework has been provided in [11] where the cognitive functions are present at all levels. A probable solution for large volume of data and energy efficiency issues are also mentioned. A three dimensional architecture model for IoT has also been proposed in [10] where the cognitive functionalities of IoT have been explained in detail. In [9], the CIoT architecture has been categorized into physical world, cyber world and social world. Some architecture models have been surveyed in [2] and five-layered CIoT architecture model has been discussed. A cloud enabled CIoT architecture has been suggested in [8] with the help of a CIoT engine. In this chapter, a general form of CR-IoT architecture has been presented by exploring the cognitive features of CR in IoT systems.

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Fig. 1 A general CR-IoT framework

The CR-IoT architecture can be represented with the following five componentswhich are physical objects, communication, data management and decision making, user interface and system management. Each block of the architecture is briefly discussed in this section (Fig. 1). • Physical objects: These contain all the sensors and devices present in the CR-IoT networks. Each object performs its own operation to collect data and prepares to transfer information for data processing. • Communication: The devices are connected with each other through CR enabled nodes. The CR nodes sense the unoccupied available spectrum, select the most appropriate channels and transfer data from devices to data processing and decision making and vice versa. CRs also coordinate the spectrum sharing among themselves and vacate the selected channels due to the arrival of PUs. They can also change the transmission parameters to obtain optimum network performance. • Data management and decision making: The raw and unprocessed collected data from sensors and devices are transferred to this component for processing to make data useful for analysis. After analyzing the data, required action is decided and decision(s) is (are) transferred all over the system for execution. • User interface: This connects the user(s) with the CR-IoT system. Request and reception of service(s) can be done by the user. The observation of the real world can be realized and the corresponding action can be seen with the help of this block. • System management: This ensures the overall CR-IoT system performances. It monitors interconnection among the blocks, quality of service, energy consumption and system security.

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2.2 Spectrum Sensing and Sharing for CR-IoT Spectrum sensing and sharing are the main functional components of CR based IoT system. On the basis of spectrum bandwidth of interest, spectrum sensing techniques can be categorized into Narrow-band and Wide-band sensing approaches. The other approaches are Cooperative and Non-cooperative sensing which are based on decision making after observing PU activity. The techniques have been described for CR-IoT applications in [15, 46]. In a CR-IoT system, the utilization of radio spectrum needs to be analyzed to determine the capacity of supportive IoT device communication. A method to find out the underutilized FM radio spectrum in an urban area have been discovered in [21] so that low powered and short ranged CR-IoT devices can utilize the unoccupied spectrum. To ensure cooperative spectrum sensing and sharing, a concept named ‘reciprocal fairness’ and a game theoretical tool is used in [13]. CR is used in a system comprising 4G with WiFi/IoT to achieve a radio communication regulatory requirement and improve its existence in the system without interference [19]. This study also has considered the importance of spectrum utilization by secondary user for the future IoT communications. In [12], the authors have suggested a convergent solution where an experimentally implemented low-cost cooperative spectrum sensing solution is proposed and a distributed software-controlled sector aware spectrum sensing architecture is presented. The proposed scheme is then integrated to a future internet architecture named “NovaGenesis”. For Internet of Vehicles (IoVs), a novel CRAVNET framework is proposed to allow the CR assisted vehicles to use the assigned licensed spectrum bands for vehicular communication [28]. A cooperative three-state spectrum sensing and allocation solution is developed where the secondary users can respond to additional spectrum resources opportunities on the basis of their positions and employs optimal sensing node allocation algorithm which assures that the available channels have been timely acquired within short period of time. The utilization of Social IoT in CR has been proposed in [47] to precisely realize the condition of the channel and implement solution corresponding to the spectrum problems. A normalized weighted energy detector is proposed by the authors in [25] to detect the randomly arrived PU. The detection probability formula has been approximately derived by means of Gaussian approximation technique and a solution has been determined by steepest descent algorithm. A method of spectrum detection using cyclostationary technique have been described in [24] for IoT applications in SigFox (a communication system using ultra-narrow band (UNB) for IoT devices) operated ISM bands, carried out noise floor measurements and provided an insight about the application of the SDR (Software Defined Radio) in IoT. The study conducted in [29] shows that spectrum utilization efficiency can be improved by using their proposed solution incorporating enhanced Jaya algorithm, named S-Jaya, and the total transmission rate with faster convergence speed can be maximized. Considering the spectrum allocation problem in terms of spectrum utilization and network throughput in CR-IoT, a concurrent transmission model is

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proposed by the authors in [26], an allocation plan is formulated on the basis of this model and Non-Dominated sorting Genetic Algorithm-II [48] is applied. For effective channel selection, an opportunistic MAC (Medium Access Control) protocol for a CRASN (Cognitive Radio Ad-hoc Sensor Network) is proposed [16] and compared to the conventional IEEE 802.11 MAC protocol with the help of existing channel selection schemes. For most of the proposed MAC protocols, the implementation of multi platform software is unavailable. Besides, the heterogeneous behavior of the MAC platform is also noticeable while merging multiple platforms in a defined network. So, the authors have described the issues in developing multiplatform MAC protocol in [17], and propose a methodology to avoid the issues due to the merging of multi platforms. In a CRASN, the users do not have common control channel and thus creates complexity in the discovery of neighbor. So, the proposed scheme in [18] involves expedition of the rate of discovery by a node by choosing available channels for hopping and assigning the channels for the node while working in a group to explore different channels at the same time by different nodes for discovering new neighbors. Addressing the adjacent channel interference (ACI) problem, in [30] the authors have developed a channel assignment mechanism with guard band awareness for CR-IoT devices in a time varying channel. A channel access scheme has been proposed in [22] to access the multiple channels in unfamiliar environment and maximize system throughput. The scheme has also included a fair channel grouping system to ensure fairness among the cognitive users of WSN (Wireless Sensor network). The channel is divided into a certain number of groups by following water filling principle and then channel group is allocated for each cognitive user by using distributed learning algorithm fairly. The strategy, proposed in [20] for channel assignment in IoT networks, has used opportunity spectrum access through cognitive radios. Traffic history is used to guide the channel allocation in a distributed manner. To prevent providing fake data by malicious users, trustful cluster-based cooperative compressed spectrum sensing (TCBCSS) algorithm is proposed [14]. The algorithm can maintain high spectrum detection rate with the help of accurate detection of the malicious user. In another study, a user sensing and power control algorithm is suggested [15] for a system using RFID and gaining access to data from passive primary receiver (PU). The protection issue for passive PUs due to the opportunity for CR nodes to perform harmful transmission is addressed in this study. So a novel sensing power control algorithm is suggested to protect the passive primary receiver. The authors have proposed a scheme in [23] to acquire the most probable positions of secondary base stations (SBSs) for CR-IoT sensor networks and choose the best suitable operating channel for maximizing the secondary system capacity while protecting the primary systems at the same time. An appearance probability matrix for secondary IoT devices is proposed and the particle swarm optimization (PSO) technique is used for finding the optimum position and operating channel of secondary base stations.

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2.3 Adaptation of Transmission Parameter in CR-IoT A cognitive radio system in CR-IoT can determine the appropriate transmission parameter for a given wireless channel environment with the help of a cognitive radio engine. However, in case of wireless multicarrier transceivers, the number of decision variables become larger and the transceiver system becomes highly dimensional. Thus Evolutionary Algorithms (EAs) are required for such situation. So, for wireless multi-carrier transceivers, a CR engine based on real-coded BiogeographyBased-Optimization (RCBBO) and fuzzy decision making [31] can be employed. By means of CR engine using meta-heuristic optimization techniques, it is also possible to determine the optimal value of transmission parameters. So it is required to perform comparative performance studies among these techniques to obtain an optimal solution for different transmission scenarios, which is done in [32]. The performance of CR engine incorporating Differential evolution (DE) [49] and Bat algorithm [50] respectively have been analyzed in [33]. Particle swarm optimization (PSO) based decision making module is designed [34] to achieve minimum transmit power, minimum bit error rate (BER) and maximum throughput.

2.4 Energy Management in CR-IoT Energy management is an essential factor for an IoT network design. Spectrum sensing and allocation mechanisms can be energy efficient if CR is combined with wireless energy harvesting. An optimization framework has been designed in [37] with the help of which the spectral efficiency and energy efficiency tradeoff is observed. Also, a low complexity heuristic algorithm is proposed to solve the optimization issue between spectrum allocation and energy harvesting. Due to the involvement of many sensors and devices in the CR-IoT network with different power consumption profiles, the design and development of energy efficient solutions is a challenge. A spectrum efficient and energy harvesting scheme has been proposed in [36] where the sensor nodes in the system can perform dedicated energy harvesting on the basis of their current energy level and maintain a balance between network performance and lifetime with the help of a cluster head selection algorithm. A two-way information exchange dynamic spectrum sensing algorithm is suggested in [38] for enhancing the energy efficiency for data transmission in licensed channels and energy efficient optimal transmit power allocation technique for better dynamic spectrum sensing and data throughput. IoT network involving large number of sensor nodes can be energy efficient if forwarding of the packets is done through nearest neighbors in multi-hop paths and ignoring access point or gateway. An energy saving channel selection scheme is studied for linear networks, which can be generalized for random networks according to the authors, and shown that the scheme can save energy as well as maintain balance in energy irrespective of the relay load, though scanning overhead marginally

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increases [39]. A smart, reliable and energy saving CR protocol with high throughput for IoT applications can be introduced by reducing the communication time among the secondary user devices over the channels, which is done in [35]. The proposed protocol, named Reliable and Energy Efficient Cognitive Radio Multi-Channel Medium Access Control Protocol for Ad-hoc Networks (RECR-MAC), is studied and compared with other selected benchmark Cognitive Radio MAC (CR-MAC) protocols.

2.5 Security and Reliability for CR-IoT Jamming is one of the common security and reliability threats faced by CR networks [51]. It impacts the availability of the network and thus causes Denial of Service (DoS) [52]. So the channel assignment problem under jamming attacks needs to be taken in consideration. A probabilistic spectrum algorithm for time-critical IoT-based CR networks has been proposed in [41]. The algorithm targets to minimize the packet invalidity ratio of each CR transmission. The statistical information of activities of the primary users, fading conditions, and jamming attacks over unused channels are used to meet the objective. This study addresses the problem associated with channel assignment under proactive jamming attacks whereas the other similar study [42] concentrates on the same problem under both proactive and reactive jamming attacks. A jamming detection scheme has been proposed in [44] which observes signals with the help of switching Dynamic Bayesian Network for a CR-IoT system when reactive jammers are present. The study conducted by the authors in [43] aims at examining and suggesting a reliable and adaptive CR protection method against jamming attack with the help of game theory and multi-arm bandit (MAB) policies. Thompson Sampling MAB policy is proved to be the superior policy among them. A Blotto game new strategic power allocation scheme, which uses Blotto game model and Nash bargaining solution as major elements, is proposed to secure the CR communication [45]. On the basis of performed simulations, this scheme outperforms the existing schemes, namely BGPA (Blotto Game based Power Allocation Scheme), CRAJ (Cognitive Radio Anti Jamming), and RLAJ (Reinforcement Learning based Anti Jamming) schemes in terms of normalized secondary user’s payoff, security efficiency and percentage of payoff decrease. Besides jamming attacks, Primary User Emulation attacks (PUE) is also a malicious attack that affects the CR-IoT network performance. So, a PUE attack detection scheme is proposed [40] which is a combination of energy detection and location determination. For each SU, more than one threshold is used to detect received energy level.

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3 Research Challenges in CR-IoT The evolution of IoT influences the system operation in different applications. CR implementation in IoT makes it more intelligent and effective. However, there are some research challenges faced by the researchers in improving the IoT technology are discussed in the following section. Hardware design issues limit the effective CR network utilization. For example, an antenna cannot detect multiple frequency spectrums due to direct relation between antenna size and frequency. The transmission power level with the surroundings also makes IoT exploration more challenging [53]. Since the gateways connect the IoT devices to the system network, it is important to incorporate the elasticity, security and power consumption in the gateway design [53]. The large volumes of data collected by CR-IoT devices can vary in nature [9]. The necessity of effective algorithms for such large-volume data processing and improvement in terms of data processing performance and duration thus arises. The presence of primary users and secondary users in same frequency bands raises the spectrum detection problem. Spectrum sensing and data transferring cannot be performed simultaneously by the cognitive radios. This requires the consideration of multiple radios and geo-location based and record keeping algorithms in the CR-IoT system design [11]. Energy optimization is also a concern for the development of algorithms. Selection of appropriate spectrum after spectrum searching is necessary to meet the specified QoS requirements. Applications such as smart grids, PANs, LANs etc. can be supported by utilizing the TV white spaces. Incorporating CR to exploit this band may be beneficial for the evolution of CR-IoT network. The standardization of protocols to fulfill this objective is thus essential. Such incorporation will give birth to some challenges which need to be investigated [3]. A key concern for CR network is the uniform security standard being inapplicable due to the heterogeneity nature of the CR nodes [53]. So, suitable security standards and regulations to all sorts of heterogeneous devices and networks are crucial. Privacy during data collection and data sharing needs to be ensured.

4 Recommendations for Future Research in CR-IoT This section provides some probable future works for further improvement in CR-IoT sector: • The application of IoT objects is rising significantly with time. As a result, new architectures might be required for massive CR enabled devices. Besides, the current and emerging technologies are also being integrated for ensuring greater accessibility and user-friendly nature. The scalability and expansion of CR-IoT schemes thus needs to be considered. It is also required to evaluate the performance and scalability of both current and future Internet architecture with different radios,

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sensing cells, interference sources arrangements and spectral sensing algorithms [12]. • The optimization of sensing interval and operational interval of a CRAHN module is essential to reduce the probability of misdetection and ensure a decent good-put of the system. The application of machine learning algorithms can increase the system good-put [16]. In the field of cooperative spectrum sensing and sharing, behavior modeling with incorporate fairness could be analyzed to develop schemes involving game models [13]. The system incorporating 4G and Wi-Fi/CR-IoT needs to be studied to utilize the free usable spectrum for uplink and downlink traffic [19]. Under dynamic channel quality conditions, spectrum assignment scheme should support simultaneous channel assignment decisions for several CR-IoT nodes and the existing proposal(s) needs to be developed to fulfill the objective [30]. The availability of and dealing with large amount of channel information can also be studied for CRAHN [22]. Quality of service requirements should also be a concern for evaluating the system performance [26, 28]. • For effective transmission, the optimal performance of CR node(s), in terms of spectrum detection and energy consumption, should be ensured. So, techniques for energy efficiency in a wireless sensor network will always be a research interest. For instance, in an IoT architecture incorporating communication system such as SigFox [24], the energy utilization by the IoT system can be improved using the new protocols. The performance of CR network nodes may be affected by the varying condition of the surroundings. So, the modeling of the diverse environment affecting the wireless transmission by a CR node into a non-cooperative game cost function with an accurate power loss exponent can help in saving battery power for low power sensor network [54]. • Jamming attacks in a CR network has been studied by many researchers as it is the most common malicious attacks faced by the network. However, there may be other types of security provisioning problems, such as PUE attacks, faced by a CR network. This may limit the use of CR in IoT paradigm. A generalized form of existing game scheme(s), like in [45], may help in modeling defense mechanisms to resist different malicious attacks. In case of jamming attacks, with the help of real dataset, the jammer detection model studied in [44] can be further improved. Also, the study of smart jammer with lower power needs to be performed. Similar to the research works performed to deal with the channel assignment problem in [41, 42], the assignment problem under function specific and smart-hybrid attacks [52] can also be studied.

5 Conclusion The cognitive approach towards Internet of Things (IoT) creates an opportunity to offer a smart environment where massive devices can communicate among themselves for transferring information and coordinating decisions. Several IoT applications can be further improved through cognition which can change the human lifestyle

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and influence both business and industrial growth. This chapter has reviewed different features of current research effort in spectrum sensing, sharing and channel allocation in CR based IoT. Security provisioning problems and energy management for CR-IoT has also been discussed. There still exist some research issues and challenges for effective management of CR based IoT networks.

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Cognition Radio Enabled IoT Md. Mahfuzur Rahman and Mohammad Abdul Matin

Abstract Internet of things (IoT) has changed human lifestyle by introducing various smart applications. In recent years, sophisticated automation systems have become an essential outcome of the IoT paradigm. Due to the characteristics of IoT applications, devices need to communicate with each other seamlessly. New networking technologies and architectures have also been designed to support the communication requirements of current and future IoT devices. Wireless and radio communications are very desirable for achieving communication among the devices with various proximity. As there is a rapid growth in the number of IoT devices networked based on wireless transmission, radio frequency resource needs to be allocated efficiently to enhance radio spectrum utilization. In wireless connection, Cognitive Radio (CR) is an opportunistic radio access technology targeted to improve the spectrum usage and to mitigate the excessive contention of radio communication. In this chapter, we have identified the functional similarities between IoT and CR, and the challenges that are important to be addressed to integrate CR technology for IoT. We have also proposed a framework for cognitive radio enabled IoT that provides efficient radio spectrum utilization for IoT.

Acronyms IoT CR M2M uCode RFID EPC LTE

Internet of Things Cognitive Radio Machine to Machine Ubiquitous Code Radio Frequency Identification Electronic Product Code Long-Term Evolution

Md. M. Rahman (B) · M. A. Matin North South University, Dhaka, Bangladesh e-mail: [email protected] M. A. Matin e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_3

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Quality of Service Ultra-wideband

1 Introduction The Internet is undoubtedly one of the most promising discoveries in the history of communication technology. The multi-fold increase in the number of connected devices through the internet (to support various smart application) has introduced the obligation of a new paradigm—“Internet of Things (IoT)”. IoT broadens the scope of smart applications by allowing real-world objects (virtually anything) to process data, and communicate through the internet. Even though the idea of IoT has been in presence since long, it is currently turning into a reality on account of the internet accessibility for low-powered tiny devices (e.g., sensors, actuators, etc.). In [1], IoT has been described as: “IoT is a worldwide network of interconnected objects uniquely addressable, based on standard communication protocols, and allows people and things to be connected Any-time, Any-place, with Any-thing and Any-one, ideally using Any-path/network and Any-service.” IoT has become successful to create a great appeal and attraction to a large research community. Due to the rise of innovations and conventions, IoT is intended to elevate the internet to the next revolution. IoT is progressing with the goal to allow every object in this world virtually be connected through the internet. IoT has already become an essential means to enhance the impact of the internet on the potential areas for information processing, decision making, data analysis, etc. Existing internet technologies is in great urgency to embrace new methodologies to support the adaptation to the growth of IoT. Everyday things like cameras, televisions, smart-phones, kitchen appliances, refrigerators, transports, healthcare facilities, etc. will be connected through the internet. Though the total number of globally connected devices is predicted to be 50 billion in 2020 [2], Cisco projects that 99.4% of physical objects will remain unconnected as there are at least 200 connectable things today present in the world for per person. The rapid growth in inter-device communication has the goal to make IoT devices to act as intelligent systems. The Machine to Machine (M2M) communication allows machines or devices to communicate with each other without the intervention of any human interaction. M2M communication is an enabling technology that facilitates the implementation of IoT applications. The adaptation of M2M communication for IoT will create an important portion of internet traffic, in the future. Imagine the situations, our alarm clock instructs the curtains of the bed-room when to open, adjusts the room lights, communicates with smart thermostats regarding the room temperature, and informs the coffee-maker to switch on during our waking up and start out in the morning; our refrigerator reminds us in a convenient way about certain grocery (that are about to finish inside the refrigerator) when we travel adjacent to a grocery store (where the product is available); our physicians adjust the medication, controls the surrounding environments of a patient staying in intensive

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care unit (ICU) depending on the information collected from various sensors attached to the patient’s body, or at the patient’s surroundings. These applications will become in reality in future through IoT. To support these type of smart applications, almost all the related devices will need to communicate with each other. IoT applications are assumed to pervade and dominate home automation, transportation, industry and manufacturing, mining and energy, construction, agriculture, merchandise, security, healthcare, education, and all other sectors related with the envision for smart cities. All the devices are targeted to be connected through the internet and a significant portion of the communication will be performed among devices (i.e. M2M communication) to develop intelligence. Wireless communication network and wireless sensor network paradigms both have adorned M2M communication with a more accessible and efficient manner. With the increasing number of IoT devices in the environment and due to economic M2M communications, non-license ISM bands are getting crowded gradually. Therefore, new challenges have emerged to indulge for managing the spectrum resources to achieve a reasonable and compelling wireless communication. The scarcity of spectrum resources (due to crowded unlicensed bands) would possibly create an obstruction for M2M communication in IoT in the recent future. CR can be a potential and attractive solution while integrated with the IoT paradigm to mitigate the spectrum shortage and to enhance overall spectrum utilization. In this chapter, we have reviewed the challenges for enabling CR in IoT and proposed a framework in integrating CR for M2M communication in IoT. In addition, this chapter will serve as a reference for new researchers interested in CR enabled IoT. The rest of the chapter is organized as follows: Sects. 2 and 3 describe the major challenges and issues with IoT and CR respectively to direct in understanding the necessity of a new framework. The existing related frameworks are discussed in Sect. 4. Section 5 describes our proposed framework for integrating CR and IoT functionalities (based on their functional similarities) before the chapter is concluded in Sect. 6.

2 Major Functionalities and Challenges with IoT Six basic functionalities have been identified for IoT by Al-Fuqaha et al. [3]— identification, sensing, communication, computation, services, semantics (Fig. 1). For M2M communication, identification is a vital requirement and various identification methods have already been proposed for IoT like electronic product codes (EPC), ubiquitous code (uCode), etc. Ubiquitous ID Center defined uCode that use RFID or barcodes to identify objects and also to collect information (known as “context”) related to things. In IoT, context information is normally very related to specific things and the things need to be identified properly. Sensing helps to collect context information as of related objects that are specific to IoT application. Numerous sensor devices integrated with single board computers like (e.g. Arduino, Raspberry PI, etc.) are also used to serve IoT sensing activities. With the existing com-

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Fig. 1 IoT-related functionalities

munication protocols (e.g., IEEE 802.15.4, Wi-Fi, Bluetooth, Z-wave, ZigBee, and LTE-Advanced, etc.), there are popular technologies (e.g. RFID, Near Field Communication (NFC), Ultra-wide bandwidth (UWB), Bluetooth Low Energy (BLE), etc.) specially used for IoT communication. IEEE 802.15.4 specifies standards for different layers of low power wireless networks. Wi-Fi generally works with 100 m range. Bluetooth is targeted to be used for short distance communication with minimum power consumption but, BLE additionally provides a coverage of 100 m. ZigBee and Z-Wave both are designed for mesh topology (i.e., the communication may happen with multiple paths). LTE is focused on high-speed data transfer for wireless communication and LTE-Advanced is extended from LTE with a maximum 100 MHz bandwidth support. RFID normally works for sensing within 10 cm to 200 m range, NFC works well for up to 10 cm with high frequency band at 13.56 MHz, and with a maximum data rate of 424 kbps. UWB is designed for low coverage with low energy but high bandwidth communication. Arduino, Raspberry PI, WiSense, Intel Galileo, BeagleBone, UDOO, FriendlyARM, Gadgeteer, Cubieboard, Z1, Mulle, and T-Mote Sky etc. hardware platforms and Contiki Real-Time Operating System (RTOS), TinyOS, LiteOS, and Riot OS operating system platforms are developed to support computations for IoT. The cloud platform is another source of computation platform for the application handling a large amount of data [4, 5]. Services can be considered as the basic activities that are used to create applications. Various services are normally described to create IoT application and those services may be categorized as identity-related services, information-aggregation services, collaborative-aware service, ubiquitous services. Semantics is the ability to extract knowledge from the collected information and also the ability to take the right decision. Resource Description Framework (RDF), Ontology Web Language (OWL), Efficient XML Interchange (EXI) are standards technologies for semantics.

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IoT application in any domain (e.g., industry, environment, society, etc.) integrates the support of heterogeneous devices. Though heterogeneity is an unpleasant feature for system designers, IoT welcomes heterogeneity to enlarge the scope and intelligence of the application. In IoT, heterogeneous devices may use diverse network connectivity with various topologies and protocols. This also creates concerns for programming languages selection, control complexity, device compliances to standard, failure in data communication, data security and privacy, etc. The promising network architecture is required for the dynamic and interoperability support of IoT. Context awareness is very fundamental characteristics of IoT application [6]. Context is mainly useful information related to any thing or any situation that is considered to characterize the behaviour of the application with the user. Abowd and Mynatt [7] have defined context awareness as—“Adapting behavior based on information sensed from the physical and computational environment”. Information answering five W’s normally constructs the basic context of anything—who, what, where, when, why. In the IoT paradigm, when the application is more context-aware, it becomes more intelligent. Context includes the meaningful information related to place, person, position, situation, circumstances, surroundings, posture, the attitude of something, etc. Devices and communication protocols need to be designed to support the required context information seamlessly and with low latency. Using the context information, IoT devices are also expected to be capable of reasoning and learning the environment semantically. The primary activities to manage contexts in IoT is shown in Fig. 4. IoT includes devices such as sensors, electronic devices, etc., that are operated with low power consumption. More energy economic device designs should be addressed to prolong the lifetime of devices as well as to support the “green design”. As the primary cause of power consumption of devices is due to the communication part, data transmission is also needed to be energy efficient. IoT applications deal with huge amount of data that are transmitted through the communication network and introduces challenges for data collection, convergence, processing, analytics, security issues. Data are meaningless unless it is used (or being useful) in making decisions. On the other hand, only helpful data needs to be transmitted by devices. For this, IoT devices are to possess a certain level of cognition capabilities. Autonomic operation is one of the major features of IoT applications. The devices should be able to reconfigure themselves and be able to understand the presence, activities, capabilities of the neighboring devices. Environment discovery, selfmanagement are assumed to be inherited properties for IoT devices. IoT devices should also be able to play various role dynamically and perform required actions depending on the obligation of the environment. Mobility is an important factor for automaticity, and efficient mechanisms are needed to support automated geo-location mobility to offer services without interruption and with zero-configuration. Many IoT applications need to support higher QoS requirements. Availability, reliability, scalability, performance, security, trust etc. are some key QoS criteria for IoT. Availability refers to the accessibility of services at any time, and at any place. One way to ensure availability is through redundant devices for the same services. Reliability ensures how much the system is functioning according to the specifica-

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tion. Reliability is very closely related to availability but is more focused on fulfilling services requirements. Reliability is critical in a system containing various components and for high reliability every hardware, as well as software component of the system, needs to ensure it. Previous studies focused on both availability and reliability QoS criteria for IoT. Scalability refers to integrating new services or operations without affecting or changing the existing services. Security and trust are most crucial QoS requirement of IoT that guarantees the exchange of information without compromising sensitive data. Interference free communication, reliable data delivery, situation-aware reasoning may ensure QoS aspects of IoT applications. In wireless communication, dynamic traffic scheduling, spectrum allocation also play an important role in ensuring QoS. For this, a novel communication infrastructure supporting new services with significant economic benefit is of great importance in IoT. Using unlicensed band for data transmission may not always ensure the QoS requirements of an application and eventually application-specific CR control is required to respect QoS.

3 Major Functionalities and Challenges with Cognitive Radio Cognitive Radio (CR) has been attracted by IoT paradigm to enhance the spectrum utilization. CR dynamically seeks for suitable spectrum in the licensed band for allowing access to the unlicensed consumer devices without making any disturbance to the licensed consumer devices [8]. Following the definition mentioned in [9], CR can be described as—“A technique to access radio resource that can change the device’s transmitter parameters based on interaction with the environment in which the device operates”. CR is very perceptive about spectrum realization and spectrum state learning. CR enabled devices normally possess the characteristics to quickly switch the spectrum to avoid coinciding transmission. CR introduces spectrum sensing, spectrum sharing, spectrum management, and spectrum decision-making techniques for wireless communication. In varying network conditions, dynamic spectrum utilization through CR ensures spectrum usage protection for legitimate devices. For example, CR enabled mobile device can switch between licensed-band and free unlicensed band, if needed to reduce the cost of calls, also to mitigate the gap of poor network coverage in certain geographical areas. Cognitive capability and Reconfigurability are two key aspects of CR enabled devices. The cognitive capability enables a device to sense the spectrum-related information from the radio communication environment. Cognitive capability helps the device to be aware of the unused portion of the spectrum at a specific time or location with the goal to select the best spectrum. The cognition is expected to be done in avoiding any interference with other consumer devices. Mitola [10] made the initial effort for achieving radio cognition using a knowledge representation language. Cognitive capability includes three basic functionalities: spectrum sensing, spec-

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trum analysis, and spectrum decision [10]. Spectrum sensing refers to the task to monitor the spectrum, to capture related information, and to detect spectrum holes (i.e. underutilized spectrums). Spectrum sensing also includes the tasks to understand the presence of primary consumer devices (both transmitter and receiver) in own vincity. To detect the primary consumer devices in the environment, there are three techniques—energy detection, matched filter detection, cyclo-stationary feature detection. In addition to that, there is a possibility of ‘co-operative detection’ where the detection is done in a group orientation. One or more devices in the group perform the sensing activities and share the useful information with the remaining devices in the group. Spectrum analysis includes the tasks to characterize the detected holes (e.g., holding time, interference level, link layer delay, etc.). Spectrum decision is the task to choose the appropriate spectrum band considering their characteristics of the available spectrums, delay, and loss for spectrum change, and also considering the requirements. These activities need to be continued in a cyclic order as the currently chosen spectrum may not remain the best always. The reason is that other more suitable spectrums may become available or the primary consumer devices may appear to perform transmission and the changing of spectrum frequently is termed as ‘spectrum mobility’. The existence of primary consumer device plays an important role in selecting a suitable spectrum. Again, there may arise situations where multiple secondary consumer devices are about to choose the same licensed spectrum as all have the same access right to the spectrum, so a sophisticated spectrum sharing or coordination methodologies are needed to prevent such collision. Reconfigurability enables the device to both receive and transmit data using the selected (‘on the fly’) spectrum among the variety of frequencies. Jondral [11] is also a pioneer in proposing an architecture of Cognitive Radio transceiver supporting CR related reconfigurability. Reconfigurability includes the tasks to change, if necessary, the operating frequency, modulation scheme, transmission power, protocol scheme, and these parameters are adjusted depending on the selected spectrum. All the basic functionalities related to CR are shown in Fig. 3. Once a particular spectrum is selected for a transmitter, the receiver should also be informed about the newly selected spectrum. In CR, spectrum cognition, allocation, reconfiguration decision-making activities can be achieved with either through the involvement of a centralized entity/device or locally at each device. Centralized strategy (termed as ‘cooperative’ spectrum sharing) may reduce interference, ensure more spectrum utilization. CR tasks at each node (or non-cooperative spectrum sharing) requires more communication (more sensing information exchange) among devices for a CR solution and it involves transmitter-receiver handshake task at each device.

4 IoT and CR Frameworks In this section, we have described various frameworks available in the literature focusing on IoT and/or Cognitive Radio. Research efforts have been made for developing various systems but most of the frameworks are focused on specific applications. The

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Fig. 2 Cognitive radio management framework (adapted from [14])

existing architectures have not yet merged into a reference model. But, few frameworks that are promising only for being a reference model for IoT are discussed in this section and our proposed framework is based on these frameworks. Wu and Zhao [12] have proposed an IoT architecture where the things (also termed as ‘nodes’) are categorized into three: device node, gateway node, access node. Device nodes are simple devices (e.g. temperature sensor, power switch, etc.) that collect the context information from the environment and also actively follow the instructions or suggestions obtained from the access nodes. Access nodes are capable of processing the context information, make necessary decisions, and depending on the type of application the access nodes recommend device nodes to perform necessary task. Gateway nodes are intermediary between device nodes and access nodes to carry out their communications, and helps access nodes to enhance control of the device nodes. Yang et al. [13] have proposed three layers for IoT functionalities in their model— perception layer, network layer, and application layer. The functionalities in perception layer are considered as the activities done using the sensing organs of the human body (e.g. eyes, ears, nose, throat, etc.), the network layer is as the activities of the nerve system in the human body, and application layer is as the brain in the human body. The main focus of the perception layer is to capture and collect information by perceiving, and recognizing things. The devices related to the perception layer are normally low-powered and low-priced. The network layer is based on the mobile telecommunication or the internet that helps to perform necessary data transmission. Application layer analyzes the data collected from the perception layer and interacts with the end-users by providing their desired services.

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Al-Fuqaha et al. [3] have discussed five layers architecture for IoT—device layer, device abstraction layer, service management layer, application layer, business layer. Device layer can also be thought of as perception layer or object layer. Device layer includes the physical sensors, actuators, etc. that are capable to collect information from the environment querying location, motion, acceleration, vibration, temperature, humidity, weight, etc. Device abstraction layer can also be termed as communication layer where data communication and network related functionalities can be planned with various techniques like 3G, Wi-Fi, Bluetooth, etc. Service management layer (also termed as ‘middleware layer’) accumulates the data from heterogeneous devices, processes the data to make decisions and to deliver required services. Application layer is to perform necessary composition of services, to create new application for supporting consumer needs. The business layer is responsible to analyze, evaluate, monitor, and ensure the QoS aspects of the remaining layers. The five-layered architecture has also been proposed by Tan and Wang [15] and Atozri et al. [16]. Sherman et al. [14] have described the components minimally required for a cognitive radio enabled system—sensing module, policy database, learning and reasoning module, configuration database, radio reconfiguration module. Sensing module accepts input from the radio components in the environment. The policy database module includes the rules to prescribe the acceptable communication behaviour under the specified circumstances. The learning and reasoning module learns based on experience. The configuration database keeps the record of the current radio configuration. A general framework for CR enabled system is shown in Fig. 2.

5 CR Enabled IoT To support IoT applications, IoT devices are required to be equipped with various functionalities including context acquisition, learning, thinking, and making decisions about both physical and social worlds, etc. Some of the basic functionalities of IoT are very identical to the required functionalities of CR. We have depicted the basic functionalites of CR in Fig. 3 and the related IoT functionalities in Fig. 4. We have also summarized the similarities between the functionalities of CR and IoT paradigm in Fig. 5. To support CR in IoT, integration of CR-related functions is required for IoT devices. Normally, devices are manufactured with the capability to communicate with very specific bandwidth range. Since the transmission power level in CR varies a lot depending on the situation, CR enabled IoT devices need to be capable in communicating with various spectrums. We have proposed a framework for integrating the functionalities of both CR and IoT. Our framework integrates the CR functionalities with IoT and is based on the IoT frameworks presented in [3]. It is realized that the five layer architecture of IoT is more effective and reliable in integrating the functionalities of CR. As a result, there are total five layers in our framework—perception layer, abstraction layer, communication layer, service layer, and application layer. The layered approach helps to group similar functionalities together and helps to make development process independent.

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Fig. 3 Cognitive radio cycle

Fig. 4 IoT context cycle

Fig. 5 Similarities among CR and IoT functionalities

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Fig. 6 CR enabled IoT framework

Each layer contains a number of sub-modules focusing on the functionalities of CR and IoT correspondingly. For example, the spenctrum sensing sub-module in the perception layer includes the functionalities related to finding underutilized spectrums in CR, and the things acquisition sub-module handles the similar type of functionalities for IoT paradigm (Fig. 6). Application layer integrates the services available in the service layer, performs service composition (if necessary), manages Quality of Service (QoS), takes care of security aspects, etc. This layer is a key interface between the end-users (including human-being, organization, and other systems) and IoT devices. IoT is getting popular gradually and the scope of the smart application is becoming larger. QoS submodule handles the non-functional requirements to be fulfilled by the application. The functionalities in each layer and the communication strategy within the layers or among the devices are important in ensuring the QoS aspects of an application. The application layer also satisfies the intelligence requirements of an application along with the functional and QoS aspects. Security is also a sub-module in the application layer that protects the collected or stored data ensuring integrity, availability, and confidentiality. Security sub-module is important for both CR and IoT, but the adaptation of CR in IoT introduces new challenges and additional complication. The security sub-module in our framework unveils or pretends the possible attacks and incorporate possible solutions using authentication, data encryption, etc. In CR, attackers may pretend to be potential consumers, and without authentication or mimicking as a licensed consumer may easily lead to false situations.

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Service layer identifies and handles the creation and management of required services that can be integrated into an application. There are data manager and knowledge base sub-modules in the service layer. The service layer includes services to make decisions based on the collected context information. CR related services intelligently select the best spectrum by reasoning with the defined policies. Both CR and IoT services need to learn about the environment and any knowledge representation language (like [17]) can be used to define the knowledge-base. There are also some XML-based languages to achieve the required reasoning from the knowledge. Baclawski et al. [18] proposed a language that can query radio spectrum information, handle machine learning, artificial intelligence, and inference aspects. Communication layer deals with the functionalities to handle the data transmission among devices and also the functionalities to connect to the internet. In this layer, communication-related decisions (e.g. intelligent routing, managing spectrum issues, etc.) are made. The spectrum manager sub-module in this layer combines the spectrum aware routing algorithms. The algorithms used in routing manager consider latency minimization, number of hops minimization, energy efficiency, etc. Abstraction layer hides the hardware heterogeneity, location mobility issues from the upper layers of the framework. The abstraction layer thinks all the sensors, actuators, etc. as virtual objects and hides the complexity related to their functionalities, operating conditions. The abstraction layer also solves the complexities related to various network topologies seamlessly. The abstraction layer finally solves the spectrum mobility (transition in using a new spectrum from the previous spectrum) and things mobility (changing the geo-location) related issues. Perception Layer is like the facial skin and the five sense organs of loT that mainly gather information, and identify objects. The Perception Layer includes 2D bar-code labels and RFID tags reader-writers, camera, GPS, sensors, terminals, and sensor network. Things acquisition sub-module in the Perception layer is focused on capturing information from the environment. The spectrum sensing sub-module in this layer identifies under-utilized spectrum frequencies either through energy detection, or through any other feature detection.

6 Conclusion Internet of things (IoT) is an emerging technology that has already introduced a large number of smart applications. IoT devices always try to understand the environment by collecting context information and applies reasoning on that information to make the application intelligent. To collect context information, the devices need to communicate with each other seamlessly. IoT assumes that all the ‘things’ will be connected through the internet. Wireless communication is a preferred way to establish the required connections due to flexibility. With the increasing number of IoT devices an efficient spectrum management scheme is needed. Cognitive Radio (CR) is also an promising technology to address spectrum scarcity issues. CR allows unused licensed spectrum to be used opportunistically by the unlicensed devices

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without interfering licensed devices. In this chapter, we have presented the integration of cognitive radio with IoT. Both technologies need to perform some similar activities. We have successfully identified the functional similarities between CR and IoT, and proposed a framework by making groups of those functionalities. In future, we will perform experiments for the proposed integration system.

References 1. Ali Khan, A., Husain Rehmani, M., Rachedi, A.: Cognitive-radio-based internet of things: applications, architectures, spectrum related functionalities, and future research directions. IEEE Wirel. Commun. 24(3), 17–25 (2017) 2. Evans, D.: The internet of things: how the next evolution of the internet is changing everything. CISCO White Pap. 1(2011), 1–11 (2011) 3. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015) 4. Rahman, M., Graham, P.: Compatibility-based static VM placement minimizing interference. J. Netw. Comput. Appl. 84, 68–81 (2017) 5. Md Mahfuzur Rahman and Peter Graham: Responsive and efficient provisioning for multimedia applications. Comput. Electr. Eng. 53, 458–468 (2016) 6. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014) 7. Abowd, G.D., Mynatt, E.D.: Charting past, present, and future research in ubiquitous computing. ACM Trans. Comput.-Hum. Interact. (TOCHI) 7(1), 29–58 (2000) 8. Matin, M.A.: Spectrum Access and Management for Cognitive Radio Networks. Springer (2017) 9. Haykin, S., et al.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005) 10. Mitola, J.: Cognitive radio for flexible mobile multimedia communications. In: 1999 IEEE International Workshop on Mobile Multimedia Communications (MoMuC’99), pp. 3–10. IEEE (1999) 11. Jondral, F.K.: Software-defined radio: basics and evolution to cognitive radio. EURASIP J. Wirel. Commun. Netw. 2005(3), 275–283 (2005) 12. Wu, J., Zhao, W.: Design and realization of WInternet: from net of things to internet of things. ACM Trans. Cyber-Phys. Syst. 1(1), 2:1–2:12 (2016). ISSN: 2378-962X 13. Yang, Z., Yue, Y., Yang, Y., Peng, Y., Wang, X., Liu, W.: Study and application on the architecture and key technologies for IoT. In: 2011 International Conference on Multimedia Technology (ICMT), pp. 747–751. IEEE (2011) 14. Sherman, M., Mody, A.N., Martinez, R., Rodriguez, C., Reddy, R.: IEEE standards supporting cognitive radio and networks, dynamic spectrum access, and coexistence. IEEE Commun. Mag. 46(7) (2008) 15. Tan, L., Wang, N.: Future internet: the internet of things. In: 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 5, pp. V5–376. IEEE (2010) 16. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15): 2787–2805 (2010). ISSN: 1389-1286 17. Mitola, J.: Cognitive radio: an integrated agent architecture for software defined radio (2000) 18. Baclawski, K., Brady, D., Kokar, M.: Achieving dynamic interoperability of communication at the data link layer through ontology based reasoning. In: Proceedings of 2005 SDR Forum Technical Conference, pp. 14–18 (2005)

SDN-Enabled IoT: Ensuring Reliability in IoT Networks Through Software Defined Networks Pankaj Thorat, Sukhdeep Singh, Avinash Bhat, V. Lakshmi Narasimhan and Gaurav Jain

Abstract Ensuring reliability for IoT networks is very crucial for the use cases like autonomous self-driving car, tactile internet, healthcare devices, etc., which requires continued communication to facilitate un-interrupted services. Software defined networks (SDN) facilitates to program the network and enables efficient control over the complicated network infrastructure like IoT. For a continued and effective implementation of SDN in IoT networks, it must solve the network reliability challenges to provision the low-latency and ultra-reliable transmission scenarios even in the case of failures in the network. In comparison with the path-based recovery, the local rerouting is a preferred solution for rapid failure recovery. For achieving the rapid local recovery, backup paths must be pre-configured for every flow on the link, which results in memory consumption of the switch for maintaining flow rules of the backup paths. Also, the efforts required for rerouting of every flow can delay the failure recovery. The book chapter will focus on the issues associated with failure management in software defined IoT networks and proposes forwarding table configuration in network, which can autonomously recover an OpenFlow-based IoT network from a link or a node failure. We firstly present Local Immediate (LIm) and Immediate Controller Dependent (ICoD) failure management approaches to overcome the shortcomings of link failure management approaches in SDN. Our proposed approaches conserve the memory of switches by reducing the backup path rules by aggregating the flow rule on the common network component using VLAN-enabled P. Thorat (B) · S. Singh · A. Bhat · G. Jain Samsung R&D India-Bangalore (SRI-B), Bangalore, India e-mail: [email protected] S. Singh e-mail: [email protected] A. Bhat e-mail: [email protected] G. Jain e-mail: [email protected] V. Lakshmi Narasimhan University of Botswana, Gaborone, Botswana e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_4

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flow labelling. The proposed approaches are expected to accomplish recovery in the range of 2–20 ms and will fulfill the stringent 50 ms recovery condition of Carrier Grade Networks (CGNs). Next, we extend our solution on single link recovery and present forwarding table configuration for the network to accomplish the switch recovery. To validate our proposed approaches for the link and switch recovery, we evaluated the performance in following points; (i) Number of flow entries can be saved to enable the protection against failures, and (ii) how quickly the recovery can be accomplished.

1 Introduction Internet of Things (IoT) is envisioned as a technology that will connect billions of sensor-enabled “things” to provide valuable services to the end users. While there has been multiple forecasts [1, 2] on the timeframe when this technology will be available to the masses, the uptake of IoT has been gradual. This is largely due to the challenge of defining a business model that will blend the cost of connecting things with a definite value to the end user of the service. Amidst this a few business cases like self-driving car, tactile internet, smart healthcare etc. are emerging where the value offered far surpasses the cost of embedding the IoT technology. These key use cases are likely to drive the popularity of IoT service, but will only be successful if they can be delivered in a reliable manner. A typical IoT ecosystem comprises of things, communication, applications and data analysis bundled into a service. Reliability of the service is vastly depends on the communication infrastructure on which the IoT services is planned. With respect to communication various network technology choices are available depending on the range of coverage and data rate required for the service. The fifth generation of cellular technology (5G) is however set to overcome all these challenges as it has been architected keeping “people” and “things” in mind. This is achieved by adding two new dimensions viz., Massive Connectivity and Ultra-high Reliability and Low Latency to the cellular technologies existing design objective of providing Capacity Enhancement. This makes 5G a compelling platform for delivering IoT services in the near future. Additional dimensions that 5G offers are likely to offer the telecom operator with plethora of business use cases, including IoT based services. However, this flexibility to operator is without its challenges, as operator now will have to juggle with various kind of service requirements on the same infrastructure. Technologies like Software Defined Networks (SDN) are likely well suited to resolve this obstacle. SDN separates the user plane from the control plane and provides the flexibility to manage the network centrally through Application Program interfaces (API) enabling Network Programmability. In this evolving new paradigm, we are encouraged to assess the capability of SDN to deliver IoT use cases like self-driving car, tactile internet, smart healthcare that warrant ultra-high reliability and low latency. IoT is inculcating SDN technology for better management of networks. For successful deployment of SDNenabled IoT networks (as depicted in Fig. 1), it is expected to provide continued

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Fig. 1 Overview of SDN-enabled IoT networks

services even in the case of network component failure. Network component failure can disturb the forwarding of traffic, which might cause service unavailability, packet loss, degradation in Quality of Service (QoS) and routing instabilities. For SDNenabled IoT, fault detection and IoT service restoration should be performed rapidly, without creating burden on data plane network components or centralized SDN controller (SDNC). We begin with a brief discussion on various options that SDN currently offers and its limitations. Subsequently, we will focus on the issues associated with failure management in software defined future networks (SDFN) and propose network designs to mitigate this problem so as to accomplish the desired reliability. The book chapter will focus on the issues associated with failure management in software defined IoT networks and proposes forwarding table configuration in network, which can autonomously recover an OpenFlow-based IoT network from a link or a node failure. We firstly present Local Immediate (LIm) and Immediate Controller Dependent (ICoD) failure management approaches to overcome the shortcomings of link failure management approaches in SDN. Our proposed approaches conserve the memory of switches by reducing the backup path rules by aggregating the flow rule on the common network component using VLAN-enabled flow labelling. The proposed approaches are expected to accomplish recovery in the range of 2–20 ms and will fulfill the stringent 50 ms recovery condition of Carrier Grade Networks (CGNs). Next, we extend our solution on single link recovery and present forwarding table configuration for the network to accomplish the switch recovery. To validate our proposed approaches for the link and switch recovery, we evaluated the performance in following points; (i) Number of flow entries can be saved to enable the protection against failures, and (ii) how quickly the recovery can be accomplished.

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2 Related Work For seamless delivery of IoT services in SDN-enabled IoT networks, the SDN controller must handle the network failures and restore the connection. In this section, we will discuss about the major research works on failure management related in SDN.

2.1 Failure Recovery Mechanisms For failure recovery CGNs rely on protection and restoration strategies [3, 4]. After detecting failure, backup paths are established in case of restoration. This leads to allocation of network resources dynamically that in turn causes delay in the recovery process. On the other hand, in protection strategy, the backup paths are configured in prior to the failure in the network. The discontinued flows are rerouted to the preconfigured backup path, if the failure is encountered in the protection mechanism. Hence, protection strategy is considered to be effective for delay in case of failure recovery. Furthermore, protection strategy can be subfurther divided into (1) link protection and (2) path protection. Path protection can be further subdivided into 1:1 and 1 + 1 path protection [4]. In 1:1 path protection, the packets are sent only on the primary path but if primary path get disrupted then the, the failed path is compensated by a backup path. In 1 + 1 path protection, duplicate data packets are produced and transmitted to backup path and primary path and further the duplicates are discarded at the destination by receivers. It decreases the sender transmission rate and increases throughput. Local recovery can not be achieved with the help of path protection methods as switches situated on the primary path that has failed is incapable of detouring discontinued flows on its own. The source switches needs a failure warning to redirect the flows to backup path. The time needed for issuing the warning in turn increases the time required for recovery and causes high packet losses. After setting the transmission to backup path in path protection mechanism, primary path’s flow rules become extinct and needs supplementary flow handling in order to release the resources of a switch. The detection of failure in carrier-grade network depends on Bi-Directional Forwarding Detection (BFD) and Loss Of Signal (LOS). LOS shifts the status of the port from “UP” to “DOWN” state of failed switches after detecting the failure [5]. Meanwhile, BFD protocol exchange the “hello” messages periodically amongst the switch pairs [6]. When link between connected switches is not functional, BFD neighbor session is cut down and the notification is fostered.

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2.2 Failure Recovery in SDN Many researchers across the globe are working on management of failure in SDN. Much of the works are on setting up the backup path for each discontinued flow [7–[10]. Flow rules for backup path are present for each of the discontinued flow on primary path. Authors in [10] proposes to install a backup path each new incoming transmission on that particular link. Considering thousands flows for each link, there may be stress produced on switch state for storing the flow rules for backup path for each flow due to proposed method. Memory limitation of network switches enforces constraints on backup flow rule storage. Research work of [11, 12] shows that the flow numbers are in millions for data centers having 100,000 computational nodes. For such scenarios, it is not possible to store flow rules for backup path because it may cause processing bottleneck and centralized controlled overload. Storing flow rules of millions of primary and backup path is impossible. Ternary Content-Addressable Memory (TCAM) are used to store flow rules but current available switches are capable of supporting upto 6000 TCAM flow rules [13]. Research work of [14, 15] uses restoration methods for recovery. However, restoration techniques take more time for recovery as compared to protection methodologies. The recovery methods used by the authors in [7, 16, 17] highly depends on optional fast failover group feature. The fast failover group is dependent on the implementation of firmware of vendor so, it is difficult to state whether switches assists given application or a SDNC. Considering all the aforesaid problems in the literature, we put forward the joint switch and link failure management methods. The proposed work is extension of our previous work based on SDN’s link recovery [18, 19]. In the next section, we provide detailed explanation of our proposed schemes, related performance evaluation and result discussions.

3 Proposed Solutions for Link and Node Recovery The ultra-reliable IoT services need uninterrupted connection for seamless delivery of the services. Even in the case of failure, the network must rapidly recover from the failure to restore the discontinued service. The failure recovery is one the most important factor of network management which can be felicitated using the OpenFlow-managed SDN. In an SDN-enabled IoT network, the protection mechanism can be realized in either a proactive or a reactive manner. In a reactive approach, after the failure detection, the forwarding entries in the reroute switch are modified to reroute the discontinued flows to the pre-configured backup path. In contrast, in the proactive approach, the switching node detects the failure of port and reroute the disconnected flows to the preconfigured backup path.

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For evaluating the performance of our proposals, we emulate the topology of AT&T network in Mininet that emulates the virtual switch running on generalpurpose hardware [28]. The emulated virtual network of 52 links 25 switches as depicted in Fig. 2 and the SDNC is hosted on the Linux server hosting with 54 GB RAM and an Intel CPU 2.60 GHz with 12 core processor. For our experiments, we use the NOX SDNC that supports Open-Flow 1.3 [29, 30]. Our NOX SDNC has a devoted interface to every virtualized OpenFlow switch of the AT&T network. A devoted connection between the SDNC and switches is characterized as out-of-band connection. It enables the separation of control and data traffics, which reduces the impact of excessive data traffic on the control traffic. Out-of-band connectivity also isolates the impact of failure at the data plane on the communication between SDNC and network switches. To measure the performance of our proposal, we use the iPerf to generate UDP Traffic. We generate packet stream at the 100 Mbps rate with each packet of size 50 bytes [31].

4 Link Recovery Solution for SDN-Enabled IoT We first implement a simple and straightforward form of controller dependent (CoD) recovery, which requires a SDNC involvement to reroute every individual discontinued flow. To minimize the rerouting overhead on the SDNC and accomplish a local immediate (LIm) recovery, we use the FF feature of the OpenFlow [20–23]. LIm recovery approach aggregates the discontinued flows into a single flow by taking the consumption of flow table memory into consideration. In Lim approach, we implemented the fast failover (FF) feature of the OpenFlow. However, the availability of FF depends on the vendor’s implementation of the switch firmware, since it is an optional OpenFlow’s feature. To overcome the shortcoming of LIm recovery approach, we

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Fig. 3 CoD recovery approach

propose the intermediate controller dependent (ICoD) recovery approach to accomplish a link recovery with minimal SDNC involvement and reduce the recovery time by avoiding the need to individually rerouting each flow.

4.1 CoD Recovery Approach In CoD recovery approach, when the link between switch switches A and D disrupts as depicted in Fig. 3, the switch A identifies the failure of port and transmits a notification of port failure to the SDNC. In reply, the SDNC transmits n flow modification messages to reroute the n discontinued flows from switch A to the switch B on the preconfigured backup path ABCD for link AD. The switch B and switch C are preconfigured with the n flow rules each to route the flows on the backup path.

4.2 LIm Recovery Approach LIm leverages the group table feature stated in the OpenFlow 1.1 specifications for grouping the incoming flows with common output port [22]. The capability of the flow entry redirect the flows to a group table facilitates various means of traffic forwarding. With the FF group, a switch can locally reroute the discontinued flows thus eliminating the need of involvement from the SDNC. A group entry consists of multiple buckets and each bucket forward packet to its uniquely associated output port. In general, the FF group gives priority to its first action bucket to execute. In LIm recovery approach, when the flow arrives at the switch, it is tagged with the upstream link ID. The link ID is encoded in VLAN tag field of the VLAN header

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Fig. 4 LIm recovery approach

of the packet. After the failure of link between switch A and D as depicted in Fig. 4, the switch A identifies the failure of port and toggles the failed port status to down from up. The action bucket of the FF groups with the output port with status down executes the subsequent action bucket that reroutes the discontinued flow to the dedicated backup path ABCD. The discontinued flow rules on the backup path are matched using its VLAN ID in the switches B and C. Using the flow-aggregation strategy enabled by VLAN, a single flow rule in the switches of backup path can restore the connectivity of n discontinued flows.

4.3 ICoD Mechanism We propose the ICoD recovery approach to overcome the shortcoming of Lim recovery approach due to its dependency on the FF group. In ICoD mechanism, we use the Indirect group type. The group entry with type indirect is a required feature for the OpenFlow switches [22]. In ICoD recovery approach, when the link between switch A and D disrupts as depicted in Fig. 5, the switch A identifies the failure of the port and transmits a notification to the SDNC. After receiving the notification of failure, the SDNC transmits a group entry modification message to change the output port of the action bucket that are affected by the failure. A single group modification message can reroute n flows on link AD to its backup path ABCD. The discontinued flow rules on the backup path are matched using its VLAN ID in the switches B and C. Using the flow-aggregation strategy enabled by VLAN, a single flow rule in each switch of the backup path can restore the connectivity of n discontinued flows.

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Fig. 5 ICoD recovery approach

4.4 Performance Evaluation To test our solution for the link recovery, we created flows from PL to CB via SF, NY and NC. We disconnected the link between switches at NY and CC. The backup path for the link CC-NY is CC-CL-PD-NY.

4.4.1

Experimental Traffic at the SDNC

In Fig. 6, the x-axis denotes the experimentation timeline, and the y-axis denotes the OpenFlow control traffic per 100 ms at the SDNC. Figure 6 depicts the variation in traffic at the SDNC from the preconfiguration of backup paths per link for 1000 flows travelling between PL to CB to the failure of link between NY and NC. At the beginning, the control traffic generated for backup pat setup creates spikes between 0 and 1.4 s. A higher control traffic is generate in the case of CoD approach in comparison to ICoD and Lim recovery approach.

4.4.2

Failure Recovery Time

We disrupt the link between switches at NY and CC to test the recovery operation. In Fig. 7, the x-axis depicts the number of flows discontinued by failure and the y-axis depicts the recovery time in ms. Figure 7 depicts that the proposed LIm and ICoD approaches realizes the recovery in 2–3 ms and 20 ms, respectively. In CoD approach, the recovery time is dependant on the number of discontinued flows.

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Fig. 6 Experimental traffic at the SDNC

Fig. 7 Failure recovery time

4.4.3

Flow Rules Installed for Link Protection

In Fig. 8, the x-axis depicts the links between the path from PL to CB and y-axis depicts the total flow rules configured to setup the backup path of each link. In this experiment, we count flow rules configured to configure the backup path for 1000

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Fig. 8 Flow rules installed for link protection

bidirectional flows for each link on the path from PL to CB (PL-ST (1), ST-CC (2), CC-NY (3), and NY-CB (4)). Since, we are aggregating flows using VLAN tag in the LIm and ICoD approaches, the number of flow rules for configuring backup path per switch has reduced from 2000 to 2. For 1000 flows on the path, we accomplish the reduction of more than 99% in configuring the flow rules of backup path setup per switch.

5 Rapid and Robust Node Recovery of Data Traffic in SDN-Enabled IoT Networks 5.1 Reactive Approach Reactive approach is the simplest approach to realize the failure recovery. After arrival of the incoming flow, the SDNC computes a forwarding path and configures the flow rules for path setup. After the setup of the primary path, the SDNC must setup a backup path for each of the core link or switch and edge link. When a switch or a link disrupts, the reroute switch identifies the failure and inform SDNC. In response, SDNC finds the backup port for the downed port and update the output port of the flow rule’s action to reroute the discontinued flow to a pre-configured backup path for the failed switch or link. When the link between switch A and B, or switch B disrupts as depicted in Fig. 9a, the switch A identifies the failure and notifies SDNC. In response, the SDNC sends the flow updation messages for every failed flow on link between switch A and B to its destined 2-hop switch via the preconfigured backup path. To protect the n flows traveling from switch A to D, the backup paths AFD and AED are setup by configuring n and m flow rules in the switch F and E, respectively. For the edge link

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(a) Protection of Core Switch

(b) Protection of Edge Link

Fig. 9 Reactive recovery based data plane protection

BD failure, the backup path BAFD is preconfigured. After the failure notification, the SDNC transmits flow updation message to switch B to reroute the discontinued flows from link between switches B and D to backup path BAFD. Flow having link BD as their core link, the switch B reroutes them to the destined 2-hop neighbor.

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5.2 Proactive Approach To eliminate the need of SDNC involvement, we implemented the FF feature of OpenFlow [2]. The FF group can reroute the flows locally and thus help in achieving the rapid and proactive recovery. However, to local rerouting, the backup paths must be preconfigured. We use flow-aggregation strategy in the proactive recovery that aggregates the flows with common immediate destination switch by flow labelling to reduce flow table memory consumption associated for storing the flow rules of backup path. On the backup path, the SDNC just configures a single flow rule, which matches the label of the rerouted aggregate flows. Upon failure of the link AB or switch B as depicted in Fig. 10a, the switch A identifies the failure, labels the packets with failed core switch ID and locally reroute the discontinued flows to the 2-hop neighbor via the preconfigured backup paths. Incoming packets on the backup path are matched using the VLAN ID in the packet’s header at switch F and it again forwards the packets to the next switch on backup path. Similarly, in Fig. 10b, the switch B reroute the packet around the link using the backup path BAFD. The proposed proactive approach attains a local recovery unlike the reactive recovery.

5.3 Hybrid Approach We propose the hybrid recovery approach to overcome the shortcoming of proactive recovery approach due to its dependency on the FF group. In hybrid recovery approach, we use the Indirect group type. The group entry of type Indirect is a required feature for the OpenFlow switches [22]. Similar to proactive scheme, we are pre-configuring the backup paths to secure the flows from switch/link failure. To reduce the memory consumption of the switch for configuring the backup paths, we are aggregating the flows using VLAN-based flow tagging mechanism. In the proposed hybrid scheme, when edge link BD disrupts as depicted in Fig. 11a, the failure affected switch B identifies the port failure and transmits a port down notification message to the SDNC. In response, the SDNC transmits the indirect group modification message to reroute the flows on link BD to the edge switch D via the backup path BAFD. The group entry modification message change the output port of the indirect group’s action bucket from a failed port to an backup port, which reroute the discontinued flows on the backup path BAFD. Therefore, the hybrid recovery scheme can accomplish the recovery for all the edge link failure affected flows with a single group entry modification message. However, for a core network component, the flows must be aggregated based on its destined 2-hop neighboring switch. In the case of a switch B or link AB failure as depicted in Fig. 11b, the discontinued flows are detoured via the backup paths to its destined 2-hop neighboring switch.

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(a) Protection of Core Switch

(b) Protection of Edge Link

Fig. 10 Proactive recovery based data plane protection

5.4 Performance Evaluation We generated the traffic on the path from PL to NV, AL, NO, HS, and SA via SF, and DL. For testing the recovery of the core-component, we raise a core switch failure at DL and a link failure between the SF and DL and for testing the edge link recovery, we disrupt the link between the DL and NV.

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(a) Core Switch Protection

(b) Edge Link Protection

Fig. 11 Proactive recovery based data plane protection

5.4.1

Failure Recovery Time

We disrupt the switch at DL to test the recovery operation. In Fig. 12, the x-axis depicts the flows discontinued by failure and the y-axis depicts the recovery time in ms. Figure 12 depicts that the proposed proactive and hybrid approaches realizes the

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Fig. 12 Failure recovery time

recovery in 2–3 ms and 20 ms, respectively. The recovery time in reactive approach is dependent on the number of discontinued flows.

5.4.2

OpenFlow Control Traffic in the Network

Figure 13 depicts the change in control traffic at the SDNC with growing amount of flows to recover. The traffic is counted from the point failure happens till the discontinued flows get rerouted. Here, we investigate the burden on the SDNC to perform the recovery procedure. In Fig. 13a, the x-axis depicts the amount of flows transmitting through the link DL-NV and the y-axis depicts the OpenFlow traffic generated for recovery at the SDNC. We select the flows travelling from the PL to NV via SF and DL. We disrupt the link between DL and NV and calculate the OpenFlow traffic generated at SDNC from failure detection to the discontinued flow recovery. In Fig. 13b, the x-axis depicts the flows passing through the switch at DL, and the y-axis depicts the size of OpenFlow traffic for recovery at the SDNC. For the results in Fig. 13b, we fail the switch at DL and measure the OpenFlow traffic generated at the SDNC from failure detection to the discontinued flow recovery. To provide protection for 4000 flows on the core switch DL, the proposed proactive and hybrid schemes reduces the control communication traffic up to 99% and outperforms the reactive scheme. Similarly, to protect the 800 flows on the edge link, the proposed proactive and hybrid schemes saves the control traffic by up to 99%.

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Fig. 13 a OpenFlow traffic for edge link recovery. b OpenFlow traffic for core switch recovery

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Backup Path Flow Rules

In this experiment, we measure flow rules required to setup the backup path in case of switch DL failure. In Fig. 14a, the X-axis depicts the backup paths for the edge links DL-AL, DL-HS, DL-NO, DL-NV, and DL-SA of the flows between PL-AL,

Fig. 14 a Backup path flow rules for edge link protection. b Backup path flow rules for core switch protection

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PL-HS, PL-NO, PL-NV, PL-SA, respectively. The backup paths are from the switch at SF to the switch at NV, AL, NO, HS, and SA. In Fig. 14b, the X-axis depicts the backup paths between the 2-hop neighbors of the switch at SF. The backup paths are from the switch at SF to the switch at NV, AL, NO, HS, and SA. The Y-axis in Fig. 14a, b depicts the total number of flow rules required for setting up the backup path. The reactive scheme configures 800 flow rules in every switch of the backup path to protect 800 flows travelling from between AL and SF. Similarly, an equal number of flow rules are configured in the backup path switch to protect the flows on the edge link. However, the proposed proactive and hybrid schemes aggregate the discontinued flows on a failed path into a single flow. Our proposed proactive and hybrid schemes minimize the backup path rules in the flow table by more than 99% and conserves the switch table memory.

6 Conclusion This chapter is motivated to configure a fault-tolerant network for rapid and lightweight recovery in SDN. The work is based on the observation that preconfiguration of the network for a local failure detection and rerouting is essential to accomplish the rapid recovery. This chapter adopts preconfiguration of the flow rules for reducing the recovery time. However, this puts the strain on the limited available switch memory and overburden the software defined networks controller (SDNC) for rerouting all the discontinued flow. Toward this, the chapter focuses on and proposes forwarding table designs for link recovery, multi-link recovery, and joint link and node recovery. To address the deficiencies of existing recovery schemes, the proposed solutions employ flow aggregation and grouping strategies to accomplish faster and scalable failure handling in SDN-enabled IoT networks. In first proposed approach, the LIm and ICoD recovery approaches overcomes the limitations of recovery approaches in traditional and SDN-enabled IoT networks. The proposed approaches significantly decrease the flow rules required for backup path configuration by aggregating the discontinued flows using Virtual Local Area Network (VLAN) labelling. The emulation results show that the proposed mechanisms accomplish recovery between 2 ms and 20 ms, respectively, and fulfils the stringent 50 ms recovery criteria of the carrier-grade network. LIm and ICoD also decrease the backup path flow storage requirement by up to 99%. The proposed switch configuration in the second solution of the chapter achieves joint link and switch recovery without knowing the type of failure. Achieving local recovery against a failure of switch becomes complicated because the switch can not store the divergence information of the flows affected by the failure. The emulation results reveal that the proposed Proactive and Hybrid recovery approaches accomplish 99% reduction in flow storage and failure recovery in 20 ms.

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References 1. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf 2. https://www.abiresearch.com/press/21-billion-iot-devices-will-ship-embedded-real-tim/ 3. Mannie, E., Papadimitriou, D.: Recovery (protection and restoration) terminology for generalized multi-protocol label switching (GMPLS). In: RFC 4427, IETF (2006) 4. Vasseur, J.P., Pickavet, M., Demeester, P.: Network Recovery: Protection and Restoration of Optical, SONET-SDH, IP and MPLS. Morgan Kaufmann (2004) 5. OpenFlow Switch Specification Version 1.3. https://www.openflow.org/, June 25. 2012. 6. Katz, D., Ward, D.: Bidirectional forwarding detection. In: RFC-5880, IETF (2010) 7. Sharma, S., Staessens, D., Colle, D., Pickavet, M., Demeester, P.: Enabling fast failure recovery in OpenFlow networks. In: The proceedings of International Workshop on the Design of Reliable Communication Networks (DRCN) (2011) 8. Sharma, S., Staessens, D., Colle, D., Mario, P., Demeester, P.: OpenFlow: meeting carrier-grade recovery requirements. J. Comput. Commun. 656–665 (2013) 9. Yu, Y., Chen., S. Xin, L., Wang., Y.: A framework of using OpenFlow to handle transient link failure. In: The proceedings of Transportation, Mechanical, and Electrical Engineering (TMEE), 16–18 Dec 2011, pp. 2050–2053 10. Sgambelluri, A., Giorgetti, A., Cugini, F., Paolucci, F., Castoldi, P.: OpenFlow-based segment protection in ethernet networks. J. Optical Commun. Netw. 5–9 Sept. 2013, pp. 1066–1075 11. Mysore, R., Pamboris, A., Farrington, N., Huang, N., Miri, P., Radhakrishnan, S., Subramanya, V., Vahdat, A.: PortLand: a scalable fault-tolerant layer 2 data center network fabric. In: The Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication, 4 Aug 2009, pp. 39–50 12. Iyer, A.S., Mann, V., Samineni, N.R.: SwitchReduce: reducing switch state and controller involvement in OpenFlow networks. In: The proceedings of IFIP Networking Conference, 22–24 May 2013, pp. 1–9 13. Curtis, A., Mogul, J., Tourrilhes, J., Yalagandula, P., Sharma, P., Banerjee, S.: DevoFlow: scaling flow management for high-performance networks. In: The Proceedings of the ACM SIGCOMM 2011 Conference (SIGCOMM’11), pp. 254–265. 14. Kempf, J., Bellagamba, E., Kern, A., Jocha, D., Takacs, A., Skoldstrom, P.: Scalable fault management for OpenFlow. In: The proceedings of Communications (ICC), 10–15 June 2012, pp. 6606–6610 15. Kim, H., Santos, Turner, J.R., Schlansker, Y., Tourrilhes, M., Feamster, J.: CORONET: fault tolerance for software defined networks. In: The proceedings of Network Protocols, Nov 2012, pp. 1–2 16. Pfeiffenberger, T., Du, J.L., Arruda, P.B., Anzaloni, A.: Reliable and flexible communications for power systems: fault-tolerant multicast with SDN/OpenFlow. In: 7th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, pp. 1–6 (2015) 17. Cascone, C., Pollini, L., Sanvito, D., Capone, A.: Traffic management applications for stateful SDN data plane. In: EWSDN 2015, Sept. 2015 18. Thorat, P., Raza, S., Nguyen, T., Im, G., Choo, H., Kim, D.: Optimized self-healing framework for software defined networks. In: The Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (IMCOM’15). ACM, New York, NY, USA, Article, pp. 1–6 19. Thorat, P., Challa, R., Raza, S., Kim, D., Choo, H.: Proactive failure recovery scheme for data traffic in software defined network. In: The Proceedings of NetSoft (2016) 20. Thorat, P., Raza, S.M., Kim, D.S., Choo, H.: Rapid recovery from link failures in softwaredefined networks. J. Commun. Netw. 19(6), 648–665 (2017) 21. Thorat, P., Challa, R., Raza, S.M., Kim, D.S., Choo, H.: Proactive failure recovery scheme for data traffic in software defined networks. In: 2016 IEEE NetSoft Conference and Workshops (NetSoft) (2016) 22. Thorat, P., Jeon, S., Choo, H.: Enhanced local detouring mechanisms for rapid and lightweight failure recovery in OpenFlow networks. In: Computer Communications (2017)

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23. Thorat, P., Jeon, S., Raza, S.M., Challa, R., Choo, H.: Scalable and efficient forwarding table design for multi-link failover in OpenFlow-enabled networks. In: IETE Technical Review (2017) 24. https://www.networkworld.com/article/3187891/internet-of-things/4-reasons-ciscos-iotforecast-is-right-and-2-why-its-wrong.html 25. https://www.3gpp.org/news-events/3gpp-news/1774-5g_wiseharbour

QoS Aware Spectrum Selection for IoT Md. Mahfuzur Rahman and Mohammad Abdul Matin

Abstract Selecting appropriate spectrum becomes one of the key challenges for IoT devices in satisfying the QoS aspects of the applications while integrated with Cognitive Radio (CR). Most of the existing research is focused on maximizing the spectrum utilization by merging CR with IoT paradigm whereas QoS aspects of IoT applications have largely been neglected. We propose a spectrum selection mechanism that can be employed by IoT devices to meet the QoS requirements of IoT applications. The approach includes identifying the QoS requirements of IoT applications, matching appropriate spectrum (in CR) satisfying the QoS requirements. This QoS aware spectrum matching strategy provides IoT applications a suitable solutions for satisfying the QoS requirements.

1 Introduction Cognitive Radio (CR) has the goal to enhance the spectrum utilization in wireless communication. CR introduces an opportunistic way to allow the usage of licensed ISM bands (e.g., GSM, TV bands, etc. allocated for licensed/ primary users/devices) by the unlicensed/secondary users/devices. As a significant portion of the licensed band often remains underutilized, the motivation of CR is based on enhancing the spectrum utilization by allowing the licensed band access to the secondary users. One of the major challenges in CR is to ensure the seamless and guaranteed access of the spectrum (and without making any type of interference) for the primary users. CR opportunistically provides the access to the licensed band to the secondary users only during the primary users’ unused period. With the rapid growth of wireless devices for emerging IoT paradigm, there will be a serious dearth of spectrum and the integration of CR with IoT devices may provide a potential solution and enhance the support for spectrum utilization. Md. M. Rahman (B) · M. A. Matin North South University, Dhaka, Bangladesh e-mail: [email protected] M. A. Matin e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_5

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Internet of Things envisions billions of things (e.g. home appliances, industry equipment, any objects in hospitals, transports, mines, agriculture, or other places) to be connected through internet, and will inaugurate a large number of smart applications. An IoT device may have the capability to transmit data either as a primary user (using its own licensed band) or as a secondary user while integrated with CR; otherwise uses the unlicensed spectrum. The applications need to ensure on demand and/or continuous data communication among the IoT devices (to store, aggregate and analyze the IoT data). Depending on the type of applications there may present various QoS requirements (e.g. delay/latency sensitive, packet-loss sensitive, runtime reconfiguration preferences or hand-off sensitive, cost sensitive, jitter sensitive, throughput sensitive etc.) for each application. The key challenge is to ensure those QoS aspects of the application while selecting the transmission method for data communication. The QoS requirements of an IoT application may vary over a period of time in a great extend, and highly depend on the type of application. Discovering as well as satisfying the QoS requirements of an application at run-time is crucial. Proper and precise estimation of the QoS requirements specifically for data transmission may lead to efficient network resource utilization and user satisfaction. On the other hand, under estimation of QoS requirements may degrade the quality of service, and similarly, over estimation may provide unnecessary allocation of network resources. Efficient spectrum allocation decision for data communication is also needed to ensure those QoS requirements. CR generally involves the activities—sensing, learning, and adapting the spectrum environment and those activities need to integrate QoS considerations to select and allocate the best suitable spectrum ensuring the required QoS aspects of data communication. The motivation of the chapter is to study and propose a QoS aware decision making strategy for CR in selecting and allocating appropriate spectrum resources for IoT applications. This decision making process includes the proper understanding of the QoS requirements of the applications, allocating appropriate spectrum resources (considering CR) to satisfy the QoS requirements in a best possible way. So, the proposed CR-enabled mechanism targets the refinement of the existing spectrum resource allocation. The rest of the article is organized as follows: Sect. 2 describes the related works, the proposed QoS aware decision making strategy is presented in Sect. 3, some initial evaluation results are discussed in Sect. 4, and Section 5 concludes the chapter.

2 Related Works Cognitive Radio (CR) needs to collect the spectrum utilization information from the environment, and use those information to learn the environment in the best possible ways. For this, the devices need to be equipped with cognition capabilities. Spectrums can be occupied in various manner and Li and Qiu [1] used Baysian network to model the spectrum occupancy pattern. They used graphical tools to understand

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and describe the occupancy co-relations, and performed reasoning on spectrum occupancy varied over time. Spectrum occupancy was also modeled as a two-state Markov chain decision process by Wellens et al. [2]. Jouini et al. [3] specifically focused on radio equipments and their constraints consideration in CR. The authors identified the basic cognitive capabilities that are essential in the context of CR and their suggested abilities include environment observation, analysis, decision, and action. CR needs to mitigate various QoS vulnerabilities, and also needs to be capable of handling the challenges of finding dynamic solutions with various conditions. Those conditions include connectivity with higher capacity, massive data transfer, lower latency (end-to-end), lower price communication, etc. Saha et al. [4] considered the QoS aspects in network routing using an efficient data flow strategy. They have considered packet-loss, transmission delay, throughput, jitter as the QoS aspects of data transmission in IoT, and finally used the best-fit strategy for QoS aware data transmission. Jin et al. [5] also identified various QoS aspects of data communication for smart city applications based on IoT devices. Duan et al. [6] described the perception, network, service and management as the basic and high-level layers in IoT communication architecture and analyzed the QoS requirements issues on each layer. Awan et al. [7] differentiated the flow-level QoS aspects from the packet level QoS aspects, and used a queuing system to handle the IoT traffic that are sensitive to delay. Pérez-Romero et al. [8] described a belief-based spectrum selection approach in CR, and provided an experimental evaluation of spectrum access interference. In [9], the authors suggested that the primary users may include various costs for using their licensed band (by the secondary users) during the under-utilized periods. The associated cost of using the licensed band (by the secondary users) can also be treated as a QoS aspect for the spectrum resource usage in CR. Current IoT applications assume the devices/things to be able to connect any time, by anyone from anywhere via any service or network path. Akhtar et al. [10] identified the limitations of 4G mobile networking in supporting the key requirements of IoT application and described a spectrum decision making framework for IoT applications in 5G networks. Zaheer et al. [11] surveyed on operational framework and theoretic solutions for integrating cognitive concepts in IoT. The authors identified various real world scenarios where current IoT paradigm is still inefficient in intelligent decision making for effective data communication. Wen et al. [12] proposed a method to select an optimal location for the information gathering station in monitoring spectrum resources and in allocating optimal spectrum to support IoT devices. The information gathering station integrates the basic CR functionalities (sense, learn, adapt, etc.) on behalf of surrounding IoT device and handles the challenges to allocate appropriate spectrum to its surrounding IoT devices.

3 QoS Aware Spectrum Allocation An IoT application demands various QoS aspects to be satisfied and, for this, IoT devices/sensors and their computation as well as communication strategies need to be analyzed. For example, some IoT applications are delay sensitive whereas

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Table 1 QoS metrics for IoT application Layer QoS metrics Communication Computation Device

Jitter, bandwidth, delay time, throughput, network connection time, monetary cost, availability, reliability, privacy, security, interoperability Scalability, reliability, dynamic adaptability, response time Weight, accuracy, stability, response time, range, sensitivity, precision, power consumption, capacity

Fig. 1 QoS aware spectrum selection framework

others are delay-tolerant and to support this delay-related QoS characteristics, optimal strategy needs to be maintained at the computation and communication level of IoT devices. Duan et al. [6] proposed a framework describing various layers of application execution and Singh and Baranwal [13] identified various QoS metrics for IoT applications in each of those layers (shown in Table 1). In Cognitive Radio paradigm, an IoT device/user is allowed to opportunistically use the licensed spectrum of a primary user and, to ensure or fulfill the QoS requirements of IoT application (specifically at the communication layer), efficient spectrum selection decision is needed to be made. A QoS aware Spectrum selection framework (adapted from [14]) has been presented in Fig. 1 that finds out the appropriate

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spectrum to be assigned to specific application mostly satisfying the quality of service for the data communication. In this framework, the QoS requirements of both primary and secondary users are properly analyzed and appropriate spectrum are allocated to them. This QoS aware spectrum selection process includes two aspects: QoS requirement model and Spectrum capability model. The models are derived from [15] described by Ding et al. for a different domain (i.e. for cloud computing paradigm [16–19]).

3.1 QoS Requirement Model QoS requirement model includes two parts: QoS demand and QoS weights. The values of both parts can be fixed manually or collected from the previous runs of the same or similar type applications. Let r is the total number of QoS features of interest. The QoS requirement of an application ai can be represented by (qi , wi ), where qi = [qi1 qi2 qi3 · · · qir ], wi = [wi1 wi2 wi3 · · · wir ]. Here, qi j describes application ai ’s QoS requirement for feature j. The values in qi are normalized and ai ’s all the QoS requirement should satisfy: 0 ≤ qi j ≤ 1, where 1 ≤ j ≤ r. The wi j describes application ai ’s priority or weight on the QoS feature j. The weight values are soft constraints and if there is no QoS requirements for some cases then the weight values are sacrificed. An application ai ’s QoS weights should satisfy: 0 ≤ wi j ≤ 1, where 1 ≤ j ≤ r, and r 

wi j = 1

j=1

We can represent the currently considered n applications’ QoS requirements and their corresponding weights using two matrices:

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⎡ ⎤ ⎡ q11 q12 q1 ⎢q2 ⎥ ⎢ q21 q22 ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ Q = ⎢q3 ⎥ = ⎢ q31 q32 ⎢ .. ⎥ ⎢ .. .. ⎣.⎦ ⎣ . . qn qn1 qn2 ⎡

⎤ ⎡ w1 w11 w12 ⎢w2 ⎥ ⎢ w21 w22 ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ W = ⎢w3 ⎥ = ⎢ w31 w32 ⎢ .. ⎥ ⎢ .. .. ⎣ . ⎦ ⎣ . . wn wn1 wn2

⎤ q13 · · · q1r q23 · · · q2r ⎥ ⎥ q33 · · · q3r ⎥ ⎥ .. .. ⎥ . . ⎦ qn3 . . . qnr ⎤ w13 · · · w1r w23 · · · w2r ⎥ ⎥ w33 · · · w3r ⎥ ⎥ .. .. ⎥ . . ⎦ wn3 · · · wnr

3.2 Spectrum Capability Model Spectrum resource selection needs to satisfy the QoS requirements of the considered applications. Let t be the total number of features associated with each spectrum s  S, where S is the set of all spectrum resources and n(S) = k · A spectrum si can be represented as: si = [ f i1 f i2 f i3 · · · f it ]. The capability of all the spectrums in S can be given by: ⎡ ⎤ ⎡ f 11 f 12 s1 ⎢s2 ⎥ ⎢ f 21 f 22 ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ S = ⎢s3 ⎥ = ⎢ f 31 f 32 ⎢ .. ⎥ ⎢ .. .. ⎣.⎦ ⎣ . . sk f k1 f k2

⎤ f 13 · · · f 1t f 23 · · · f 2t ⎥ ⎥ f 33 · · · f 3t ⎥ ⎥ .. .. ⎥ . . ⎦ f k3 · · · f kt

Different QoS feature takes values from various ranges and Min–Max normalization can be applied in a way so that 0 ≤ si j ≤ 1 where 1 ≤ i ≤ k and 1 ≤ j ≤ t. It is assumed that if a normalized value is larger then the spectrum offers better QoS support, and to support this statement some of the normalized values are taken by reciprocals (e.g. cost, etc.).

3.3 QoS Aware Spectrum Selection Spectrum selection process considers currently available spectrums in a certain time and finds out a set of spectrums that support the QoS aspects of applications. QoS

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aware sepctrum selection includes two algorithms: spectrum matching algorithm and QoS matching algorithm. Spectrum matching algorithm initially tries to find a set of spectrums suitable for each application. The matching process then selects the best suited spectrum (from the considered set of spectrums) to support the QoS requirements of each application. Algorithm 1 Spectrum-Matching Algorithm (ai , S, θ ) Input: qi : application ai ’s QoS requirements, S: a set of spectrum, θ : threshold Output: Fi : a set of spectrums satisfying ai ’s QoS requirements 1 2 3 4 5 6

Fi ← φ for each s  S do if matching(q i ,s) ≥ θ then Fi ← Fi {s} end if end for

For each application ai , the spectrum matching algorithm initially considers currently available spectrums—S that can be used for each application. Each spectrum s in S contains different capabilities to satisfy the QoS requirements of the application. The matching algorithm finds out a subset of spectrum—Fi (from all the spectrums in S) satisfying ai ’s QoS requirements (in a certain level denoted as θ ) using Eq. 1:

f

qik q(s, k)

f 2 2 k=1 qik k=1 q(s, k)

matching(qi , s) = 

f

k=1

(1)

Here, q(s, k) is the QoS capability of spectrum s considering feature k. If the matching(qi , s) value is greater than or equal to θ , then the spectrum s is considered to be suitable to satisfy ai ’s QoS requirements and is included in Fi . Algorithm 2 QoS Matching Algorithm (Fi , ai , wi ) Input: Fi = a subset of S, qi : application ai ’s QoS requirements wi : application ai ’s weights on QoS requirements Output: tμ : the selected spectrum with the highest satisfaction to ai

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μ = 0 for each t ∈ Fi do temp ← QoSMet(ai ,t) if temp ≥ μ then μ = temp tμ ← t end if end for

QoSMet(ai , t) =

f  k=1

⎧ ⎪ ⎨

wik , γ (t, ai , k)

q(t, k) − qik , if q(t, k) − qik > 0 | q(t, k) − qik | + max | q(t, h) − qik |, if q(t, k) − qik < 0 γ (t, ai , k) = h∈Fi   ⎪ ⎩ min | q(t, k) − qik |= 0 , if q(t, k) − qik = 0 h∈Fi

(2) In Eq. 2, γ (t, ai , k) is the measurement of closeness for the application ai with the spectrum t considering QoS feature k. The spectrum t exactly satisfies the QoS requirement k of ai when q(t, k) − qik results as zero. QoSMet(ai , t) provides how closely the QoS requirements of ai are met by the spectrum t considering the weights of ai on all the QoS features. With the QoSMet(ai , t) results, the best suited spectrum—tμ is selected for the application ai using Eq. 3. tμ = Selected Spectrum(ai ) = argt∈Fi max QoSMet(ai , t)

(3)

The QoS satisfaction of the application ai (due to selecting spectrum tμ ) can be estimated using Eq. 4. Satisfaction(ai , tμ ) = 1 −

f 1  | q(tμ , k) − qik | × wik f k=1 qik

(4)

4 Evaluation To verify the correctness of the QoS aware spectrum selection, we have assumed very simple scenarios of IoT based data communication and have used hand-craft data to run some experiments. In these experiments, we have associated three QoS aspects with five different applications and those QoS requirements are matched against the QoS feature of total five spectrums. The QoS requirement values of each application are normalized and presented using the matrix Q. For example, the QoS requirement value of application 1 is in the row q1 in the matrix Q, row q2 describes the QoS

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requirements of application 2, and so on. Similarly, the applications’ weights on QoS requirements are presented using matrix W . Finally, the spectrums’ QoS supporting capabilities are presented using matrix S. ⎡ ⎤ ⎡ 0.5 q1 ⎢q2 ⎥ ⎢ 0.3 ⎢ ⎥ ⎢ ⎥ ⎢ Q=⎢ ⎢q3 ⎥ = ⎢ 0.7 ⎣q4 ⎦ ⎣ 0.4 0.8 q5 ⎡

⎤ ⎡ w1 0.3 ⎢w2 ⎥ ⎢ 0.5 ⎢ ⎥ ⎢ ⎥ ⎢ W =⎢ ⎢w3 ⎥ = ⎢ 0.3 ⎣w4 ⎦ ⎣ 0.6 0.4 w5 ⎡ ⎤ ⎡ s1 0.3 ⎢s2 ⎥ ⎢ 0.8 ⎢ ⎥ ⎢ ⎥ ⎢ S=⎢ ⎢s3 ⎥ = ⎢ 0.4 ⎣s4 ⎦ ⎣ 0.7 0.5 s5

0.2 0.3 0.2 0.4 0.1

⎤ 0.3 0.4 ⎥ ⎥ 0.1 ⎥ ⎥ 0.2 ⎦ 0.1

0.5 0.3 0.3 0.2 0.3

⎤ 0.2 0.2 ⎥ ⎥ 0.4 ⎥ ⎥ 0.2 ⎦ 0.3

0.5 0.2 0.3 0.6 0.3

⎤ 0.7 0.4 ⎥ ⎥ 0.5 ⎥ ⎥ 0.1 ⎦ 0.2

The initial experiments show that, the QoS satisfaction rate is 84% (considering all of the QoS requirements) for application 1 with the selected spectrum using the proposed strategy. The QoS satisfaction rate of the application 5 is the highest as 93% and the lowest satisfaction rate 78% is measured for the application 2. The satisfaction rate highly depends on the availability of the spectrums with the required QoS support. If there is a large number of spectrums with the required QoS support, the more number of applications will be able to obtain spectrum that closely satisfies their QoS requirements. Further experiments will be conducted in future to prove the efficiency of the proposed framework in a larger scale.

5 Conclusion This article introduces a QoS aware spectrum matching algorithm for the IoT applications. Cognitive Radio (CR) provides the opportunity of getting increased number of spectrums in the environment that can be used for the data communication of IoT devices. All the available spectrums may not support the QoS requirements of the IoT applications. The proposed QoS aware spectrum selection strategy provides

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an effective solution for satisfying the QoS aspects of the application. The initial experiment results show the correctness of the strategy and further experiments will be conducted in future to describe its effectiveness in a larger scale.

References 1. Li, H., Qiu, R.C.: A graphical framework for spectrum modeling and decision making in cognitive radio networks. In: 2010 IEEE Global Telecommunications Conference GLOBECOM, pp. 1–6. IEEE (2010) 2. Wellens, M., RiihijäRvi, J., MäHöNen, P.: Empirical time and frequency domain models of spectrum use. Phys. Commun. 2(1–2), 10–32 (2009) 3. Jouini, W., Moy, C., Palicot, J.: Decision making for cognitive radio equipment: analysis of the first 10 years of exploration. Eurasip J. Wirel. Commun. Netw. 2012(1), 26 (2012) 4. Saha, N., Misra, S., Bera, S.: QoS-aware adaptive flow-rule aggregation in software-defined IoT. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 206–212. IEEE (2018) 5. Jin, J., Gubbi, J., Luo, T., Palaniswami, M.: Network architecture and QoS issues in the internet of things for a smart city. In: 2012 International Symposium on Communications and Information Technologies (ISCIT), pp. 956–961. IEEE (2012) 6. Duan, R., Chen, X., Xing, T.: A QoS architecture for IoT. In: 2011 International Conference on Internet of Things and 4th International Conference on Cyber Physical and Social Computing, pp. 717–720 (2011) 7. Awan, I., Younas, M., Naveed, W.: Modelling QoS in IoT applications. In: 2014 17th International Conference on Network-Based Information Systems, pp. 99–105. IEEE (2014) 8. Pérez-Romero, J., Raschellà, A., Sallent, O., Umbert, A.: A belief-based decision-making framework for spectrum selection in cognitive radio networks. IEEE Trans. Veh. Technol. 65(10), 8283–8296 (2016) 9. Zou, J., Huang, L., Gao, X., Xiong, H.: Joint pricing and decision-making for heterogeneous user demand in cognitive radio networks. IEEE Trans. Cybern. 1–14 (2018). https://doi.org/ 10.1109/TCYB.2018.2851620. ISSN: 2168-2267 10. Akhtar, A.N., Arif, F., Siddique, A.M.: Spectrum decision framework to support cognitive radio based IoT in 5G. In: Cognitive Radio in 4G/5G Wireless Communication Systems. IntechOpen (2018) 11. Zaheer, K., Othman, M., Rehmani, M.H., Perumal, T.: A survey of decision-theoretic models for cognitive internet of things (CIoT). IEEE Access 6, 22489–22512 (2018) 12. Wen, J., Yang, Q., Yoo, S.-J.: Optimization of cognitive radio secondary information gathering station positioning and operating channel selection for IoT sensor networks. Mob. Inf. Syst. 2018 (2018) 13. Singh, M., Baranwal, G.: Quality of service (QoS) in internet of things. In: 2018 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–6 (2018) 14. Alshamrani, A., Shen, X.S., Xie, L.: QoS provisioning for heterogeneous services in cooperative cognitive radio networks. IEEE J. Sel. Areas Commun. 29(4), 819–830 (2011) 15. Ding, D., Fan, X., Luo, S.: User-oriented cloud resource scheduling with feedback integration. J. Supercomput. 72(8), 3114–3135 (2016) 16. Chowdhury, M.S., Osman, M.A., Rahman, M.M.: Preference-aware public transport matching. In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6 (2018) 17. Rahman, M., Graham, P.: Compatibility-based static VM placement minimizing interference. J. Netw. Comput. Appl. 84, 68–81 (2017)

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18. Rahman, Md.M., Graham, P.: Responsive and efficient provisioning for multimedia applications. Comput. Electr. Eng. 53, 458–468 (2016) 19. Rahman, Md.M., Thulasiram, R., Graham, P.: Differential time-shared virtual machine multiplexing for handling QoS variation in clouds. In: Proceedings of the 1st ACM Multimedia International Workshop on Cloud-Based Multimedia Applications and Services for e-Health, CMBAS-EH’12, New York, NY, USA, pp. 3–8. ACM (2012)

Cognitive M2M Communications: Enablers for IoT Rezwana Ahmed and Mohammad Abdul Matin

Abstract In recent years, there has been a significant development of Internet of Things (IoT). The IoT is seen as an important part of the Future Internet. The number of machines which will be connected to the Internet is expected to reach billions in near future. This enormous number of heterogeneous devices, both physical and virtual, are operated using various different communication protocols to form extended global networks. This puts a massive burden and challenges on present communication technologies in terms of performance and connectivity due to spectrum crisis and inefficient spectrum utilization. Incorporating cognitive radio technologies in such scenarios can help to overcome such challenges. This chapter provides an overview of cognitive machine-to-machine (M2M) technologies along with some potential applications.

1 Introduction Internet technologies have seen a dramatic growth in the last few decades. With these developments, a new paradigm named the “Internet of Things (IoT)” has emerged. This emerging paradigm is thought of as a part of the Internet of the future and is envisioned to comprise an enormous number of intelligent communicating ‘things’ connected heterogeneously that expand the borders of the world further with both physical and virtual components [1]. While human-to-human communication has always been given importance, an entirely different type of communication has emerged recently that is between “machines”. These machine-to-machine communications are comprised of completely automatic generation and exchange of such data, as well as processing and the actuation of these machine generated data between smart machines, without any outside intervention or with very low human intervention. A vast amount of machines with various functions are autonomously arranged to make R. Ahmed (B) · M. A. Matin North South University, Dhaka, Bangladesh e-mail: [email protected] M. A. Matin e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_6

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up an M2M network. The Internet of Things (IoT) vision is fulfilled when M2M networks are connected among themselves and to the Internet while set up in the physical world for fulfilling their sensing and controlling functions [2]. Due to the widespread availability of wireless technologies, flexibility of wireless techniques, inexpensive M2M modules, the market for M2M is predicted to grow very rapidly which will vastly increase the number of wireless devices and the machine data these devices produce. Moreover, an enormous portion of the allocated/licensed bands is drastically underutilized in both the spatial domain and the time domain [3]. This results in a few challenges including resource constraints, machines heterogeneity, Quality-of-Service support etc. One likely resolution to the spectrum scarcity issue is to use cognitive radio technologies [4]. Using CR technology in existing machine-tomachine communication, a new kind of communication called cognitive machineto-machine (CM2M) communication is introduced which is able to deal with the problems that arise from the rapid increase of the quantity of wireless devices and the data they produce. Cognitive M2M communication sets to tackle these issues by enabling the devices that comprises the network with cognitive radio to increase flexibility and reliability of the network while making the network more energy efficient by sensing and exploiting the unused frequency bands in the surrounding. In CM2M communication, the unlicensed/secondary systems can access the frequency band/channel dynamically in case the primary network machines are not present, but are required to leave the band/channel in case a primary network machine (licensed system) is detected. In this chapter, the authors have discussed the necessities of cognitive radio technologies in the existing IoT networks. This will tackle the problem of spectrum crisis that has arisen due to the dramatic increase of devices in wireless networks in recent times. If this spectrum crisis issue goes unaddressed, it will be getting worse in the future. Moreover, an overview of cognitive M2M communications and how this can enable the vision of IoT is also being discussed. The medium access control protocol (MAC protocol) is essential in accessing vacant licensed spectrum while avoiding interference with the primary devices and thus the spectrum sensing component must be taken into account when designing MAC protocols for cognitive M2M networks [5]. Different MAC protocols specifically used for cognitive M2M networks have also been reviewed in this chapter. The rest of the chapter has been organized as follows: Sect. 2 gives an overview of the cognitive M2M network along with a review of different MAC protocols used for such networks, and the advantages of using cognitive radio technologies in M2M communications. Section 3 discusses how incorporating cognitive radio technologies in machine-to-machine (M2M) communications enables IoT. The various applications of cognitive M2M networks are provided in Sect. 4 while Sect. 5 concludes the paper.

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2 Overview of Cognitive M2M Communications M2M communications is a communication paradigm which provides ubiquitous connectivity among devices while communicating autonomously without any or minimal intervention from humans [6, 7] for better cost effective and time management [8]. With the addition of the cognition aspect, cognitive M2M becomes intelligent and adaptable. It also results in more potential for the cognitive M2M network compared to the conventional M2M network [2]. Cognitive M2M networks are composed of a massive number of M2M machines, M2M gateways equipped with CR technology, geo-location databases, independent spectrum sensing machines, CR base station, and M2M server. It uses cognitive radio technologies and dynamic spectrum capabilities to increase spectrum utilization and exploit other spectrum opportunities. It is equipped to tackle the challenges of energy efficiency, managing interference, and heterogeneity of the devices in the network. In addition, it gives rise to new application areas for M2M communications [9]. In cognitive M2M communications, each device (1) possesses cognitive capability, i.e., is able to sense radio environment and (2) has reconfigurability, which means every M2M device in the network is able to reconfigure its own operating parameters to transmit on a different frequency band [10]. However, MAC protocols need to be designed such that they are able to handle these unique challenges and provide the necessary requirements for successful performance of cognitive M2M communications. In summary, some of the communications scenario in cognitive M2M communications can be depicted as: – Cognitive machines can use the primary network spectrum opportunistically in the absence of primary network activities to communicate with the M2M gateway. – Parallel transmissions of primary network machines and cognitive machines might exist in case the primary network is prepared to share its spectrum, provided the interference due to cognitive M2M communications is below a threshold. – Cognitive machines can communicate by accessing the unlicensed frequency bands/channels. – Cognitive machines that are nearby can communicate with each other directly. This kind of communication is referred to as device-to-device communication, or D2D communication. – In CM2M, machines can use multi-hop cognitive communication to communicate with the M2M gateway for processing and communication purposes by forwarding data to their next hop neighbours.

2.1 MAC Layer Protocols of Cognitive M2M The MAC layer is mainly accountable for channel access by machines in a network using a shared medium. The MAC layer protocol being used determines medium access techniques, power consumption, transmission range, data rates and other

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characteristics of the network [11]. M2M networks have a high degree of system automation and consists of a large number of machines that need to autonomously communicate with each other [12]. Moreover, these machines have different service requirements and traffic characteristics which need to be considered when providing them channel access. M2M communications need to be energy efficient as the machines in the network are mostly low powered and battery operated. Security against malicious attacks such as denial of service and hacking is also necessary, while ensuring the various Quality-of-Service requirements of the machines are met [13]. In the following subsections, the MAC protocols that have been specifically designed for cognitive M2M communications are discussed.

2.1.1

PRMA-Based Cognitive MAC Protocol

A packet reservation multiple access (PRMA) based MAC protocol is designed for use in microcellular environments by the authors in [13]. For the design of the protocol, a standard M2M architecture is considered where the M2M devices (referred to as MTCDs) and M2M gateway (or, MTCG) are connected in a master-slave configuration. The MTCG behaves as a centralized controller and is in charge of keeping up the operation of all the MTCDs that are connected to it while also responsible for detecting channels via spectrum sensing techniques. PRMA can be described as a protocol that integrates slotted ALOHA, TDMA, and a reservation scheme. It permits reservation only in case of voice sources. Data sources are given the voice slots that are vacant, that is, the intervals which are silent during a conversation, which are identified using speech activity detection. This protocol uses TDMA to divide the available cognitive channel into a certain number of time slots having a fixed length. Each of these time slots are able to carry one packet. Along with TDMA, TDD mode is also used to make the use of the cognitive channel more efficient. The number of UL (uplink) time slots (for sending data from MTCD to MTCG) is larger than DL (downlink) time slots, which are used for transmitting data from the MTCG to the MTCDs. This is because most of the traffic is in the UL. The DL time slots are used for DL communications and broadcasting the UL time slots status, that is, if they are available or reserved, as well as for acknowledgements (ACKs). A specific number of time slots are grouped together to form a multiframe, which begins with a channel detection period followed by a preamble (also called multiframe control header). The MTCG carries out spectrum sensing operation and if it finds an unoccupied channel, it uses this preamble to broadcast an enabling signal containing information about this unoccupied channel’s availability. The MTCDs that want to transmit data scan for such an enabling signal and connect with the MTCG once they receive such a signal. At first, the UL time slots in a frame are all open for contention. This contention procedure conforms to a slotted ALOHA scheme where a MTCD with data to transmit contends for a vacant UL slot and sends a packet having a certain permission probability to the MTCG. Upon successful reception, the MTCG uses the DL time slot to send back a positive ACK signifying that the slot the MTCD is contending for has been reserved for the following frames in that multiframe. On the other hand, if

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there is a collision with another MTCD contending for the same slot, the contention procedure is carried out in a different time slot according to a random backoff value. This backoff value is part of the protocol’s backoff procedure to ensure the MTCDs’ Quality-of-Service (QoS) requirements are kept in mind while assigning resources. The backoff procedure makes sure the QoS requirements are met in case of collision during the contention period and a backoff has to be taken, so that MTCDs with critical QoS requirements are given a smaller backoff in order to give them priority while assigning channel. All MTCDs need to contend for reservation at the start of a new multiframe. In other words, the reservation cycle length is restricted to a single multiframe. This is done to make sure fairness is maintained between all the MTCDs. Additionally, due to the periodic update of channel availability information according to regulatory constraints, there is a possibility that the underlying cognitive channels might not be available the next time the MTCG carries out spectrum sensing.

2.1.2

CRB-MAC Protocol

A receiver-based cognitive MAC protocol is introduced called CRB-MAC protocol in [14]. This protocol has been designed for M2M devices working in difficult wireless scenarios, keeping in mind their reliability and energy efficiency requirements. Using the preambling sampling approach, CRB-MAC takes on the issue of idle listening while also avoids the use of synchronization overheads when supporting sleep/wakeup modes. The reliability requirements are ensured by the opportunistic forwarding approach with several receivers CRB-MAC takes, exploiting the wireless medium’s broadcast nature. If we consider a node that wants to send data to a sink/gateway node by forwarding it to its first neighbor hops within the transmission range, at first it needs to carry out spectrum sensing to find any primary system activity. If activity is detected, the sender node enters sleep mode, and this process is again repeated after a certain interval, called the checking interval. In case of no PU activity in the channel, the sender node starts sending the preamble and then the data. The preamble is composed of multiple micro-frames of a certain duration. It also possesses identification information used by neighboring nodes to differentiate between sensor node transmission and primary system transmission. The neighboring nodes of the sender node detect and sample a few of the preamble micro-frames to extract any required information such as sequence number of the data etc. The neighboring nodes of the sender node are allowed to forward the data in the direction of the sink node. Each of these nodes wake up to receive the transmitted data from the sender node. No Acknowledgement (ACK) message is sent to the sender node, and if the data packet is found to be erroneous, it is discarded. Moreover, these neighboring nodes set a timer prior to sending the data towards the next hop. The node with the shortest timer, set relative to the distance between the node and the sink, is the closest to the sink and usually sends the data towards the sink. Once the timer expires, all the neighboring nodes perform sensing operation. Detection of primary system activity in channels results in the nodes falling back to sleep mode

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for one checking interval. On the other hand, detection of sensor node transmission results in each node comparing its own sequence number with that of the transmitted data. A match indicates the same data being transmitted by a different node, and the node discards the data packet. In case of no match, a free channel indicates that this particular node is the winner, and so, it begins sending the preamble. The sender node checks if the data has been forwarded successfully by any of the nodes involved within the contention window, duration of which is determined by the transmission radius of the sender nodes, by carrying out the sensing operation right before termination of the contention window (passive ACK). Failure to forward the data packet causes the sender node to resend the data. For multiple hops, this same process is repeated until the data has been successfully received by the sink.

2.1.3

SD-MAC—Spectrum Database-Driven MAC Protocol

A MAC protocol for spectrum database-driven cognitive M2M networks has been designed by the authors in [15], called SD-MAC. It is an adaptive protocol capable of choosing ASM (available spectrum map) or local sensing scheme in an adaptive manner for machines to gather spectrum information in the presence of dynamic primary system machines and channel conditions that varies with time. The network considered in the paper is a spectrum database driven cognitive M2M network consisting of several cognitive M2M networks, each having a base station and a certain number of devices, and a spectrum database. The base station of each of these networks are in control of the sensing and access operations of the devices which are all capable of opportunistically accessing the vacant licensed spectrum channel while avoiding interference with the primary network’s activities. For this proposed protocol, the device and base station operations are based on time frames. A time frame is divided into two parts: announcement duration, and usage duration. The base station uses the announcement duration to announce the beginning of the time frame and send information regarding the ASM and usage order to devices in the network that want to send data. The usage duration is sectioned into a certain number of equal time slots, each of which is used by the devices one by one according to the usage order provided by the base station to perform spectrum sensing and transmission. The spectrum sensing scheme (ASM scheme or local sensing scheme) the devices uses is determined by the base station at the start of every time slot. At the beginning, the base station of the cognitive M2M network broadcasts a control message announcing the start of a frame to all the devices in the network. The announcement duration is partitioned into several time slots. During each of these time slots, a device which wants to transmit data sends acknowledgements (ACKs) to the base station through the control channel while devices with no data to send go into sleep mode in order to conserve energy. When all the ACKs have been received or the timer has run out, the base station acquires ASM relevant to its network from the spectrum database and sends this information along with the usage order to different devices. Once the information has been sent, it starts the usage duration. Each of the equal time slots is further divided into two parts, namely the sensing phase and

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the transmission phase. During the sensing part, the device uses adaptive sensing strategy to sense the spectrum channel once it is assigned a time slot. The sensing phase also includes the negotiation between the device and the base station where the base station decides what sensing scheme this particular device is going to use at the start of a time slot. In case the ASM scheme is picked, C-RTS and C-CTS packets (used for sending negotiation information between base station and machines) are exchanged between the base station and the machine through the control channel to ensure an available channel is used for device in the transmission part of the time slot. Once channel sensing is over, the machine sends an S-RTS packet to the base station which, upon reception of the packet, replies with an S-CTS packet in order to exchange information regarding channel availability. After successful completion of this exchange, spectrum availability of this specific channel is observed then the information updated. The sensing operation is stopped and the transmission is started in case an eligible channel is obtained. During the transmission part of the time slot, the radio modules of the machines which want to transmit data are turned on and the data that might include several data packets and their equivalent ACK packets are sent to the base station. After sending the ACK packets, devices wait for feedback from the base station. Failure to receive any feedback from the base station within the time slot results in the data transmission to be considered a failure as well. Upon successful reception feedback from the base station, the devices will turn off their radio modules at other time slots. In cases when the transmission duration has ended but the device still has data to send, the device turns its radio module off and enters sleep mode until it awakes again at the start of the next frame to begin a new cycle of sensing and access to this new frame.

2.1.4

DACT—Data Aided Cognitive M2M MAC Protocol

A data aided cognitive M2M MAC protocol is designed by the authors in [16]. For the design of this protocol, the authors consider an OFDMA (Orthogonal Frequency Division Multiple Access) based primary system where the network bandwidth is partitioned into a certain number of orthogonal subcarriers, and the time axis is also separated into time symbols. As a result, all the subcarriers and the time symbols can be considered as independent units, and each set of time symbol and subcarrier is seen as independent resource capable of being appointed to a communication. In OFDMA, the resource is occupied in a scattered manner throughout the network bandwidth resulting in spectrum holes in the system, and so can be exploited by a suitable MAC protocol to form a secondary M2M network that is completely independent from the primary system. This cognitive secondary system can exploit these unused spectrum holes, i.e. the unused frequencies in a specific time symbol which are not being used by the primary system, to send data. The proposed protocol intends to exploit the in-band signaling in the framing structure to obtain information about these unused resources. OFDMA-based networks use a frame-oriented resource management [16] where a frame is further divided into an uplink subframe and a downlink subframe. The downlink subframe contains two fields per frame that are used to send infor-

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mation regarding resource allocation for the uplink and downlink subframes that follow. So, an in-band signaling may be perceived as an information carrying field that has knowledge concerning whether the frame that follows is occupied or not. The secondary system is an independent M2M network based on OFDMA, consisting of machines that autonomously communicate with each other and uses the primary system’s physical interfaces. As a result, the network avoids causing access control channel congestion in the primary system and is able to use the in-band signaling of the primary system’s base station to obtain frame-by-frame information about the state of the subcarriers occupancy. This decreases transmission delays compared to other methods as no additional resource has to be used to sense channels. The DACT protocol uses the polling technique. This approach is centered on the token passing technique and is capable of handling a large quantity of machines in a simple manner by adjusting to the inconstant traffic produced by all the M2M machines in the secondary system. The machine list has been assumed as managed off-line which means all the machines are conscious of all the other machines in the M2M network and the polling order. A specific machine is kept in a state of waiting until it gets its turn with the token. Once it gets the token, it holds on to it for specific amount of time which might be fixed or can dependable on the data the M2M machine wishes to send. The base station broadcasts in-band signaling to all the M2M machines in the cognitive secondary system. These machines pass the token amongst them. The machine that possesses the token can transmit data to all the other machines. Whether data transmission occurs depends on the availability of free resource in the primary system which is learned from the base station. Moreover, the data transmission duration varies with the technique we elect to use. These different approaches are: – Gated approach: the machine transmits all its data present in the queue at the instant it comes in possession of the token. – Fixed approach: a fixed amount of time is allocated to each machine during which it can hold on to the token. – Exhaustive approach: a machine holds on to the token to send data until the device’s queue has been emptied. – Limited approach: in this approach the machines are only able to send a fixed amount of data each. – Adaptive approach: each machine is able to transmit a specific quantity of data in accordance to an established cost function, Once a machine gets the token, it obtains the in-band signaling and deciphers the information it contains. If there are free resources in the primary system frame, it uses this unused portions to transmit data by broadcasting it among all the M2M machines. When transmission has ended, this machine passes on the token to the next M2M machine in the network. Even if a machine receives the token, it might not be able to transmit any data if there are no free resources present in the primary system while it is possession of the token.

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2.2 Advantages of Cognitive M2M There are some advantages of using cognitive radio technologies in M2M communications which are: 1. Introducing cognitive radio technologies in M2M communications ensures the machines in the network are able to opportunistically access the unused spectrum hole, also called spectrum hole, are thus able to utilize the traditional spectrum [17]. So, cognitive M2M networks are capable of handling a much larger number of machines. 2. Machines in cognitive M2M networks are able to change their transmission power levels adaptively based on the environments they are operating under, while avoiding spectrum pollution and interference, which makes cognitive M2M communications energy efficient [9]. 3. Software-reconfigurability capabilities afforded by cognitive radio to the machines in an M2M network enables these machines the ability to swiftly change between wireless modes in order to minimize or avoid interference among other machines in the network, thus improving the performance of M2M communications [9]. 4. When all machines in an M2M communication system have cognition abilities, the machine and protocol heterogeneity is no longer an issue and the network is, as a result, efficient and flexible since all devices are adequately smart for communicating among each other freely [2].

3 Cognitive M2M as Enabler for IoT Internet of Things (IoT) has given rise to a unique evolution towards a new universal communication infrastructure providing new services and thus having a significant impact on the economy and the industry. However, with the advantages of IoT comes new challenges. The number of mobile-connected devices is expected to be 11.6 billion by 2020. With such an enormous increase in devices, demands on spectral resources will be tremendous and result in overcrowding in the unlicensed bands. Moreover, IoT applications are diverse and encompass many different types of devices and various heterogeneous technologies that connect these devices to the network. Energy efficiency of the network is also an issue for IoT. Employing cognitive radio technology for M2M communications can be a promising solution for IoT and tackle many of these issues thus fulfilling the vision of IoT [18]. During a cognitive cycle [19], CR uses spectrum sensing and analysis to detect spectrum white space, which are frequency bands not occupied by primary users, the secondary users choose the best one among them with the help of spectrum management and handoff. CR also manages spectrum access among all the secondary users through dynamic spectrum access within the cognitive cycle. In case of an appearance from any primary users, it is also responsible for withdrawing of any secondary users from

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that frequency band. Furthermore, self-existing and co-existing interference heavily degrade the performance of M2M communications. Cognitive radio offers software re-configurability to the machines in M2M network, which can then perform rapid switches among other wireless modes to reduce interference drastically with other devices [2]. The large number of devices and multiple network technologies of IoT makes it difficult to manage all the devices, and the traffic they produce. The scalability of the network will also be an issue since a significant extent of expert human intervention is required to manage and organize such networks [18] which will not scale as well with an increase in devices, and the network’s heterogeneity and complexity. Self-organization capabilities such as self-management and self-healing are required of the networks to enable scalability and extending their coverage. Cognitive radio enabled machines in the M2M network are able to carry out self-organization for spectrum management, control channel setup, opportunistic access of spectrum using autonomic dynamic spectrum allocation in order to obtain scalability as a network.

4 Applications of Cognitive M2M Communication The addition of cognitive radio to M2M communications give rise to added functionality and performance of the network that can benefit many applications (Fig. 1).

4.1 Home Multimedia Distribution and Sharing More and more devices, machines, and terminals are being included in home networks nowadays, from mobile phones to TVs and personal computers and other electronic appliances. M2M communications are set to become a prevailing communication paradigm in home networks due to the drastic penetration of embedded devices. One of the main application and service in a home network is the multimedia distribution and sharing. Home networks often make use of the ISM band for such purposes. As this frequency band is becoming more and more crowded, dynamic access to additional spectrum provided by the cognitive radio makes cognitive M2M communications an attractive option for home multimedia distribution and sharing [2].

4.2 Intelligent Transportation Systems Intelligent Transportation Systems (ITS) make use of sensors in both roads and vehicles to provide autonomous connection between road vehicles and traffic lights,

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Fig. 1 Applications of cognitive M2M communications

roads, emergency crews etc. with the help of M2M. The Dedicated Short Range Communication (DSRC) band used for such communications is expected to become congested in the future as massive amounts of data exchange will be required to carry out applications such as traffic monitoring, management of such traffic, and smart parking etc. Dynamic spectrum sharing provided by cognitive M2M can provide better spectrum utilization and efficient communication between the smart objects in the vehicles and the roadside ITS sensors in order to provide longer range of communication and better data rates [20].

4.3 eHealthcare Cognitive M2M is expected to play a vital role in the tackling of radio resource scarcity, green issues and network heterogeneity issues in the eHealthcare system. Persistent pervasive monitoring of patients is a requirement of eHealthcare system. eHealthcare applications also usually comprise of interconnection of hybrid networks and transmitting heterogeneous traffic in a green manner. Cognitive radio can help with all these requirements [2].

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4.4 Environmental Monitoring In order to monitor the environmental conditions in a smart city, a vast number of heterogeneous devices are required to be placed in key locations that can monitor temperature, humidity, noise, air quality, CO and CO2 emissions etc. Dedicated vehicles equipped with sensor devices and location determination devices (e.g. GPS) moving on the city roads is another option. In such cases, cognitive radio technology should be implemented for reliable and energy-efficient operation of these huge number of devices. The dynamic spectrum management and reconfiguration capabilities of CR-based smart objects would ensure overcrowded spectrum would not be an issue and data can be transferred for longer distances and in areas with no network coverage [20].

4.5 Smart Grid The amount of energy related data is enormous. This becomes an issue for existing communication networks as well as any future smart grid network. Smart grids utilizing cognitive radio facilitates better spectrum utilization and improves communication capacities so as to support large scale data transmission [20].

5 Conclusion In this chapter, the authors present cognitive machine-to-machine (CM2M) communication. M2M solutions mainly use ISM bands are thus susceptible to interference since the ISM band is also used by multiple other wireless technologies which will only get worse in the future. It is expected that M2M without cognition will only be a drain on the current network infrastructure as time goes on. Using the cognitive radio capabilities, M2M communication effectively utilize the radio resources at a time where spectrum scarcity is becoming a big issue. Moreover, cognitive M2M technologies is a must in order to realize the vision of IoT.

References 1. Li, S., Da Li, X., Zhao, S.: The internet of things: a survey. Inf. Syst. Front. 17(2), 243–259 (2015) 2. Zhang, Y., Rong, Y., Nekovee, M., Liu, Y., Xie, S., Gjessing, S.: Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw. 26(3), 6–13 (2012) ˇ 3. Cabri´ c, D., Mubaraq Mishra, S., Willkomm, D., Brodersen, R., Wolisz, A.: A cognitive radio approach for usage of virtual unlicensed spectrum. In: 14th IST Mobile and Wireless Communications Summit. Citeseer (2005)

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4. Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005) 5. Palicot, J.: Cognitive radio: an enabling technology for the green radio communications concept. In: Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly, pp. 489–494. ACM (2009) 6. Whitehead, S.: Adopting wireless machine-to-machine technology. Comput. Control. Eng. 15(5), 40–46 (2004) 7. Wu, G., Talwar, S., Johnsson, K., Himayat, N., Johnson, K.D.: M2M: from mobile to embedded internet. IEEE Commun. Mag. 49(4), 36–43 (2011) 8. Chen, M., Wan, J., Li, F.: Machine-to-machine communications: architectures, standards and applications. KSII Trans. Internet Inf. Syst. 6(2) (2012) 9. Aijaz, A., Hamid Aghvami, A.: Cognitive machine-to-machine communications for internetof-things: a protocol stack perspective. IEEE Internet Things J. 2(2), 103–112 (2015) 10. Ejaz, W., Ibnkahla, W.: Machine-to-machine communications in cognitive cellular systems. In: 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), pp. 1–5. IEEE (2015) 11. Oliveira, L., Rodrigues, J.J.P.C., Kozlov, S.A., Rabêlo, R.A.L., de Albuquerque, V.H.C.: MAC layer protocols for internet of things: a survey. Future Internet 11(1), 16 (2019) 12. Hegazy, E., Saad, W., Shokair, M.: An efficient proposed MAC protocol for M2M networks. Wirel. Pers. Commun. 96(2), 2253–2269 (2017) 13. Aijaz, A., Hamid Aghvami, A.: A PRMA based MAC protocol for cognitive machine-tomachine communications. In: 2013 IEEE International Conference on Communications (ICC), pp. 2753–2758. IEEE (2013) 14. Aijaz, A., Ping, S., Akhavan, M.R., Aghvami, A.-H.: CRB-MAC: a receiver-based mac protocol for cognitive radio equipped smart grid sensor networks. IEEE Sens. J. 14(12), 4325–4333 (2014) 15. Liu, Y., Rong, Y., Pan, M., Zhang, Y., Xie, S.: SD-MAC: spectrum database-driven MAC protocol for cognitive machine-to-machine networks. IEEE Trans. Veh. Technol. 66(2), 1456– 1467 (2016) 16. Tarchi, D., Fantacci, R., Marabissi, D.: An M2M cognitive MAC protocol for overlaid OFDMA environments. Trans. Emerg. Telecommun. Technol. 28(3), e2955 (2017) 17. Dehalwar, V., Kalam, A., Kolhe, M.L., Zayegh, A.: Review of machine to machine communication in smart grid. In: 2016 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), pp. 134–139. IEEE (2016) 18. Rawat, P., Deep Singh, K., Marie Bonnin, J.: Cognitive radio for M2M and internet of things: a survey. Comput. Commun. 94, 1–29 (2016) 19. Matin, M.A.: Spectrum Access and Management for Cognitive Radio Networks. Springer (2017) 20. Tragos, E.Z., Angelakis, V.: Cognitive radio inspired M2M communications. In: 2013 16th International Symposium on Wireless Personal Multimedia Communications (WPMC), pp. 1–5. IEEE (2013)

Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making Sotirios K. Goudos

Abstract The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine (M2M), while posing new challenges. Cognitive Radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary Algorithms (EAs) are suitable techniques to solve the above-mentioned problem. In this chapter, we propose a new approach for designing a CR engine for wireless multicarrier transceivers using monarch butterfly optimization (MBO). Moreover, we also apply a modified MBO version that includes a Greedy strategy and a self-adaptive Crossover operator, called Greedy Crossover MBO (GCMBO). Additionally, the CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. The simulation results show that the GCMBO driven CR engine can obtain better results than the original MBO and outperform other popular algorithms. Moreover, GCMBO is more efficient when applied to high-dimensional problems in cases of multicarrier system. Keywords Cognitive radio (CR) · Monarch butterfly optimization (MBO) · Optimization methods · Evolutionary algorithms

1 Introduction In recent years, there is a rapid development of wireless communications and a widespread adoption of devices (smartphones, tablets, laptops) which are running demanding applications. As a result, the need for additional wireless spectrum has S. K. Goudos (B) Radiocommunications Laboratory, Department of Physics, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_7

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been dramatically increasing, leading to the spectrum scarcity problem. Furthermore, advanced software applications impose high data throughput and reliable connections and at the same time there is a big necessity to design eco-friendly systems. Additionally a study of electromagnetic spectrum utilization by Federal Communications Commission (FCC) [1] has reported that on an average, there is a 10% of the allocated spectrum that is used. Most of the spectrum bands are only utilized for limited time and in limited places. The Internet of Things (IoT) paradigm describes communication not only human to human (H2H) but also machine to machine (M2M) without the need of human interference. Recently Long Term Evolution-Advanced (LTE-A) supports M2M communications (called LTE-M) [2]. Moreover, the IoT nodes are assumed to be interconnected using cellular or other wireless communication technologies. However, the need for spectrum resource for IoT applications is becoming significant. Such an issue can be addressed using smart IoT objects that will have cognitive capabilities [3, 4]. Current technology trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) [5, 6] into IoT. Cognitive Radio (CR) is a promising technique which has been proposed as the technology which can effectively cope with these problems [7]. Mitola suggested that CR systems have the ability to monitor and detect the conditions of their operating environment, and reconfigure their transmission parameters in order to provide the best service which satisfies the user’s demands [8, 9]. The CR system is informed about its operating environment by the environmental parameters which also determine the effectiveness and the accuracy of the decisions made by the system. Moreover, it is worth noting that in order to determine the possible directions of the system, we must define the performance objectives which will be satisfied by it. Evolutionary Algorithms (EAs) are suitable techniques for solving the abovementioned problem. The optimization of CR systems has been performed in the literature using several evolutionary optimization techniques. Among others these approaches include Genetic Algorithms (GA) [10–14], Particle Swarm Optimization [15, 16], or Ant Colony Optimization (ACO) [17]. Moreover, the authors in [18] use the Biogeography-based optimization (BBO) algorithm for CR system optimization. In a recent paper, we apply real-coded BBO (RCBBO) for the CR problem optimization [19]. Moreover multi-objective EAs have also been used to the CR problem [20, 21]. A survey of all these techniques can be found in [22]. Monarch butterfly optimization (MBO) is a recently proposed evolutionary algorithm that models the migration of monarch butterflies that live in North America [23]. MBO has been applied successfully to several real world engineering problems like the dynamic vehicle routing problem [24], the Chinese TSP problem [25], for denoising brain images [26], PID controller design [27], localization in wireless sensor networks [28], and for optimal power flow for IEEE 30 and 118-bus systems [29]. Moreover, the authors in [30] have a proposed a new MBO version that uses greedy strategy and has a self-adaptive crossover operator the GCMBO. In this chapter, the CR system will be optimized using the two different MBO algorithms: the original MBO, and the GCMBO. Additionally, in order to evaluate the

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algorithms performance we compare the MBO algorithms with popular evolutionary algorithms from the literature. Namely, we compare with Biogeography-Based Optimization (BBO) [31], Particle Swarm Optimization [32], a stud genetic algorithm (StudGA) [33], and evolutionary strategies (ES) [34, 35] The simulations are carried out for both single carrier and multi-carrier systems. The simulation results show that the GCMBO algorithm achieves better performance than the original MBO algorithm, as well as better than the other algorithms. The simulation results are finally combined with a fuzzy decision maker in order to find the best compromised solution.

2 Problem Formulation The basic concept of a cognitive engine is real-time tuning of transmission parameters according to the environment information. Therefore, first the environmental and the transmission parameters should be defined. The environmental parameters are the inputs for the cognitive engine in order to make a decision about the transmission parameters. The optimization problem may therefore be defined by searching for the best possible transmission parameters subject to the known environmental parameters. The environmental parameters used in this paper are the signal-to-noise ratio (SNR), bit error rate (BER), and background noise power (N) [11]. The transmission parameters are the unknowns or the decision variables of the cognitive radio engine. The list of the transmission parameters [18] is shown in Table 1. From the Table 1 it is worth noting that the modulation type used in our system is Binary Phase Shift Keying (BPSK), for modulation index = 2, Quadrature Phase Shift Keying (QPSK), for modulation index = 4, and Quadrature Amplitude Modulation (QAM), for modulation index > 4. The other transmission parameters can take any value (continuous variables) between the specified minimum and maximum values. Table 1 Transmission parameters Parameter name

Description

Value Min

Max

Transmission power

Amount of transmitted power

0.158 mW

251 mW

Modulation index

Number of symbols for the current modulation

2

256

Bandwidth

Bandwidth of the transmitted signal in Hz

2 MHz

32 MHz

TDD

Percentage of transmit time

25%

100%

Symbol rate

Number of symbols per second

125 Ksps

1 Msps

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2.1 Cognitive Radio Engine Objectives The five objective functions used in this section are formulated in [18] and kept the same for comparison reasons. The total fitness is obtained by summing the objective functions using suitable weight values. The single objective fitness functions for a CR system with K independent subcarriers are described below. The objective function of power consumption minimization is expressed as [18] K ¯ = Fmin-power ( p)

pi

i=1

(1)

K × pmax

where pi is the transmitted power on the i-th subcarrier, and pmax is the maximum available transmit power, and p¯ = ( p1 , p2 , . . . , p N ) is the vector of the transmitted power. The objective function of BER minimization is defined as [18] log (0.5)   K10

¯ m) ¯ = Fmin-ber ( p, log10

i=1

Pbei (m i , pi ) K



(2)

where m i is the modulation index on the i-th subcarrier, m¯ = (m 1 , m 2 , . . . , m N ) is the vector of the modulation indices and For BPSK modulation the BER is defined as [36]  Pbei ( pi ) = Q

pi N0



Also for M-ary PSK the BER is expressed as [36]  Pbei ( pi , m i ) =

   2 π pi Q 2 log2 (m i ) × × sin log2 m i No mi

Additionally for M-ary QAM modulation the BER is given by [36]  Pbei ( pi , m i ) =

4 log2 m i

   1 3 log2 (m i ) pi 1− √ Q mi (m i − 1)No

We assume that No = −104 dBm as set in previous papers in order to compare results. The objective function of throughput maximization [11] is defined as  K Fmax-throughput (m) ¯ =1−

log2

i=1

mi



N

log2 (m max )

(3)

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where m max is the maximum modulation index. The objective function of interference minimization [18] is given by 



¯ tdd = ¯ b, Fmin-interference p,

K i=1

(( pi + bi + tddi ) − ( pmin + bmin + 1)) K ( pmax + bmax + 100)

(4)

where bi and tddi are the bandwidth and the time division duplexing on the i-th subcarrier respectively, b¯ = (b1 , b2 , .. . . . , b N ), tdd = (tdd1 , tdd2 , . . . , tdd N ) are the corresponding vectors of bandwidth and time division duplexing respectively, and Bmin , Bmax are the minimum and maximum bandwidths available. The objective function of spectral efficiency maximization [18] is expressed as  Fmax-specteff m, b, rs = 1 −

K

m i rs,i bmin i=1 m max rs,max bi

K

(5)

where rs,i is the symbol rate on the i-th subcarrier, rs = (rs,1 , rs,2 , . . . , rs,N ) is the corresponding vector of the symbol rate and rs,max is the maximum symbol rate. The above described objective functions could sum in order to create a total objective function. The requirement to fulfill multiple objectives has lead to the use of a weighted sum approach. Therefore, a total objective function may be defined according to the following sum Ftotal (x) ¯ =

k

wi Fi (x) ¯

(6)

i=1

where, w1 , w2 , . . . , wk , 0 ≤ wi ≤ 1 for i = 1, 2, . . . , k are suitable weight values subject to w1 + w2 + · · · + wk = 1

(7)

where k is the number of objective functions. As a result, we form the multiple objective fitness function for a CR system with K independent subcarriers:  ¯ tdd, r¯s = w1 Fmin-power ( p) ¯ m, ¯ b, ¯ + w2 Fmin-ber ( p, ¯ m) ¯ Fmulti-carrier p,

 ¯ tdd ¯ b, + w3 Fmax-throughput (m) ¯ + w4 Fmin-interference p,  ¯ r¯s + w5 Fmax-specteff m, ¯ b, (8)

The weights w1 , w2 , w3 , w4 , w5 define the search direction of the evolutionary algorithm and must conform to the limitations of (7). Table 2 holds five different weight vectors that represent different scenarios of the CR engine configuration. Each weight vector emphasizes on different performance objective. This fact leads the evolutionary algorithms to a solution set that is related to a specific scenario. We have chosen these weight values based on a trial and error procedure. We have

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Table 2 Weighting values

Scenario

Weight vector [w1 , w2 , w3 , w4 , w5 ]

Minimize power consumption

[0.65, 0.05, 0.15, 0.10, 0.05]

Minimize bit-error-rate

[0.05, 0.70, 0.05, 0.05, 0.15]

Maximize throughput

[0.05, 0.10, 0.70, 0.10, 0.05]

Minimize interference

[0.05, 0.05, 0.15, 0.70, 0.05]

Maximize spectral efficiency

[0.05, 0.15, 0.10, 0.05, 0.70]

Balanced mode (equal weights)

[0.2, 0.2, 0.2, 0.2, 0.2]

found that better solutions can be obtained using these weight values. Additionally, in order to evaluate the choice of equal weights the balanced mode scenario has been also selected for comparison reasons. Please note that the weight values taken are different than those in [11, 18].

2.2 Best Compromised Solution In order to choose the best compromised solution from the set of solutions a suitable decision maker has to be used. The fuzzy set theory has been used as a decision maker in several applications in the literature like transportation planning, vendor selection, etc. [37, 38]. The satisfaction degree of each objective function is represented by a linear fuzzy membership function which for minimization problems is expressed as

μk =

⎧ ⎪ ⎨ 1 max ⎪ ⎩

z k −z k z kmax −z kmin

0

if z k ≤ z kmin if z kmin < z k < z kmax

(9)

if z k ≥ z kmax

where z k the value of k-th objective function, z kmin , z kmax are the minimum and maximum value of the k-th objective function respectively. The best compromised solution is found by using s=

n obj 1

n obj

μk

(10)

k=1

where n obj is the number of objectives and s is the degree of satisfaction. For each solution, we calculate the value of s. The point with the maximum s value is the best compromised solution.

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3 Monarch Butterfly Optimization MBO concept is based on the migration behavior of the monarch butterflies in nature. MBO divides the population in two lands (or sub-populations). The decision variables are the positions of the monarch butterflies. In order to update these positions MBO uses two operators which are applied sequentially. The first is the migration operator, that generates offsprings that can be adjusted by the migration ratio. The second is the butterfly adjusting operator that tunes the butterflies’ positions. Thus, the exploration of the search space is done by these two operators. Additionally as the authors claim in [23] these two operators can also be applied simultaneously. Therefore, MBO algorithm can also be easily implemented in parallel.

3.1 Migration Operator The migration behavior of monarch butterflies can be modeled using the following rules. (1) There are two subpopulations of the monarch butterflies located in Land 1 and Land 2. (2) The new child butterflies (individuals) are created using the migration operator in Land 1 or Land 2. (3) The best individuals survive to the next generation. A control parameter of MBO is the ratio r pop of monarch butterflies in Land 1. Thus, if the total population size is N then the population size of Land 1 is N pop1 = ceil(r pop × N ) where the ceil() operator denotes the rounding to nearest integer greater or equal. Accordingly the population in Land 2 is N pop2 = N − N pop1 . We consider butterflies positions (vectors) of problem dimensions size D, given by b¯ = (b1 , b2 , . . . , b D ) For each individual in population during the migration process the algorithm generates a random number as r nd = rand(0,1) × Period where rand(0,1) is uniformly distributed number within the interval (0, 1), and Period is the migration period. This is suggested by the authors in [23] to be set to 1.2. The migration process is then formulated as [23] t bi,t+1 j = br 1, j if r nd ≤ r pop t bi,t+1 j = br 2, j otherwise

(11)

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where bi,t+1 j is the value of the i-th butterfly position at the j-th dimension at the next generation, brt 1, j is the value of newly generated position at the j-th dimension of a randomly selected butterfly r1 from subpopulation 1, and brt 2, j is the value of newly generated position at the j-th dimension of a randomly selected butterfly r2 from subpopulation 2 respectively.

3.2 Butterfly Adjusting Operator MBO uses also a butterfly adjusting operator, which can be described as follows [23] t bi,t+1 j = bbest, j if rand(0,1) ≤ r pop t bi,t+1 j = br 3, j otherwise

(12)

t where bbest, j is the value of the best so far individual at the j-th dimension of the whole population, and brt 3, j is the value of the position at the j-th dimension of a randomly selected butterfly r3 from subpopulation 2. Moreover, the butterflies position can be further modified by the expression [23]

 t bi,t+1 j = bi, j + a db j − 0.5

if rand(0,1) > B A R

(13)

where BAR is the butterfly adjusting rate, db j is the walk step of the individual I that can be calculated by Lévy flight, and a is a weighing factor calculated as a=

Wmax t2

(14)

where Wmax is the maximum walk step of a monarch butterfly in a single move.

3.3 GCMBO GCMBO is a new improved version of MBO introduced in [30]. GCMBO modifies the migration and the butterfly adjusting operator as follows. During the migration process a greedy strategy is applied which for minimization problems can be expressed as [30]  b¯t+1,i =

b¯t+1,new , if f (b¯t+1,new ) < f (b¯t,i ) b¯t,i , otherwise

(15)

where f (b¯t+1,new ), f (b¯t,i ) are the fitness values of a new generated and an old i-th individual respectively.

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Additionally, GCMBO includes also a crossover operator in the butterfly adjusting phase. According to [30] the crossover operator modifies the i-th butterfly generated using (13) by bC R t+1,i = b¯t+1,i × (1 − C R) + b¯t,i × C R

(16)

where bC R t+1,i is the new generated individual with crossover operator and C R is a self-adapting crossover parameter given by f (b¯t,i ) − f C R = 0.8 + 0.2 ×  f b¯t,wor st −

 b¯t,best  f b¯t,best

(17)

  where f b¯t,best , f b¯t,wor st are the fitness values of the best and worst individual respectively. Again after the crossover generated individual a greedy selection strategy is also applied given by  b¯t+1,i =

bC R t+1,i , if f (bC R t+1,i ) < f (b¯t,+1i ) b¯t+1,i , otherwise

(18)

where f (bC R t+1,i ) is the fitness value of the new crossover generated butterfly.

4 Numerical Results In this chapter, we compare results for a single carrier and multiple subcarriers using the two MBO algorithms, BBO, StudGA, ES, and PSO. We compare the algorithms on the five objective functions presented in the previous section. All algorithms ran for 20 independent trials. A population of 200 vectors was selected. The total number of iterations was set to 200. The stopping criterion was the iteration number. For the MBO algorithms, we set the max step size Wmax = 1 the butterfly adjusting rate to 5/12, the migration period to 1.2, and the migration ratio to 5/12. For the BBO algorithm, we set the habitat modification probability, P mod , to 1 and the maximum mutation rate, m max , equal to 0.01. The maximum immigration rate I, and the maximum emigration rate E are both set to one. The elitism size is set to two for all algorithms. For the ES, we set λ = 10 produced offspring in each generation, and standard deviation σ = 1. Moreover, for StudGA we set the crossover probability to 1, and the mutation probability to 0.01. For PSO we have set the inertia weight to 0.3, and both the cognitive constant and the social constant to one. The first design example is that of a single subcarrier using all five objective functions with different weight scenarios. The corresponding weight values for each case are those given in Table 2. The total fitness values for the whole range of the five algorithms versus the objective functions are depicted in Table 3. It can be noticed

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Table 3 Comparative results of the total fitness values for single carrier system. The smaller values are in bold font Scenario

BBO

ES

StudGA

MBO

PSO

GCMBO

Minimizing power mode

0.015849

0.015837

0.015837

0.015828

0.016616

0.015828

Minimizing BER mode

0.027078

0.027078

0.027079

0.027078

0.027078

0.027078

Maximizing throughput mode

0.009476

0.009479

0.009476

0.009476

0.009476

0.009476

Minimizing interference mode

0.006389

0.006396

0.00639

0.006389

0.006418

0.006389

Maximizing spectral efficiency mode

0.009476

0.009477

0.009476

0.009476

0.009476

0.009476

Balanced mode (equal weights)

0.025566

0.025556

0.025566

0.025555

0.025727

0.025555

that the algorithms MBO and GCMBO have produced the same results in all the five objective functions. Moreover, they have obtained the best results in all cases. However, all algorithms perform well in this case and obtain similar results. The problem has only five decision variables so it is not difficult for all algorithms to find good solutions. Next, we expand the previous case study with multiple subcarriers like 16, 32 and 64 and obtain the corresponding results. Using more subcarriers makes the problem high dimensional. In this case the total number of decision variables for N c subcarriers is 5N c . Therefore for 16, 32, and 64 subcarriers the total number of decision variables is 80, 160 and 320 respectively. First, we consider the 16-carriers case. The corresponding results are reported in Table 4. We notice that GCMBO achieves the best performance in most of the cases. StudGA also obtains a good performance and is first in two cases. The original MBO did not obtained the best results in this case. The PSO algorithm obtained the worst results for this case. BBO also performs satisfactory in most of the cases. Next, Table 5 lists the comparative results for the 32-carriers case. In this case, GCMBO outperformed the other algorithms in three scenarios, while was second in the rest of the scenarios. StudGA also again performs well and obtained the best performance in two scenarios. BBO obtained the best result in one scenario and is third in most of the other scenarios. MBO performs better than PSO in all cases, however again the results differ from GCMBO. Additionally, Table 6 holds the five objective function scenarios obtained best results for a 64-carrier system. The problem complexity increases in this case. Also, we note that the algorithms again are evaluated with the same population number as in previous cases. In this case, CGMBO performs better than the other algorithms

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Table 4 Comparative results of the total fitness values for 16-carrier system. The smaller values are in bold font Scenario

BBO

ES

StudGA

MBO

PSO

GCMBO

Minimizing power mode

0.04695

0.11918

0.03273

0.06901

0.24191

0.03442

Minimizing BER mode

0.10448

0.16034

0.06687

0.16958

0.15798

0.06232

Maximizing throughput mode

0.04803

0.09362

0.01641

0.08212

0.15748

0.01572

Minimizing interference mode

0.03696

0.08434

0.01110

0.06949

0.21232

0.02219

Maximizing spectral efficiency mode

0.29115

0.31004

0.11237

0.57107

0.56931

0.12055

Balanced mode (equal weights)

0.11966

0.2443

0.05722

0.22612

0.30177

0.05395

Table 5 Comparative results of the total fitness values for the 32-carrier system. The smaller values are in bold font Scenario

BBO

ES

StudGA

MBO

PSO

GCMBO

Minimizing power mode

0.05787

0.20742

0.05944

0.08524

0.29854

0.06339

Minimizing BER mode

0.15558

0.18535

0.12605

0.19524

0.19755

0.10294

Maximizing throughput mode

0.06003

0.12856

0.03799

0.07246

0.19446

0.03826

Minimizing interference mode

0.05592

0.1661

0.05098

0.23323

0.27239

0.05455

Maximizing spectral efficiency mode

0.33217

0.42536

0.28875

0.59275

0.63304

0.22092

Balanced mode (equal weights)

0.15444

0.2622

0.13083

0.29651

0.37362

0.11837

Table 6 Comparative results of the total fitness values for 64-carrier system. The smaller values are in bold font Scenarios

BBO

ES

StudGA

MBO

PSO

GCMBO

Minimizing power mode

0.09555

0.27252

0.09156

0.08644

0.33926

0.06787

Minimizing BER mode

0.14324

0.20382

0.15395

0.20983

0.20964

0.17043

Maximizing throughput mode

0.0588

0.15655

0.06284

0.14169

0.20862

0.05487

Minimizing interference mode

0.08044

0.24043

0.10698

0.09687

0.32457

0.07237

Maximizing spectral efficiency mode

0.3779

0.52714

0.44421

0.66211

0.67295

0.43491

Balanced mode (equal weights)

0.16396

0.33116

0.20629

0.25338

0.4005

0.20663

92 Table 7 Algorithm ranking first and second for all cases

S. K. Goudos Algorithm

Ranking 1st

Ranking 2nd

BBO

4

3

ES

0

0

StudGA

5

9

MBO

2

2

PSO

0

0

11

7

GCMBO

in 3 scenarios. Additionally, BBO also obtains a similar performance. It seems that CGMBO is second in the scenarios that BBO is first and vice versa. The other algorithms performance varies. PSO obtains the worst performance, while StudGA performs satisfactory in most of the scenarios. Based on previous results, Table 7 holds the algorithm ranking for all cases and shows clearly that GCMBO has the majority of 1st positions. StudGA follows with five best positions out of 24. Also BBO obtained the best result in four cases. The original MBO performed well in two cases, while ES and PSO did not obtained first or second place in any case. Moreover, in order to further evaluate the results we have conducted non-parametric statistical tests. Namely, the Friedman test and the Wilcoxon signed-rank test. These tests are commonly performed in order to compare results from evolutionary algorithms [39]. These tests are conducted on the results found in Tables 3, 4, 5 and 6. Table 8 holds the average rankings achieved by Friedman test. It is clear that GCMBO emerges as the best algorithm with the higher ranking. StudGA and BBO follow with a close difference between them. MBO comes fourth, while ES and PSO are in fifth and sixth place respectively. Additionally, the Wilcoxon signed-rank test is conducted with a significance level at 0.05 in order to find if GCMBO is significantly better than the other algorithms. The results are listed in Table 9. The p-values for all algorithms are below 0.05, thus GCMBO is significantly better than the other algorithms. Figure 1 shows the convergence rate graph for the single carrier case. It seems that all algorithms except PSO converge at similar speed. BBO converges slightly faster than the other algorithms. The convergence rate graph for the 16 subcarriers case is depicted in Fig. 2. It seems that BBO and GCMBO converge at similar speed faster than the other algorithms. Table 8 Average rankings achieved by Friedman test

Algorithm

Average rank

Normalized values

Rank

BBO

2.73

1.49

3

ES

4.56

2.49

5

StudGA

2.50

1.37

2

MBO

3.92

2.14

4

PSO

5.46

2.98

6

GCMBO

1.83

1.00

1

Cognitive Radio Engine Design for IoT Using Monarch Butterfly … Table 9 Wilcoxon signed-rank test between RCBBO-G and the other algorithms GCMBO

p-value

BBO

0.0194

ES

0.0000

StudGA

0.0348

MBO

0.0002

PSO

0.0001

Fig. 1 Convergence rate graph for single subcarrier case

Fig. 2 Convergence rate graph for 16 subcarriers case

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This is also the case for Fig. 3 which depicts the convergence rate graph for the 32 subcarriers case. Again GCMBO and BBO converge at similar speed. Figure 4 shows the converge rate graph for the 64 subcarriers case. Overall, in all cases GCMBO and BBO converge at similar speed so that the use of any of the two algorithms does not offer an advantage regarding convergence rate speed. In order also to evaluate the algorithms performance in terms of distribution of the obtained results, we produced the box plots for all cases. Figure 5 shows the box plot of the algorithms results for the single carrier case. It is evident that GCMBO, BBO and StudGA obtain the smaller distribution of values and perform similarly. Next, Fig. 6 depicts the box plot for the 16-subcarriers case. GCMBO obtained the smaller distribution of values. StudGA and ES also have small distributions. MBO results have the larger spread of values.

Fig. 3 Convergence rate graph for 32 subcarriers case

Fig. 4 Convergence rate graph for 64 subcarriers case

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Fig. 5 Boxplot graph for the single subcarriers case

Fig. 6 Boxplot graph for the 16 subcarriers case

The box plot for the 32-subcarriers case is plotted in Fig. 7. Again clearly GCMBO has the smaller distribution of values. BBO and StudGA also perform well in this case. Similar results are obtained for the 654-subcarriers case. GCMBO obtained the best result, while BBO and StudGA follow. Overall it seems that GCMBO manages to produce a small distribution of values in all cases. Thus, also in terms of value distribution GCMBO performs well (Fig. 8).

4.1 Best Compromised Solutions The best compromised solutions using the fuzzy decision making rules described in the previous section across the full range of weighted vectors and the applied algorithms are reported in Table 10. One may notice that the best compromised

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Fig. 7 Boxplot graph for the 32 subcarriers case

Fig. 8 Boxplot graph for the 64 subcarriers case Table 10 Best compromised solutions for the cases of 1, 16, 32 and 64 carriers Number of carriers

Minimum power consumed (mW)

Minimum BER

Maximum throughput

Minimum interference

Maximum spectral efficiency

Algorithm

1

0.0537

0.0741

1

7.504E−7

1

GCMBO

16

0.0592

0.0736

1

8.44E−4

0.9579

GCMBO

32

0.1087

0.0622

1

0.0018

0.8506

GCMBO

64

0.2359

0.0571

0.9961

0.0066

0.6824

GCMBO

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solutions found are obtained in all cases by GCMBO algorithm. Figures 9, 10 and 11 depict in a graphical manner the best compromised solutions found by the fuzzy decision maker for 16, 32, and 64 subcarriers respectively.

Fig. 9 Best compromised solution found by fuzzy decision maker for 16 subcarriers

Fig. 10 Best compromised solution found by fuzzy decision maker for 32 subcarriers

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Fig. 11 Best compromised solution found by fuzzy decision maker for 64 subcarriers

5 Conclusion In this chapter, we have proposed a GCBMO approach for the optimization of Cognitive Radio IoT systems engine combined with a fuzzy decision maker. The main characteristic of this algorithm is that it models the monarch butterfly behavior and includes migration with greedy selection and crossover operators in order to improve its performance. Simulation results show that the GCBMO algorithm outperforms the original MBO and other popular algorithms. From the above figures we can see that the GCBMO algorithm converges faster and obtain better results than the original MBO. It is also worth noting that the differences between the algorithms become more significant as the number of subcarriers is increasing. Moreover, using fuzzy set theory we have managed to find the best compromised solution which is given by GCBMO algorithm.

References 1. Spectrum policy task force report. In: Federal Communications Commission (FCC’02), pp. 745–747 (2002) 2. Nokia: LTE Evolution for IoT Connectivity (2016) 3. Rawat, P., Singh, K.D., Bonnin, J.M.: Cognitive radio for M2M and internet of things: a survey. Comput. Commun. 94, 1–29 (2016) 4. Khan, A.A., Rehmani, M.H., Rachedi, A.: When cognitive radio meets the internet of things? In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Sept 2016, pp. 469–474 (2016) 5. Baban, S., Denkoviski, D., Holland, O., Gavrilovska, L., Aghvami, H.: Radio access technology classification for cognitive radio networks, pp. 2718–2722 (2013)

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6. Gavrilovska, L., Atanasovski, V., Macaluso, I., Dasilva, L.A.: Learning and reasoning in cognitive radio networks. IEEE Commun. Surv. Tutorials 15, 1761–7177 (2013) 7. Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23, 201–220 (2005) 8. Mitola, J.: Cognitive radio: an integrated agent architecture for software defined radio. Doctoral Dissertation, Stockhold, KTH (2000) 9. Mitola Iii, J., Maguire Jr., G.Q.: Cognitive radio: making software radios more personal. IEEE Pers. Commun. 6, 13–18 (1999) 10. Newman, T.R., Rajbanshi, R., Wyglinski, A.M., Evans, J.B., Minden, G.J.: Population adaptation for genetic algorithm-based cognitive radios. Mobile Netw. Appl. 13, 442–451 (2008) 11. Newman, T.R.: Multiple objective fitness functions for cognitive radio adaptation. Dissertation, University of Kansas (2008) 12. Newman, T.R., Barker, B.A., Wyglinski, A.M., et al.: Cognitive engine implementation for wireless multicarrier transceivers. Wirel. Commun. Mobile Comput. 7, 1129–1142 (2007) 13. Hauris, J.F.: Genetic algorithm optimization in a cognitive radio for autonomous vehicle communications. In: Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007, pp. 427–431 (2007) 14. Zhang, Z., Xie, X.: Application research of evolution in cognitive radio based on GA. In: 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA, pp. 1575–1579 (2008) 15. Tan, X., Zhang, H., Hu, J.: A hybrid architecture of cognitive decision engine based on particle swarm optimization algorithms and case database. Ann. Telecommun. 69, 593–605 (2014) 16. Yu, Y., Tan, X., Xie, Y., Chen, J.: Cognitive radio decision engine based on binary chaotic particle swarm optimization. J. Inform. Comput. Sci. 10, 3751–3761 (2013) 17. Zhao, N., Li, S., Wu, Z.: Cognitive radio engine design based on ant colony optimization. Wirel. Pers. Commun. 65, 15–24 (2012) 18. Kaur, K., Rattan, M., Patterh, M.S.: Biogeography-based optimisation of cognitive radio system. Int. J. Electron. 101, 24–36 (2014) 19. Paraskevopoulos, A., Dallas, P.I., Siakavara, K., Goudos, S.K.: Cognitive radio engine design for IoT using real-coded biogeography-based optimization and fuzzy decision making. Wirel. Pers. Commun. 97, 1813–1833 (2017) 20. Chen, W., Li, T., Yang, T.: Intelligent control of cognitive radio parameter adaption: using evolutionary multi-objective algorithm based on user preference. Ad Hoc Netw. 26, 3–16 (2015) 21. Pradhan, P.M., Panda, G.: Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making. Swarm Evol. Comput. 7, 7–20 (2012) 22. Pradhan, P.M., Panda, G.: Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: a survey. Ad Hoc Netw. 17, 129–146 (2014) 23. Wang, G.G., Deb, S., Cui, Z.: Monarch butterfly optimization. In: Neural Computing and Applications (2015) 24. Chen, S., Chen, R., Gao, J.: A monarch butterfly optimization for the dynamic vehicle routing problem. Algorithms 10 (2017) 25. Wang, G.G., Hao, G.S., Cheng, S., Qin, Q.: A discrete monarch butterfly optimization for chinese TSP problem. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 165–173 (2016) 26. Aravindan, T.E., Seshasayanan, R.: Denoising brain images with the aid of discrete wavelet transform and monarch butterfly optimization with different noises. J. Med. Syst. 42 (2018) 27. Sambariya, D.K., Gupta, T.: Optimal design of PID controller for an AVR system using monarch butterfly optimization. In: IEEE International Conference on Information, Communication, Instrumentation and Control, ICICIC 2017, pp. 1–6 (2018) 28. Stromberger, I., Tuba, E., Bacanin, N., Beko, M., Tuba, M.: Monarch butterfly optimization algorithm for localization in wireless sensor networks. In: 2018 28th International Conference Radioelektronika, RADIOELEKTRONIKA, pp. 1–6 (2018)

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Physical Layer Security of Cognitive IoT Networks Güne¸s Karabulut Kurt and Özge Cepheli

Abstract Internet of Things (IoT) networks have the potential to drastically change our daily lives with a diverse set of applications. Healthcare, manufacturing, transportation and various other industries may encounter their impacts. As the IoT nodes evolve, it is only natural to expect an increase in their capabilities. Cognitive IoT (CIoT) networks contain IoT nodes with cognitive capabilities that can sense, and analyze the environment. These nodes can also act based on their analysis results. With such a potential, CIoT networks may also become a target for malicious users or attackers. In this chapter, we provide an overview of the main security requirements. Main attack types are summarized. Possible attacks on the CIoT nodes, including primary user emulation attacks, sensing data falsification attacks, objective function attacks and eavesdropping attacks are detailed. A case study targeting primary user emulation and sensing data falsification attacks based on a trust metric is detailed. Open issues are also highlighted.

Acronyms AWGN CA CIoT CPK DoS DDoS ECDLP IBE ITU

Additive white Gaussian noise Certificate authority Cognitive IoT Combined public key Denial of service Distributed denial of service Elliptic curve discrete logarithm problem Identity based encryption International telecommunication union

G. K. Kurt (B) Istanbul Technical University, ITU Maslak, 34469 Istanbul, Turkey e-mail: [email protected] Ö. Cepheli (B) Eumetsat Allee 1, 64295 Darmstadt, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_8

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ICMP IoT IDS IPS MCIM MIM OSI RFID ROC WEP

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Internet control message protocol Internet of things Intrusion detection system Intrusion prevention system Machine communication identity module Man in the middle Open system interconnection Radio-frequency identification Receiver operating characteristics Wired equivalent privacy

1 Introduction Wireless security has been a challenging and developing research area since wireless transmission channel is a shared medium. With the expanding use of wireless applications, the research activity in this area has started recently. Many technical challenges are still to be resolved. This chapter presents a survey of security aspects in Internet of Things (IoT) networks, with a focus of on its extension through wireless nodes with cognitive abilities. A broad definition of IoT communication includes the remote control of machines and monitoring, sensing/data collection from machines (telemetry) and the environment, targeting ubiquitous connectivity towards a global network that integrates the physical world with the cyber world. Achieved through sensing, data collection, processing and analysis, IoT promises to connect all devices through communication networks. From a wireless perspective, IoT enables a communication link between an IoT node and a portable node, or between an IoT node and a back-end information system. A typical IoT network model is shown in Fig. 1, composed of four interconnected components. It can be seen from the figure that the IoT node is connected to a wireless communication network without any human interaction, along with a remote connection service like monitoring, control, maintaining or localization and a user interface. Today, there exist many standards regulating IoT networks and IoT communications with active standardization activities conducted by ETSI, IEEE and 3GPP [1]. Wireless sensor nodes are the primary building blocks of IoT networks. They are deployed in a diverse set of applications such as smart home systems, vehicle to vehicle communication systems including smart transportation systems, health monitoring and alarm systems, smart metering, and industrial automation. IoT nodes are typically required to be small and inexpensive. With limited battery lives, IoT nodes should able to operate unattended by humans for extended periods of time using the available wireless networks. These real-life deployment requirements make it difficult to deploy classical security precautions on these systems. Starting from this point of view, this chapter investigates the security requirements of IoT networks. Our discussion is then extended to cognitive IoT (CIoT) networks, where the nodes

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Fig. 1 Typical IoT network model

have cognitive capabilities to improve the network efficiency and application performances. Our focus is on the physical layer, as especially the cognitive abilities of the CIoT networks heavily rely on opportunistic spectrum access and distributed control mechanisms for proper functionality, as detailed below. In the following section, we explain the evolution of IoT networks towards CIoT networks. Security requirements of general IoT networks are detailed in Sect. 3. The main attack types are classified in Sect. 4. In Sect. 5, we focus on the attacks on the cognitive access of CIoT networks. A case study against main attacks is detailed in Sect. 6. Finally conclusions are drawn and the open issues are listed in Sect. 7.

2 Evolution of Cognitive IoT Networks The IoT term is first coined by Ashton in 1999 [2]. The formal introduction of IoT to the literature dates back to 2005, in when the International Telecommunication Union (ITU), where four technological enablers are identified as the radio-frequency identification (RFID) technologies, sensor technologies, smart technologies and nanotechnology [3]. The IoT European Research Cluster (IERC) states that a massive number of uniquely addressable sensors, actuators and a diverse set of heterogeneous devices need to communicate with each other over pervasive networks, and also remain connected to the Internet [4]. To address the research challenges associated with IoT networks, researchers have been hard at work. According to [5], over 200,000 publications have been published on IoT between 2012–2017. This popularity mainly stems from the fact that the IoT paradigm has a wide impact potential in our daily lives, with economic, social, and technical implications. However, providing cost effective access techniques in wireless networks using the already scarce spectrum remains a challenge for the

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densely deployed heterogeneous devices. One solution can be to use new spectrum, however to preserve the cost effectiveness of the IoT networks service providers may not afford the bill for reaching multiple billion dollars. To address the cost effectiveness of IoT devices, one option is to re-utilize the idea of cognitive radio networks. In cognitive radio networks, the cognitive devices are clustered as primary nodes (users) and secondary nodes, and the dynamic spectrum access techniques target to address the dense deployment challenges. The empowerment of IoT networks with devices that are equipped with cognitive networks is termed as CIoT. The CIoT paradigm implies the seamless integration of IoT networks with nodes that support cognitive abilities. The integration of cognitive abilities to wireless devices, termed as cognitive radio, is first proposed by Mitola in 1999 [6]. The proposed model initially implied reconfigurable radio parameters, then its capabilities are extended to the integration of signal-processing and machine-learning techniques for their implementation by Haykin [7]. To this day, cognitive radio techniques remain an active research field, mostly targeting operational considerations, targeting performance and efficiency aspects of cognitive radio networks [8–10]. The extension of the idea of cognitive radio aspects to IoT paradigm is proposed in [11], focusing on smart and sustainable city development application, targeting an autonomic selection of the most relevant object. In [12], the authors target to seamlessly integrate the human cognitive abilities in the design process of CIoT networks, where a systematic discussion is proposed on the fundamental cognitive tasks. The authors in [13], propose the integration of cooperative and cognitive aspects to the classical IoT network. Further related references include [14, 15]. Contrary to the extensive literature on IoT, the literature on CIoT is quite sparse. One gap that remains a challenge in the CIoT networks is the security aspects. Even when the security issues of IoT networks are still under investigations and constant development and improvement, the cognitive front-ends of IoT networks extended the vulnerabilities of these networks to even more attacks, mostly from the physical layer.

3 Security Requirements In this section, we introduce the security aspects of IoT networks along with possible attacks related to each one. Secrecy, authentication, data integrity awareness and robustness to channel impairments constitute the main security requirements of wireless systems. Random nature of mobile communication systems makes it more difficult to realize physical attacks in a long time period, lowering the risk of having enduring security breaches. However, as special case of wireless systems, IoT and CIoT networks, are expected to work properly with no human supervision for long time periods, and generally in a predictable fashion, hence these security requirements become even more important for them. We provide these requirements with details below.

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3.1 Secrecy Data secrecy implies that the information is decoded only by the intended and authorized destination node(s). In wireless communication systems the information is actually broadcast due to the nature of the wireless channel. Hence this broadcast information can be received by the authorized and unauthorized nodes. In wired communication systems, when the sender and the receiver are connected through a cable, it is often assumed that the data cannot be captured by an illegitimate receiver. This generalization is not 100% true, as there exist many attack types of wiretapping in wired communications, but can be accepted as a practical approach as most wired channels are physically unavailable by being located inside walls or underground, and cable ends are usually secured by locked system rooms or racks, preventing physical access for everyone but authorized nodes. In addition to that, special equipment is necessary to wiretap a channel and the attempt becomes noticeable if the channel is physically interfered, which are disincentive factors. However in wireless environments, any receiver that is located within the coverage area of the transmitter can capture the communication signals. Major attack types against data secrecy are the passive attacks such as eavesdropping and traffic analysis. Eavesdropping is the act of illegitimately capturing and decoding the communication signals of the transmitter. In traffic analysis attacks, the illegitimate node can not decode the data. However, the attacker still but gathers the data traffic that can include sensitive information such as data transmission rates, source/destination addresses, transmission protocols and data types. Major countermeasures to these attacks include encryption, beamforming and artificial noise, as detailed in Sect. 5.

3.2 Authentication The authentication process targets to identify the target node and to verify that the corresponding node is allowed to access the offered services. In case when a non-legitimate node gets authenticated, restricted services and information can be accessed, even introducing the risk of information alterations, risking the performance of IoT networks. Authentication process is executed through the means of a secret key mechanism. Such secret keys can be obtained from ID-based systems, hardware tokens, preshared passwords and location information [16]. From the physical layer security perspectives, channel coefficients can also be used to generate the keys. ID-based cryptography, which is introduced by Shamir [17] in 1984, is also applicable for the authentication process. Identity based encryption (IBE) algorithm [18] and combined public key (CPK) algorithm [19] are frequently used systems. In IBE, node identity corresponds to its public key, and the chain of certificate authority (CA) is not used. But online databases are still required. CPK-based authentication

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systems eliminate the need for an online database or a trusted third party [20]. CPK can process the user identity by its key. Brute-force attacks, eavesdropping attacks, man in the middle attacks and authentication cloning attacks are the frequently encountered authentication attacks.

3.3 Data Integrity Awareness Data integrity awareness target the trustworthiness of the received data. Its essential attributes include completeness, currency/timeliness, accuracy/correctness and validity/authorization [21]. Data integrity is a critical requirement of IoT networks, because of their remote controlled nature. If the data are not trustworthy, which means it has been altered or corrupted, the receiver system should notice it and data should not be trusted. Major data integrity attacks are jamming attacks and message modification/ spoofing attacks. On the physical layer, jamming attacks are based on transmitting interfering signals to reduce the received signal’s quality and hence to degrade the communication system’s performance. Message modification are based on insertions or deletions to actual data. On the physical layer, spoofing based message modification attacks may also be encountered.

3.4 Robustness IoT networks need to be designed to be more robust than many other humansupervised systems against jamming and/or wireless channel based effects such as fading, noise or interference. It is usually not possible to reduce these natural or artificial (intentional) impairments [22]. Robustness can be enhanced by resource diversification techniques, such as having back up resources.

4 Attack Types The general picture of the categorized attacks with related countermeasures and security aspects are shown in Table 1. The attacks in the table are the major attack types on IoT and CIoT networks and are categorized according to the security requirement it threatens. Threat types I, II, III and IV refer to secrecy, authentication, data integrity and system robustness respectively. The available countermeasures to prevent these attacks are also introduced in the table. Physical layer security solutions are shown by “PHY” label. Similarly, data link layer solutions are shown by data link label, and other solutions are categorized under higher layer, in order to comprise the whole solutions available.

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As shown in the table, there exist security enhancing solutions in different layers of Open System Interconnection (OSI) model. Supervised communications systems can be monitored with intrusion detection system (IDS) or intrusion prevention system (IPS) equipments combined with human supervision [23, 24]. Network targeted attacks can be prevented by network or application layer solutions such as IDS and IPS, denoted as higher layer solutions in the table. There are also solutions that work in data link layer, such as encryption systems. Encryption provides a good secrecy enhancement as it makes data incomprehensible for unauthenticated users. Encryption and higher layer security solutions are available for a long time, as they are also applied to wired communication systems and are very successful on many security issues. However, it should be noted that these solutions are not efficient against all physical attack types, which means more precautions should be deployed for wireless communication systems. This is the main motivation that physical layer security concept has become very popular in the recent years. Beamforming, artificial noise, error correction coding, and spread spectrum coding are considered as physical layer countermeasures. There are a number of possible security attacks for the IoT nodes and the wireless communication networks. These security attacks are described in the following categories.

4.1 Physical Attacks These attacks may include passive attacks as eavesdropping and traffic analysis, as well as active attacks like denial of service (DoS), information disclosure and message modification attacks. In passive attacks attacker does not give an input to the target system, which makes the attacks undetectable. Taking precautions are the only countermeasure against passive attacks. On the contrary, attackers usually can be detected and prevented uniquely in active attacks, as they have an input to the system. • DoS attacks: A DoS attack is one of the general class of attacks that aim exhausting the resources available to its legitimate nodes. Jamming is widely used to execute DoS attacks at the physical layer. Jamming signals can be utilized by an adversary in order to obstruct communication between two nodes. Jamming signals are especially malicious if they can affect a system required communication in IoT networks, for example GPS signals which are used for localization and moving direction decisions in many vehicular IoT networks [25]. DoS attacks can also be executed by distributed nodes to reduce the risk of being detected. Such DoS attacks are referred to as distributed denial of service (DDoS) attacks [26]. Frequently, anomaly detection systems are used to detect DoS/DDoS attacks [27]. Due to the distributed nature of attackers DDoS attacks are more difficult to detect [28].

Active

Passive

Traffic analysis Eavesdropping Brute force Jamming Message modification Information disclosure Denial of service (DoS) Masquerade ID theft Man in the middle Network injections Configuration attacks Protocol attacks Core network attacks

Attack

✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

✓

✓ ✓ ✓

✓

II

I

Threat type

✓ ✓

III

✓ ✓ ✓ ✓

✓ ✓

✓

IV

✓ ✓ ✓

✓

✓ ✓

BF

✓

✓ ✓

AN

Countermeasures PHY

✓

✓ ✓

ECC

✓

✓ ✓

SSC

✓ ✓ ✓

∼ ✓ ✓ ✓ ✓

∼ ∼

Data link Higher layers Encrypt. IDS/IPS

Table 1 Attacks and countermeasures for IoT networks. Threats: I. Secrecy, II. Authentication, III. Data integrity, IV. Robustness. BF: beamforming, AN: artificial noise, ECC: error correction coding, SSC: spread spectrum coding

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• Information disclosure: A node can be compromised to leak confidential information to unauthorized nodes. These attacks can be realized in many ways, by using a specific system vulnerability or deceiving the authentication system to send information to the malicious node. Information disclosure attacks can be seen as an active version of eavesdropping attacks. • Message modification: This term refers to an attack type in which an intruder performs altering to the network communication data [29]. This type of attacks violate data integrity requirement of wireless IoT networks, and become an extensive security threat considering the results it may bring along. Since IoT networks are typically remote controlled, it is easy to see that an intrusion and modification of the controlling data can be very harmful. These attacks can be prevented by improving secrecy, using effective authentication systems and using data integrity check algorithms [21]. • Eavesdropping and traffic analysis: Eavesdropping is a way for an unintended receiver to intercept a message. The attacker is called an eavesdropper. Eavesdropping can be performed even if the messages are encrypted, the encryption algorithm can be solved eventually. Encryption makes a data secret for some time, which is determined by technology. The time that an encrypted data can stay secret is the time fastest processor of latest technology can solve the encryption key by trying every option one by one, which is called brute-force attack. It is estimated by Kaliski that in order to an information stay secure for 20 years in 2010, one should use an encryption key having minimum length of 2048 bits [30].

4.2 Attacks on Compromise of Credentials Many malicious attempts on wireless communication systems are based on compromising credentials by unauthorized users, to access secret data. Comprising brute force attacks on tokens and weak authentication algorithms, physical intrusion as well as malicious cloning of authentication tokens on the Machine Communication Identity Module (MCIM). • Masquerade attacks: In a masquerade attack, an intruder acts as a legitimate node, deceiving the authentication system. A masquerade attack can be implemented in many ways, including social engineering or capturing the authentication sequences to access information illegally [31]. Note that a masquerade attack can also be categorized as a physical attack if authentication algorithm is based on physical layer attributes. • Identity theft: The attacker determines the identities of the target devices in an identity theft, by capturing network traffic. Frequently, identity (ID) filtering based authorization is applied in wireless networks. However, network sniffing capable devices can capture the transmitted data through the wireless broadcast channel. Such devices can also imitate the authorized devices’ ID as their own in order to

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Fig. 2 Man in the middle attack illustration

get authenticated. Identity theft is possible by brute force attacks [32] where the attacker tries all the possible ID keys [33]. • Man-in-the-middle attacks: The attacker makes separate connections with the source and destination in the man in the middle (MIM) attacks, eliminating the direct communication between these nodes. As both the source and the destination are unaware of the attacker, modified messages can be received as actual messages. The communication process is controlled by the attacker, as shown in Fig. 2. MIM attacks can be very dangerous to systems, as the attacker gets authenticated by the service agency and control the IoT node as he desires [34]. • DoS attacks: We have already classified DoS attacks under physical layer attacks, but these attacks can also be used in order to analyze security weaknesses and exploit them to gain unauthorized access to the system. For example, in weakly encrypted systems such as wired equivalent privacy (WEP), where there exist tools that can execute a brute force style attack based on the “model” security key captured during the network recovery caused by numerous DoS attack packets [35]. • Network injection: An attacker can make use of devices that are exposed to nonfiltered network traffic in network injection attacks [36]. Then the attacker is able to inject re-configuration commands that affect different intelligent network devices and do malicious actions to whole system network.

4.3 Networking Attacks The network attacks mainly target configuration changes of network nodes, protocol and signaling aspects and the capabilities of the core network. These attacks are exemplified below. • Configuration Attacks: These attacks include software updates, and configuration changes. Configuration attacks are rarely performed as a single attack, it usually includes compromise of credentials type of attacks. However, sometimes attackers do not need to compromise credentials. Authenticated users, who have the privilege to change configuration of system, can do malicious acts to an IoT network, whether accidentally or intentionally. • Protocol Attacks on the Device: These are the attacks that directed against the IoT node. Examples include MIM attacks upon first network access, DoS attacks, which we already introduced. We can give smurf attack as an example of protocol

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type DoS attacks, which uses extensive Internet Control Message Protocol (ICMP) echo request (ping) traffic to IP broadcast addresses to degrade the service quality. Obviously, these attacks are dependent on which protocols are being used by the IoT communication network. • Attacks on the Core Network: These include attacks traffic tunneling between impersonated devices, mis-configuration of the firewall in the modem/router/ gateways and DoS attacks against the core network. These attacks are not directly related to an IoT network, and mainly a major threat for network operators. The IoT network can be protected by having a back up different connection type, which can be switched to if there is a disconnection or service degradation occurs on the master network.

5 Attacks on the Cognitive Access In CIoT networks, due to their sensing capabilities, a special set of attacks can constitute a threat to the network. These attacks are mostly based on the physical layer, and need to be cautiously handled as they may also cause a communication disruption. Despite the importance of the threat, the literature on these attacks is yet quite sparse. In the following section, we focus on possible attacks on CIoT networks through their cognitive abilities. The potential security threats targeting spectrum sensing of CIoT networks can be categorized as primary user (node) emulation attacks, sensing falsification attacks, objective function attacks, hamming attacks and eavesdropping attacks. Note that this list is by no means complete and constantly being updated by the researchers. Although the security threat is extremely critical, finding effective, practically applicable, and efficient countermeasures still remains a challenge.

5.1 Impact of the Cognitive Access Cognitive radio concept encompasses opportunistic spectrum access paradigm. Although targeting to improve the spectral efficiency in the valuable frequency bands, implementation of opportunistic spectrum access constitutes the most challenging issue in deployments of cognitive radio networks [37]. Typically, cognitive radio equipped wireless nodes can be classified as primary or secondary nodes. Primary nodes, the licensed users, correspond to the wireless nodes, can also be IoT node, that have priority in the usage of a target spectrum band. Secondary nodes, the unlicensed users, on the other hand correspond to the nodes with lower priority. These nodes need to access the spectrum without interrupting the communication of primary nodes. Through this two-tier classification, opportunistic spectrum access can be enabled as either underlay or overlay systems. In the underlay systems, the secondary nodes are allowed to simultaneously use the spectrum as long as their interference is limited.

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Fig. 3 Cognition phases of CIoT nodes

This is frequently achieved through spread spectrum based access techniques [38]. In the overlay systems, simultaneous transmission is not allowed and the secondary nodes can utilize the target frequency band only when the band is vacant. In presence of a primary node, secondary nodes must immediately terminate transmission. To be able to handle spectrum coordination with the target performance parameters, secondary nodes need to accurately sense the radio transmission related activities of the primary nodes. This introduces a challenge on the sensing aspects and hence is widely studied in the literature [37]. The cognitive cycle of CIoT nodes include four phases, as shown in Fig. 3. These phases are the sensing phase, the analysis phase, the switching phase and the acting phase [39]. The spectrum monitoring takes place in the sensing phase. Both the narrowband and wideband channels can be monitored. This phase is followed by the analysis phase where features associated with the environmental, network and usage conditions are characterized and extracted. The next set of parameters and the response strategies are determined. The determined parameters are applied and the responses are executed during the acting phase. This is then followed by the sensing phase again. Although the benefits of the cognitive abilities are clear in CIoT networks, mainly from radio resource utilization efficiency perspective, these capabilities also trigger security concerns in system deployments. Considering the layered reference models, physical layer, the medium access layer and the network layer are prone to attacks. However, as the main challenge associated with spectrum sensing is the essential feature of CIoT nodes, most attacks target the spectrum sensing capability of secondary nodes or they aim to block primary nodes’ communication.

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5.2 Primary User Emulation Attacks One of the main threats to cognitive nodes with dynamic spectrum access capabilities is the primary user emulation attack. In this attack type an adversary emulates the characteristics of a high priority node, targeting the reduction of available resources to secondary nodes, increasing the access failure probability. To increase the negative impact, more than one adversary can also collaborate during an attack. The depleted resources can be exploited by the attackers with the goal of increasing their data rates. An exemplary attack is visualized in Fig. 4. Introduced in [40], primary user emulation attacks can be classified into two classes; selfish attacks and malicious attacks. As a countermeasure, a transmitter verification scheme is proposed in [40]. The proposed scheme relies on the assumption that the primary node and the attacker are physically separated. This work is then extended in [41], where the authors propose to use the detected energy levels jointly with the locations of the transmitters. A Bayesian multiple criteria approach is proposed in [42]. Game theoretic approaches targeting to optimize sensing strategy are also considered as a countermeasure in the literature [43]. A database assisted attack mitigation approach is proposed in [44], where a multi-threshold energy detection, fingerprint-based location verification and a two-level database are jointly utilized. In case when the localization information is not available, classical authentication approaches can also be resorted to, as proposed in [39].

Fig. 4 An exemplary visualization of the primary user emulation attack in the time/frequency plane

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5.3 Sensing Data Falsification Attacks These attacks target the falsification of the sensing measurements at secondary nodes by the injection of misleading sensing data in the cognitive radio network. The attacker targets the dynamic spectrum access mechanism, as the performance of this mechanism is of critical importance to the overall network performance, especially in overlay systems. Based on the falsified sensing data, a secondary node can either stays inactive, which reduces the system throughput, or it transmits its data and disrupts a primary node’s transmission. In case when the attackers are spread in space, they may only have a limited impact on the overall performance. Typically cooperative sensing techniques are used as a countermeasure against such attacks [45, 46], as the accuracy of the sensing data can be improved by combining the received measurements from multiple devices. Thrust based techniques can also be employed on the relay nodes to improve the accuracy of the sensing decision [47]. The attack visualization of the countermeasure approach is shown in Fig. 5. Another form of data falsification is possible by masking the primary node’s transmission, targeting to trick the secondary nodes to believe that the spectrum is vacant. Such attacks can also be combatted by cooperating secondary nodes.

5.4 Objective Function Attacks Proposed in [48], objective function attacks require a smarter attacker model, targeting to interfere the cognitive cycle of CIoT nodes. A malware (virus) can be used so that the attack self-propagates. The attack targets the learning engine’s objective function that the CIoT nodes to determine the next states, including the frequency band, transmit power level, modulation type of medium access control associated parameters. Analysis, switching and acting phases of the cognitive cycle, as shown

Fig. 5 Visualization of the sensing falsification attack and the countermeasure approach

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in Fig. 3 can also be affected, possibly disrupting the complete communication network. As a countermeasure [48] suggest the incorporation of a common-sense idea in the CIoT nodes. Using threshold values to update the parameters is proposed in [49]. Also classical intrusion detection systems can be modified as a detection approach against these attacks [50].

5.5 Jamming Attacks Jamming attacks correspond to DoS attacks in the physical layer [51]. The denial-ofservice vulnerabilities of cognitive radio networks are studied in [52]. These attacks can be classified as single-channel, or multi-channel jamming attacks [53]. Singlechannel attacks involve continuous transmission of jamming signal. The multiplechannel attacks may also be executed with continuous transmission however, such a strategy would be power-expensive from the attacker perspective. Hence the attacker can also resort to cognitive functionalities to determine the channel to be attacked, with the objective of disrupting communications. In order to detect jamming attacks, noise levels can be constantly monitored [51]. Direct sequence spread spectrum or frequency hopping spread spectrum are the main techniques against jamming attacks [54]. Spatial retreat of the nodes are also possible. Beamforming based approaches also improve the resilience against jamming attacks [55].

5.6 Eavesdropping Attacks in CIoT The eavesdropping attacks target capturing information from an ongoing data transmission are also a threat in CIoT. The attacker, specifically the eavesdropper, is an illegitimate node that is not authorized to capture information. Guided by the seminal work of Wyner [60], eavesdropping attacks are usually studied under the three-node topology however collaboration among multiple eavesdroppers is also possible [56]. However, upon detection of the communication channel, due to the broadcast nature of the wireless environment, the eavesdropper can capture/record/detect the transmitted data in CIoT networks. Countermeasures against eavesdropping attacks mainly include adaptation of classical cryptographic solutions to cognitive radio networks [57], physical layer security techniques based on information theoretical secrecy, or a combination of both approaches [58]. Beamforming, power control and the artificial noise based approaches are the most frequently used solutions, however, the effectiveness of filtering and also full-duplex communication based techniques are also shown [55, 59]. Error correction coding and spread spectrum coding can also be used as countermeasures in CIoT networks.

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6 Case Study Based on [47], in this chapter we investigate the advantages provided my the multiantenna arrays through the use of beamforming approaches. Our target is to detect and prevent possible attacks on CIoT networks. A two-phase approach is considered; in the first phase the attack is detected and in the second phase a beamforming based countermeasure is taken into account. Sensing falsification attack is combatted with a trust based cooperative approach. A trust factor is calculated and tracked for secondary nodes by the so-called guard agents. Attackers can also be deceived through the use of artificial noise based physical layer security techniques.

6.1 Network Model Our CIoT network model targets cooperative spectrum monitoring, composed of N secondary CIoT nodes, that are deployed in a spatially spread manner. A fusion center, to where the secondary nodes report their spectrum sensing measurements, is employed to determine the spectrum usage status. Let E m denote the sensing energy level for the mth secondary CIoT node. In case of a well-behaved (non-attacker) node, E m ’s distribution is [47]  Em ∼

2 χ2ρ , H0 2 χ2ρ (2γm ), H1

(1)

where γm is the signal to noise ratio (SNR) of the mth node, ρ is the time-bandwidth 2 denotes the chi-square distribution with 2ρ product. In the equation above, χ2ρ degrees of freedom. The hypothesis of the absence (presence) of a primary CIoT node is denoted by H0 (H1 ). When the node is an attacker, it targets to falsify the spectrum report. It increments its sensing measurement by λ. The sensing energy threshold is denoted by Υ . In case when the sensing energy is already higher than Υ no modification is required at the attacker node. The hypothesis associated with the status of the nodes are denoted by node G 1 , in case of an attacker node, and G 0 in case when the CIoT node regularly participates to the decision process. Hence Eq. (1) can be modified as ⎧ 2 ⎪ ⎨χ2ρ ,  G 0 , H0 2λ 2 E m ∼ χ2ρ , G 1 , H0 , σ2 ⎪ ⎩ 2 χ2ρ (2γm ), H1

(2)

where σ 2 represent the corresponding additive white Gaussian noise (AWGN) power.

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6.2 Trust Calculation In order to monitor the status of the secondary CIoT nodes, a trust metric can be used. Such techniques are widely applicable in attack detection schemes in the literature. We define our trust metric for primary user emulation and spectrum sensing data falsification attacks as the trustworthiness of a client node may highly depend on values like previous trust metric of the node and report similarity with trustworthy nodes in the system. In our system model, the n-length trust vector t is determined by the fusion center, where t (m) donates the trustworthiness of the mth node. The following hypothesis tests are conducted to update the trust vector. The fusion center first evaluates whether there is a primary CIoT node by using the following inequality n j=1, j=m

P(E j = e j |H1 ) P(E j = e j |H0 )

H1

≷ Υ,

(3)

H0

where e j refers to the possible energy levels. Recall that Υ denotes the spectrum sensing energy threshold. After detection of the presence of a primary CIoT node, the fusion center performs a second hypothesis test to determine a suspicious report of any secondary node P(E m = em |G 1 , H0 ) P(E m = em |G 0 , H0 )

G1

≷ ν,

(4)

G0

where ν indicates the level of suspicion that can be tolerated in the system. A binary indicator function is defined about the report of the mth node at time k as  1, True Report ck (m) = . (5) 0, False Report The beta function trust model in [61] is taken as the guideline in our system model. Accordingly, the trust value can be calculated as,

k tm (k) =

j=1

c j (m) + 1

k+2

.

(6)

In case when ti (k) < ψ, the node is labeled as an attacker. Here, ψ denotes the chosen trust threshold. When an attacker is labeled, its reports are not considered in the spectrum sensing process.

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6.3 Beamforming-Based Attack Prevention Multi-antenna based techniques can be used to alleviate the impact of the attacks in CIoT networks. Here, we consider artificial noise and beamforming based tools against sensing falsification and primary user emulation attacks in a centralized decision system. Spatially separated guard agents are also used. The fusion center detects the attackers based on the trust metric in Eq. (6). The guard agents transmit artificial noise to the attackers by using the properly tuned beamforming coefficients. The aim is to transmit artificial noise solely to the attacker node as a countermeasure. Other secondary CIoT nodes should not be affected by the transmission of the artificial noise. Another design target is to minimize the total transmit power to keep energy efficiency as high as possible. On the attacker side, the received signal can be modeled as rm (k) =

Ng 

h Hjm (k)z j (k) + u(k) + x P (k).

(7)

j=1

Here, at time k the channel vector between the jth guard agent and the mth secondary CIoT node is denoted by h jm (k). N g denotes the number of guard agents. The length of this vector is N T j , depending on the number of transmit antennas at the jth guard agent. z(k) is the N T j -length artificial noise vector. Components of this vector are Gaussian distributed. Σ j (k) represents its spatial covariance matrix at time k. u(k) is the AWGN term with zero mean and variance σ 2 . x P (k) is the received signal from the primary CIoT node at time k, which has a non-zero value only when the node is transmitting. As the artificial noise is directed towards the attacker by the use of beamforming, the other nodes will not encounter its deterioration effects. On this node only by beamforming, the other nodes in the network will not get affected and hence they can continue with their communication scheme. Let us denote the SNR in presence of an active guard agent as γm AN . The optimization problem to determine the beamforming coefficients can be formulated as minΣ

Ng  j (k)∈H

NT

j

Σ j (k)) Tr(Σ

j=1

s.t. γm AN ≥ Υ Σ j (k) ≤ Pmax . Σ j (k)  0, ∀ j ∈ [1, n],

(8)

where Pmax represents the maximum transmit power level of the guard agent. Tr(·) denotes the trace operator. This optimization problem can be solved by convex solvers like CVX or SeDuMi. Here we use MATLAB and SeDuMi. A single attacker CIoT node is considered.

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Optimized

0

P( H1 | A1, H ) ( Countermeasure successful)

1 0.9 P

AI

=9P

s

PU

0.8 0.7 PAI = Ps

PU

0.6 0.5 0.4 0.3 PAI = 0

0.2 0.1 0

0

0.1

0.2

0.3

0.5

0.4

0.6

0.7

0.8

0.9

1

P ( H | A , H ) ( False positive) 1

0

0

Fig. 6 The receiver operating characteristics with varying interference powers

2 guard agents are used, each of which are equipped with 4-element antenna arrays. 1000 Monte-Carlo runs are completed. Receiver operating characteristics (ROC) curves denote the relation of shows the probability of true positives, P(H1 |A1 , H0 ), with respect to the probability of false positives, (H1 |A0 , H0 ). True positives represent the cases where the we deceive the attacker about the presence of PU signal, which is actually absent. False positives represent the cases that attacker assumes the presence of a primary CIoT node. ROC curves of three different scenarios are shown in Fig. 6. The ROC curves. The sets shows the performance under different interference power values. Considering the absence of any artificial interference, PAN = 0, we encounter a diagonal line, and observe a low performance. As the power of artificial noise increases to line, PAN = Ps , the performance of the proposed approach becomes visible. At a high power cost with PAN = 9 × Ps the performance approaches to the optimum case, also shown in the figure.

7 Conclusions and Open Issues Despite the known countermeasures and methods of enhancing security, there are many open issues in IoT networks, some of which stems from wireless security aspects. First, the variable use of different wireless communication technologies in IoT networks, make it difficult to maintain security. Since there is not a single

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standard about which technologies to be used in IoT networks, all these issues can be affect these systems depending on the scenario. As a result, it is not possible to consider IoT networks as secure in all cases. Adding to this issue, the cognitive abilities of the nodes in CIoT networks increase the potential threats to the network. In this chapter, we identified the requirements for the deployments of IoT/CIoT networks while relating them with the main security threats and challenges in the IoT applications. Here, we provide an overview of the IoT and CIoT wireless security requirements and potential system vulnerabilities, then we focus on the attacks on the cognitive aspects. Although there are a lot of resolved security issues, new security attacks will continue to challenge CIoT networks with the arrival of new technologies. The main open issue remains addressing multiple requirements while satisfying the security targets. The main requirements can be listed as • Low Power Efficiency: Although security is of premium importance, any kind of countermeasure needs to be power efficient to enable long battery lifetimes of lightweight CIoT nodes. Hence power efficiency must be considered as a design metric and the proposed solution must target a low power consumption. • High Spectral Efficiency: CIoT devices are envisioned to be smart but lightweight devices, that will dynamically access the spectrum. While providing this spectral access, the spectral efficiency must be kept high to keep overall data rate sufficiently high. • Low Hardware and Computational Cost: The cost of using supplementary nodes as security agents, such as the guard agent in the case-study, or including additional hardware capabilities can be a tool to improve the resilience against diverse set of attacks. Furthermore, the computational complexity of the proposed solution needs to be affordable from the CIoT nodes’ perspective. • Robustness to Implementation Problems: Channel estimation errors, frequency and timing offsets are unfortunately facts of receiver designs. Hence the proposed security solution needs to be robust and resilient against such estimation errors that possibly reduce the effective SNR. Overall, the promise of CIoT networks are indispensable. Hence the security issues need to be handled in an effective and practical manner, and the cost of the security solutions are expected to be justified based on the expected benefits in our societies with a diverse set of applications including smart cities, smart healthcare, and smart factories. Mostly focusing on the physical layer, the listed gaps in the literature need to be addressed for the seamless integration of the CIoT paradigm in our daily lives. Acknowledgements This work is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 115E827.

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Internet of Energy Harvesting Cognitive Radios O. Cetinkaya, M. Ozger and O. B. Akan

Abstract The Internet of Things (IoT) offers enhanced connectivity so that any system, being, or process can be reached from anywhere at any time by perpetual surveillance, which results in very large and complex data sets, i.e., Big Data. Despite numerous advantages, IoT technology comes with some unavoidable drawbacks. Considering the number of devices to be added to the current electromagnetic spectrum, it is a fact that wireless communications will severely suffer and eventually become inoperable. Furthermore, as wireless devices are equipped with limited capacity batteries, frequent replenishments and/or maintenance will be needed. However, this is neither practical nor achievable due to the excessive number of devices envisioned by the IoT paradigm. Here, the unification of Energy Harvesting (EH) and Cognitive Radio (CR) stands highly promising to alleviate the current drawbacks, enabling more efficient data generation, acquisition, and analysis. This chapter outlines a new vision, namely Internet of Energy Harvesting Cognitive Radios (IoEH-CRs), to take the IoT-enabled Big Data paradigm a step further. It discusses the basics of the EH-assisted spectrum-aware communications and their implications for the IoT, as well as the challenges posed by the unification of these techniques. An operational framework together with node and network architectures is also presented.

O. Cetinkaya (B) · M. Ozger · O. B. Akan Next-Generation and Wireless Communications Laboratory (NWCL), Department of Electrical and Electronics Engineering, Koc University, Istanbul 34450, Turkey e-mail: [email protected] M. Ozger e-mail: [email protected] O. B. Akan e-mail: [email protected]; [email protected] O. B. Akan Internet of Everything (IoE) Group, Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_9

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Acronyms ACC APs CR CTs EFEG EH EH-CRs EM FCC HANs IoEH-CRs IoT IPs ISM ITU KEC MEEG MFEG MPTT NANs PPDR PUs PV QoS RF SCs SGs TEG WANs Wi-Fi WSNs

Autonomous connection circuit Access points Cognitive radio Current transformers Electric field energy harvesting Energy harvesting Energy harvesting cognitive Electromagnetic Federal communications Home area networks Internet of energy harvesting cognitive radios Internet of things Internet protocols Industrial, scientific, and medical International telecommunication union Kinetic energy conversion Mechanical-to-electrical energy generators Magnetic field energy harvesting Maximum power point tracking Neighborhood area networks Public protection and disaster relief Primary users Photovoltaic Quality of service Radio frequency Smart cities Smart grids Thermoelectric generation Wide area networks Wireless fidelity Wireless sensor networks

1 Introduction Wireless communications have experienced tremendous progress, such as higher data rates and better Quality of Service (QoS). These advancements have triggered new wireless networking architectures and related communication services. The Internet of Things (IoT) [1], one of these services, is a vision that extracts voluminous data from surroundings, and accordingly analyze and manage the extracted information with decision-making procedures. It necessitates the employment of key information

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technologies, such as Wireless Sensor Networks (WSNs) [2]. The main factor that makes the WSNs as the main pillar of the IoT is advanced sensing capabilities supported by Internet connectivity. The ever-evolving utilization of the IoT paradigm, however, brings its own challenges. Since the IoT offers a seamless connection for any object, the number of devices connected to the Internet is predicted to exceed 16 billion by 2020 [1, 3, 4]. This refers to the generation of very large and complex datasets, i.e., Big Data [5], which will necessitate novel methods for handling. The colossal growth in the number of wireless devices will result in unprecedented levels of wireless data traffic, inevitably incapacitating the available spectrum. Furthermore, the fixed allocation of the spectrum bands will worsen the efficient use of the spectrum. Although the licensed bands are underutilized, the unlicensed ones suffer from over-crowdedness. When the current scarcity in the radio spectrum is considered, the foreseen problem requires quick action to prevent wireless communications from a possible failure easing the efficient extraction of the data from surroundings. To mitigate the ongoing spectrum scarcity and inefficiency constraints, Cognitive Radio (CR) technology has been proposed [6]. It facilitates opportunistic access to the spectrum bands to boost the spectrum utilization efficiency [7]. However, due to high power-consuming CR cycles/functions, another problem called energy inadequacy has arisen. Although the majority of wireless devices operate intermittently, a generic power source, i.e., battery, depletes in less than a year. This fact points out the need for an auxiliary or even a completely distinct power source to ensure proper system operation. However, the size-constrained nature of the sensor nodes precludes additional battery employment in most cases. In this regard, Energy Harvesting (EH) comes to the forefront as a strong candidate in alleviating the energy constraints of the WSNs by opportunistically exploiting the ambient resources [8]. The challenges mentioned above solicit novel methodologies to obtain the desired spectrum efficiency with advanced power provisioning schemes enabling proper generation of the Big Data in an IoT-enabled scenario [9]. The most promising solutions for these problems are EH and CR technologies. This chapter therefore mainly focuses on the unification of state-of-the-art EH and CR techniques, i.e., energy harvesting cognitive radios (EH-CRs), to facilitate self-sustaining, spectrum-efficient and interconnected next-generation communication networks and services. By following this agenda, the chapter is organized as follows. Section 2 offers a review of CR and EH-aided communications. In Sect. 3, the discussion is extended to motivations for spectrum-aware and self-sustaining communications in the IoT domain. In Sect. 4, the newly emerging IoT architecture, i.e., the IoEH-CRs, is presented. Existing EH techniques and applications of CRs are discussed in consideration of potential challenges posed by both EH and CR methods. In Sect. 5, the general framework of IoEH-CRs is presented. Operation overview, node and network architectures of IoEH-CRs are described. Current and future applications and open research directions for Big Data-enabled IoT are also mentioned in this section. Finally, Sect. 6 concludes the chapter.

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2 Background This section focuses on the basics of CR and EH-aided communications for Big Dataenabled IoT. While CR is utilized for efficient spectrum use, EH fosters the energyefficient operation for a limited power supply regime. With this motivation, this section outlines how CR can exploit the underutilized spectrum bands to mitigate the spectrum scarcity problem. EH-aided communications for enabling longer lifetime and/or battery-free operation are also investigated to reveal the requirements of future Big Data-enabled IoT.

2.1 Requirements of Big Data-Enabled IoT As the volume of WSNs and the number of corresponding devices are expected to exceed unmanageable levels in near future, the need for novel approaches for efficient extraction, acquisition, and analysis of the generated data is obvious. Considering the nature of IoT vision, we can mention two major problems for the WSNs, which are further intensified by the IoT technology. Diminishing spectrum and energy constraints of the WSNs will certainly alleviate the ongoing limitations, and therefore immensely contribute to Big Data-enabled IoT vision. With this motivation, we focus on EH and CR technologies and their joint utilization in the IoT domain to make ever-efficient data extraction and handling possible.

2.2 Cognitive Radio Communications for Big Data-Enabled IoT Wireless devices become more and more proliferated due to the emergence of IoT technology [1]. These devices primarily use Industrial, Scientific, and Medical (ISM) bands. Since these bands are already crowded, packet errors and collisions occur frequently making the communications difficult. Due to these reasons, precious spectrum resources need to be managed in a more efficient manner [2]. The excessive use of ISM bands causes spectrum scarcity [2, 5]. Licensed bands are underutilized and unlicensed bands are already congested. To overcome the problem of spectrum scarcity and to increase the utilization efficiency of the licensed spectrum bands, CR technology is proposed [6]. The main feature of this technology is agile spectrum utilization. Changing the operating frequency if necessary provides opportunistic usage of the spectrum such that the CR can use licensed bands until it interferes with communication of license holders. The licensees are mostly called primary users (PUs).

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Cognitive cycle operations consist of three main functions, namely spectrum sensing, spectrum decision, and spectrum hand-off. They are explained in detail as follows. • Spectrum Sensing: CRs should be aware of the radio environment to choose the spectrum band for their communication. Hence, the spectrum sensing operation is a crucial part of the cognitive cycles since it provides information about the usage statistics of the Electromagnetic (EM) spectrum. Spectrum opportunities are determined according to the results of the spectrum sensing. There are three different categories for spectrum detection: transmitter detection, cooperative detection, and interference-based detection [7]. In the transmitter detection, CRs try to detect the primary transmitters. There are three mechanisms for transmitter detection, namely matched filter detection, energy detection, and cyclostationary feature detection. In cooperative detection, CRs exchange their sensing results related to the local spectrum with each other to determine the existence of PUs. In interference-based detection, there is an interference limit that the PUs can tolerate. As long as CRs do not exceed this limit, they can utilize the spectrum bands. • Spectrum Decision: Spectrum sensing reveals a list of vacant spectrum bands. Using this list, the CR should choose a band to transmit according to the requirements of secondary networks such as data rate. CR can use a centralized or distributed approach in the decision-making process. While the centralized approach requires an excessive number of control packets, the decision process is performed locally. • Spectrum Handoff: The CR should vacate the licensed band it uses when a PU begins transmission on the channel. The functionality of the spectrum handoff prevents interference with the PUs. This feature also provides the mobility of the spectrum. The CR can vacate the busy channel and continue its transmission on another vacant channel. The cognitive cycle functions try to avoid PUs interference and to increase the overall spectrum utilization efficiency. This decreases the burden on the ISM bands. The CR capabilities also decrease the probability of collision due to the utilization of less congested channels.

2.3 Energy Harvesting Communications for Big Data-Enabled IoT As a corollary of small form factors that the sensor nodes necessitate, the WSNs are mostly resource-constrained. One of the most important factors that restrict sensor operation is limited batteries. This issue results in an inevitable trade-off between sensor lifetime and the number of observations, i.e., reporting frequency, where the reliability of communication and the QoS provided are adversely affected due to this intrinsic limitation. Since the cognitive cycle operations require a considerable

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amount of energy, there is a certain need for an auxiliary and/or a distinct power source to sustain the desired sensory operations. In this context, a well-studied phenomenon, i.e., EH, comes to the forefront to alleviate the power limitations of the wireless devices. In the simplest terms, EH refers to exploiting a stray source or converting energy from one form to electricity [10, 11]. Energy harvesters, i.e., transducers, actively probe the devices’ medium to acquire power from environmental and/or non-environmental sources such as lights, airflows, temperature variations, Radio Frequency (RF) signals, and EM fields [12–15]. EH techniques support power provisioning systems to enable wireless devices to operate for longer durations. With the EH capability, the untethered operation of the wireless devices can be maintained. EH contributes to the aim of building autonomous devices/services distributed pervasively in both indoor and outdoor domains. In addition to the above-mentioned advantages, the limited storage resultant maintenance, replacement, and operation costs can be also relieved by the EH mechanisms. In this context, EH stands as a perfect candidate to extend the lifetime of wireless devices achieving the required QoS while supporting additional operations, such as CR cycles.

3 Motivations for Using Energy Harvesting Cognitive Radios in Big Data-Enabled IoT As discussed, wireless devices in the IoT domain severely suffer from two major constraints in executing their tasks: limited-capacity batteries and congested EM spectrum. To resolve these issues and ease the building of Big Data-enabled IoT, the following subsections elaborate on the motivations for spectrum-aware and selfsustaining communications offered by CR and EH technologies.

3.1 Motivations for Spectrum-Aware Communications The IoT concept offers enhanced connectivity to the wireless devices thanks to the Internet Protocols (IPs) employed [1]. International Telecommunication Union (ITU) states that any device can be reachable from anywhere at any time with the IoT [16]. This feature poses challenges to the communication between the IoT devices. One of the major challenges is the heterogeneity of the IoT devices. This decreases the communication efficiency. Furthermore, they commonly utilize ISM bands by adopting various protocols, such as Bluetooth, ZigBee, and 6LowPAN. However, these bands are overcrowded [2]. Hence, CR technology helps to overcome this challenge by supporting spectrum-aware communications. This type of communication enables the opportunistic use of underutilized licensed channels, which gives

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spectrum-awareness to IoT devices [17]. The main motivations of the spectrum-aware communications can be listed as follows. • Energy Efficiency: Spectrum-aware communications exploits the idle bands, which decreases the probability of collision among the IoT devices. Since they are all resource-constrained, spectrum-aware communications offer energy-efficiency and lifetime extension to these devices. • Adaptation to different spectrum regulations: Since IoT devices are heterogeneous, they encounter different spectrum regulations. CRs can overcome this challenge by the spectrum-aware communications since they can operate on any band available [18]. • Dynamic spectrum access: Apart from the fixed spectrum allocation, the spectrum-aware communications granted by the CR provides the ability of dynamic access to the licensed spectrum. • Increase in the reliability: Spectrum-aware communications provide less collision probability and packet losses due to opportunistic use of the licensed channels. This, therefore, increases the reliability of the communication [18]. The cognitive cycle functions try to avoid PUs interference and to increase the overall spectrum utilization efficiency. This decreases the burden on the ISM bands. The CR capabilities also decrease the probability of collision due to the utilization of less congested channels.

3.2 Motivations for Self-sustaining Communications Wireless autonomous devices are the key enablers for pervasive surveillance and intelligent control mechanisms. The observations of these devices can be utilized to grant a set of benefits from public to commercial services in both indoor and outdoor domains. With the unification of state-of-the-art technologies, application ranges of these devices have reached a new level as enabling any area, system, and/or process to be observed and managed remotely. The IoT is the foremost factor that extends the operation of wireless devices into new fields. With the IoT technology, any parameter of interest can be periodically monitored in an energy-efficient manner within acceptable intervals. The ongoing problems of current communication architectures can be therefore mitigated by the eligible features that the IoT paradigm proposes. However, the unprecedented growth of the IoT will cause some inevitable drawbacks. Energy hunger can be regarded as one of these side-effects. Even though the wireless devices operate intermittently, batteries generally deplete before their expected operation times. This issue necessitates periodic maintenance and/or replacement for proper system operation. However, when the number of devices that will be included in wireless services with the emergence of the IoT paradigm is envisioned, neither maintenance nor replacement will be possible. Furthermore, the heterogeneous structure of the IoT-enabled networks is expected to result in random battery depletions, which is another factor that forces the wireless devices to find sufficient alternatives

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for perpetual power provisioning. Additional battery employment sounds promising for high power demanding devices; however, this may or may not be possible in every case, due to size, weight, and volume restrictions. Thus, each process requires the most efficient operation possible to extend the lifespan of sensor nodes [19]. Stage level energy saving and management procedures should be maintained for sustainable use of the available resources. If the operational costs are decreased under a certain energy level, enough to be scavenged from the environment, the self-sustaining operation of wireless devices becomes possible. This idea encourages EH procedures to build wireless systems without energy constraints. As the lifetime of sensor nodes is extended, more robust, reliable, and durable operations can be realized. Hence, EH has great importance to satisfy the demands raised by today’s emerging technologies.

4 Internet of Energy Harvesting Cognitive Radios Here, the outstanding characteristics of EH and CR technologies are revealed to better anticipate the potential gains and achievements with the realization of IoEHCRs. Furthermore, how CR functions, supported by EH procedures, can be applied to various IoT-assisted domains, such as Smart Cities (SCs), Smart Grids (SGs), and smart transportation systems are discussed. The challenges posed by CR, EH, and their unification in the IoT domain are also specified to better realize the newly emerging IoEH-CRs paradigm. In the end, the integration of EH-CRs to the Internet is explored.

4.1 Energy Harvesting Methods in Big Data-Enabled IoT This subsection overviews the existing EH methods utilized in the IoT domain. These methods can be broadly separated into five groups as light propagations, heat and motion variations, EM radiations, and field distributions, in which availability, controllability, and predictability of these sources determine the models and the specifications of the harvesting procedures that are going to be employed [10, 12, 20]. By following this categorization and the frequency of preference, today’s leading EH mechanisms are discussed below, and a more illustrative comparison is provided in Table 1 [11].

4.1.1

Light Energy Harvesting

Power provisioning from ambient light sources, either from the sun or artificial ones, can be accepted as a well-established method of the EH [20]. This method is simply based on converting light energy into utilizable electric power with respect to a chemical phenomenon, i.e., the photovoltaic (PV) effect. Here, the amount of the

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Table 1 Comparison of energy harvesting techniques [10, 11, 21] Technique

Energy availability

Characteristics

Advantages

Disadvantages

Solar

Poor

Ambient Uncontrollable Predictable

Environmental Independent of grid High output voltage

Depends on sunlight Deployment constraints

Thermal

Poor

Ambient Uncontrollable Unpredictable

Environmental Independent of the grid Scalable

Not always available Requires eff. heat sinking Low power density

Airflow

Good

Non-ambient Uncontrollable Unpredictable

Environmental Independent of grid Available day and night

Fluctuating power density Hard to implement Requires construction

Motion

Fair

Non-ambient Controllable Unpredictable

Compact configuration Lightweight

High charge leakage Highly variable output

RF

Good

Non-ambient Uncontrollable Predictable

Abundant in urban areas Enables mobility

Scarce in rural areas Low power density

M-field

Good

Non-ambient Controllable Predictable

No ext. power source Easy to implement Lightweight

Requires high and perpetual current flow Safety vulnerabilities

E-field

Excellent

Non-ambient Controllable Predictable

No need of current flow Easy to implement Always available

Being capacitive Mechanical constraints

obtainable energy varies considerably depending on the angle and intensity of the incident light, besides the size and type of the employed PV cells [20]. Sunlight EH is utilized to energize wireless autonomous devices deployed in outdoor to monitor and control power delivery systems, such as SG components. Although the application range of the solar energy is getting broader in parallel to enhancements in PV cell technology, reliability of sun-power is highly questioned due to its time-varying and uncontrollable nature. In addition, the conversion efficiency of solar cells is still a bottleneck for this technique. Therefore, solutions such as maximum power point tracking (MPPT) are required to acquire as much energy as possible from the ambient propagations [15]. For indoor solutions, specialized PV materials, which are better suited for diffused lights, are used to run minuscule of power requiring wireless devices. Unlike its sunlight-based counterparts, artificial light EH operates regardless of environmental parameters such as season, weather,

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and/or daytime. Furthermore, flexible and sufficient power rating characteristics ease its implementation in time uncritical Smart Home/Building solutions.

4.1.2

Kinetic Energy Harvesting

Kinetic Energy Conversion (KEC) can be classified into two main groups: flowbased and mechanical stress-related approaches [14]. Flow-based EH, i.e., air and/or liquid flows, means generating electricity by converting the linear motion of flows, i.e., flow resultant kinetic energy. For air-flow EH, wind turbines and, on a smaller scale, anemometers are employed. However, these converters are mostly bulky and can only operate within certain levels of wind speeds. For liquid-based EH, water flows (moving or falling) are utilized to generate useful energy. Mechanical EH, in particular, is based on taking advantage of vibrations, pressure variations, and stress-strains by using mechanical-to-electrical energy generators (MEEG) [14]. Electromagnetic, electrostatic, and piezoelectric mechanisms can be regarded as the main components of the MEEG [12, 15]. Piezoelectric generators, the most utilized, attain energy from highly random and mostly unpredictable kinetic resources in both indoor and outdoor domains [11, 13, 20]. The main shortcoming of piezoelectric effect-based devices is requiring source-specific design procedures since the conversion efficiencies are highly varying with the resonant frequency of the vibrations. This issue necessitates multi-band, i.e., frequency tuneable, design alterations for the MEEG [22]. Piezoelectric materials are commonly utilized in low-power consumptive mission-uncritical IoT applications.

4.1.3

Thermal/Heat Energy Harvesting

Thermal EH, i.e., thermoelectric generation (TEG), takes its basis from two distinct abilities of materials, i.e., thermo-electricity and pyro-electricity [23]. Thermoelectricity, the most utilized, is characterized by a physical phenomenon, namely the Seeback Effect, in which a junction constituted by two distinct conductors bends in a certain way due to a temperature gradient. The amount of this mechanical movement resultant energy can be adjusted by simply changing the way of material connections from series to parallel or vice versa [20]. Due to its ubiquitous nature, TEG attracts both academia and industry efforts for its further exploration. Thanks to their miniature size and long-life characteristics, thermo-generators are widely used in energizing low-power consumer electronics attached to human bodies [14]. However, a fundamental limit, namely the Carnot cycle, affects the maximum power gathered by the TEGs [24]. This issue necessitates intermittent operation, i.e., time-bounded execution of the obligated tasks, to guarantee reliable communications. In outdoor, TEG is of utmost importance for IoT-enabled SG communications due to high temperature swings in abundance between the power grid assets and the environment [10, 11]. However, similar to solar-based methods, its performance

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markedly alters due to the non-deterministic characteristics of the source that is being exploited [21].

4.1.4

Radio Frequency Energy Harvesting

RF EH is based on exploiting ambient and underutilized EM signals to operate lowpower devices. Due to the broadcasting nature of wireless propagation, RF signals can be easily attainable and efficiently utilizable [11, 12]. RF energy harvesters are composed of large aperture power receiving and rectifying antennae (rectennas), RF filters, voltage multipliers, and/or couplers depending on the application requirements [20]. For urban areas, in particular, RF EH has received significant attention in recent years due to the increasing number of television and radio transmitters, cell phone towers, and wireless fidelity (Wi-Fi) routers because of the unprecedented growth in wireless services. This advance has led research efforts to profit from underutilized RF signals to operate IoT-enabled SC services without any battery constraints. Although this method offers reliable solutions regardless of the environmental limitations, it still has several shortcomings, such as low power conversion efficiencies, necessitating close deployment around the EM wave emitting sources, requiring additive/complementary components, being distortive to wireless communications and unpredictable in nature [11]. Nevertheless, RF EH has recently achieved extensive growth in both indoor and outdoor domains promoting self-sustaining IoT networks and services which are free from battery constraints.

4.1.5

Magnetic Field Energy Harvesting

Power provisioning from the EM fields is a well-known method of EH, which is mainly utilized in the vicinity of power grid assets [10, 21, 24]. This technique is based on the inductive coupling of the magnetic fields (M-fields) around the alternating current-carrying conductors to be clamped with current transformers (CTs), i.e., current sensors. In this regard, it can be said that the M-field energy harvesters have two main functions, which are extracting energy and measuring the level of draining current on the conductor of interest [20]. Although this technique provides sufficient power rating and less complexity, energy output fluctuates severely depending on the amount of current flowing through the power line. Since M-Field occurs due to alternating current, the line must be loaded to drain sufficient level of current to let the harvester scavenges enough energy as supporting the attached device. Another restrictive characteristic of the M-field EH (MFEH) is requiring close deployment, for the best performance achievable, as the field intensity exponentially decreases with the distance from the field-emitting source [20]. This configuration challenges the whole process due to the high current presence, which necessitates additional components and/or procedures to mitigate safety concerns [23].

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Electric Field Energy Harvesting

According to the basics of electrostatics, if a voltage applied conductor is also carrying current, there occurs a time-varying field at any point in the space [25]. This points out the presence of both magnetic and electric fields, according to the Poynting vector, which can be extracted via either inductive or capacitive coupling, respectively [26]. Electric field (E-field) EH is based on exploiting ambient field flows around the voltage-applied conductors to support battery-constrained devices. On the contrary of previously explained approaches, E-field EH (EFEH) does not require specialized harvesters, where the field resultant electric charges are extracted by simple conductive materials. This relatively recent approach was first employed on high- and middle-voltage overhead power lines for online condition monitoring, due to the E-fields in abundance [24, 27, 28]. Initial results encouraged its utilization in low-voltage systems by mounting household AC power cords with metallic sheaths [10, 29, 30]. These efforts revealed that EFEH could provide sufficient solutions for applications operated in both indoor and outdoor. There are also some application-specific approaches employing this emerging method [11, 25, 31]. E-field is neither intermittent nor dependent on the load [24]. Since the voltage and the frequency are firmly regulated and exactingly maintained, E-field is stable and predictable in its behavior. Therefore, it can be referred to as a highly promising candidate to ensure self-sustaining IoT services regardless of the ambient factors [10, 11, 21].

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Hybrid Energy Harvesting

The methods discussed above are of great use in today’s wireless networking applications; however, environmental sources, in particular, alter markedly in terms of availability and power density. The very same issue is also valid for non-environmental resources, in which the harvesting performance is highly affected by the nondeterministic nature of the ambient variables. Therefore, harvesting energy from multiple sources seems highly promising to alleviate the varying availability of harvestable sources. In other words, each source is partly responsible for energy acquisition as complementing each other when any of them fails to provide an adequate rate of power. Thus, multi-source EH, i.e., hybrid solutions, become more and more important in sustaining information- and/or time-critical communications [12, 21, 32]. Since this approach increases the overall system reliability, it becomes possible to run wireless devices as they have a constant and durable power source. It is believed that this method will alleviate the bottlenecks of energy-constrained IoT services and broaden the scope of EH communications in the near future [10, 12].

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4.2 Applications of Cognitive Radio in Big Data-Enabled IoT CR usage in the IoT domain results in a variety of applications. It provides flexibility for the utilization of the EM spectrum, increasing the number of IoT devices covered as the unused spectrum bands are successfully utilized. Thus, this operation breaks the barriers of spectrum regulations. In the following subsections, some basic application areas of the IoT are discussed in consideration of how CR capabilities affect the operation of IoT services. These applications are outlined according to [33].

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Smart City

Cities have become more populated since the quality of life in urban areas keeps increasing with technological developments. However, the increase in the population causes some problems in the management of cities. Hence, this situation necessitates efficient resource utilization, management, and control via advanced communication techniques. This leads to a new concept, namely Smart Cities [34]. The use of information and communication technologies (ICT) in the management of cities provides efficient utilization of resources and makes cities better places to live [35]. The main motivation of the SC concept is sensing with ICT technologies, which enable self-monitoring and response without any supervision [36]. The physical world is connected to the virtual world by the sensing systems of the cities, which deliver their observations to the city officials. This enables the intelligent management of cities and the interconnection between the core systems [37]. IoT technology is employed to support the SC vision [38]. By the SC concept, the cities can be remotely observed by the Internet-assisted networking architectures. However, the transportation of the observation data to the administration units increases the use of the EM spectrum, especially in the ISM bands, since the IoT devices mainly utilize them. The ISM bands are already crowded according to the Federal Communications Commission (FCC) [2]. The emergence of the IoT in the SC context further increases the spectrum scarcity problem in urban areas. Hence, CR is a decent solution to overcome this problem. One of the application areas of CR in the IoT domain is the SCs. The observations coming from the WSNs deployed in the key systems of the cities are opportunistically conveyed to the Access Points (APs) of these networks, where the sensor data become accessible from anywhere at any time thanks to the IoT paradigm. Furthermore, EH extends the lifetime of WSNs employed in SCs. Hence, CR and EH utilization in SCs can provide more reliable and efficient services.

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Smart Grid

The traditional electrical power grid is prone to failures; hence, the grid evolves to have intelligence, higher efficiency, and self-control by next-generation wireless networking technologies [38]. This concept is known as Smart Grid. The most important enabler of this concept is the realization of two-way communications between the customers and utilities [39, 40]. Smart meters and WSNs are the main pillars of the SG concept [41]. SG network is spread over large geographical areas, and this poses challenges for the operation of this network. Hence, for the easy management of the grid, the network becomes a multi-tiered network, consisting of home area networks (HANs), neighborhood area networks (NANs), and wide area networks (WANs). Due to the size and multi-tiered structure of the network, the SG application can have different requirements in terms of reliability, data rate, bandwidth, and latency [42]. Furthermore, the existing network tends to grow to even further areas, which makes the errorless flow of the data difficult. Therefore, CR is applied in the SG backhaul and distribution networks [42]. The application of CR enhances the performance of the SG networks in terms of soft limit on capacity, wide coverage area, fault tolerance, and self-healing [42].

4.2.3

Smart Transportation and Mobility

The IoT concept can also be used in transportation systems to enable easy and safe travel [33]. The concept of the Internet of Vehicles (IoV) is emerged to enable smart transportation with energy consideration [43]. The IoT can help to observe and control the traffic, enabling autonomous cars, and increasing the energy efficiency of transportation [33, 44]. The capabilities of IoT provide control of the vehicles and management of the transportation system in an easy and intelligent manner. This can be done by enabling reliable communications between the elements of the transportation infrastructure [33]. Hence, inter-vehicle communication, vehicle-toinfrastructure communication, and vehicle connection to the Internet are of utmost importance. CR technology can be utilized to enable efficient and reliable communication and access to the Internet without experiencing the limitations of spectrum regulations in the IoT.

4.3 Challenges Posed by Internet of Energy Harvesting Cognitive Radios As discussed, the IoT devices suffer from limited resource capabilities. These restrictions can be related to any part and/or component as well as communication-based

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parameters. On the other hand, the envisioned CR operation stands as a potential contributor to the resource scarcity problem. Since cognitive cycle operations include spectrum sensing and handoff, there will be an extra need for energy. In addition to the energy budget, the communication structure will be affected by the execution of CR cycles. Here, some potential challenges are discussed. • Bandwidth variation: Future services will demand even more bandwidth since applications are demanding higher and higher data rates. CR capability can fulfill this requirement. However, distinct characteristics of licensed channels will cause variations in the utilized bandwidth. This will pose challenges for the IoT applications that require steady bandwidths. • Quality of Service: IoT has a heterogeneous structure that consists of different technologies with different levels of QoS requirements. CR has the flexibility to adapt to these changes by cognitive capabilities. However, the PU activities pose challenges in satisfying the different QoS levels. • Coverage area: IoEH-CRs should allow the networks to cover large geographical areas while considering the PUs’ activities. This issue challenges the overall coverage since the nodes in a region may or may not have sufficient spectrum opportunity for a particular duration. As this may result in lack of information from those regions, IoEH-CRs should cover as much as area possible. • Scalability: Remote monitoring and control of the large geographical areas require excessive numbers of sensors to provide full coverage. This issue necessitates scalable solutions to sustain proper networking functionalities. Hence, network infrastructure needs to be designed flexible and adaptive to any changes in the network parameters and/or addition of EH-CRs. • Reliability and delay: The PU activities may cause a change in the channel conditions for the IoEH-CR nodes. Interference from the PU degrades the channel quality and the reliability of the communication. Furthermore, the connectivity of these nodes highly depends on the activity of the PUs, which increases the delay. • The limited computational capability of EH-CRs: Performing complex tasks cannot be done due to the limited energy budget of the sensor nodes. Hence, the collective efforts of the nodes are highly essential to complete high energyrequiring complex tasks. • Intermittency in EH resources: EH techniques severely suffer from the intermittent profile of the exploited resources, because ambient available sources are mostly unpredictable and uncontrollable in their natures. They are strictly affected by environmental conditions, ambient parameters and/or random human interventions. As this issue poses a challenge for continuous power extraction, IoEH-CRs need to be structured by considering the capabilities of the source that is being utilized. • Service differentiation: ZigBee, 6LoWPAN, Z-Wave, Wi-Fi, and NFC are the technologies that are commonly employed in the IoT domain. As the IoT paradigm proposes heterogeneous network architecture, wireless devices must be compatible with different protocols to allow an interconnected infrastructure that communicates over the Internet.

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The IoT environment is harsh since too many nodes contend for the spectrum with limited power supplies. CR and EH techniques are therefore utilized to overcome these difficulties. However, spectrum-aware and self-sufficient communication techniques pose some additional challenges. These challenges are intensified by the ever-evolving demands of the IoT services.

4.4 Integration of Energy Harvesting Cognitive Radios with the Internet To realize the concept of IoT, EH-CRs must be seamlessly integrated with the Internet. This integration is vital to enable CR-capable EH wireless devices in extracting energy and information from their surroundings without any constraints [45]. The main pillar of the IoT is WSNs. Hence, to enable the concept of IoT, WSNs must be connected to the Internet. The first approach to integrate the WSNs to the Internet is to make them all-IP networks [46]. This is challenging because the WSNs use application-specific networking and data-centric routing protocols. Furthermore, the WSNs are resource-constrained, and the size of their packets is small. If they use unique IP addresses for the addressing on the Internet, the packet size will extend, and hence the energy consumption for the packet transmission will increase. However, EH-CRs can overcome this challenge by employing efficient EH techniques. Although the energy constraint is overcome, address management of such a massive network would be difficult to handle. Hence, the full IP stack on WSNs may not be achievable due to the low capability of sensor nodes and their deployment in large numbers [46]. The integration of WSNs with the Internet to realize the IoT can be performed from a different perspective [47]. They can be classified based on the employed stack and topology. In stack-based integration [48], approaches that connect the WSNs to the Internet are Front-end, Gateway, and TCP/IP. In the Front-end approach, the WSNs and the Internet are two separate domains. The connection between them is provided by a centralized device that can communicate with the sensor nodes and is connected to the Internet. This device is always traversed by centralized devices. In the Gateway approach, the base station behaves as the application layer gateway, which can provide web services to the IoT users [47]. In TCP/IP solutions, the protocol stack of the Internet and the WSN is the same. The sensor nodes are directly connected to the Internet, which means full integration. In other classification, namely topology-based, the integration approaches are hybrid and AP solutions [47]. In the hybrid approach, the nodes with higher computational capability are located at the edge of the network to connect the WSNs to the Internet. AP solution fosters the Internet-enabled nodes to behave as APs in the WSNs for the integration with the Internet. The above discussions are for the WSNs without CR and/or EH capabilities. However, if CR and EH capabilities are considered in the integration of such networks

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with the Internet, the procedures/schemes to be followed should be reconsidered in a different perspective. The energy constraint of the sensor nodes is the most significant limitation of these systems. Furthermore, the spectrum scarcity problem intensifies this limitation due to the packet loss caused by collisions. Hence, CR and EH are two important techniques to enable ever-efficient IoT services. The EH-CRs can overcome these difficulties and provide seamless operation in the IoT domain. On the other hand, an energy-efficient and spectrum-aware solution needs to be considered to address the challenges posed by the IoT and the integration of the WSNs to the Internet. The advancement for the low-power and lossy devices pave the road for the connection to the Internet using IPv6 with 6LoWPAN [49].

5 General Framework of IoEH-CRs for Big Data Applications Up until this point, the general concepts, motivations, challenges, and possible application areas of CR and EH technologies are discussed. By following all these aspects, this section provides a general framework for the IoEH-CRs. Operation overview, as well as node and network architectures of IoEH-CRs, are explained in a comprehensive manner. Furthermore, potential applications and possible future solutions that can be delivered by this newly-emerging paradigm are also pointed out considering Big Data-enabled IoT vision.

5.1 Operation Overview In system architecture, an EH-CR node transmits its readings opportunistically to a CR-capable AP, i.e., a gateway, which is connected to the Internet. The readings, therefore, become observable from anywhere at any time as they are stored in remote servers. This operation paves the way for online monitoring, control, and management of SC and SG components and any other smart environment. To realize this, the EH-CRs organize in two ways, namely homogeneous and heterogeneous. In the homogeneous architecture, all nodes are identical in terms of computational capability. Hence, there should be a gateway to provide Internet connectivity. Homogeneous architecture can be seen in Fig. 1, where all the EH-CR nodes (i.e., Secondary Users) have the same hierarchy. CR and EH capabilities can ease the problems of spectrum scarcity and packet collision. The queries from the remote IoT users are conveyed to the IoT devices by traversing the gateways. On the other hand, the nodes with higher spectrum availability and more EH opportunities may have different roles in the operation, such as having IP connectivity. These nodes form an overlay IP network for the connectivity of the EH-CRs to the Internet. This architecture refers to the heterogeneous operation, which can be seen in Fig. 2.

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Internet

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Fig. 1 Homogeneous operation architecture of IoEH-CRs

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Fig. 2 Heterogeneous operation architecture of IoEH-CRs

5.2 Node Architecture An EH-CR node consists of four main units, which are ultra-low-power communication, ultra-low-power processing, autonomous connection, and power provisioning and conditioning units. The proposed node architecture is illustrated in Fig. 3. The power provisioning and conditioning unit has the following subsystems: energy harvester, rectifier, regulator, and storage. This unit aims to convert the ambient and underutilized resources into utilizable electric power. The applied EH method is determined depending on the application and its specifications. For example, if the network is going to be established in a wide-scaled rural area, solar and/or airflow solutions will be more beneficial. For an urban environment, however, RF and piezoelectric options will be preferable. E-field and artificial light-based approaches will stand out for indoor applications.

Internet of Energy Harvesting Cognitive Radios Power Provisioning and Conditioning Unit

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Fig. 3 Node architecture of IoEH-CRs

Since the majority of harvestable resources give AC output, rectification is one of the most crucial processes for EH-capable sensors. Rectifiers are for both converting the alternating current into utilizable DC power, and preventing the harvested energy from back feeding. Since the energy that can be scavenged by existing EH approaches are of the order of micro-to-milliwatts, rectifiers must be as efficient as possible to avoid undesired losses. Regulators adjust the wave/signal shape of the rectified voltage/energy to allow the connected circuit to operate properly. Depending on the energy output, buck and/or boost converters can accompany the regulators to provide the most convenient voltage gap for the devices attached. Since the great portion of standalone devices suffers from limited energy storage capabilities, which also encourages EH procedures, harvested energy should be condensed in an efficient storage which is determined by capacity, leakage, and charge/discharge cycle characteristics. For increased longevity, i.e., node/network lifetime, quick-charged, long-lasting, high-power condensed, and low leakage super-capacitors must be employed. Autonomous connection circuit-unit (ACC) needs to be utilized to separate the harvester and the resting circuitry from each other. It acts as a switch between harvesting and nodal operation stages. In other terms, it provides isolation. This circuit simultaneously observes the voltage level on the storage element, i.e., super-capacitor, and accordingly enables charge conveyance when the harvested energy is high enough for sensor operations and disengages the super-capacitor from the circuitry to turn back the harvesting period when the voltage level descends below a certain threshold. This operation, held under the control of the processing unit, not only prevents the undesired discharge of the super-capacitor but also allows more frequent transmission cycles by shortening the time exerted for the harvesting process [10]. The processing unit, consisting of a microprocessor and a memory, decides the operating parameters and manages the whole systems/subsystems of the Internet-enabled EH-CR node.

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The ultra-low-power communication unit provides the capability of channel switching, channel sensing, modulation, and power control. It has an RF front-end, a demodulator, and a decoder to provide the communication over the IPs with CR functionalities. In short, the described EH-CR architecture will sense the parameters of interest, process the gathered data, and notify a gateway over the Internet where the conditions of applications/systems/things are monitored, controlled, and managed by authorized personnel.

5.3 Network Architecture The IoT has unique features, such as pervasiveness, ubiquitousness and access to the Internet. These features necessitate different consideration of the WSNs in the IoT domain since the employment of IoT networks are more complex than the WSNs due to the excessive number of devices, two-way communication between IoT servers and devices, heterogeneity in the sensed data, various wireless radio access technologies and IoT gateways, etc. [50]. Due to these reasons, the majority of current deployment techniques and architectures employed in the WSNs are not applicable to the IoT domain [50]. Furthermore, these architectures are affected by the use of CR and EH enabling the IoEH-CRs. In the IoT architecture, the IoT devices deployed in the application areas, i.e., environment, traffic, and structure monitoring, smart building, SC, etc., send their collected information to the IoT APs or IoT servers [50]. On the other hand, in this chapter’s context, the IoT nodes are planned to be equipped with CR and EH capabilities. The CR capability provides flexibility for the spectrum utilization, where EH eases energy management. Hence, the network architectures that can be proposed in the IoT domain may be used for the IoEH-CRs. Regarding these, some possible network topologies for this paradigm are explained as follows. • Ad Hoc IoEH-CRs: The IoEH-CR nodes are homogenous, and management of the spectrum is done cooperatively among them. In this architecture, no central entity is present for the regulation of spectrum-aware communications. Exchange of control packets is performed to establish spectrum-aware communications. An example of this type of network architecture can be considered as the network in Fig. 1. • Heterogeneous IoEH-CRs: In this architecture, special nodes with higher computational capabilities are used to perform tasks that need high power. These nodes may have special responsibilities, such as spectrum bargaining [10]. The network architecture in Fig. 2 is a typical example of heterogeneous IoEH-CRs. • Clustered IoEH-CRs: Spectrum availability is one of the most important parameters in establishing communication between IoEH-CRs in a dynamic radio environment. The nodes within the same geographical location can have similar

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Fig. 4 Clustered network architecture in IoEH-CRs

vacant channels. In such environments, the nodes are grouped to efficiently utilize the same spectrum bands among the members of that group. These groups are called clusters, and they offer energy-efficient and reliable communications. Figure 4 shows a hierarchical network structure, where cluster-heads behave as local coordinators. • Mobile IoEH-CRs: Dynamic spectrum usage is intensified if the nodes are mobile since the mobility affects the spectrum availability in the IoEH-CRs. Solutions for dynamic spectrum access should also consider mobility in this architecture.

5.4 Discussion on Existing and Future Applications of IoEH-CRs for Big Data The IoT has diverse application areas, which are health, agriculture, industry and automation, SCs, body area networks, SGs, smart metering, and intelligent transportation [51]. Furthermore, the existing applications may lead to the new future application areas by the addition of CR and EH capabilities to the IoT nodes. Future applications will benefit from the flexibility of spectrum usage and energy consumption. Potential applications of CR capable IoT are classified into indoor and outdoor use cases [51]. Indoor use cases are smart homes, smart metering, and cognitive femtocells, whereas outdoor cases are related to vehicular communications. From the perspective of the IoEH-CRs, the SG elements, such as sensors and meters, use ISM bands to convey their sensed data to the communication facilities. However, this

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communication may be interrupted by the dynamics of the wireless radio environment [52]. Hence, possible advantages/disadvantages of the EH techniques should be analyzed, and the combination of CR functions with these EH techniques should be investigated for the seamless operation of the IoEH-CRs in an SG environment promoting Big Data paradigm [53]. Home appliances will be connected to the Internet by the IoT paradigm. These devices will mostly operate on the ISM bands, which are already crowded [51]. Here, the congested bands can be avoided by utilizing the television white spaces [54]. Furthermore, CR is also utilized in next-generation public protection and disaster relief (PPDR) networks [55]. Resilient and sustainable communication is of utmost importance in emergencies. However, the communication systems that are employed in the emergency areas are facing challenges, such as coverage losses, energy outage, network partitioning, and spectrum shortages [55]. However, these systems should be connected to the Internet to deliver information about the disaster to remote areas in a continuous fashion. Hence, the PPDR network nodes should be designed as IoT nodes supported by CR and EH capabilities. The operating time of these networks is the times that ISM bands are heavily used. Hence, the CR capability of the nodes provides resilient communications to the PPDR networks. Furthermore, these nodes may suffer from energy outages for reaching the AP. Smart transportation is also an application area of the IoEH-CRs, since protocol stack used for the vehicular communication, which is IEEE 802.11p Wireless Access in Vehicular Environments (WAVE), may also experience congestion [56]. Hence, according to the delay threshold, CR network principles are utilized. Furthermore, in SCs, key systems of the cities are observed, and the gathered data is sent by the sensor nodes to the city officials to increase the quality of life of their citizens [57]. The IoEH-CRs can be utilized in SCs since IoT nodes suffer from the difficulties in terms of available spectrum and battery replenishment. Hence, CR and EH techniques can be considered as key enablers of the SCs, where the more reliable and robust operation of the SC services are guaranteed.

6 Conclusion The combination of two promising technologies, i.e., CR and EH, in the IoT domain paves a road for more connected, spectrum-efficient, and self-sufficient networks and services. However, the existing challenges posed by these technologies are intensified by their combination to enable IoEH-CRs. The key issues realizing the IoEH-CRs are energy scavenging techniques with efficient spectrum management protocols in the context of the IoT. The limited energy capabilities of wireless standalone devices and the spectrum scarcity that occurs due to a large number of wireless devices will promote the employment of this architecture to overcome the current difficulties while enabling efficient generation, acquisition, and analysis of the Big Data. Hence, future communication solutions are expected to include the IoEH-CRs in many applications, such as SG, SC, smart transportation, and PPDR networks.

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The proposed paradigm will grant more flexibility and efficiency in the utilization of spectrum and energy resources, which will provide promising services for Big Data-enabled IoT in the very near future.

Glossary Big Data is a term that describes huge amount of structured, semi-structured and unstructured data. Energy Harvesting (EH) is a phenomenon which refers exploiting a stray source or converting energy from one form to another. Cognitive Radio (CR) is a technology that enables accessing the spectrum opportunistically via sensing the spectrum. Energy Harvesting Cognitive Radios (EH-CRs) are the wireless devices with energy harvesting and cognitive radio capability. Internet of Energy Harvesting Cognitive Radios (IoEH-CRs) It is the Internet of Things, in which the things have energy harvesting and cognitive radio capability. Wireless Sensor Networks (WSNs) are the autonomous sensors that monitor a parameter of interest such as temperature, humidity, presence etc. and accordingly convey the gathered data through the network to an authority. Industrial, Scientific, and Medical (ISM) radio bands are the vacant bands that are reserved for industrial, scientific and medical purposes in international order. Primary Users are the licensed users and can access to their spectrum bands without any limitations. Spectrum Sensing is one of cognitive cycle operations that reveals information about the spectrum usage. Spectrum Decision is the determination of operating spectrum band after sensing the spectrum bands. Spectrum Handoff is the cognitive cycle operation that ceases the transmission of cognitive radio when PU starts to communicate on the channel CR operates. Photo-voltaic (PV) Effect is a chemical phenomenon, in which PV cells emit electrons when they exposed to light. This effect yields in generating electrical energy. Smart City (SC) is a vision to enable information and communication technologies to observe the city elements to provide better management of the city resources/services. Smart Grid (SG) provides two-way communication between the utilities and homes to provide better electric services. Maximum Power Point Tracking (MPPT) is a method that maximizes power extraction from (mostly) environmental sources under all conditions. Kinetic Energy Conversion (KEC) is a process, which is simply based on converting movement resultant energy into utilizable electrical power.

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Mechanical-to-Electrical Generators (MEEG) are devices, which contribute KEC process by taking advantage of vibrations, pressure variations and stress-strains. Thermo-electric Generation (TEG) is a process of converting temperature difference resultant energy into utilizable electrical power. Autonomous Connection Circuit (ACC) operates as a switch between harvester and nodal circuitry in a EH sensor node to efficiently manage the harvested energy.

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Cultural IoT Framework Focusing on Interactive and Personalized Museum Sightseeing Sotirios Kontogiannis, George Kokkonis, Ioannis Kazanidis, Michael Dossis and Stavros Valsamidis

Abstract Museum visitors are very focused and demanding. Immersive technologies as virtual and augmented reality, interactive haptics, 3D scanning and plotting, content digitization, and personalized automatic navigation must be exploited by museums in order to stimulate museum visitors and extract their attention. The authors of this work propose an open source IoT InteRactive Museum Experience (IRME) framework. IRME offers information classified in thematic sections. The visitors have the opportunity to explore specific thematic sections of interest. Navigation instructions and artwork guidelines are obtained with the help of a smart phone application. Data-mining, artificial intelligence and cognitive services offer the ability to learn from visitor’s preferences and respond more accurately to future requests and in this way enhance visitor’s experience in the museum. IRME provides a real-time, responsive and personalized navigation to museum visitors. It includes indoor positioning technology, IoT sensors and actuators, haptic devices orchestrated over cloud services. Wherever possible, IRME uses low power technology such as Bluetooth Low Energy devices, led plates-spots-cubes and 3D printing S. Kontogiannis · I. Kazanidis Laboratory of Distributed Microcomputer Systems, Department of Mathematics, University of Ioannina, Ioannina, Greece e-mail: [email protected] URL: http://kalipso.math.uoi.gr/microlab I. Kazanidis e-mail: [email protected] G. Kokkonis (B) Department of Business Administration, Western Macedonia University of Applied Sciences, Grevena, Greece e-mail: [email protected] M. Dossis Department of Computer Science, Western Macedonia University of Applied Sciences, Kastoria, Greece e-mail: [email protected] S. Valsamidis Department of Accounting and Finance, Technological Educational Institute of Eastern Macedonia, Kavala, Greece e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. A. Matin (ed.), Towards Cognitive IoT Networks, Internet of Things, https://doi.org/10.1007/978-3-030-42573-9_10

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modeling capabilities, in order to promote museum artifacts and to enhance the visitors’ knowledge acquisitions and entertainment. Moreover, the reflection of such recreational improvements to the visitors is also measured using IoT sensors and the results are used as feedback for future thematic land planning, and IoT illustration techniques. Keywords IoT · IoT protocols · BLE technology · Haptics · Indoor positioning beacons · Smart agents · Cognitive services · Chat-bots · Classification algorithms · Data mining algorithms · Mobile phone applications · Museums

1 Introduction Virtual and Augmented Reality are two of the most disruptive and popular technologies [1]. Real Reality is the environment of the user without the use of any device while Virtual Reality is the reality that visitors’ experience, which is unrelated with their environment and is completely machine generated [2]. Virtual museums are an artificial recreation of a real museum environment. In the case of Augmented Reality (AR), users see and interact with the real world, while digital assistive content is provided to the visitor. This technology is assisted with sensors which help to generate enhancements on to the existing visitor real world with the use of displays. Virtual reality can recreate real life in a digital world, while augmented reality enhances real world with virtual displays. Merging those two different concepts, Milgram and Kishino [3] proposed the idea of Mixed Reality (MR), which is a mixture of real and virtual worlds. Milgram’s Mixed Reality continuum is a one-dimensional array from the Real Environment to the Virtual Environment (Fig. 1). Augmented and Virtual realities have the ability to alter our perception of the world but they differ in the perception of time. Virtual Reality and Augmented Reality have similarities in the technology they use and the field of tourism and museums that they are applicable. Virtual Reality and Augmented Reality have differences in the immersion they provide, the devices they use and their applications. While virtual reality creates its own reality that is completely computer generated and driven, augmented reality enhances experiences by adding virtual components such as digital images, graphics, or sensations as a new layer of interaction with the real world. Mixed reality (MR) does not just overlay virtual objects on the real-world environment but anchors virtual objects to the real world. There are two forms of reality technologies that are referred to as mixed reality: (a) Mixed reality that starts with the real world, where virtual objects that are overlaid on the virtual world (real Fig. 1 Mixed reality continuum

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Fig. 2 Order of reality concepts ranging from reality (left) to virtuality (right)

to virtual), (b) Mixed reality that starts with the virtual world and the virtual world content is anchored to real world devices (virtual to real) [4]. Figure 2 depicts the categorization between the different versions of reality and virtuality [5]. Mobile Augmented Reality (MAR) enhances the real world of a user with computer-generated virtual content through his mobile. The features and capabilities of mobile phones take advantage of ubiquitous computing, and 3G Internet access, computer vision, and mobile cloud computing contributed to the rise of MAR [2].

2 IoT Interactive Museums The Internet of Things (IoT) is a new network technology which enables machines and devices to interact with each other [6] and allows everyone and everyday objects to be connected anytime and anyplace [7]. Internet of Things (IoT) includes the augmentation of the Internet to little and minimal effort “things” that are thought to realize smart environment with a specific end goal to provide new services to the users [8]. Internet of Things (IoT) computing consists of the application of intelligent sensors and technologies [9]. The basic idea of the IoT is a pervasive presence around us of a variety of things or objects such as RFID tags, sensors, actuators, mobile phones, etc. which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to produce the desired output [10]. The IoT technologies that are widely used for the deployment of virtual IoTbased museum are: the Radio Frequency Identification (RFID), the Wireless Sensor Networks (WSN), the Middleware protocols, the Cloud computing and the IoT application software [11]. IoT actuators, sensors, haptic user interfaces, and VR headsets are responsible for transferring digital content to the user’s perception through the virtual reality world. This will maximize user interactivity and augmentation. Haptic interfaces enable user to interact with the virtual artifacts, and to observe and virtually manipulate museum exhibits. This kind of interaction immerses the user to virtual reality and gives the feeling of “virtually being there” [12]. This technology apart from tourism promotion can help people with impairments and disabilities to virtually interact with 3D representations of the tourist attractions and monuments that would have been otherwise incapable to visit in the real world.

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Museum is one of the places where objects of historical, scientific, artistic, or cultural interest are stored and exhibited. More specifically, a museum is an institution that protects a collection of artifacts and other objects of artistic, cultural, historical, or scientific importance. Some public museums make these artifacts available to the public which can view the artifacts through exhibits that may be permanent or temporary [8]. Technology can play a crucial role in supporting museum visitors and enhancing their overall museum visit experiences, and museum executives to provide content and delivery relevant information, and at the same time allow visitors to get the level of detail and the perspectives which they are interested in Kuflik [13]. Many museums have already benefited from “digitalization” based on information and communication technology (ICT). A traditional way for the digitalization is deploying a database or even a museum information system [14]. The purpose of the museum project was to enhance access to the scientific collections and to upgrade internal info by building joint national databases [15]. The progress in IoT technology has already led to such extent, that some museums provide exhibits equipped with IoT-enabled digital equipment [9, 16]. An IoT device for smart museum able to support a static cultural space that becomes intelligent thanks to the definition of an innovative model of sensors and services is presented in Chianese and Piccialli [17]. A smart electronic guide for museums which has the capability of provide the visitors of a museum with pictorial, scripted, and vocal information about each object was proposed in [18]. Museums are good environments for using BLE to provide location-awareness since usually the building and its contents do not allow changes due to preservation policies [19]. Museums are nowadays a tool of entertainment and provide visitors either with paper booklets or with audio guides. It is difficult to define in advance a tour for all the visitors, because interests may vary from person to person. Interactive and personalized museum tours need to be developed; a significant contribution can be given by the Internet of Things (IoT), which involves the extension of the Internet to small and low-cost “things” that are thought to realize smart environments in order to provide new services to the users [20]. The problems that museums face in determining what knowledge is suitable for dissemination purposes, and looking at how this process presents itself and how it can be supported by information and digital resources, are alleviated when existing knowledge about the process is distributed across a wide number of objects, like the ones which represent a museum’s permanent collection examined by Chen et al. [21]. This chapter presents the related work that has been done in the areas of tangible media, augmented reality, haptics and virtual models and services focused on museums. It outlines the necessary equipment and software needed in order to create 3D media with tangible interactive content. Furthermore, it presents the IRME framework that can support museum augmented and virtual services, the interconnection of the above hardware sensors with the 3D rendering and illustration software layouts, as well as the description of interactive user services that can be supported by a framework compatible system. The next chapter deals with existing and related work.

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3 Related Work on Cognitive IoT Museum Services IoT museums consist of visiting systems and recommendation systems which provide advanced services to museum visitors and executives [16]. An example is the “SmartMuseum” system, in which visitors can gather information about what the museum displays and customize their visit based on specific interests [22]. This system, which integrates PDAs and RFIDs, brought an interesting novelty when first released, but it has some limiting flaws. An interior location-aware design for smart museums was designed and valid by Gite and Pandey [15]. It consists of a process centre, wherever the particular business logic is responsible to (i) retrieve from the Cloud the cultural info associated with the determined artworks and (ii) manage the standing of the indoor surroundings in accordance to users’ position. The design and the application of location-based services and technological tools applied to Cultural Heritage environments are proposed by Chianese and Piccialli [17] and Amato [23]. The design, creation and use of tangible smart replicas in a large-scale museum exhibition is described by Marshall et al. [24]. These replicas were used to create a layer of narrative content that was offered in addition to the more factual content traditionally presented in museums. A smart museum concept where information services are not limited with straightforward provision of record-based description of exhibits, as it happens in traditional museum information systems is proposed by Korzun et al. [25]. The concept is extended with services of high intelligence level when additional historical sources can be used to semantically enrich the museum collection, including knowledge acquired from visitors and museum personnel. Within the research field of loT, efforts have been made on a particular sub-area called cognitive loT (CloT) [26], which aims to incorporate cognitive capability into the conventional loT framework to some extent [27]. Cognitive data processing integrates the human cognition process into IoT applications. Rather than being programmed to deal with every possible data-processing need, a cognitive dataprocessing application is trained using artificial intelligence algorithms to sense, predict, infer, and learn tasks and environments. The notion of a cognitive dynamic system (CDS) provides guidelines to build cognition into loT in a systematic way [28, 29]. Feng et al. [30] explore the cognitive loT paradigm in the context of smart homes. They argue that by using the notion of cognitive dynamic systems, which build on perception-action cycles, memory, attention, intelligence, and language, it is possible to engineer loT applications that cover a wide spectrum of tasks with minimum human intervention. Different kinds of cognitive actions can influence different components of the system (Haykin et al. 2012). Alleto et al. [20] developed an indoor location-aware system to make interactive cultural displays in a museum with BLE beacons combined with an image recognition wearable device. The system automatically provides users with cultural contents related to the observed artworks.

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A multi-tier approach dealing with sensor fusion and IoT aspects in a modular way proposed by (Cicirelli and Spezzano 2016). The approach relies on the use of the agent metaphor, state charts and on the Rainbow multi-agent platform. The overall goal is to move from the IoT towards the Cognitive IoT where objects interact and operate by acquiring knowledge from the surrounding environment and by following a context-aware perception-action operational cycle [31, 26].

4 Related Work on System Architecture of Smart Museums The Architecture of the proposed system uses best practices of existing systems and adds values by using tangible media, augmented reality, haptics and virtual models and services focused on museums. Tahyudin et al. [32] proposed a system architecture using interactive Mobile Augmented Reality (MAR) as illustrated in Fig. 3. This architecture consists of the main actors, namely the readers and editors. The editors are AR content that creators who connect to the cloud interface. Meanwhile, the reader is a user who views some content by running the AR with app for Android interface platform. The details of the system architecture are portrayed in Fig. 3. Jinu et al. [33] proposed an Indoor Location Aware Architecture IOT Based Heterogeneity Smart Museum. The proposed system is done in phases: (a) Cloud Contents and Room Creation, (b) Cloud Contents and Media Upload, (c) Background Subtraction Algorithm and (d) Processing center. The architecture of the Indoor Location Aware Architecture IOT Based Heterogeneity Smart Museum is illustrated in Fig. 4. An interior location-aware design for smart museums was designed and valid by Gite and Pandey [15]. It consists of a process centre, wherever the particular business logic is responsible to (i) retrieve from the Cloud the cultural info associated with

Fig. 3 The mobile AR architecture

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Fig. 4 The architecture of an indoor location aware architecture IOT based heterogeneity smart museum

the determined artworks and (ii) manage the standing of the indoor surroundings in accordance to users’ position. The structure of the system architecture is portrayed in Fig. 5.

Fig. 5 Overall structure of the proposed system architecture

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Fig. 6 Architecture diagram

An indoor location-aware framework depending on a wearable device outfitted with image recognition and confinement capacities to automatically furnish users with cultural contents identified with the observed arts [8]. The overall structure of the proposed system is depicted in Fig. 5. It consists of four main building blocks: Updating Cloud Contents, Uploading Media with Localization service, Image-processing and Artwork content Delivery (Fig. 6). Chianese and Piccialli [9, 17] defined the architecture to represent and manage the smartness inside cultural spaces, adopting the IoT paradigm and supporting this direction with the design of a set of sensor nodes. The proposed system consists of three layers: (i) the sensing layer, which is devoted to the data transfer and acquisition, and nodes collaboration in short-range and local networks, (ii) the network layer, which is aimed at transferring data across different networks and applications, and (iii) the application layer, where the IoT applications are deployed together with the middleware functionalities. Figure 7 illustrates the proposed three-layer architecture. Another indoor location-aware architecture for smart museums was designed and validated by Alleto et al. [20]. The system relies also on a wearable device equipped with image recognition and localization capabilities to automatically provide users with cultural contents related to the observed artworks. The ability to identify the visitor’s position is guaranteed by an infrastructure of BLE transmitters. The architecture also consists of a processing center, where the actual business logic is in charge

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Fig. 7 A representation of the IoT architecture for a cultural space

to: (1) retrieve from the Cloud the cultural information related to the observed artworks and (2) manage the status of the indoor environment in accordance to users’ position. Finally, the system accesses the Cloud also to store multimedia contents produced by the user and to share environment-generated events on his/her social networks. Figure 8 shows the overall structure of the proposed system architecture. It

Fig. 8 Overall structure of the indoor location-aware system

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Fig. 9 Architectural design of smart spaces-based software infrastructure

is composed, as described below, of three main building blocks: Localization service, Image-processing algorithm and processing center. Korzun et al. [25] implemented a semantic layer, enriching the collection stored in a traditional Museum Information System (MIS). The architectural design is depicted in Fig. 9. In the section that follows a more detailed IRME architecture description is presented and IRME services functionality is outlined.

5 IRME Framework Services and Protocols The InteRactive Museum Experience framework (IRME) offers a variety of services. As it focuses on maximizing visitors’ experience, it is interacting with the visitor via automatic content discovery and proposed navigation. It tries to immerse and stimulate all visitors’ senses and interact with him/her via audio, vision, and haptics. Cloud services host the digitized material and offered to the visitor via a cloud multimedia portal. The Information System (IS) offers identification, interactive haptic services and thematic guidance. The system will interact with the visitor mainly through the visitor’s cell phone with the help of mobile applications running on the phone. Navigation bots and voice guidance are used to navigate the visitors through the museum exhibits. Blind people will have the opportunity to hear the description of the artifacts and explore the exhibits with the help of haptic interfaces (sensors and actuators). All the proposed services are offered via a coherent way, so as not to disturb the time line of the historical and the cultural events. The supported services offered by the IRME are illustrated in Fig. 10.

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(2)Authentication service Information System of Digitized Content Images, Audio/Video content 3D content Content tagged with descriptive information Additional meta-data that link to other content (sequential linkage) Additional content of applications (games), questionnaires for self evaluation and/or other source

(6) Interactive service Wireless communication protocols channel 3G/4G/Wi-Fi

(4)Thematic tour service

Visitor Mobile phone Application with 3D capabilities that offers: User authentication and positioning (User Anonymity provisions) One single App for All offered services with 3D content delivery output Transparent sensory feedback uploaded to the Information system Cognitive services and smart bots

Data mining and user profile (3)Navigation Sensors-mobile phone, wireless classification bot service communication protocols channel (Wi-Fi, BLE) Content classification agent User preferences cognitive miner (7) Sense and (5) Augmented projection service Automated guidance bot congnitive service Smart displays Automated suggestions bot Heart rate sensor rotating steppers Self evaluation agent Skin conductance servo spot-leds Skin temperature rgb led cells-surfaces Haptic gloves Accelerometer led cubes (8) Haptic Gyroscope service ECG sensors (head strip)

Fig. 10 Services of the interactive museum experience framework

In each museum site, Eddystone beacons are placed to identify the location of each identified visitor [34]. These beacons help the automated guided tour within the museum spaces. Specifically, the following services are offered to the final visitor of the museum through the application of the mobile phone: Service 1. IS Content Manager and content distribution service. A multimedia database storage combined with a web-based content manager is used in order to distribute digital content wirelessly and simultaneously to multiple visitors. The HTTP/JSON based content request-response application protocol should be enforced in order to offer a stateless REST architecture. The Information System (IS) services should be offered through Internet cloud services as PaaS hosted services. A Digital content multimedia manager will be responsible for uploading, tagging and managing content to thematic axis and content sections. A mobile phone application will be created in order to deliver the multimedia streams directly to the visitor either via the museum’s Wi-Fi infrastructure or appropriate 4G service provider. Visitor’s thematic selections and location acquired by the navigation service will continuously update the augmented navigation information on the visitors mobile phone application. Thematic axis and sections will be selected by visitors in order to modify interactively his/her navigation proposition and course towards the museum exhibits. Interactive propositions and suggestions for indoor navigation will be offered by services described in the following sections. Service 2. Authentication service of the visitor. Visitors personal data will be stored to the cloud IS with the help of a smart phone application. Personalized services

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and content will be provided to the user according to his/her interests. As personal data are involved, authentication service is crucial for the proposed framework. IRME user authentication process can be completed either with username/password or a unique ticket token handed out to the user at the museum entry when shared mobile device is used. This token will combine a randomly generated id, unique visitor IS profile id-name or visitor e-mail, creation time information and token expiration date. This information shall be encoded in a simple form for user input-authentication via the mobile phone application. For that reason, authors’ IRME proposes this auth information to be handed out to the visitors by the museum curators with the form of an e-tag, which will either be a QR printed card or a wrist attached NFC tag. The interaction between the mobile phone application and the e-tag will authenticate the user to the cloud IS and will offer the capability to parameterize its tour with section selection and content delivery parameters configuration (offered by content distribution service). The visitors will be identified by the mobile phone application of the visitor via wireless technology or Wi-Fi with the museum or 3G/4G through appropriate authentication protocol. Service 3. Navigation service consists of two subservices: The location service and the artwork navigation service. The location service of the visitor uses the BLE transponder and GPS receiver of the visitor’s mobile phone (smart phone). For the museum exterior monuments the navigation service utilizes the GPS location service and in the museum interior installments it uses the Bluetooth Low Energy (BLE) technology and internal Eddystone beacon protocols suite (Eddystone UID for the process of acquiring beacons power signal and Eddystone TLM for the process of receiving beacons’ sensory telemetry data such as room humidity, temperature or even beacon acceleration for moving beacons) [35]. The location signals extracted from GPS (for the exterior site of the museum) are used for the process of displaying navigation directions to predefined sights using google directions and geocoding APIs. For the museum building interior installments, the BLE beacons UID transmissions, are processed in order to display navigation information to the exhibit cases and monuments placed internally. Switching from one technology to the other is performed automatically, based on the number of beacons received at the visitors’ smart phones. If more than 3 beacons announced or less than five locked satellites are experienced, the Eddystone navigation protocol takes over. For the process of navigation the Eddystone based IRME application protocol uses BLE beacons that are installed in the ceiling of the museum corridors or adjacent museum walls following a fixed height pattern of 2 or 2, 5 or 3 m of beacons placements. Beacon’s distribution usually determines the indoor positioning accuracy. Regarding BLE indoor position accuracy, Wirola et al. [36], suggest 1 m horizontal accuracy over a detected floor, 1 Hz minimum navigation info update rate and zero latency delay in which position is available to the visitor, for the process of identification of supermarket shelves. Nevertheless, such accuracy is more than adequate for museum exhibits and the requirements for Ambient Assisted Living are suggested as [37]. These are a 0.5–1 m 2D positioning accuracy compared to reference and a 0.5 s update rate of the navigation system for a typical area of 90 m2 . A useful metric for the position

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quality, if normal distribution is assumed, can be expressed with the use of the standard deviation of the subtraction of estimated positions at a given time minus real measured positions, in a navigation route. Mean standard deviation values between 0.1 and 0.5 m denote a positioning system of high accuracy. The indoor positioning algorithms can be implemented in either Android or IOs applications depending on the type of the smart phone used by the visitors of the museum. The artwork navigation service will use the current visitors calculated position in order to deliver a service which guides the visitor using digitized pictures of museum sites. The location service communicates the location info to the artwork navigation service. The latter communicates, via Wi-Fi to the IS and the IS responds with digitized artwork and personalized navigation instructions to the user [33]. The impact and effect of location awareness to the artwork navigation service offers visitors a unique contextual and augmented reality experience. For the process of indoor locale-awareness, the RSSI (Received Signal Strength Indicator) is the indication of how strong is a BLE received beacon transmission. The receiving location service on the user’s phone receives the signal from the transmitting BLEs and it determines the location, and the museum room based on proximity index d one each for each beacon signal, according to the following Eq. 1: RSS I = −(10n log 10d + A)

(1)

where d is the distance from the Eddystone beacon, A is the received signal strength at 1 m distance, and n is the signal propagation constant, the phone application determines its position with the lowest d which corresponds to the closest beacon [20, 38]. That is, for smart phones position locks of 1–2 m accuracy, trilateration is used of at least three wall placed beacons for a 36 m2 room for less than 1 m accuracy. These beacons must form the edges of an equilateral or an isosceles triangle placed at a certain fixed height as mentioned previously. Since beacons don’t have a directional antenna, only an estimation of the distance to the beacon can be received and not from which direction the signal is coming from. In order to understand that, you need to have a line-of-sight to three or more beacons, and compare the RSSI values to each of them. The RSSI calculations can pinpoint to one specific floor plan room location. One exception to this is narrow corridors, where position calculation can be performed using two beacons [39]. For more accurate (less than 1 m) calculations, a dense beacons installment is preferred by forming polygons (hexagons for room of 40 m2 < S < 80 m2 , where beacons are placed at the hexagon edges using distances equal to the circle radius that includes the hexagon). For large rooms (S > 80 m2 ) a triangle split approach is used where each the total area forms a polygon of rectangular triangles: S = T1 + T2 + T3 + · · · + Tn , where each one of the n triangles covers areas between 3 and 4 m2 [39]. All these floor plan segments and their subdivisions of 0.25 m2 circular blocks of RSSI BLE values are features of a custom GeoServer floor plan layer [40]. The reason that a dedicated map service has been proposed is due to the small scale, easy to use and to configure, in contrast to the limited Google indoor maps.

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Every Eddystone beacon sends via the Bluetooth protocol its location data as well as its transmitted signal strength value, to be used by the location service on the user’s phone to determine the visitor’s current room and location. The location information has been used elsewhere to create an exercise application that displays the route was taken on one’s morning run, or a memo application that lets visitors write themselves reminders that pop up automatically as soon as they reach a specific location. In the context of the current work, the location information is used to offer augmented reality environment effect for the museum’s visitors. After the location is determined by the location service, it is passed to the artwork processing service (implemented also in Android or IOs) which communicates it to the IS via a suitable Wi-Fi protocol. The IS processes the location data and starts a thematic processing run on the museums thematic and artwork image and related info database which is located normally in the cloud [15]. In this way the visitor’s experience can be broadcasted to external Internet and social media applications to inform also the public, the museum’s personnel, executives and external visitors who want to enter the museum about the queues’ status in front of the exhibitions, the expressed interest about the museum’s items and exhibits. Thus, useful statistics are collected to aid the museum administration about executive decisions for future improvements. Moreover, and most important of all, the IS with its image and sound processing applications sends sound and image data to the visitor’s navigation service (and thus its artwork processing sub-service) application, so that the museum visitor can live a unique cultural experience with navigation and cultural sound instructions and images about the museum exhibitions. In addition, the navigation service also includes image and sound improvement algorithms which e.g. cut out destroyed sound and blurred image data. Furthermore, the IS can inform the visitors about the consequent artworks and their history. The museum personnel can also inform the visitors, in case of an emergency to point out the emergency routes and exits. Therefore, the usability of the combination of the navigation service and the Eddystone beacons is many fold range from unique cultural experience to the extreme health and safety situations. Service 4. The main bot-based thematic tour service and the subsystem of tagged sections digitized material presentation are integrated in the mobile phone application. This service will also offer the capability of adaptive on user demand content renewal and context switching. The IRME thematic tour service allows visitors to have automated guided experience in a museum according to predefined paths. Each museum may store various tagged section paths combined with specific key phrases that provide different navigation and delivers different IS content to the visitors. For example, possible predefined paths (thematic axis) could be provided to the visitors that do not have a lot of time to spend on the museum or are of targeted content-sight interest. Such paths would navigate them only at the most axis-related museum artifacts and provide only axis related (sections) content. Another example possible predefined path (axis) could be incorporate exhibits from a specific period of time. Based on a cognitive bot, the thematic tour service will interrogate the visitor of his preference, text mine and isolate key word phrases of user text preferences or searches, cross-check key

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phrases- tagged sections relation and offer to the visitor the IS sections of highest mining relation [41]. It has to be mentioned that this service bot will adapt-order its selected offered sections according to the user location inside the museum dynamically according to the visitor indoor placement having in mind sections layout and trying to minimize both visitors’ paths as well as maintain a logical (mostly chronological) sequence of the offered sections content (as set by the sections numbered tags at Fig. 11). For example, if someone supposes that two visitors (see Fig. 11) have chosen the different thematic axis content sections and these sections are located in different rooms and places in the museum. The service bot will propose a guidance that will incorporate each axis section exhibit, with different paths for the two visitors in order to maintain their input requests (searches). Thematic tour service could help visitors choose the best guidance according to their needs also interactively via timed bot question breaks-called outage cognitive suggestions (see visitor B(4)—interactive service cognitive suggestion), or on visitor request (called external on-demand bot requests).

Thematic tour service Cloud Services - Digitized Content Storage and Content Manager

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Fig. 11 Tagged thematic tour service functionality and interaction with the user interaction service and navigation service

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Service 5. Enhanced augment projection service by visitor interaction with small screens of additional information to be placed on selected exhibit displays or protected monument sites (Interactive Video Displays—IVDs). These smart displays will be activated by selecting the user when s/he is in a close position on that exhibit [12]. Service 6. User Interaction service. The UI service is a supplementary service that offers to the visitor alternative section suggestions, questionnaires or section evaluation quizzes. It interacts both with the cloud IS thematic advancement module (service bot) of the thematic tour service and cloud IS previously recorded and categorized visitors’ museum section paths (service 1). This bot will perform text mining on other visitors’ selections in accordance to visitor profile characteristics and offer targeted guidance information to each virtual user followed by self evaluation questions, quizzes and games. The virtual visitors selected thematic content to visit or visit roadmap could be altered dynamically by this service either automatically or with the form of a suggestion issued to the suggestions bot. Part of the interactive service is also the profile classification bot that tries to classify users according to their selections and profile information in order to create groups of visitors and issue invitation to group joins via the suggestions bot. Moreover, the content classification agent is part of the content manager and works independently, checking classification bonding confidence, evaluates classification tree paths and creates new routes based on visitors’ selections. Service 7. Sense cognitive service. In the context of the proposed cognitive service, interactive and suggestions services offer cognitive Artificial Intelligence (AI) driven information as augmented to the visitor. In depth, the use of smart bots, data mining algorithms, fuzzy algorithms and self constructing tree path algorithms cover a part of the cognitive services supported by IRME. Nevertheless, the authors propose an additional sense cognitive service driven by sensory feedback, for post processing evaluation of the museum visitors’ enthusiasm or interest regarding the offered knowledge. The sensory feedback will be offered by wrist watch BLE capable sensors that will interact with the visitors’ mobile phone app. These sensors will provide real-time information about the virtual visitors’ cognitive state as well as sensory monument evaluation statistically derived from feedback sensory data. Service 8. Haptic exploration of museum exhibits. As touching museum exhibits is often forbidden, visitors are only able to visual explore the exhibits. Haptic technology offers the visitors the ability to haptically explore virtually digitized museum artifacts. Haptics refers to human-computer interaction that encompasses tactile feedback. 3D scanners can analyze museum artifacts, collect data of their appearance and shape and produce digital 3d models of the exhibits. Haptic interfaces such as haptic gloves and styluses with the help of virtual reality can offer the visitor the opportunity to interact with museum artifact and explore their physical properties such as static and dynamic friction, stiffness and elasticity, weight, fragility, density. Geometric properties such as roughness, ripples and wrinkles are also available for tactile exploration. This technology is rather helpful to people with visual impairments, as it gives them the ability to interact with the exhibits and explore them. In order to provide the above service, Haptic devices such as haptic gloves and styluses, virtual

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reality headsets and 3d monitors should be placed nearby museum exhibits, available to visitors, Fig. 12. Service 9. 3D scanning, CAD/CAM modeling and 3D printing. Modern museums should offer the visitor a digital 3D model of the exhibits. 3D scanners can produce these 3D models quite accurate, Fig. 13. These objects can be further exploited by the visitors with the help of CAD/CAM software. Visitors can redesign the artifacts and manufacture their own personalized prototypes. This process will stimulus visitors attention and force visitors to digitally interact with the exhibits.

Fig. 12 Haptic exploration of virtual 3d models

Fig. 13 A 3d object of Greek ancient statue Aphrodite of Milos. Hellenistic period around 150–50 BC, located at Museum of the Louvre

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Service 10. Online artifacts exhibition. As all museum artifacts will be digitally 3D scanned and modeled for the services 8 and 9, it will be rather easy to convert a classic museum to a digital, 3D, online, haptic museum. This transmutation will offer people with mobility impairments to visit the museum through Internet and explore its exhibits. People will not have to spend travel in order to visit a museum on the other side of the world. The time, money and distance needed to reach the place of the museum will be diminished. All the sensory information such as sounds, vision, and haptics will be digitally delivered through internet to online visitors [12]. Haptic interfaces should be acquired by online visitors, in order to haptically explore the virtual 3D models. Such interfaces could be haptic styluses as the Touch™ Haptic Device [42] or a haptic glove such as CyberGrasp™ [43] and the Master II [44].

6 IRME Communication Protocols and Services Interoperability In this section the main protocols that constitute the IRME architecture and services as well as protocols core functionality and technical characteristics are presented. In the subsections that follow the following protocols functionality is outlined: 1. The data exchange protocol of the thematic tour and interactive services and the authentication-interfacing between the visitors’ mobile phones and IS. 2. Navigation service indoor positioning protocol functionality over Eddystone capable beacons [35]. 3. Sense cognitive service protocol that implements the smart watch sensory data acquisition over BLE. 4. Enhancement Augmented projection protocol over BLE GATT [45, 46] and its core functionality on sending thematic IDs to smart displays embedded in museum showcases, for the purpose of displaying specific showcase item information. 5. Experimental testing of a haptic protocol using gloves for the process of touch sense encoding-decoding and transferring of selected artifacts, as part of the haptic service implementation. 6. 3D scanning and modeling protocol and functionality.

6.1 Data Exchange Protocol of the Thematic Tour and Interactive Services For the process of exchanging informatory data between the visitors’ mobile phone and the IS, the HTTP protocol is used. It is used by the Authentication service to transfer personal data from the user’s mobile to the IS. The HTTP GET/POST/PUT

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methods are used to in order to negotiate and start a session between the visitor’s smart phone and the IS. In order to transfer messages reliably that require acknowledgment, such as the interactive service notifications, and bot suggestions, asynchronous TCP push notifications are used. All the alerts that the IS sends to the visitors are also send over the TCP protocol. The asynchronous push notification mechanism of the IRME involves a direct database instance connection of each visitor to a visitor’s temporary database table prefixed by the visitors’ authentication id, where all push notifications directed for each visitor are stored by the IS. The visitor polls this database space, performing direct TCP database connections similar to a user that checks his/her e-mail correspondence via POP3 or IMAP service, every 10–30 s. This short polling period gives the visitor the impression of an asynchronous message exchange. The HTTP protocol is also used for transferring the periodic personalized navigation service messages and navigation direction info over the BLE or the Wi-Fi infrastructure to each visitor. The period update interval which the mobile phone asks the IS for new direction message-information is set to 3–5 s. Content-media distribution is performed over the Real-time Transport Protocol (RTP) protocol. It is a network protocol responsible to deliver streaming multimedia such as audio and video. It is a end-to-end, real-time that runs in conjunction with the RTP Control Protocol (RTCP) over the UDP protocol. RTCP is used to enforce the Quality of Service (QoS) of the transmission, records statistics and synchronize the multimedia streams.

6.2 Navigation Service Protocol The navigation service protocol requires a number of devices called beacons located in the indoor museum areas of interest, following a placement pattern of opposite corners placement in case of rooms of less than 60 m2 sizes and every 40 m ceiling placement in corridors or staircases. In cases of large rooms, the placement policy followed is a mixed one of corners and ceiling beacon placements [47]. The purpose of these placement policies is to have at least 3 beacon signals received on every location. Beacons are BLE devices that broadcast small data packets at regular time intervals and commonly operate on coin-cell batteries. Beacons achieve optimal power consumption by remaining asleep most of their operating time, only waking up to broadcast at predefined intervals [45], programmable set in this case to 500 ms. BLE broadcast packet has a payload limit to 31 bytes, where the Eddystone beacon protocol is encapsulated [35]. Eddystone protocol is different from the iBeacon protocol regarding frame format, broadcasting intervals and functionality. Eddystone protocol includes also multiple types of frames such as the UID frame used for indoor positioning, URL frames for the process of performing mobile phone URL redirections similar to NFC URL NDEFs and TLV frames used for sensory telemetry data broadcasts. Typically, sensors placed in Eddystone beacons are temperature, humidity, accelerometer, smoke

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sensors, buzzers and leds [48]. TLV protocol can be very handy in the replacement of the old fashioned and expensive fire alarm systems and exit signs, an interesting functionality that can be easily extended as a security service feature of the IRME architecture. The proposed IRME navigation protocol follows the Eddystone UID frame specification of a 16B device ID and 1B beacon transmission power, configures to be sent every 500 ms, followed by a periodic (every 3 min) TLV Beacons sensor values and battery status transmission used for museum management and safety purposes. These signals are captured by the visitor’s mobile phone where the navigation protocol state machine tries to estimate visitor’s position inside the museum. This is performed as follows: The museum area top view is divided into polygons of 1–2 m2 . Since indoor accurate positioning is not a requirement of this service but a more of polygon of interest visitor’s placement. Initially, from the three beacons RSSI values, a polygon fingerprinting method is performed. Fingerprinting uses the RSSI values of a group of devices to create a signature of a specific polygon. This is done by storing these values, along with the beacon addresses, as records in a database table. The mobile phones during authentication download this ephemeris fingerprinting data and based on those data a least distance value of the ephemeris data and the visitors beacon vector is calculated. If these least distance values are more than one pinpointing to different polygons, then a secondary triangulation method starts. The triangulation method is a method that uses ephemeris lookup table data of Beacon IDs, RSSI values and distance in meters from the beacon. From this table, a triangulation method is performed geometrically using three beacons and the tangent point is then looked up to which polygon it belongs from the point distances from the nearby building walls. This method is set to be more accurate but with bigger fluctuations for continuously moving users. The indoor position polygon of a visitor is then uploaded to the IS using HTTP GET protocol and the reply of this request includes the section ID which the current visitor is (each museum thematic section is assigned a number of location polygons as polygons of interest). This current section ID value is also used by the interactive service to inform the visitor of a wrong thematic section course, or advice the visitor for a thematic section change (bot of the interactive service) and by the thematic tour service in order to calculate the next thematic tour section for each visitor to follow. The visitor navigation between the current thematic section and the section that follows is also performed by the navigation service using a top view map and navigation arrows followed with recorded verbal directions.

6.3 Cognitive Service Protocol Cognitive protocol is a middleware protocol responsible for the communication between the visitors’ mobile phone and the BLE smart watches, followed by timestamped mobile phone sensory data uploads to the IS. Cognitive protocol includes

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fields for heart rate sensor, skin conductance sensor, skin temperature, room temperature, step measurements, 9 fields for ECG sensor values and capability of encapsulating to the protocol additional up to 100 measurements in the option header and option header length fields, for future added sensors. The sensors measurements are collected periodically from the smart watch microprocessor. When the smart watch is close enough to a BLE capable mobile phone, pairing process initiates and the user is notified while the cognitive service starts in the mobile phone background. Then, the sensory measurements are captured periodically by the BLE capable service [45] and then the sensory data are transmitted transparently (to the visitor) to the IS via CoAP protocol [49] to the responsible for the measurements IS management CoAP service [50]. CoAP utilizes the UDP protocol performing periodic uploads of minimum latency without handshakes, in accordance to the TCP capable streams. CoAP is an open IETF standard, and it uses a ReST-full JSON representation for the IS uploaded data. The cognitive protocol upload packet data unit structure is no more than 70– 270 Bytes. It is used by low processing power-devices in low bandwidth channels. The implemented cognitive protocol does not include an acknowledgment or stream control—packet retransmission mechanism. The IS cognitive manager is responsible for the process of user location tracking, as well as field joining of visitor position in the museum, user current activity (current museum section visited taken from the tour navigation service) and user cognitive status evaluation from the incoming sensor measurements. For example, let’s assume that a user is in front of an exhibition case presenting medieval weapons. The user interacts with the thematic tour service requesting informational content for the tools, while the navigation service locks the visitor’s current position from the navigation service beacons. If the user is interested on the alleged information, then the interest will show up in the cognitive sensor measurements as a heart pulse rate increase followed by a skin temperature increase. On the other hand, if the visitor feels bored an opposite sensory reaction will emerge. Furthermore, if the visitor has lost track of his interest and starts walking around the cases, a vast increase of body temperature and skin conductance will occur, followed by an increase in the visitor’s number of steps from the gyroscopic-accelerometer sensors. Post-processing IS cognitive service agent will be initially used for the process of analysis, clustering and therefore classification of visitors’ sensory responses to a fixed number of visitor profiles, visitors’ path-sections data mining needs to be performed from visitor’s thematic tour service logs and navigation service logs. Then, a supervised training process will adjust and calibrate the retrieved visitors’ sensory data to each validated profile, using neural processes or fuzzy logic. Finally, the trained profile sets will be used to classify incoming visitors’ experiences and issue either thematic-section change requests via the interactive service bot. The cognitive service via this data mining approach will be able to offer statistical cognitive information regarding users’ interest, disappointment or distraction over specific exhibits.

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6.4 Enhanced, Augmented Projection Protocol This section proposes an Enhancement Augmented projection protocol over BLE GATT and its core functionality on sending thematic IDs to smart displays embedded in museum showcases, for the purpose of displaying specific showcase item information. BLE was developed for the purpose of broadcasting data without the need of a connection. BLE devices are normally used in one-way communications where receivers pick up data passively. The main advantage of this technology is that the system enables the user to interact with the exhibit without even touching it, using his/her mobile device. Interaction is achieved through the visitor mobile application and a museum smart showcase that receives data via BLE GATT and accordingly adapts its specific provisions and structures such as a smart display screen, dynamic lighting, rotating artifacts in stepper motors, rotating spots, multicolor spots, busts which are dynamically illuminated according to the visitor choices. For the communication between the visitors’ mobile phone and the proposed smart museum showcase the BLE GATT protocol is used. The proposed augmented protocol includes a number of specific characteristics under a unified BLE service or services. Such characteristics include the following: (1) stepper motors that rotate artifacts, (2) rgb led strips-3D led cubes that blink and change color when an artifact is selected, (3) Stepper motors that slightly elevate and rotate an artifact, (4) Led displays inside the cases that offer content information of the selected artifact and vocaly narration in multiple languages, (5) Rotating spot leds that target in the selected case area artifact placement, and cases led strips and surfaces that change color either randomly, on IR sensor driven visitor’s proximity or visitor BLE connection. The embedded smart displays, rotating artifacts by stepper motors, rotating spots, multicolor spots, dynamic photorealistic lighting moves and actions are based on predefined settings. The visitor is capable to only notify (enable/disable) or read the specified characteristic’s properties values. The difference with other augmentation methods is that in the IRME augmented service the mobile device is only used for one-way communication so as to send information to the system. Visitors receive the requested information each museum showcase artifact itself directly through the IS. Furthermore, the displayed information is either stored internally in the smart display or downloaded directly from the IS. This mixed reality environment gives the user a sense of immersive experience and makes him a co-creator of the exhibition since s/he is able to influence the way the exhibit is presented. When the visitor’s mobile device is close to the smart showcase, the visitor’s mobile is authenticated and a pairing process initiates, notifying the mobile phone user. At this point, the service is locked for other external users and mobile phones for a certain time. When the pairing process is completed, the user can interact with the smart showcase augmented service smart embedded display, sending through the proposed mobile application, a text ID that corresponds to specific presentation parameters. This text ID can be the number of a particular artifact, in a showcase

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with many artifacts inside, or a code that corresponds to specific showcase actions. This text ID is sent through the mobile application with a 50 byte payload. When the sensors receive this, the showcase dynamically adapts the artifact presentation through a mixed reality environment. In particular, using this protocol the user can view information, photographic material, text and video related to the exhibit, through a presentation at the embedded display screen that is combined with other actions such as rotation of the exhibit, properly illumination of specific parts of the exhibit or with specific lighting, etc. The proposed system is illustrated at Fig. 14 and consists of (i) the connection module, (ii) tracking module, (iii) context manager, (iv) presentation module, and (v) data logging module. The Connection module which is responsible to receive data from users devices through Bluetooth, authenticate the user, initiate the pairing of the devices, and manage other users’ connections. It has to deal with situations such as what happens when two or more users want to connect, who has priority and how many time available to interact with the exhibit. The decisions are made according to many parameters such as connection time and duration, user potential disabilities etc. The tracking module is responsible to receive, analyze and pass to the context manager, the active user choices and actions after a paired connection has established. The context manager, receives this data from the IS via the Internet using museum Wi-Fi/3G or 4G gateways and communicates with the IS system database in order to

Fig. 14 Enhanced, augmented service components and modules

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decide which actions are the best according to the user choices and profile. In order to propose the best case presentation scenario, it also takes into account the data analysis module suggestions. The presentation manager, actually implements the proposed by the context manager presentation scenario. In order to do this, it receives from the database the actions that has to be completed and sends in specific time the appropriate signals to the showcase devices. Finally, the data logging module stores all the interaction data (such as user actions, interaction duration, user preferences etc.) to the IS central database, for the IS system to perform clustering analysis in order to find out typical users and propose improved presentation scenarios. That way, user input can give an added value to the museum exhibition design and lead to better user experience lead from logged data. The philosophy of the proposed service and embedded end to end service protocols is to allow the visitor to interact with the exhibit via the mobile phone. This can be useful for the simple visitor where it can adapt the presentation according to its preferences but also for people with difficulties and disabilities who are unable to understand all the information available from the exhibits.

6.5 Haptic Protocol Real time haptic interaction is involved at services 8 and 10 at Sect. 5. Haptics involves tactile and kinesthetic interaction between the user and the 3d virtual object. As the 3d virtual model of the artifacts will be hosted on a cloud service the haptic interaction will be transmitted over the Internet. Haptic services should be transmitted over a special haptic transport protocol. They are relatively sensitive to delay and jitter when transmission through a computer network is involved. Table 1 depicts the higher tolerable thresholds of delay, jitter, packet loss, update rate, packet-size and offered bandwidth of a network [12]. Above this order, the Quality of Experience (QoE) of the user has not to be decreased. In the same table the thresholds for real time transmission of haptics, audio, video and graphics are depicted, so as a comparative analysis can be made. All the media are called supermedia applications. From Table 1, it is obvious that haptics are more sensitive to delay and jitter and update rate than video, audio and Graphics. The depicted QoS alteration between Table 1 QoS requirements for supermedia applications QOS

Haptics

Video

Audio

Graphics

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≤2

≤30

≤30

≤30

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≤50

≤400

≤150

≤100–300

Packet loss (%)

≤10

≤1

≤1

≤10

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≥1000

≥30

≥50

≥30

Packet size (bytes)

64–128

≤MTU

160–320

192–5000

Throughput (kbps)

512–1024

2500–40000

64–128

45–1200

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audible, visual and haptic application derives from the fact that haptics refer to the sense of touch, and the tactile and kinesthetic sensitivity, while the other applications refer to the sense of vision and hearing. The high update rate in conjunction with the small jitter and delay demand a fast network without congestions. This QoS alteration means that the RTP protocol, which is responsible for the transmission of video, audio and graphics, should be modified, in order to satisfy the needs of haptic transmission. The new protocol that is proposed is called Real Time Haptic Transmission Protocol (RTHTP). It is a light-weight, real-time middleware protocol responsible for transmitting haptic interactions through the network. As haptics demand timely delivery of information, the RTHTP should run over the UDP protocol. The Time to Live (TTL) of a packet should be smaller than doubled value of the maximum affordable delay Delaymax . Most of the lost packets should not be retransmitted as they will be devalued information when they reach the haptic user. The retransmission is meaningful only when the mean network delay is lower than the quarter of the Delaymax (delay < Delaymax /4). The reordering of the packets is also meaningful only when the network delay is smaller than the maximum affordable delay Delaymax . In order to detect packet loss and reorder detection, a sequence number of 2 bytes is needed at the RTHTP header. For synchronization between the supermedia streams a timestamp of 4 bytes is also needed at the header. The synchronization technique that is proposed is the enhanced Virtual Time Rendering (VTR) media synchronization algorithm [51]. For jitter compensation a buffer is installed at the receiver side. The packets are deliberately postponed at the receiver side in order to be delivered to the haptic user equally distributed. This will lower the end-to-end jitter at affordable levels. When the mean delay of the network is smaller than the maximum affordable delay, then the buffer size in time units is equal to difference between the mean delay Delaymean and the maximum affordable delay Delaymax (see Eq. 2). Receiver_Buffer_sizems = (Delaymax − Delaymean )

(2)

The size of the buffer regarding packet units is relative to the sending rate of the source (Eq. 3). Receiver_Buffer_sizepackets = (Delaymax − Delaymean ) ∗ Packet update rate (3) The Session Initiation Protocol (SIP) [52] is used to initialize, maintain, and terminate the haptic session between the museum visitor and the virtual 3D Object. As haptic payloads are relatively small (smaller than 128 bytes), in order the RTHTP protocol to be efficient, the RTHTP should be as small as possible. The RTHTP packet is created at the application layer and is transmitted over the UDP transmission protocol over IP networks. When the network is under congestion, the grouping of packets is necessary. This technique helps the network to avoid congestion and lowers the mean delay and jitter [53].

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6.6 3D Modeling and Printing Protocol The 3D modeling and printing protocol is responsible for the generation of 3D artifact models from either IS existing models or camera images using a photogrammetry software that can render 3D models from a few artifacts captures. Photogrammetry software is a 3D reconstruction software that takes as input 2D image surfaces. Such 3D reconstruction from motion software can be offered by open source and simple user access tools such as Meshroom [54], a 3D reconstruction app that uses the AliceVision framework [55] or Regard3D [56]. That is, these tools take as input at least 4 object picture moving around an object taken from different 900 user-artifact angles. For better accuracy in 3D representation, it is preferred to use at least 9 pictures taken from different angles of 40–450 with a very good result for a 160 × 160 × 160 bust around 24–36 photos of 15 and 10 degrees accordingly. The most important when using such a 2D–3D approximation is that the object requires a constant light (using a white object background in all directions so as to avoid problems of shadow changes as the object moves). For non cased objects that give the visitor the ability to scan around the object itself, constant artifact lightning can be achieved using constant light on top of the object. Shadow images will show off in the printed object’s texture. Moreover, glossy and glass objects are hard to print using this approach. The proposed IRME 3D modeling and printing process-protocol, works as follows: 1. The visitor selects and object that s/he wants to get a 3D imprint and informs the IS with an HTTP GET imprint request. 2. The IS asks the visitor (response) if the specified object is in a case or standalone. 3. The visitor responds to the request by sending asynchronously to the IS via HTTP the request ID followed by the artifact case ID and/or the Object ID. 4. The IS detects from its database, if the object is in a case and if existing 3D object model exists. If not then notifies the curator for a missing 3D model request. If the object is not in a case, then it checks whether the object-artifact is placed in a 3D camera placement seat. If it is placed then initiates the visitor’s photogrammetry-photos acquisition process. Otherwise, if an already uploaded and printed approved model exists at the IS, then this object model is presented to the user for final approval. If the artifact is placed in a wall or a corner spot, then if an object-artifact 3D model exists in the IS database, it is preselected, otherwise the 3D printing process terminates with a notification sent to the curator for a missing 3D model of an object in an out of 3D capable position. 5. If the object is in a 3D capable placement position but lacks of a 3D model in the IS, then the mobile phone application allows the visitor to take several images of it with his mobile phone camera (at least 8 MP) round the object, at almost constant angles (marked with angle-signs at the base of the scanned object). In this case, upon each successful photograph passed from a shadow Gaussian filter check for shadow validation, each photo is uploaded using back to back HTTP

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requests to the IS with its corresponding angle id (numerical id starting from the artifact from and moving clockwise). 6. Upon images upload completion, the IS notifies the museum staff of a 3D model printing request and queues the request to the desktop computer of the museum’s 3D printing office. Inside the 3D printing office, there is a desktop computer running appropriate 3D software such as Ragard3D with a shared folder updated file content by the IS for 3D model generation and a pool for 3D model printing (connected to a wireless 3D printer of at least 50microns layer resolution printing either on ABS, PET, wood or metal of ancient bronze or aluminum filaments. 7. Since the process of 3D printing especially for metal is quite expensive (60– 100 e/Kgr), a printing validation is required from the user prior to 3D model printing. That is, the constructed 3D model is converted to an STL file presented to the visitor using an asynchronous HTTP generated content notification that contains a URI of the generated STL file. The user can preview the STL object with appropriate mobile phone STL viewer and respond to the previously sent notification with a notification acknowledgement, using the notification ID, followed by a print, remove, or reject control command. In case of an issued print command then the mobile phone application offers the ability to the user to select object normalized dimensions (small-medium-large) and printer filament to be used as well as an estimation printing time, and costs, pricing options, and online payment. In most cases even for ABS printing, 3D printers printing time is excessively long (at least 25 min for a 50 mm × 50 mm printout in a professional 3D printer). This means for most cases the final printout objects will have to be delivered to the end visitors using the visitors’ information (address) from the IS. In case of a reject command, the images are stored to the visitor’s IS storage place (for later use). In case of a remove command the images are removed from the visitor’s IS storage place and the 3D printing process either restarts or terminates.

7 Conclusions According the authors’ related work survey on the IT, IoT cognitive museums and museum augmented services offered; it is obvious that there is no unified solution for the perspective of an augmented reality framework for the museums that covers all the edges of existing IT and IoT technologies. Sporadic efforts lay around in bibliography of subsystems and augmented reality studies that cannot cope with the problem of museums trying to increase their traffic and income by agitating the visitors with the offering of new services. Furthermore, the system architectures of smart museums proposed in the bibliography, are more of generic descriptive content accompanied by case studies narrowed down to a very small portion of their architecture. For this purpose the authors of this chapter propose a framework for an IT/IoT, InteRactive-responsive Museum Experience, called IRME. This framework tries to exploit all the available immersive technologies to stimulate the visitor and attract

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his/her attention. It offers automated guidance based on visitors preference, AI visitor—smart bot interaction, visitors cognitive sense analysis and augmented reality exhibition of the museum artifacts via actuators such as leds, 3D printing, smart displays, and artifacts elevating and rotating using actuators. The purpose of the IRME framework is to include in a unified ensemble, all contemporary IT and IoT technologies in order to further promote and enhance museum artifacts exhibition. Since museums tend to be the historical knowledge playgrounds for kids and adults, IRME tries to take advantage of this trend using the visual acoustic and tactile stimuli to excite the visitor. All available modern technologies have been taken into account, and the authors focused in the interoperability and cross functionality of these services so as to improve the overall system performance and efficiency. The IRME digitized content is provided by the information system to the visitors through the proposed data transfer protocols. A location service helps producing customized media transmitted to the visitor from the IS cloud. The services transport protocols used are the UDP, the HTTP-TCP, TCP, the CoAP, the RTP/RTCP, the BLE and the Eddystone protocol for indoor positioning. The application protocols used follow the stateless ReST architecture. The services provided from the IRME to the visitors include content distribution service, authentication service, navigation service thematic tour service, augmented reality projection service, interaction service, haptic exploration, 3D scanning, 3D printing and online exhibition service. Haptic devices, 3D printers, 3D display capabilities, smart leds following artifacts, beacons, smart phones and smart watches are used in order to perform user interactive functionalities maximizing visitors interest, knowledge passing and experience.

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