Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions 9783031155413, 9783031155420

Table of contents :
Foreword
Preface
Part I: On Integration of Data Systems and Traditional Computing Research
Part II: Data-Driven Decision-Making Systems
Part III: Data-Intensive Systems in Healthcare
Acknowledgment
Contents
Part I On Integration of Data Systems and Traditional Computing Research
Energy Conscious Scheduling for Fault-Tolerant Real-Time Distributed Computing Systems
1 Introduction
2 Energy Management
2.1 Dynamic Power Management
2.2 Dynamic Voltage and Frequency Scaling
3 Fault Tolerance
3.1 Fault-Tolerant Techniques
4 Joint Optimization of Energy and Fault Management
5 Conclusion
References
Secret Data Transmission Using Advanced Morphological Component Analysis and Steganography
1 Introduction
2 Suggested Method's Proposed Goals
3 Review of Literature
4 Suggested Method
5 Results and Discussion
6 Conclusions
References
Data Detection in Wireless Sensor Network Based on Convex Hull and Naïve Bayes Algorithm
1 Introduction
2 Related Work
2.1 Challenges and Problem Statement
2.2 Contributions
3 Proposed Methodology
3.1 *-25pt
3.1.1 N-Gram Extraction
3.2 Attribute-Based Encryption
3.3 Symmetric-Key Algorithm
3.4 Cipher Text Attribute–Based Encryption
3.5 Performs the Naïve Bayes Classifier on the Remaining Data Points
4 Experimental Results
4.1 Dataset
4.2 Performance Evaluation Parameters
5 Conclusions and Future Work
References
DSPPTD: Dynamic Scheme for Privacy Protection of Trajectory Data in LBS
1 Introduction
1.1 Problem Statement
2 Related Work
3 Our Proposed Scheme
3.1 Trajectory Processing Model
3.2 Trajectory Generator
3.3 Multilayer Perceptron and Deep Neural Network
3.4 K-Paths Trajectory Clustering
3.5 Trajectory Release
4 Performance Analysis of Privacy Protection Scheme
5 Conclusion
References
Part II Data-Driven Decision-Making Systems
n-Layer Platform for Hi-Tech World
1 Introduction
2 Information Management Issues
3 Indian Administrative System
4 System Model
5 System Architecture
6 Unique Citizen Identification Code (UCIC)
7 Implementation and Performance Study
8 Discussion
9 Conclusion and Future Work
References
A Comparative Study of Machine Learning Techniques for Phishing Website Detection
1 Introduction
2 Literature Review
3 Phishing Websites Features
3.1 Address Bar Features
3.2 Abnormal Features
3.3 HTML and JavaScript Features
3.4 Domain Features
4 Proposed Method
5 Results and Discussions
6 Conclusions
References
Source Camera Identification Using Hybrid Feature Set and Machine Learning Classifiers
1 Introduction
2 Literature Review
2.1 Correlation-Based Method
2.2 Feature-Based Method
3 Method and Model
3.1 Image Preprocessing
3.2 Feature Extraction
3.3 Classification
4 Experiment and Result Analysis
5 Conclusion
References
Analysis of Blockchain Integration with Internet of Vehicles: Challenges, Motivation, and Recent Solution
1 Introduction
1.1 Organization and Reading Map
2 Architecture of IoV
2.1 Physical Layer
2.2 Communication Layer
2.3 Computation Layer
2.4 Application Layer
3 Challenges of IoV
3.1 Privacy Leak
3.1.1 Leakage in the Communication Layer
3.1.2 Vulnerabilities in the Computation Layer
3.1.3 Vulnerabilities in the Application Layer
3.2 High Mobility
3.3 Complexity in Wireless Networks
3.4 Latency-Critical Applications
3.5 Scalability and Heterogeneity
4 Overview of Blockchain Technology
4.1 Blocks
4.2 Miners
4.3 Nodes
5 Types of Blockchain Network
5.1 Public Blockchain
5.1.1 Benefits
5.1.2 Drawbacks
5.1.3 Applications
5.2 Private Blockchain
5.2.1 Benefits
5.2.2 Drawbacks
5.2.3 Applications
5.3 Consortium Blockchain
5.3.1 Benefits
5.3.2 Drawbacks
5.3.3 Applications
5.4 Hybrid Blockchain
5.4.1 Benefits
5.4.2 Drawbacks
5.4.3 Applications
6 Motivations of Using Blockchain in IoV
6.1 Decentralization
6.2 Availability
6.3 Transparency
6.4 Immutability
6.5 Exchanges Automation
7 Recent Solutions for IoV Integration with Blockchain
8 Applications of Blockchain in IoV
8.1 Incentive Mechanisms
8.2 Trust Establishment
8.3 Security and Privacy
9 Use Cases of Blockchained IoV
9.1 Supply-Chain Management
9.2 Manufacturing and Production
9.3 Settlements of Insurance Claim
9.4 Management of Fleet
9.5 Tracking of Vehicle
10 Future Scope of Blockchained IoV
10.1 Off-Chain Data Trust
10.2 Evaluation Criteria
10.3 Management of Resources
10.4 Data-Centric consensus
10.5 Blockchain for the Environment
10.6 Administration of Blockchain Platform
10.7 Evaluation of Performance
10.8 Design of New Services
10.9 Future Architecture Integrations
11 Conclusion
References
Reliable System for Bidding System Using Blockchain
1 Introduction
1.1 Features of Blockchain
1.1.1 Decentralization
1.1.2 Traceability
1.1.3 Immutability
1.1.4 Currency
2 Literature Survey
3 Notations Used in the Study
4 Proposed Work
5 Security Analysis
6 Conclusion
References
Security Challenges and Solutions for Next-Generation VANETs: An Exploratory Study
1 Introduction
2 Security Requirements
2.1 Security Services
2.2 Security Attacks
3 Security Mechanisms
3.1 Hybrid Device to Device (D2D) Message Authentication (HDMA) Scheme
3.2 Blockchain-Based Secure and Trustworthy Approach
3.3 Searchable Encryption with Vehicle Proxy Re-encryption-Based Scheme
3.4 Secure and Efficient AOMDV (SE-AOMDV) Routing Protocol
3.5 Socially Aware Security Message Forwarding Mechanism
3.6 Puzzle-Based Co-authentication (PCA) Scheme
3.7 Intelligent Drone-Assisted Security Scheme
3.8 Efficient Privacy-Preserving Anonymous Authentication Protocol
4 Comparative Study of Security Solutions
5 Conclusion and Future Work
References
iTeach: A User-Friendly Learning Management System
1 Introduction
2 Literature Review
3 Proposed Model
4 Comparison
5 Users Feedback Analysis
6 Conclusion and Future Scope
References
Part III Data-Intensive Systems in Health Care
Analysis of High-Resolution CT Images of COVID-19 Patients
1 Introduction
2 Review of Literature
3 Materials and Methods
4 Results and Discussion
5 Conclusion
References
Attention-Based Deep Learning Approach for Semantic Analysis of Chest X-Ray Images Modality
1 Introduction
2 Literature Review
2.1 Image Captioning
2.2 Attention Mechanism
2.3 Medical Report Generation
3 Methodology
3.1 Overview
3.2 CNN Encoder
3.3 LSTM Decoder
3.4 Attention Mechanism
3.5 Model Architecture
4 Experiments
4.1 Dataset
4.2 Exploratory Data Analysis
4.3 Pre-processing and Training
4.4 Model without Attention Mechanism
4.4.1 Encoder Architecture
4.4.2 Decoder Architecture
4.4.3 Model Training
4.4.4 Model with an Attention Mechanism
4.4.5 Model Evaluation
4.5 Results
4.5.1 Case 1
4.5.2 Case 2
4.5.3 Case 3
4.5.4 Case 4
4.5.5 Case 5
4.5.6 Case 6
4.5.7 Conclusion
References
Medical Image Processing by Swarm-Based Methods
1 Introduction
2 Swarm-Based Methods
3 Feature Selection
4 Image Segmentation
5 Image Classification
6 Image Registration and Fusion
7 Conclusions
A.1 Appendix A. Flowcharts of Swarm-Based Algorithms
References
Left Ventricle Volume Analysis in Cardiac MRI Images Using Convolutional Neural Networks
1 Introduction
1.1 Convolutional Neural Networks
1.2 Network Layers
1.3 CNN Tuning
1.4 Max Pooling
1.5 ReLU
1.6 Sigmoid
1.7 Dropout
1.8 Hyperparameters
1.8.1 Learning Rate
1.8.2 Patch Size
1.8.3 Batch Size
1.8.4 Epochs
1.9 Augmentation
1.9.1 Shift Augmentation
1.9.2 Rotation Augmentation
2 Literature Review
3 Overview of Our Work
4 Methodology
4.1 Dataset
4.2 Preprocess
4.3 Load and Split Dataset
4.4 Augmentation
4.5 Model
4.6 Training the Model
5 Results and Discussion
5.1 Environment
5.2 Evaluation Metrics
5.3 Training Details and Evaluation
5.4 Comparison with Other Models and Pre-processing Methods
6 Conclusion
References
MRI Image Analysis for Brain Tumor Detection Using Deep Learning
1 Introduction
2 Related Work
3 Proposed Work
3.1 Dataset Description
3.2 Data Augmentation
3.3 Loading and Splitting Augmented Data
3.4 CNN Architecture
4 Result and Analysis
5 Conclusion
References
Index

Citation preview

EAI/Springer Innovations in Communication and Computing

Sarvesh Pandey Udai Shanker Vijayalakshmi Saravanan Rajinikumar Ramalingam   Editors

Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions

EAI/Springer Innovations in Communication and Computing Series Editor Imrich Chlamtac, European Alliance for Innovation, Ghent, Belgium

The impact of information technologies is creating a new world yet not fully understood. The extent and speed of economic, life style and social changes already perceived in everyday life is hard to estimate without understanding the technological driving forces behind it. This series presents contributed volumes featuring the latest research and development in the various information engineering technologies that play a key role in this process. The range of topics, focusing primarily on communications and computing engineering include, but are not limited to, wireless networks; mobile communication; design and learning; gaming; interaction; e-health and pervasive healthcare; energy management; smart grids; internet of things; cognitive radio networks; computation; cloud computing; ubiquitous connectivity, and in mode general smart living, smart cities, Internet of Things and more. The series publishes a combination of expanded papers selected from hosted and sponsored European Alliance for Innovation (EAI) conferences that present cutting edge, global research as well as provide new perspectives on traditional related engineering fields. This content, complemented with open calls for contribution of book titles and individual chapters, together maintain Springer’s and EAI’s high standards of academic excellence. The audience for the books consists of researchers, industry professionals, advanced level students as well as practitioners in related fields of activity include information and communication specialists, security experts, economists, urban planners, doctors, and in general representatives in all those walks of life affected ad contributing to the information revolution. Indexing: This series is indexed in Scopus, Ei Compendex, and zbMATH. About EAI - EAI is a grassroots member organization initiated through cooperation between businesses, public, private and government organizations to address the global challenges of Europe’s future competitiveness and link the European Research community with its counterparts around the globe. EAI reaches out to hundreds of thousands of individual subscribers on all continents and collaborates with an institutional member base including Fortune 500 companies, government organizations, and educational institutions, provide a free research and innovation platform. Through its open free membership model EAI promotes a new research and innovation culture based on collaboration, connectivity and recognition of excellence by community.

Sarvesh Pandey • Udai Shanker • Vijayalakshmi Saravanan • Rajinikumar Ramalingam Editors

Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions

Editors Sarvesh Pandey Computer Science Banaras Hindu University Varanasi, India

Udai Shanker Madan Mohan Malaviya University of Technology Gorakhpur, Uttar Pradesh, India

Vijayalakshmi Saravanan University of South Dakota South Dakota, SD, USA

Rajinikumar Ramalingam Deutsches Elektronen-Synchrotron DESY Hamburg, Germany

ISSN 2522-8595 ISSN 2522-8609 (electronic) EAI/Springer Innovations in Communication and Computing ISBN 978-3-031-15541-3 ISBN 978-3-031-15542-0 (eBook) https://doi.org/10.1007/978-3-031-15542-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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

Foreword

Data analytics and machine learning technologies, particularly in a decentralized scenario, are offering cost-effective solutions for many real-life problems. Recent developments in computer technology have led to increased research interests in the field of modern data-intensive distributed computing systems. Today, when user requirements are becoming exponentially complex, it is not possible to meet the expectations of society by applying core knowledge of any single research area of computer science; rather, there is a need for integrated efforts with the umbrella of research topics. This prompted the researchers to think about the multi-disciplinary nature of work to provide a solution for the challenges set forth due to various future requirements. In this direction, data systems serve as a strong component that we are either using or would be using in near future. Advancement in the field of modern computing will continue to be critical for computer science researchers and a matter of concern for the end users. Therefore, the objective of the book Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, edited by Sarvesh Pandey, Udai Shanker, Vijayalakshmi Saravanan, and Rajinikumar Ramalingam, is to introduce the reader to recent research activities in the field of modern-day data-driven decision-making processes. It is an excellent example of a collection of advanced works applied to relevant problems. It covers areas like real-time systems, machine learning, data analytics, medical imaging, and applications of all these areas considering evergrowing user demands. Some of the chapters of this book provide interesting information on the integration of this wonderful and disruptive technology with modern applications. Also, one chapter introduces the readers to a system model for detecting the original camera that clicked a particular image – this would help in solving many real-life issues in the near future. Research addressing performance issues of these systems is a relatively novel area, and the contents in the chapter are good enough to evince the interest for developing innovative solutions to the open technical challenges. This book will be very helpful to students, researchers, scientists, and industry professionals working in the field of computing. A genuine attempt is made to increase the understanding of how data is going to play a central role in many of the v

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emerging research domains. It would empower the readers to work on new research domains, which would be useful for society. At last, this book indeed provides future insights on the performance issues with modern data-intensive systems. Director at IIIT, Pune, Maharashtra, India

Anupam Shukla

Preface

This book, titled Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, is centered on discussing various new opportunities created by the fast-computing power and big data collectively. There were more than 40 submissions; out of these, 16 submissions have been finally included in this book proceeding after rigorous review. We appreciate everyone who considered this venue for the possible publication of their research articles; congratulations to all the authors whose book chapters are included. To better organize the contents, this book is divided into three sections. Part I, which consists of four chapters, is mainly on integration of data systems and traditional computing research. Part II, which consists of seven chapters, is about how data-driven decision-making is now a reality. Finally, Part III, which consists of five chapters, discusses the critical role of data management in healthcare functioning. The themes of the accepted book chapters are discussed below in brief so that audience can understand what this book has to cater.

Part I: On Integration of Data Systems and Traditional Computing Research Chapter 1 talks about energy-conscious scheduling of resources for fault-tolerant distributed computing systems. This chapter emphasizes the point that reliability should be given equal weightage as that to deadline aspect of such system design. Chapter 2 discusses how advanced morphological component analysis and steganography could be utilized for the purpose of secret data transmission. Chapter 3 puts light on cyber-security aspects of data management in wireless sensor networks. Chapter 4 proposes a dynamic privacy protection scheme for trajectory data.

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Part II: Data-Driven Decision-Making Systems Chapter 5 proposes an idea of how integration of mobile agent systems with egovernance can lead to better/transparent and dynamic infrastructure with no loss to reliability and fault tolerance. When we are living in a world where a countless number of websites are on the Internet, it is important that we should design a system to make sure that end users do not fall into the trap of phishing websites. Chapter 6 not only discusses this problem but also attempts to resolve this issue by using some of the existing machine learning techniques. Source camera identification method, which can be used to identify the source camera of the images/photos, plays a very important role in today’s era, especially in the domain of digital image forensics. In Chap. 7, using machine learning classifiers, authors attempted to predict device-specific information from picture data. Dependence on vehicles has increased manifold in the twentieth century. Now, with advent of the Internet, researchers started working on the idea of “Internet of Vehicles (IoV).” After that, since 2015, a cross-injection of IoV and blockchain technology has continued to be a research area with lots of potential. Chapter 8 puts light on all these aspects. Traditional bidding system can also benefit from blockchain technology. Chapter 9 discusses this. With integration of blockchain, without any doubt, transparency of bidding process would increase. Chapter 10 talks about vehicular ad hoc networks (VANETs). Various security challenges one may face with VANET-based systems are nicely discussed in this chapter. This exploratory study also lists future promising solutions. Chapter 11 is all about providing a user-friendly GUI to the learners. In the recent past, we faced an unprecedented threat of COVID-19. This has proven yet again that online learning systems are our friends and can co-exist with traditional classroom teaching methods, and by utilizing both, we could improve the outcomes to a greater extent.

Part III: Data-Intensive Systems in Healthcare After the COVID-19 outbreak, the first thing we struggled with was the need for an efficient medical kit to test whether someone is COVID-19 positive or not. In the fight against COVID-19, it has been an accepted practice that CT scans could be relied on for testing. Chapter 12 details on the aspect of analyzing high-resolution CT images for COVID testing. Chapter 13 proposes the use of an attention-based deep learning approach for the analysis of X-ray images. The efficacy of swarm-based methods in processing medical images is discussed in detail in Chapter 14.

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Chapter 15 talks about analyzing cardiac MRI images using convolution neural networks to detect cardiovascular diseases. Along the line of Chap. 15, Chap. 16 focuses on analyzing brain images using deep learning to detect brain tumors. In constrained circumstances, where people with medical expertise may get overwhelmed, the techniques presented in Chaps. 15 and 16 could be of great assistive help. To summarize, we are of the view that this book has perfectly covered various application areas with central focus on big data. Varanasi, India Uttar Pradesh, India SD, USA Hamburg, Germany

Sarvesh Pandey Udai Shanker Vijayalakshmi Saravanan Rajinikumar Ramalingam

Acknowledgment

It is a matter of immense pleasure for us to write the acknowledgment part of this book; it took more than 2 years to complete – the longest assignment we worked on till date. At the same time, when we look back, it feels fulfilling that most of the goals we had thought of before accepting this opportunity have been met. This book is truly an international one as it attracted submissions from multiple countries around the globe. Acknowledgments are not just a part of a book; instead, they remind us that a network of coordination among like-minded of people could be a blessing, we believe. It’s a result of effort from a lot of people who directly/indirectly helped us in making this book a reality. We thank all the authors who contributed to this book. A hearty thank you goes to all the reviewers; you all have really made our job easy by giving your timely, valuable insights on submissions. As a small token of appreciation, we are sharing the names of reviewers: 1. Dr. Karthikeyan Chinnusamy, Veritas Technologies LLC, USA 2. Dr. Abdullah Alghamid, Najran University, Najran, Saudi Arabia 3. Dr. Savina Bansal, Maharaja Ranjit Singh Punjab Technical University Bathinda, India 4. Dr. Ajey Kumar. Symbiosis International Deemed University, Pune, India 5. Dr. Sri Hari Nallamala, Lakireddy Bali Reddy College of Engineering (Autonomous), India 6. Dr. Saurabh Pal, Veer Bahadur Singh Purvanchal, University, Jaunpur, India 7. Dr. Gargi Srivastava, Rajeev Gandhi Institute of Petroleum Technology, Amethi, India 8. Dr. Nagendra Pratap Singh, NIT, Hamirpur, India 9. Dr. Vibhav Prakash, MNNIT, Allahabad, India 10. Dr. Sanjay Kumar, NIT, Jamshedpur, India 11. Dr. Mohit Kumar, NIT, Hamirpur, India 12. Dr. Awadhesh Kumar, BHU, Varanasi, India 13. Dr. Manoj Mishra, BHU, Varanasi, India 14. Dr. Rohit Tiwari, M. M. M. University of Technology, Gorakhpur, India xi

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15. Dr. Sukhvinder Kaur, Swami Devi Dayal Institute of Engineering and Technology, Haryana, India 16. Dr. Arvind Tiwari, KNIT, Sultanpur, India 17. Dr. A. Shaji George, Indian Institute of Integrated Science Tech. and Research, Chennai, India 18. Dr. Anil Kumar, Mody University, India 19. Dr. Ankit Jaiswal, Bennett University, Greater Noida, India 20. Dr. Ravi Sharma, IMS Engineering College, Ghaziabad, India 21. Dr. Suryabhan Pratap Singh, Institute of Technology and Management, Gorakhpur, India 22. Dr. Ajay Kumar Gupta, an Independent Researcher from New Delhi, India 23. Mr. Ravi Yadav, Research Scholar, BHU Varanasi, India 24. Mr. Santosh Tripathy, Research Scholar, IIT (BHU) Varanasi, India 25. Ms. Anupama Arun, Research Scholar, IIIT Pune, India 26. Ms. Ruchi Pathak, Infosys Limited, Mysore 27. Mr. Ankit Aakash, Stone Business Development Executive, Pidilite Industries Limited, India We would also like to thank our families. There is no such thing like worklife balance; obviously, at many points, we felt like we were not able to devote enough time to our beloved ones because of being too busy with our professional responsibilities. Almighty GOD has always been alongside us in all we do.

Contents

Part I On Integration of Data Systems and Traditional Computing Research Energy Conscious Scheduling for Fault-Tolerant Real-Time Distributed Computing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Savina Bansal, Rakesh Kumar Bansal, and Kiran Arora Secret Data Transmission Using Advanced Morphological Component Analysis and Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binay Kumar Pandey, Digvijay Pandey, Ankur Gupta, Vinay Kumar Nassa, Pankaj Dadheech, and A. Shaji George Data Detection in Wireless Sensor Network Based on Convex Hull and Naïve Bayes Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Edwin Hernan Ramirez-Asis, Miguel Angel Silva Zapata, A. R. Sivakumaran, Khongdet Phasinam, Abhay Chaturvedi, and R. Regin DSPPTD: Dynamic Scheme for Privacy Protection of Trajectory Data in LBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ajay K. Gupta and Sanjay Kumar

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Part II Data-Driven Decision-Making Systems n-Layer Platform for Hi-Tech World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. B. Patel, Lalit Awasthi, M. C. Govil, and Rachita A Comparative Study of Machine Learning Techniques for Phishing Website Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Farhan Khan, Rohit Kumar Tiwari, Sushil Kumar Saroj, and Tripti Tripathi

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Source Camera Identification Using Hybrid Feature Set and Machine Learning Classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Ankit Kumar Jaiswal and Rajeev Srivastava Analysis of Blockchain Integration with Internet of Vehicles: Challenges, Motivation, and Recent Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Manik Gupta, R. B. Patel, and Shaily Jain Reliable System for Bidding System Using Blockchain . . . . . . . . . . . . . . . . . . . . . . 165 N. Ambika Security Challenges and Solutions for Next-Generation VANETs: An Exploratory Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Pavan Kumar Pandey, Vineet Kansal, and Abhishek Swaroop iTeach: A User-Friendly Learning Management System . . . . . . . . . . . . . . . . . . . . 203 Nikhil Sharma, Shakti Singh, Shivansh Tyagi, Siddhant Manchanda, and Achal Kaushik Part III Data-Intensive Systems in Health Care Analysis of High-Resolution CT Images of COVID-19 Patients . . . . . . . . . . . . 225 A. Joy Christy and A. Umamakeswari Attention-Based Deep Learning Approach for Semantic Analysis of Chest X-Ray Images Modality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Rishabh Dhenkawat, Snehal Saini, Shobhit Kumar, and Nagendra Pratap Singh Medical Image Processing by Swarm-Based Methods . . . . . . . . . . . . . . . . . . . . . . . 265 María-Luisa Pérez-Delgado and Jesús-Ángel Román-Gallego Left Ventricle Volume Analysis in Cardiac MRI Images Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Palakala Sai Krishna Yadhav, K. Susheel Kumar, and Nagendra Pratap Singh MRI Image Analysis for Brain Tumor Detection Using Deep Learning . . . 321 Prachi Chauhan, Hardwari Lal Mandoria, and Alok Negi Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337

Part I

On Integration of Data Systems and Traditional Computing Research

Energy Conscious Scheduling for Fault-Tolerant Real-Time Distributed Computing Systems Savina Bansal, Rakesh Kumar Bansal, and Kiran Arora

1 Introduction An integration between computing and physical world with upcoming advances in technology has made it possible to fulfill the growing computational demands and needs of industry and individuals. Pervasive computing devices employ controllers to read physical inputs through sensors, perform data processing, and feed tangible outputs to actuators. Real-time functions especially based on artificial intelligence such as computer vision and sensor fusion are gaining popularity due to costeffective availability of needed hardware owing to advances in VLSI and related technologies. Real-time applications, as in avionics and aerospace engineering, automobile sectors, mission and safety-critical application in defense and medical fields, for which timely completion within a given time deadline is very crucial along with logical accuracy, demand usage of real-time systems. Timeliness is essential for real-time application as beyond the specific time window or time instant (also referred as task deadline of a task) even a logically correct outcome is of no use. Failing to honor deadline can lead to serious consequences—from loss of signal quality, as during video-conferencing, to some bigger financial loss or may even cost human lives [37, 49]. Real-time systems are capable of producing accurate results within the given deadline provided the tasks are scheduled properly over

S. Bansal · R. K. Bansal Department of Electronics and Communication Engineering, Giani Zail Singh Campus College of Engineering & Technology, Bathinda, India e-mail: [email protected]; [email protected] K. Arora () Department of Computer Science and Engineering, Baba Hira Singh Bhattal Institute of Engineering & Technology, Lehragaga, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_1

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Fig. 1 Embedded system market [14]

them. Scheduling, in general, relates to allocation and assignment of incoming tasks to the underlying computing processor/s such that deadlines of all tasks get honored. The major concern of this work is on scheduling of real-time systems. The development of systems with real-time capability is increasing at a fast pace (Fig. 1) to satisfy the needs of our day-to-day lives. More specifically, these systems have application that affects our social and personal lives directly or indirectly such as bank transactions, automobiles, traffic signal controller, medical care, video-conferencing, smart home, and firefighting [40]. As per the new Global Info Research study, it is projected that worldwide market growth for embedded systems will rise from 86,500 million US dollar in 2020 to 11,620 million US dollar in 2025, at a compound annual growth rate of approximately 6.1% [39]. For instance, contemporary cars have hundreds of processing units equipped to provide basic features such as vehicle control to specialized facilities for safety and comfort. To recognize its surroundings, perception subsystem in the vehicle should be able to process enormous data that demands huge computational power necessitating the use of multicore or multiprocessor systems [35] in order to achieve higher throughput, reduced response time, and increased reliability. Substantial advancement in the performance of present-day computing systems has led to considerable rise in power consumption. In fact, the amount of heat generated by them is quickly growing to level equivalent to nuclear reactors as shown in Fig. 2 [46, 57]. As projected by Moore’s law, energy utilization of computing systems has increased at an exponential rate from last few decades. Such rise in energy consumption results in ecological and monetary problems due to which energy management has turn out to be a prime design concern for computing systems [1, 13, 20, 38]. In the scientific and technical literature, the interrelation between energy, economy, and environment is recognized with “3E”

Energy Conscious Scheduling for Fault-Tolerant Real-Time Distributed. . .

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Fig. 2 Rise in power densities [46]

[56]. United Nations Development Organization proposed at the beginning of the twenty-first century that this triple bond is of global importance and is among the eight Millennium Development Goals (MDGs) in the global economic scenario. The evolution of objective of ensuring environmental sustainability promotes the importance of energy management. In light of this, 3E is more conspicuous today than ever before, making energy management a premier research problem for computational systems. However, the main target for energy saving in such systems is processor, as a major fraction of total power is consumed by CPU alone. As the complexity and computational power of real-time computing systems grow, it leads to high operating temperature generation due to excessive transistor integration on small size chips. Miniaturization further aggravates energy consumption of processors. State-of-the-art processors consume a substantial amount of energy. For example, Intel Core i7-975 drains estimated 83 W of power in idle state, and AMD FX 8350 processor has a peak power consumption of 210 W [41]. Various assessments [5, 8, 72] recommended that main focus should be on power efficiency while designing complex real-time systems. Hence, it becomes necessary to consider energy management as a mandatory parameter for real-time multiprocessor scheduling algorithms. Along with the timing precision, real-time systems must be reliable, but a precisely designed system may fail, which can lead to unexpected situations. Massive heat dissipation adversely affects reliability and performance of semiconductor devices as well and also contributes to global warming [17]. Another serious threat to reliability is caused by high operating temperature, which is a direct consequence of high power consumption generated owing to excessive transistor integration in

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small size. As reported by Srinivasan et al. [53], maximum rise in temperature realized by 180 nm processor is 15 ◦ K lesser than realized by 65 nm processor, and scaling to such small value leads to 316% growth in error rate. For safetycritical real-time computing systems, reliability is a vital feature because faults may cause deadline violations, which can also be disastrous at times. To avoid this, fault detection and tolerance features should be incorporated in the system to achieve high reliability so that it can operate proficiently even in case of faults. Therefore, reducing energy consumption while maintaining reliability of a real-time system is a challenging problem and requires consideration.

2 Energy Management The presence of miniaturized electronic components and chips in the contemporary computing systems makes energy consumption scenario worst ever. The most prevailing digital electronic technology is Complementary Metal-Oxide Semiconductor (CMOS), whose peak power dissipation occurs during state transitions of transistors. To handle power consumption of CMOS circuits, static power dissipation (based on leakage voltage) and dynamic power dissipation (based on supply current) need to be minimized [31–33]: – Static power: It arises as an after-effect of leakage current flowing through transistors. Leakage current increases exponentially with reduced thickness of insulating region and leads to rise in static power. – Dynamic power: It is a consequence of repeated charging and discharging of capacitance of several hundreds of gates in contemporary chips. To reduce static and dynamic power consumption, commonly used techniques are dynamic power management (DPM) and dynamic voltage and frequency scaling (DVFS), respectively. These techniques are overviewed in the following subsections.

2.1 Dynamic Power Management Intel, HP, and Microsoft presented an enhanced framework, called Advanced Configuration and Power Interface (ACPI) for device configuration and monitoring [7, 60]. Basically, ACPI provides a simple and adaptable interface to operating system for configuring and discovering peripherals. The motive of ACPI-based power management is to put the whole system or devices that are unused or less used into low-power states when possible. Due to arbitrary workloads during operation time, DPM attains energy efficiency in the system by judiciously lowering the performance of system components and by switching off the processor in idle periods, thereby saving energy. However, putting

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system component to power-saving state and taking back to the active state involve energy and time overhead. Every processor has some minimum transition time from one state to another called break-even time b0 . When an idle interval is greater than the break-even time, only then processor is put to sleep mode to take advantage of DPM for reducing energy consumption. DPM technique involves taking decision of putting the system components to a power-saving state based on the size of forthcoming idle period. For example, energy budget of switching between states will be larger than the energy saved in the sleep state if the idle period is relatively small. Thus, transition to powersaving state must be done only when idle interval is greater than break-even time. The smallest value of b0 is the one that consumes exactly an equal amount of energy if kept in active state or transition it from active to power-efficient state.

2.2 Dynamic Voltage and Frequency Scaling Growing computing capabilities demand usage of higher operating frequency of processors, which lead to higher energy consumptions. To sustain necessary processor performance by using higher operational frequencies, a number of integrated transistors per chip are growing day by day [12]. Fast switching of a large number of transistors increases the frequency of a processor and also makes them dissipate more dynamic power. Dynamic power consumption of a processor and supply voltage have quadratic relation between them such that: ρdyn = ℘ζef υ 2 f,

(1)

where ρdyn is the dynamic power, ℘ is the gate activity factor, ζef is the switched capacitance, υ is the supply voltage, and f is the operating frequency. DVFS dynamically adjusts voltage/frequency to reduce processor’s power consumption; however, it trades energy with performance since reducing frequency will in turn increase execution time of application. The challenge for DVFS technique for realtime applications is how to preserve the feasibility of a system while reducing voltage so that all deadlines can be honored and energy consumption is decreased. So, care must be taken while using DVFS for real-time applications, as they have stringent timing constraint. Nowadays, processors being launched in the market have DVFS capabilities enabled on it, such as an AMD R-series [2]. Thus, in contemporary processors, it is possible to dynamically regulate the supply voltage and operational frequency to cut down dynamic power consumption using DVFS but at the price of elongated circuit delay [6, 9]. Real-time DVFS techniques can be differentiated based on time of speed adjustment as inter-task and intra-task.

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– Inter-task DVFS: With inter-task DVFS techniques, a job runs at the same frequency level until it finishes its execution after being dispatched or is preempted by a high-priority job. The speed may be readjusted when it restarts execution after preemption depending on the available slack at that particular time. A majority of DVFS algorithms are based on inter-task technique as it has low run-time overhead. – Intra-task DVFS: The intra-task algorithms adjust the speed at the welldetermined power-management points (PMPs) at run time and focus on reducing dynamic energy consumption. But they involve extra energy and time overhead owing to a large number of speed changes. Decrease in processor frequency leads to reduction in frequency-dependent power, but it increases execution time of task, which in turn results in rise in static and independent power. To overcome this problem, a critical frequency fcrit , also called energy-efficient frequency, has been proposed in the literature [29], below which the DVFS does not remain effective. So, tasks should not be executed at frequency lower than fcrit .

3 Fault Tolerance Rapid advancement in scale and complexity of real-time multiprocessor computing systems has made reliability an increasingly challenging issue. Due to the aggressive scaling of transistors, CMOS devices become more susceptible to extrinsic effects such as high-energy radiations and electromagnetic interference. Thus, computing systems have become prone to various types of faults that may introduce some errors in results. In a combinational logic circuit from 600 nm to 50 nm feature size [52], the soft error rate (SER) per chip increases by nine orders of magnitude. If scaling process remains at the same pace, then for 16 nm technology, per day per computer chip will have at least one failure [23, 34]. Despite being designed perfectly, a system may fail abnormally owing to unpredictable fault occurrence. A fault is a situation of unusual response due to some defect in the system. A fault may be a hardware defect or an implementation flaw in the software. In other words, a system is supposed to have failure when service provided by it diverges from the desired service. For example, a computing system that observes the state of critical patients in the hospital must take an action as soon as the patient’s state changes. A remedial measure must be taken if patient’s blood pressure decreases/increases beyond a specific threshold, such as giving an alarm or injecting medicine in patient’s body. This process must be performed strictly in a defined time limit (or deadline). Thus, computing system employed in hospitals especially in intensive care units (ICU) should guarantee that even if the processor incurs fault, the task is executed within its deadline [18]. Another example is in flight control systems where often tasks are activated by the controllers depending

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on the output seen on screen. However, if system incurs a fault, it should be able to handle fault before the deadline [36]. Processor faults are broadly classified as permanent and transient faults: – Permanent faults: Hardware failures lead to permanent faults caused due to manufacturing defects at the time of fabrication or due to wear and tear because of aging. The sole way to tolerate permanent faults is hardware redundancy (to employ additional hardware components). Permanent faults cause damages to processors and can hamper its working. – Transient fault: This type of fault generates soft errors (or single-event upsets (SEU)), which is not persistent and may cause errors in computation or corruption in data. Moreover, with continuing scaling of CMOS technologies, approximately all digital systems are prone to transient faults along with systems that work in outer space [70]. Studies showed that transient faults appear more often as compared to permanent faults [11, 19]. Many techniques have been proposed for detecting faults based on hardware and software [30, 42]. The well-known error detection mechanisms are fail-signal processors, alarms or watchdogs, signatures, and acceptance tests (ATs) [10, 23, 45].

3.1 Fault-Tolerant Techniques Fault tolerance is basically concealing error by switching to another unit of work at the time of fault occurrence. Redundancy is generally applied in the form of extra resources to mask faults for preserving required levels of performance in the system [19]. To integrate fault tolerance in the computing system, approaches have been suggested to tolerate faults that are generally based on redundancy of various resources such as hardware, software, time, and information. – Hardware redundancy is achieved by deploying extra hardware in the system for the replacement of a faulty component. – Software redundancy employs substitute implementations of program that can be used in case the initial version encounters a fault at run time. – Information redundancy techniques are used to handle faults that occur while transferring or storage of data such as error detection and correcting codes. – Time redundancy uses extra CPU time for re-execution of a faulty task or executes a secondary task in case of a fault. To tolerate permanent faults, hardware redundancy is essential, but repeating the execution of task fully or partially helps in tolerating a transient fault [48]. Re-execution and checkpointing are two most commonly used time redundancybased techniques for tolerating transient faults that repeat task fully and partially, respectively.

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– Checkpointing: This technique saves the snapshot of current state of system to stable storage during the execution at regular intervals called checkpoints, where every checkpoint comprises all the context information required to restart process from that point of time. On detecting a fault, system re-executes faulty segment from the most recent correct checkpoint. This technique is able to tolerate g-faults in a task. – Re-execution: Under this technique, re-execution of original task in a failure situation is done and is widely used to tolerate transient fault. If the system is safety critical, task duplication/replication is used to tolerate transient faults to provide required reliability level. However, redundancy increases resource overhead such as rise in energy consumption. Owing to the rising concern for energy management and reliability in contemporary world, energy-saving techniques must be incorporated in fault-tolerant real-time task scheduling.

4 Joint Optimization of Energy and Fault Management Computing systems are nowadays affecting almost every facet of our everyday life. Due to the increased responsibilities, it becomes essential that computer systems should provide both safety and reliability. For many years, researchers have addressed the emerging problems of system reliability, which come along with this thriving evolution of VLSI technology and raised it as prime design concern for realtime systems. Energy management has also become as an essential design parameter for real-time systems due to various environmental, economic, technical, and social issues such as hike in green-house gas emission, cooling infrastructure cost due to more heat dissipation, and damage to public health. If not judiciously handled, high energy consumption and degraded reliability will restrict the advancements to be made to real-time multiprocessor computing systems in upcoming future. Systems such as avionics, defense, and space exploration with real-time constraints need to be reliable as well as energy-efficient. Conventional approaches focused solely on timing constraints, whereas recently additional design issues such as thermal, energy, and reliability have gained attention, which has made the scheduling problem more complex. Hence, it is desirable that task scheduling algorithms for real-time systems must consider different constraints such as timing, energy, and reliability and be designed systematically to accomplish the specified design objectives. Together, reliability and energy management are conflicting design goals for a real-time system. Redundancy-based reliability/fault-tolerance enhancement techniques increase energy consumption due to overhead of the additional resources/computation. Researchers have also observed that there exists an inverse relationship between supply voltage and the rate of transient faults. As a result, reducing energy consumption makes the system more vulnerable to transient faults.

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The ability to achieve timeliness for real-time applications increases on multiprocessor systems with an increased number of computing units. With a greater number of processing units, the possibility of enhancing reliability/fault tolerance improves by having increased prospects for replication of tasks. However, redundancy of tasks raises energy consumption due to multiple executions. Owing to this reason, energyaware task scheduling algorithms remain a pressing concern for the fault-tolerant real-time multiprocessor system. However, it is challenging to reduce energy while tolerating faults because both are conflicting issues having a trade-off between them. These concerns have motivated the research for joint optimization of energy and system reliability. Several schemes are available in the literature that deals with the joint problem of power and reliability management on single as well as multiprocessor platforms. Reexecution, checkpointing, and replication along with voltage scaling and shutdown methods are frequently used strategies to preserve desired level of reliability/fault tolerance and power management in the system. Not only the task ordering for execution on a given processor but task mapping to various processors also affects energy consumption and reliability of the system. Hence, there are various aspects of fault-tolerant task scheduling on a real-time multiprocessor system where energy efficiency can be improved. The research fraternity has shifted to examine the problems at the intersection of fault tolerance and power management in recent past. Task scheduling techniques for joint management of fault tolerance and energy efficiency are discussed below as per the classification shown in Fig. 3.

Fault tolerant energy aware RTS Techniques

Re-execution with voltage scaling based

On uniprocessor platform

Check-pointing with voltage scaling based

On multiprocessor platform

Standbysparing techniques

Task-duplication with voltage scaling based

On multiprocessor platform

M-of-N hardware redundancy techniques

Y-replication techniques

Fig. 3 Classification of real-time scheduling techniques with joint management of energy and reliability

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Re-execution with Voltage Scaling A combination of time redundancy and voltage scaling is used to tackle the joint problem of fault tolerance and energy management on uniprocessor system. Based on re-execution strategy, reliability-aware power management (RA-PM) refers to the unified approach of energy management and fault tolerance based on time redundancy and has been explored in the literature with different aspects. It refers to the notion of original reliability, which is the probability of successfully executing all real-time tasks at maximum CPU speed with no transient fault. RA-PM works by utilizing the available slack for slowing down the tasks with DVFS policy as well as for executing backup copy of scaled tasks in case of fault [69]. Zhu et al. [69] proposed RA-PM over periodic realtime tasks by considering both EDF [69] and RM [71] as underlying scheduling algorithms and showed that RA-PM approach maintains the original reliability of all tasks while saving energy. In another work based on aperiodic tasks, Zhu [70] exploited dynamic slack for further lowering the frequency of tasks and to assign backup tasks to enhance reliability with RA-Greedy algorithm. He also proposed checkpointing for utilizing dynamic slack when recovery placement is not possible due to small size of available slack. The energy-constrained version of reliability-aware power management (ECRM) has been presented by Zhao et al. [65], where they focus on achieving maximum reliability for a real-time system that works in a limited energy budget. For fixed-priority real-time system with weakly hard QoS constraint, Niu et al. [44] proposed reliability conscious energy-aware scheduling (FPRMK-EM) algorithm by reserving space for recovery of mandatory jobs in case of failure and reducing frequency of other tasks for energy efficiency. Zhang et al. [61] targeted to improve energy savings of real-time system with shared resources under the constraint of reliability with EDF/DDM as underlying scheduling algorithm. They proposed Dynamic Low-Power Scheduling Algorithm for Periodic Tasks with Shared Resources (DLPSR) algorithm that exploits dynamic slack for reliability preservation and energy conservation. Considering the preemption overhead, Xu et al. [59] proposed reliability-aware power-management algorithms that effort to reduce execution time and energy consumption of real-time tasks by minimizing the number of preemptions. They proposed greedy energy efficiency scheduling algorithm (GEE) based on greedy strategy of maximally utilizing slack time. Further, GEEPU and GLEEPU have been proposed that reduce frequency based on processor utilization, and DGAET exploits dynamic slack for improving energy saving. Zhao et al. [66] proposed Generalized Shared Recovery (GSHR) technique, where in spite of separate recovery copies for scaled tasks, one or more global shared recovery blocks are reserved, which can be used by any task at whatever time in the situation of fault. In case a task encounters a fault, it uses the recovery block, and the rest of the tasks are then executed at the maximum speed. This scheme improves the reliability of a system to great extent due to the ability to tolerate multiple faults by same task with multiple shared recovery blocks. Thus, it can be used for safety-critical systems where it is essential to maintain certain arbitrary level of reliability in an energy-efficient manner. The authors proposed shared recovery

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scheme for common-deadline-based tasks (Incremental Reliability Configuration Search—IRCS) [64, 66], periodic tasks [67], and precedence constrained tasks (SHR-DAG) [68], where frequency of tasks and the number of recovery blocks are determined based on the given reliability target. Qi et al. [50] proposed global-scheduling-based reliability-aware powermanagement scheme for individual and shared recovery schemes on multiprocessor system. They showed that it is NP-hard problem to find an optimal solution for the selection of subgroup of tasks for energy and reliability management. Algorithms to exploit dynamic slack have also been proposed by them to improve energy savings. Reliability-aware dynamic power management (RA-DPM) has been presented by Fan et al. [17] with shared recovery blocks on single processor system. As soon as a minimum number of tasks execute successfully, time reserved for recoveries is used for reducing frequency for extending energy savings dynamically. Huang et al. [28] proposed energy-efficient fault-tolerant mapping and scheduling for precedence constrained tasks with mixed-integer linear programming formulation on heterogeneous multiprocessor system. They proposed List-based Binary Particle Swarm Optimization (LBPSO) algorithm that is based on particle swarm optimization to obtain high-quality solution in terms of energy saving and reliability.

Checkpointing with Voltage Scaling In order to guarantee reliability and energy efficiency, an adaptive checkpointing scheme (ADT-DVS) has been presented by Zhang et al. [63] assuming Poisson fault model. They adjust checkpoint intervals dynamically to tolerate a fixed number of faults for a set of periodic tasks with EDF scheduling policy on a single processor system. For fixed-priority scheduling algorithm, Zhang et al. [62] proposed a unified approach for checkpointing and DVFS (both task-level and application-level speed scaling) for tolerating g-transient faults while lessening energy consumption for periodic real-time task sets. The authors used genetic-algorithm-based approach (GA) to find the optimal frequency assignment with exhaustive search, which is computationally unfeasible for heavy workload applications on the processor with a large number of available discrete frequency levels. Using adaptive checkpointing technique, work was done by Wei et al. [58] based on the behavior of tasks and fault rate at run time while complying with tasks’ deadline constraints. Two offline DVFS scheduling algorithms—application-level DVS (A-DVS) and task-level DVS (T-DVS)—were proposed for fixed-priority real-time tasks by exploring dynamic slack to minimize energy consumption. Another non-uniform checkpointing technique combined with DVFS for power management has been presented by Melhem et al. [43], which has an advantage of improved energy saving over uniform checkpointing. They considered EDF scheduling algorithm for periodic tasks on a single-core processor with the constraint of having at most one failure in the system. To reduce the number of checkpoints for the sake of minimizing energy consumption, two-state checkpointing (TsCp) concept has been introduced by Salehi et al. [51] where non-uniform

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checkpointing is applied in the fault-free scenario but as soon as the fault occurs, system shifts to uniform checkpointing policy. Duplication with Voltage Scaling A majority of strategies that have been proposed in the literature for fault-tolerant energy-aware task scheduling based on duplication of task copies are for homogeneous platform. Research works done on multiprocessors using duplication of task have been divided into three categories based on the number of duplicate/replicated copies of tasks as follows: – Standby-sparing techniques: Standby-sparing strategy uses one level of replication, such that each task has exactly one replica to execute for fault handling on dual processor system. The workload handled by this technique is not greater than the maximum utilization bound of single processor because extra processor is just employed to provide fault tolerance by scheduling duplicate task copies on it. For independent periodic tasks with common deadline, Ejlali et al. [15] proposed that instead of using standby-sparing scheme with hot or cold spares, Low-Energy Standby-Sparing (LESS) is more effective in saving energy while providing reliability. LESS reduces voltage of primary tasks by applying DVFS and delays backup tasks maximally keeping the deadline constraint fulfilled. They considered reduced energy model and reliability model by considering energy and time overheads as well as static-energy consumption. Aminzadeh et al. [3] did the comparative analysis of system-level energymanagement schemes based on DVFS and DPM for standby-sparing systems and proposed a Markov model to analyze their energy and reliability parameters. They proposed that hybrid method of postponing secondary tasks and frequency reduction of primary and backup tasks on standby-sparing system always save more energy as compared to simple DVFS and DPM methods. For fixed-priority scheduling, Haque et al. [26] suggested that executing primary tasks at lower voltage and backing up tasks at maximum voltage maintain reliability of the system as well as save energy. They proposed StandbySparing Fixed-Priority (SSFP) algorithm for periodic tasks that uses dual-priority scheduling approach on spare processor to delay backup tasks and applied deallocation strategy as well to save energy by canceling backup tasks whose corresponding primary tasks have finished successfully. Dynamic slack has been exploited to enhance energy saving by reducing speed of tasks on main processor and further delaying of backup tasks at run time. Ansari et al. [4] followed the similar concept of [26] for energy- and reliability-aware scheduling on standby-sparing system by using dual-priority strategy for earliest deadline first scheduling algorithm. They presented a new Adaptive Dual-Queue scheduling (AdDQ) algorithm and showed that their work saves 14% more energy than ASSPT and CSSPT algorithms [24]. – M-of-N hardware redundancy techniques: Optimistic TMR has been proposed by Elnozahy et al. [16] to reduce the energy consumption for conventional TMR systems. Two out of three machines run at lower frequency and their result is

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matched. The output is released, but in case of deviation in results, output of third machine that was slower than other ones is used as tie-breaker. Benefit of multiprocessor platform has been exploited by Salehi et al. [51] with N-modular redundancy to improve reliability by masking errors on multiple processing units; however, it imposes substantial energy overhead. They suggested to work in two phases to carry out N-modular redundancy. Half-plusone copies for every task are executed in the first indispensable phase, and the rest of the copies are executed in the on-demand phase only if fault appeared in the earlier phase thereby saving energy in fault-free scenario. – Y-replication techniques: For a set of independent periodic tasks, Unsal et al. [55] proposed an energy-aware fault-tolerance technique with primary–backup scheme, which defers the execution of backup tasks as late as possible to minimize overlap between the execution of primary and backup copies. Energy consumption is reduced by canceling the backup copy on successful completion of primary copy. For the heterogeneous systems, Tosun [54] proposed energy- and reliabilityaware task scheduling and achieved 62% energy saving against energy-oblivious schemes. He presented an integer linear-programming-based framework for mapping and scheduling tasks to heterogeneous multiprocessor system on chip (HMPSoC) for periodic real-time tasks. For highly safety-critical systems, to achieve target reliability level, a certain number of replicas are required. But to generate an energy-efficient schedule, tasks must be executed at reduced frequency value. For preemptive periodic realtime applications, Haque et al. [27] analyzed the interplay between the energy, replication, frequency, and reliability. They proposed a method to create energy– frequency–reliability (EFR) table [25] and then how to use it for determining the extent of replication and frequency reduction for lowering energy consumption with the help of energy-efficient replication (EER) algorithm. Poursafaei et al.[47] used EFR table and presented an algorithm that works in two phases. The first phase is offline replication in which extent of replication and frequency reduction is determined for every task depending on the given reliability target. Later on, at run time, the online phase prevents the execution of redundant copies of task whose one of the copies has finished successfully. By extending the concept of standby-sparing scheme to multiprocessor system, Guo et al. [22] proposed paired-SS and generalized-SS task configuration schemes for independent periodic real-time tasks with dynamic-priority scheduling algorithm. They used worst-fit decreasing strategy for task allocation and showed that generalized-SS is a more energy-efficient configuration for dynamicpriority task set on multiprocessor system. Later on, they extended the concept for mixed scheduling where in spite of standby-sparing configuration, tasks are allocated in mixed manner, such that every processor has a mixture of primary and backup tasks provided that copies of same task are not allocated to the same processor with POED-Mix algorithm. POED algorithm is used to schedule primary tasks with ASAP preference and backup tasks in ALAP manner [21] to save energy by delaying backup tasks for reducing overlap in the execution of two copies of same task as well as minimizing the number of executed backup.

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5 Conclusion With the growing availability of multiprocessor technology, hardware redundancy has emerged as a suitable candidate for providing fault tolerance in real-time systems. Duplicating tasks on separate processing units has turned up as a fitting technique to meet stringent reliability requirements. But efficient scheduling techniques are required to handle the after-effect of replicating task resulting in increased energy consumption. Use of dynamic voltage scaling and dynamic power-management techniques has been the choice of researchers for designing energy-efficient scheduling algorithms. However, in fault-tolerant systems, careful application of energy-management schemes is required, as execution on processor at lower voltage raises fault rate.

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Secret Data Transmission Using Advanced Morphological Component Analysis and Steganography Binay Kumar Pandey, Digvijay Pandey, Ankur Gupta, Vinay Kumar Nassa , Pankaj Dadheech, and A. Shaji George

1 Introduction The World Wide Web is among the most popular and easiest mediums for individual people to convey digital information, and yet one of the most common threats to transmission is that somebody else might undoubtedly acquire those kinds of details, and the Internet by itself offers so little protection for this kind of data. Meanwhile, a transmitter favors enforcing a few other security procedures for this kind of digital data to thwart users from trying to access it. The information has indeed been dispersed all over the end points inside a computer network, utilizing

B. K. Pandey Department of IT, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India D. Pandey () Department of Electronics Engineering, Institute of Engineering and Technology, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India A. Gupta Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak, Haryana, India V. K. Nassa Department of Computer Science Engineering, Rajarambapu Institute of Technology, Islampur, Maharashtra, India P. Dadheech Computer Science & Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India A. S. George Department of Information and Communication Technology, Crown University, Int’l. Chartered Inc. (CUICI), Santa Cruz, Argentina © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_2

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packets of data of differing and fixed sizes. Most protected techniques are designed to utilize information at only the software level; as such safe encryption would’ve been packetized and sent to the lower levels of the OSI architecture. Whenever an intruder receives all the data packets, he or she might very well procure encrypted information while appropriately placing an order for all of the data obtained from the data packet. To follow that, efforts were made to violate the user’s secure methodology. An attacker would not identify the essential nature of data gathered or forwarded or the organization of pertinent information from distinguishable data packets if it is hard to eradicate data further on accumulation of data inside the incoming packets all through transmission of data. As little more than a consequence, the exclusive process of safeguarding transmitted information from all kinds of risk and securely transferring pertinent data to the receiver was already finished. This is exactly what a proposed technique based on morphological component analysisbased steganography and a hybrid convolution neural network could accomplish. Morphological component analysis provides a methodology for distinguishing picture components that appear to possess multiple morphologically important properties. Morphological component analysis must be used for picture contrast enhancement and image segregation, and it is incredibly competent at dividing pictures between texture and smooth components [2]. Morphological component analysis combined with total variation [5] regularization looks to become a particularly effective approach for distinguishing a picture into piecewise smooth contents and textures. The usage of a curvelet [7] dictionary for morphological component analysis produces “rings of aberrations inside the piecewise smoothed content part.” A total variation regularization approach [27] was applied to eliminate ringing distortions in piecewise smoother portions. The Daubechies wavelet has been used to forecast the TV regularization technique. The cartoon component of a picture has indeed been transformed to a Daubechies wavelet transform, which has been assisted with soft thresholding of such a component. The updated cartoon elements of text-based pictures [29] characteristics are then integrated into the cover picture and delivered via Internet-of-things connections. A steganographic approach modifies an initial cover image just minimally in order to incorporate a textual image element on the inside of the cover photo. As a result, a stego image would be generated. Numerous picture obfuscation techniques have been developed to conceal sensitive material within digital photographs. One of the most prevalent approaches appears to be the least significant bits (LSB) technique, which combines secret information into part of the cover picture’s least significant bit (LSB). The LSB approach seems to provide the benefits of being easy to quantify with carrying a large payload comprising data that might be included in a cover image while preserving acceptable picture fidelity. In these kinds of cases, an LSB-based technique would’ve been commonly utilized. Minor changes in pixel edge areas are less sensitive, but little changes in smooth parts become significantly more responsive.

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The extended wavelet convolution layers’ persistent search efficient method with quotient multipicture element value discretization could then be posited for concealing text-based image characteristics all across cover media and enabling obfuscation techniques just at the transmitter end as well as steganalysis just at the receiver end for full-size input images. During this multiple procedure, including ingraining, a stereo-image would’ve been transported to the extreme opposite via a protected site built upon the network of things. Therefore, just at the receiver side, the textual image has indeed been rebuilt via applying the complete opposite of steganography to obtain text-based visual attributes contained in the cover picture. These characteristics would subsequently be used to construct a hybrid convolutional neural network. This hybrid convolution neural network’s performance would be assessed by contrasting it to the data set. The main objective of this study would be to improve the reliability of textbased picture transmissions every second using morphological component analysis, steganography, and textual recognition on only the recipient side, particularly employing a hybrid convolution neural network. As a result, the work suggests two approaches. The very first employs morphological component analysis in combination with an image steganography technique, whereas the other employs this image steganography approach in conjunction with a hybrid convolution neural network. In reality, information security is carried out two times. As per results, the proposed approach surpasses the traditional technique in areas of existing quality measures, including peak signal-to-noise ratio, structural similarity index measures, and accuracy.

2 Suggested Method’s Proposed Goals The proposed approach enables safe textual data transmission and extraction from complicated damaged photos, which employs both obfuscation techniques and deep learning to accomplish its ultimate goal. • To investigate and study the plethora of textual image features, including steganography techniques, that communicate text-based picture information via untrusted connections. • To create a solid data set collection. • Start imposing an efficient MCA-based approach onto text-based pictures that might better accurately separate both textured and smoother regions of a text image while enhancing corresponding efficiency measures such as peak signalto-noise ratio (PSNR), accuracy, and structural similarity index measure (SSIM). • Using the total variation method, divide the image into smoother and textured sections. After recovering piecewise smooth shapes and textures included in a picture, obfuscation has been utilized to disguise the smoother forms and textures of textual-based pictures separately inside cover images.

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• To create a suitable procedure for generating raised stego images for data protection, employing the smoother shape and textures of textual-based pictures, in order to enable maximum security transmission of classified content on the web. • Reversing steganography techniques are applied now at the receiving end to produce a smoother shape and texture of textual-based pictures using highquality cover images. • After that, the textual picture would’ve been recreated by employing inverse morphological component analysis on the smoother shapes and textures of textual-based pictures. • The application of an adapted optimization method boosts the efficiency of the text-based picture reformation methodology. • Furthermore, the efficacy of the proposed approaches would have been assessed by correlating them with all currently accessible approaches.

3 Review of Literature From the initial periods of automation, secure text data transmission and recognition were considered attractive issues for different experts. In recent years, multiple specialists have been engaged in the creation of theoretical approaches and measurement devices to build, discriminate, replicate, and classify these pictures. An overview of the respective efforts has been provided beneath. In Caselles et al. [9], the potential for total variation could restore picture discontinuity and inspire their usage as a regularization concept for imagery challenges. These are based upon its various applications, which include noise removal, optic flows, and stereoscopic images, including 3-D surface reconstructions, segmentation, and interpolation, to mention just a few. On the other hand, it will go over the main conceptual considerations that are being advanced in favors of such a proposition. But on the other hand, it will cover the main computational techniques for solving different models with total variation and also present the basic iteration strategies and the optimization algorithm methods relying on maxflow techniques. In this [20] study, we initially divide every picture into areas that correspond to a few of the three morphology elements, namely, contours, texturing, and smoothness, using the region energies of alternating coefficients(AC) of a discrete cosine transform (DCT). Next, for every morphology element, decide on a block. I have used the lowest block size for the contouring elements, the moderate block size for the texture components, and also the greatest block size for the smoothness elements. To better preserve picture features, a multistep technique is being used to achieve visual noise removal, with each phase comparable with a BM3D methodology except for using adaptable dimensions and distinct transformation. Experiment findings reveal that this suggested methodology provides higher PSNR and MSSIM values than the BM3D approach, as well as improved viewing clarity of

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decoded pictures than that of the BM3D mechanism and some other state-of-the-art methodologies. A novel variational structural and texture-based decomposing methodology has been presented in this study [6]. The primary components of the suggested methodology seem to be as follows: (1) using a low-pass filtration level-set curvature of a source image as a given image and (2) texture suppression by minimizing a changeable exponent vitality, where the changeable multiplier has been managed to gain knowledge from the curvature-guided image filtration result. This approach appears compatible with the present state of the art in structural and textured picture segmentation, according to quantitative studies. Numerous petitions have been under consideration. The framework in [28] of the multi-resolution evaluation method for compression techniques was provided in this study, and also how a 2-D picture could be segmented into four segments, such as the approximation image and detailed picture, was provided. These wavelet coefficients are then subdivided into four subband pictures. A 2D–DWT multi-resolution decomposed would be used to achieve the picture approximations. The source picture was reconstructed via obtaining the least frequency sub-band images (i.e., LL) from three-level reduction outputs. The wavelets were modified by reconstructing the actual image, by utilizing only the approximations. The experiment performed in the MATLAB framework had the lowest error rate. As a result, the two-dimensional DWT technique becomes extremely effective in attaining a satisfactory result. As pertaining to [1], data protection relies mainly on cryptography, with obfuscation techniques functioning as a supplementary tier of security in some cases. Steganography would be a scientific method of concealing the existence of such a textual picture in encrypted transmissions. Several steganographic approaches support this idea, and the majority of techniques produce significantly relevant changes to the covering carriers, particularly as actual textual payloads grow in size. This work [23] presents a deep learning-based weighted naive Bayes classifier (WNBC), which can identify letters and letters in image files. Real-scene photos often include a few tiny disruptions, which have been eliminated throughout the preprocessing phase via supervised imaging filtration. Not only the Gabor transforms, but also stroke width transformation approaches have been used to retrieve critical data through classification. Using those returned properties, WNBC, along with deep neural network-based adaptive galactic swarm optimization, eventually obtains textual identification and character detection. Accuracy, F1-score, precision, mean square error, and recall evaluations are being used to judge the competence of a suggested methodology. Baran et al. [4] presented the modern technology of character recognition and text analysis detection. This requires a connected component-based approach, which makes considerable use of such a detection scheme for maximally stable extremal region characteristics. Contour-oriented and geometrical filtering was also used to identify non-text and text MSERs. The remaining textual sections were then split into phrases and sentences. After that, novel filtering techniques have been deployed to exclude superfluous words and non-text regions that are not even sufficiently

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aligned with anticipated characteristics. OCR technology has been applied to detect key terms and phrases that lasted all throughout the final phase. Finally, a content identification and dissemination framework were used to achieve this plan. As in Starck et al. [25] utilized multi-scale portrayal processes like the ridgelet as well as curvelet transform to reinstate the picture, yet compared one’s research results to generally recognized methodologies based on the wavelet coefficients thresholding and demonstrated that the curvelet transform enhances the data’s characteristics. As per Feng et al. [16], several cutting-edge digital data concealment algorithms base their insertion modifications primarily on the centers of one-shape forms. Nevertheless, the embedding need of this kind offers an imbalanced correction to the border framework. The paper proposes an image steganography method, which utilizes introducing additional entities as well as l-shape sequence insertion criteria to recognize freshly built content-adaptive digital picture information covers. It starts by looking at whether different one-shape patterns affect the flow of a particular 4 × 3 research methodology in the study. In terms of efficiency, four structural categories that indicate a combination of two picture components concentrated all throughout the scope of pattern alteration were employed to create a 32-dimensional steganographic set of features. In Aujol et al. [3], the characteristics of various norms that seem to be mirrodin of Sobolev and Besov standards were conflated, which had been supplemented by Y. Meyer’s previous innovations, which decomposed concepts into texture and simple geometric elements. A newly perceptual methodology is then applied to a picture, which has been partitioned into three components: the picture’s framework, texture, and noise. However, one decomposed framework comprises three semi-norms, which include the total variability of the simple geometric constituent, a detrimental Sobolev norm for both texture components, and now a negative Besov benchmark for distortion. As in Xinbo, Gao et al. [17], they proposed using morphological component analysis to dissolve mammogram pictures into piecewise components and also add a texture portion to improve mass identification accuracy. Mammogram mass recognition would be widely used during breast cancer diagnosis, but distinguishing masses from normal places would be difficult due to its rich morphological characteristics and uncertain margins. A texture component has been utilized because this effectively diminishes relational usable interruptions and blood vessel effects, and to anticipate multiple kinds of significant impacts in a mammogram, two classical circumferential surface standards have been established. An explanation for negentropy approximations for nonlinearity operations was indeed given in Prasad et al. [24] to enable the effective utilization during estimation of frequency-domain-independent component analysis (FDICA). They posited a nonlinear conceptual model predicated on natural science forecasting models of time-frequency series of speech (TFSS) through GGD processes, which enhance separation effectiveness while also speeding up centralization. The research strenuously supports the proposed nonlinear operations. As per Starck et al. [26], a new procedure relying on sparse signal representation, particularly regarding Morphological Component Analysis (MCA), would be focused on the concept that

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every transmitter behavior really should be differentiated, as well as a dictionary would seem to occur that facilitates its advancement utilizing just a sparse signal depiction. Furthermore, to acquire a correct separation, a pursuit approach toward such a sparsest depiction might well be applied. The study additionally offers a number of picture feature usage findings, conceptual findings that justify the overall separation procedure, and a description of several methods that help with the suggested method. There seems to be a fundamental framework of several repetitive multi-scale transformations, including the Ridgelet and Curvelet transformations. This chapter [22] describes a feature preference strategy, which correlates with the most sharable information hypothesis. This same technique incorporates specific alternatives for sequential-independent component analysis-based transforms with just an effective and accurate consensual knowledge appraiser, with the understanding that minimizing a probability-based error margin could be deduced by optimizing similarity measuring systems among features. A linear independent component analysis transform has been used to separate combined attributes into approximately vectors, enabling single-dimensional consensual information assessment. An independent component analysis transform would be approximated while also tackling an overall eigen desiccated issue that is also feasible and dependable throughout aspects of computation. The current methodology would be premised on linear independent component analysis, which doesn’t always generate distinctive attributes, which counters the integral dissolving of interactional understanding theory. Elad et al. [15] discuss a variety of signal computing issues, such as the process issue, which is the data loss from a physical metric; the cocktail party issue, which is the separation of one audio from a mixture of many other captured audio at a club; and the decomposition of the picture and signal into superposed achievements from distinct images. Another concern seems to be the separation of a picture into multiple parts, like texture and cartoon (piecewise smooth) elements. Hyvärinen and Oja [21] presented the theoretical background and deployments of ICA, as well as one of the most recent studies on the topic. ICA would be a relatively new phenomenon for whom the goal would be to find a linear summary of non-Gaussian and random information that really is uncorrelated with one another. This even makes it easier to build a necessary model in a variety of applications such as feature extraction. They also put a spotlight on sequential methodologies like principal component analysis, confirmatory factor, and others. There is now a description regarding wavelets in a temporal frame [13], which provides a basis for expanding wavelet techniques to issues which are not periodical and not delimited within full Euclidean space. In fact, the properties of such wavelets are indeed being studied, including both applications, in addition. Following that, they explored the stability evaluation of wavelet transforms, focusing primarily on timeframes, and proposed strategies for enhancing the latter. The curvelet transformation of elements that really are smoother apart from discontinuity along the whole curves has been examined in Donoho and Duncan [14] recently offered a new multi-resolution portrayal. The suggestions were created using the function specified for that over continuum plane R2. The implementation of the

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aforesaid transformations for digital data was already hampered by the need to define a strategy approach for computing digitally curvelet transformations and also a computing framework and Curvelet256. Then, in the case of 256,256 images, apply this technique. Furthermore, they discussed some of the research carried out utilizing it. As per Garofalakis and Gibbons [18], wavelet transformations would effectively decompose activities into the tiers as per Garofalakis and Gibbons [18]. The function’s wavelet transform is generally made up of either coarse as a whole or approximate solution and also depth parts that affect the functional in distinct layers. Numerous recent studies have demonstrated the efficacy of such a wavelet transform in allowing estimated queries to be processed across large data sets. In other words, using wavelet transform using input information to construct a layered summary of data with just a reasonable quantity of wavelets would’ve been a fine decision. The wavelet transform’s power decrease, and also decorrelation features aid in the construction of meaningful as well as appropriate approximation depictions. Wavelet transforms could possibly be calculated within a fixed time, culminating in exceedingly complicated results. As reported in Candès et al. [8], two digital curvelet transformation algorithms, both 2-D and 3-D, are used. The first is concerned with discrete fast Fourier transforms (USFFT), while the second seems primarily concerned with the packing of such particular Fourier samples. When transforming curvelets with each size, a spatially consistent grid has been employed, and the slope varies among different instances. Most electronic transformations include a list that includes digitized curvelet values that are linked either by scaling, orientation, or spatial position requirements. When it comes to n-by-n Cartesian matrices, almost all deployments have been efficient, with a computation time complexity of O(n2 log n), but they also appear to be invertible. To facilitate implementation, the proposed electronic transformations become simpler, faster, and significantly less repetitious than the initial generation using curvelets. Four textural characteristics in creating morphological components have been explored in this work (Xiang, [31]): content, coarseness, contrast, and directionality (including horizontal and vertical). Furthermore, to evaluate and develop morphological features, a classification has been done using both remote-sensing hyperspectral and polarimetric synthetic aperture radar (SAR) pictures, demonstrating all the proposed techniques’ ability to handle numerous varieties of remote sensing imagery. Furthermore, despite having a sufficient number of training instances, the results demonstrated that the proposed MCA architectural can produce extremely acceptable classification results in a wide range of analytic situations. This chapter [11] provides a method for improving dimension minimization by employing a partially EZW methodology. EZW, an evolving image compressing approach, would be an enhancement over Shapiro’s embedded zerotree wavelet method. The recommended Partially EZW Approach handles the EZW challenge while sacrificing efficiency while moving to a lower bit plane. Throughout this study, integer wavelet transforms and region of interest [10] encoding were

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introduced into Partial EZW, bringing it superior to both EZW and the SPIHT Methodologies.

4 Suggested Method The morphology component analysis methodology is being employed just at the transmitter end to separate a picture into independent units that have encompassed a naturalistic scenery element and also the texturing, as seen in the workflow of the methodology proposed in Fig. 1. According to analytical outcomes, curvelet transformation exceeds wavelet transformation for identifying a realistic picture component. The picture’s starting values would be assigned to implicit vectors. The discrete cosine transformation with curvelet conversion of the residue is then calculated. The curvelet coefficient was treated with hard thresholding after getting a curvelet transform of the residue.

Sender side Dictionary built by combining several transformations

Textual Image

Data base of cover image

Preprocess cover image

Morphological component Analysis

Cover Image Smoother part of textual image

Texture part of textual image

Stegnography Optimal pixel selection

elsb

Steganalysis (textual image feature extraction)

Smoother part of textual image

Texture part of textual image

Features of textual image Receiver side

Fig. 1 Flow diagram of suggested methodology

Based on features of textual images Hybrid convolution neural network is train to identify textual image

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The curvelet transformation returns a frequency component of the matrix. Image distortion is a feature with greater frequency. Coefficient values having a value much less than that threshold really aren’t taken into account. The retrieved features were primarily put inside the cover image using ELSB-based steganography, and the stego image was likewise broadcast over the Internet. The features of a textual picture were separated either from the cover image and the recipient sides via reversed obfuscation techniques, and the image coefficient was determined via inverse transformations of acquired matrices. Following performing an irreversible curvelet transform, a picture containing ring artifacts was created. As a consequence, to properly overcome this matter, the whole variance regularization methodology is used. The coefficients of a wavelet transformation were simply inadequate. The preponderance of a coefficient becomes essentially zero by adopting a distortionfree wavelet transformation. As a result, the image restoration challenge could be reframed as one about recovering picture coefficients that become “better” over the Gaussian white noise backgrounds. As a result, smaller magnitude coefficients should be distorted and lowered to near zero. The wavelet threshold level is a method for evaluating each coefficient to a cutoff to determine whether or not it represents a useful portion of the initial impulses. This residual is then estimated via a discrete cosine transform. This thresholding of the wavelet coefficient is normally performed on just the picture’s informational coefficients, not the approximation coefficient, since the former reflects “low-frequency” notions that typically comprise crucial components of such a signal and therefore seems to be considerably less affected by distortions. The approximated coefficients pertain to a lower frequency element, whereas the complete coefficients contribute to the “frequency part.” The effectiveness of the previous MCA methodology, as well as the current strategy, has been tested using various beginning thresholds. The coefficient having an absolute value lower than a set threshold level was fixed at zero to retrieve an essential coefficient. Using reverse wavelet transformation, every original photo is also recreated. The discrete cosine transformation of a residue was computed almost instantly. Then, two images were combined that were created by calculating the reverse discrete curvelet transformation and also the reverse discrete cosine transforms of a residual image. After that, this number would be removed from the overall image and be assigned as if it were a new residue. This residual image is therefore used toward an outcome that has been computed because the reversed curvelet transforms were measured. Determine the curvelet transformation of such an output yet again, and then proceed with the techniques below. Following numerous repetitions, the outcomes were eventually obtained. The following seems to be the methodology utilized at the sending end to identify the features from a textual picture by utilizing morphological component analysis and afterward encrypt those features utilizing an eLSB-based steganography technique: 1. Invoke Kmax but also threshold λ = δ * Kmax .

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Where Kmax seems to be the total number of iterations for every layer N and λ seems to be a hard-threshold with such a threshold level δ. 2. For J = 1 to M; Where M is number of images. Compute the residual term Res, with assumption of current value of xn is variable and value of xt is fixed: x consists of set Res = x- xt -xm ; for a given sample of m textual images,  of m textual images {xi }i = 1,2,3, . . . . . . ..t, such that x= ni=1 x i having distinct morphology. 3. Compute the curvelet transformation of xn + Res and obtain χn = ϕn+ (xn + Res n ) For each i = 1 to n, ϕ represent a set of bases of dictionary such that for every value of i, yi sparse in ϕi , not in ϕi , or at least not sparse. 4. Apply hard threshold on the coefficient χn with threshold value λ and obtain   χn = λδ ϕn+ Resn 5. For S = 1 to M; Compute the residual-term Res, with postulation of current value of xn is fixed and value of xt is changeable. Res = x-xt -xn ; for a given sample of m textual images,  x consists of set of n textual images {xi }i = 1,2,3, . . . . . . ..t, n such that x= ni=1 x i having different morphology. 6. Compute the discrete cosine transforms of xt + Res and gained χt = ϕt+ (xt + Res t ) For each i = 1 to t, ϕ denote a set of bases of dictionary such that for every value of i, xi sparse in ϕi , not in i , or atleast not sparse. 7. Now apply hard threshold on the coefficient χt with threshold value λ and obtain   χt = λδ ϕt+ Resn 8. Reading all usable frames of a cover image as well as the value of χt , χn , Res as features extracted from morphological variable analysis of textual image. 9. Change value χt , χn , and Res in binary format. 10. Now, calculate the LSB of each picture element in a cover picture that would be transferred over the channels within the transmission process. 11. Substitute all binary values with binary values χt , χn , for the LSB of every pixel of the cover picture to really be communicated. 12. Subsequently, stego images are captured and communicated via the World Wide Web.

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The following algorithms should be used at the receiver side to extract textual images: 1. Analyze a stego image obtained. 2. Compute the least significant bit of each picture element in a stego image received mostly on the receiver side and communicated from the sender side. 3. Get the number of bits, and then convert each 8 bit to a character to get the value of χt , χn , and Res. 4. Rebuild xn by xn = ϕn χn where ϕn is inverse of ϕn+ . 5. Rebuild xt by x = ϕt χt where ϕt is inverse of ϕt+ . 6. Rebuild x by using the method given below: Res = x - xt - xn . 7. Modify the threshold by λ = λ – δ. 8. If δ > λ, continue the rebuilding procedure. 9. Else, finish.

5 Results and Discussion The major purpose of that kind of work would be to retrieve textual graphics concealed inside a cover image. The textual image extraction technique is usually partitioned into two stages, one on the sender side and the other on the receiver side. Initially, a proposed methodology for morphology-based component analysis was used for a textual image in an attempt to improve the textual image variances across distinct texturing all throughout the picture. The accompanying examples demonstrated well how to breakdown a textual image into different parts based on various textural qualities, employing a specified dictionary for MCA. Because of the coarseness, the image becomes separated into coarse (strengthening) and tiny (weak) components, wherein coarseness seems to be an estimate of the number of sides in a local square neighborhood having a radius (see Fig. 2). The image is separated into high-contrast (strong) and low-contrast (weak) portions based upon contrast. (See Fig. 3). Because the image is composed of horizontal and vertical orientations, it is also segmented into those two portions even if they do not exist (see Fig. 4). As per line likeness, the frame is converted to the line-like (strong) and non-linelike (weak) elements (see Fig. 5). Following the decomposition [12] of a textual picture into different parts, the textual images of such constituents were improved by employing appropriate image improvement techniques and then merging those components to obtain the manufactured textual image. The changed textual images displayed in Fig. 6 are then hidden inside the cover image borders seen in Fig. 7 using an enhanced least significant bit steganographic approach, and the resulting stego picture is shown in Fig. 8.

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Fig. 2 Decomposition of image by coarseness

Fig. 3 Decomposes of images by contrast

Figure 6 depicts a proposed technique, which uses eLSB to optimize quality images such that the methodology could be performed appropriately. Because the proposed steganographic technology functions in a spatial arena, this is separated into two stages. In the first stage, metadata has been established. The earliest few

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Fig. 4 Images decompose based on directionality

bytes of a cover image include header data. The hidden data was inserted into the cover-image in just the way that it would be optimized. Figure 9a and b show the outcome stego image for two distinct pictures from each stage; they are source images of two distinct text images, features for preprocessed images and a stego image (b). The embed method’s secret key has been subsequently provided to such a textual information extraction procedure, mostly on the receiver end. The cryptographic keys have been used to extract textual images, and the embedded content is then obtained from the cover picture by utilizing a deep learning-based hybrid convolutional neural network (CNN). An adaptive optimization method is applied to further improve the effectiveness of deep learning algorithms. The peak signalto-noise ratio (PSNR) and structural similarity have been utilized to evaluate the effectiveness of the presented secret text retrieval technique. According to Eq. 1, MSE provides a particular evaluation process that describes a tier of similarity, or alternatively, a magnitude of variance, but there is deterioration here between primary and decompressed picture frames. MSE would be not only formulated as having for an M × N main images I but also decompressed images K:

Secret Data Transmission Using Advanced Morphological Component. . .

Fig. 5 Images decompose based upon line likeness Fig. 6 Textual images that have been manipulated

Fig. 7 Cover Image that has been used to hide textual image

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Fig. 8 Stego image that has resulted

MSE =

−1 M−1  N 1 [I (i, j ) − K (i, j )]2 M ×N

(1)

i=0 j =0

As described by Eq. 2, the peak signal-to-noise ratio (PSNR) seems to be a scientific articulation for a proportion of a signal’s maximum promising strength to the strength of perverting noise that impairs the representation accuracy. Since numerous transmissions seem to have a wide, versatile scope, PSNR would be calculated by taking the average of a logarithmic decibel scale. Lossy picture compaction encoder restoration has indeed been utilized to evaluate the accuracy of lossy picture restoration. Therefore, in the illustration, a signal seems to have been surrounding the preliminary contribution, and the dissonance appears to become an encoding defect. When encoding codecs are considered, PSNR would simply be a change to scientific comprehension for renovation efficiency when encoding codecs are considered.   MAX2I P SNR = 10log10 (2) MSE where MAXi would be the picture’s highest pixel valuation. The structural similarity (SSIM) evaluation provided by Eq. 3 is a metric for ascertaining how related two pictures emerge. The SSIM [30] measurement might well be a valuable reference measurement. Such that it further genuinely quantifies picture quality by using an initial uncompressed or reverberation picture as a baseline. SSIM has been planned to be based on conventional methodologies that have been demonstrated to be incompatible with human eye detection, including PSNR and MSE.

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Fig. 9 (a) and (b) represent feature extraction using morphological component analysis and encoding of textual image by using steganography

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The difference between SSIM and other outlined methods, like MSE or PSNR, is that all these methodologies assess perception inconsistencies, whereas SSIM wants to cure image regression as a number of changes throughout quality. The term “structural information” reflects the fact that picture element values have substantial interdependencies, especially once they seem to be temporal equivalents. These interactions offer critical information about the layout of the visual acuity image area. The measure between two windows x and y of common size N × N is:    2μx μy + c1 2σxy + c2   SSI M (x, y) = μ2x + μ2y + c1 σx2 + σy2 + c2

(3)

with μx the average of x, μy the average of y, σx2 the variance of x, σy2 the variance of y, σxy the covariance of x and y. c1 = (k1 L)2 and c2 = (k2 L)2 two variables to stabilize the division with weak denominator. L the dynamic range of the pixel values (typically this is 2bits per pixel ). k1 = 0.01 and k2 = 0.03 and by default. The eventual result is the SSIM index, which has a numeric value between −1 and 1, but scoring 1 will only be feasible if two comparable data sets have been used. This is typically measured using 8 × 8 window frames. The window frames in images could be replaced pixel by pixel, but still, it would seem that a subset of the obtainable window frames has been used to decrease quantification intricacies. Performance analysis can be performed to investigate and evaluate the adequacy of suggested methods. The evaluation revealed that the suggested technique outperformed the existing approach in terms of the structural similarity index measure, accuracy, and peak signal-to-noise ratio for a variety of thresholds (see Tables 1, 2 and 3). Once the TV regularization approach is approximate using Haar and Daubechies wavelet, then the impact on PSNR, SSIM, and correctness of textual images with different threshold levels is shown in Figs. 10, 11 and 12.

6 Conclusions At the moment, in which the primary form of engagement involves mobile technology that has Internet connectivity to transmit data, the key problem appears to become the safeguarding of such secret information. For secure communications,

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Table 1 Illustrates the influence on PSNR of the Haar and Daubechies filtering techniques for cover image shown in Fig. 6 Threshold (γ) 5 7 9 10 12 14 16 18 19 20 21 24

PSNR with Haar (existing methods) 40.12 40.25 40.50 41.00 41.25 41.60 42.70 43.80 43.76 44.03 44.34 45.42

Table 2 Illustrates the influence on SSIM of the Haar and Daubechies filtering techniques for cover image shown in Fig. 6

Threshold (γ) 5 7 9 10 12 14 16 18 19 20 21 24

PSNR with Daubechies (proposed methods) 39.75 39.70 40.25 40.50 42.00 44.20 45.70 47.20 48.09 48.24 48.54 49.54 SSIM with Haar 0.9964 0.9981 0.9974 0.9976 0.9981 0.9979 0.9980 0.9983 0.9967 0.9977 0.9976 0.9978

SSIM with Daubechies 0.9972 09975 09980 09980 0.9981 0.9981 0.9982 0.9985 0.9984 0.9985 0.9967 0.9986

information extraction and validation of textual information across a public network connection, this methodology uses morphology component analysis, total variance, enhanced LSB (e-Least Significant Bit) steganography, a deep learning-based weighted naive Bayes classifier, and an adaptive optimization method. To secure information transfer in a general internet data transmission connection, a combination including morphology component analysis, total variance, steganography, and a weighted naive Bayes classifier-based deep learning algorithm was used in many stages. The very first stage employs morphological component analysis and total variance to generate image-based components on coarseness, directionality, contrast, and line likeness. By using a spatially steganographic method, the morphological components of a text-based image were divided more and inevitably embedded into the least significant bit of cover picture as in the second stage.

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Table 3 Illustrates the influence on accuracy of the Haar and Daubechies filtering techniques for cover image shown in Fig. 6 Accuracy with Haar filtering techniques 95.645 96.998 97.923 96.782 96.976 97.135 98.695 98.456 98.443 98.544 98.556 98.561

Threshold (γ) 5 7 9 10 12 14 16 18 19 20 21 24

PSNR with Haar filter

Accuracy with Daubechies filtering techniques 96.343 97.123 98.126 97.945 97.997 98.342 99.182 99.385 99.461 99.465 99.532 99.623

PSNR with Daubechies filters

49 48

47.2

47 Peak Signal to Noise Ratio (PSNR)

45.7 46 45

44.2 43.6

44 43

42.7

42 42

41.6

41 40.12

40.25

40.25 40.50

41.5

41.25

40 39 38 5

7

9

12

10

14

16

18

Threshold ()

Fig. 10 Peak signal-to-noise ratio comparisons for the Haar and Daubechies filters utilized in the MCA and steganography techniques

The secret key acquired from textual image incepting methods had been sent to the textual picture retrieving technique on that receiver side in order to collect the textual picture, and the incorporated textual picture was ultimately detected utilizing simply a weighted naive Bayes classifier. Adaptive optimization methodology can

Secret Data Transmission Using Advanced Morphological Component. . . SSIM with Haar

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SSIM with Daubechies

0.9988 0.9985 0.9986

Structural similarity index measure (SSDM)

0.9984

0.9982

0.9982

0.998

0.9981

0.996

0.9981 0.9981

0.9983

0.9981 0.998

0.9980

0.9979

0.9978 0.9976

0.9975 0.9976 0.9974 0.9972

0.9974 0.9972 0.9970 0.9968 0.9966

0.9964 0.9964 0.9962 0.9960 7

5

9

10

12

14

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Threshold ()

Fig. 11 A comparison of a structural similarity index measure (SSIM) for such a Haar filter and the Daubechies filter used for the MCA and steganography procedures Accuracy with Haar Filtering Techniques

Accuracy of proposed method with Daubechies Filtering Techniques

100.0 99.385 99.182

99.5 99.0

98.695 98.342 98.5

98.126 97.923

98.0 Accuracy

98.456

97.997

97.945

97.5

97.123 97.135

96.998

96.976

97.0

96.782 96.343

96.5 96.0 95.645 95.5 95.0 5

7

9

10

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14

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Fig. 12 A comparison of the accuracy of the Haar and Daubechies filters employed in the MCA and steganographic processes is presented

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commonly be utilized to increase the effectiveness of a weighted naive Bayes classifier. This suggested scheme, which uses a debauched filtration mechanism to create text-based picture elements, improves the conventional process, which uses the Haar filter to create text-based image features. The suggested model not only improves on PSNR but also outperforms it in respect of SSIM relevance. The suggested methodology correctly conducts textual extraction just at the receiver side, although the letters may also be overlooked sometimes, or an identical letter may indeed be retrieved repeatedly, leading to the recovery of incorrect textual data. As a result, an integrated solution to prevent such errors across all textual recognition should be established in the near future. In addition, many types of fuzzification algorithms were addressed. An ant colony optimization approach could be used to achieve favorable outcomes.

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Data Detection in Wireless Sensor Network Based on Convex Hull and Naïve Bayes Algorithm Edwin Hernan Ramirez-Asis, Miguel Angel Silva Zapata, A. R. Sivakumaran, Khongdet Phasinam, Abhay Chaturvedi, and R. Regin

1 Introduction Wireless sensor networks (WSNs) play a major role in the world because of its applications in wildlife tracking, military movements sensing, health care system, building health monitoring, and storing environmental observations [7]. Geographic routing-based distributed sensor systems have applications in Internet of Things (IoT) domain. The mechanism of covetous sending is considered one of the excellent geographic directing plans due to its straightforwardness and proficiency. The eager mechanism for data transport has a demerit, e.g., communication void. Most of the geographic directing plans comprise two components: (1) covetous sending and (2) reinforcement. In this chapter, we propose a novel void dealing

E. H. Ramirez-Asis · M. A. S. Zapata Santiago Antúnez de Mayolo National University, Huaraz, Peru e-mail: [email protected]; [email protected] A. R. Sivakumaran Information Technology, Malla Reddy Engineering College for Women, Secunderabad, India K. Phasinam School of Agricultural and Food Engineering, Faculty of Food and Agricultural Technology, Pibulsongkram Rajabhat University, Phitsanulok, Thailand e-mail: [email protected] A. Chaturvedi Department of Electronics & Communication Engineering, GLA University, Mathura, Uttar Pradesh, India e-mail: [email protected] R. Regin () Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_3

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technique that identifies the void boundary. Moreover, a transfer hub is used that bundles from source run around the void to build a way with less bounce tally. The nodes in the WSNs interact with the surrounding environment using sensors and actuators. Sensor nodes in WSN are equipped with severe energy constraints [6]. Difficulties that arise due to power, processing, sensing, and location unity in the hardware of the sensor units may result in hardware failure. Also, badly written sensor program may result in software failure. Moreover, complications in the transceiver of the sensors lead to communication failure in the WSNs. The data are classified based on the WSNs sensed data [12]. Voronoi polygons are haphazardly isolated into the inactive hubs of the WSNs within the two-dimensional square-checking zone. Furthermore, the key zones of the static nodes are associated within the counterclockwise heading to decide the gap zones. Concurring to the distinctive concave shapes, convexity worsens into an arched body, and the raised body center is utilized as the base point. The Delaunay triangulation is combined with the bulge vertices to calculate the position of the virtual repair hub. Finally, based on the hubs’ relative removal of the hubs, remaining vitality & hub of criticality, multi-factor cooperative energy coordinating choice table between the virtual repair hub & the versatile hub concurring the choice table, the portable hub performs the limited separate development to realize the repair optimization of the scope gap. Recreation tests are designed for calculating existing scope gap. AEL-HO calculation moves forward to arrange scope and expands the arranged life cycle.

2 Related Work Data are available in various forms, i.e., lost data, offset data, out of bounds data, gain data, spike data, stuck-at data, noise data, or random data. Detection of data in the WSNs becomes complex due to restricted sensors and different placement fields. In recent years, with the advancements in ad hoc network, numerous researchers are actively contributing to the domain of IoT and sensor systems. Notwithstanding, in contrast to MANET, the versatility of vehicles in IOT is commonly compelled by predefined streets. The speed of the vehicle is additionally confined in the parts of speed limits, level of blockage in streets, and traffic control components. Along these lines, building up a practical versatility model of IOT is critical for assessing and structuring steering convention. This linkage is utilized to safeguard the dynamic transmission that enhances the transmission scope of the vehicle as indicated by situations of nearby traffic. The effect of vehicular traffic for the most part on Matt’s traceroute (MTR) breaks down when thickness changes from steady trade to greatest congested driving conditions. Clients must perceive the urban infill which is not simply to copy the length of vehicles. In addition to this, the automobile overloads bring down vehicles at normal speed. The assumption of nearby nonrenouncement encourages a hub to join a Bayesian sober mindedness inside the nearby neighborhood, where the hub is fit for perceiving the neighbors.

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Data detection techniques play a major role in obtaining an assured operating condition of WSNs. Different types of data detection algorithms are used in WSNs, which contain the basic operation of supervised or unsupervised learning. Support vector machine (SVM) classifier and statistical learning theory [8] can be introduced to detect data in the networks. A classifier’s training is performed using kernel functions that contain radial basis, polynomial, and linear kernel functions. The indoor dataset of single hop is not considered. The trend analysis of least squares SVM [4] is developed for improved sensor data diagnosis to overcome these problems. The error-correcting output matrix used for data classification has limitations. SVM with Statistical Time-Domain Features [10] and one-class SVM with Stochastic Gradient Descent [3] methods are presented for data detection in WSNs, which are suitable only for binary classification of data. For multiple data classification, data classifier identification process is combined with convolutional neural networks (CNN) [11] and Random Forest (RF) [13] utilized for consumption of energy in the sensors during anomaly detection. However, there is no detailed study on evaluation of hybrid classifiers. The comparison is made by applying this algorithm on different datasets and their feature values are utilized for the classification of sensed data.

2.1 Challenges and Problem Statement The WSNs face various challenges in data detection mechanism due to the following reasons. • The resources at the node level will make use of node’s utilize classifiers [2] only in restricted ways, since there is no need of difficult calculation. • In hazardous and uncertain atmosphere, there is a need for placement of sensor nodes. • Medical information detection techniques [14] must be accurate and random to eliminate loss. For example, the method would identify the changes among normal data and sensor data. As a result, it lacks encompassing in obtaining inaccurate information, which might lead to a misrepresentative response.

2.2 Contributions • Adaptive ensemble learning with hyper-optimization classifier is utilized to detect health information in WSNs. • In addition to that, three more classifiers are utilized on the datasets. A wide experimental evaluation is accompanied to find WSN. In this chapter, SVM, RF, and CNN classifiers are utilized for comparing the proposed AEL-HO classifier.

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• Performance of classifiers is analyzed based on the parameters of the measures such as F1-score, detection accuracy (DA), Matthew’s correlation coefficient (MCC), and true positive rate (TPR). In the work discussed in this chapter, a novel adaptive ensemble learning with hyper-optimization algorithm has been developed to reduce the complication of the AEL-HO algorithm dealing with comprehensive training datasets. The proposed technique stores related data points and cleans irrelevant data points present in the dataset. Initially, the k-mean clustering process was applied to specify training data points. Next, the quickhull algorithm [5] collects the single class label data points from each cluster in the convex hull. The data belonging to the convex hull vertices and clusters of more class label’s data points are ultimately considered the specified training data points of the AEL-HO algorithm classifiers. The experimental outputs of the large dataset demonstrate that the proposed technique minimizes the total training data points without reducing the accurateness of the training data points. Here, reduction of 90% training time is achieved in comparison to the AEL-HO method. This chapter is organized as follows. The general explanation of WSNs is given in Sect. 1. Related works and the proposed contribution are presented in Sect. 2. The proposed methodology of the AEL-HO classifier is explained in Sect. 3. The system model result is structured in Sect. 4, and the conclusion of the chapter is given in Sect. 5.

3 Proposed Methodology 3.1 Preprocessing The aim of preprocessing is to eliminate unwanted words from the bug report. During analysis, unnecessary words are removed since they may worsen the learning performance. Thus, the space for the feature set is minimized making it easy for learning and performing data analysis. This process comprises three steps: tokenization, stop-word removal, and stemming. First, a sequence of text is partitioned as numbers, words, punctuation, etc., which are termed as tokens. Then, every punctuation is substituted with spaces; escape characters that are nonprintable are eliminated and all words are changed to lowercase. Here, the common stem of words is substituted and saved as selected features. For instance, words like “moving,” “moved,” “moves,” and “move” are substituted with the word “move.” The words obtained, once preprocessing is completed, are termed as features as given in Eqs. (1) and (2). BR = {S1, S2, S3----Sj}

(1)

Data Detection in Wireless Sensor Network Based on Convex Hull and Naïve. . .

Sj = {f1, f2, f3----fk}

3.1.1

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(2)

N-Gram Extraction

While extracting the features, information present in the bug report is expressed as a feature vector. It supports extending the network by transforming the information of the bug report into various sets of n-gram data. Thus, features are represented in a better way. N-gram method is deployed for observing the semantic relationship and for estimating the frequency of feature order. N-gram technique is reduced as k − 1 and organizes the series of i features into unigram, bigram, and trigram. P (f i/f 1, f i/f 2, . . . . . . ..f i-1) = p (f i/f i-k + 1, . . . . . . ..f i-1)

(3)

where, fi and p() represent the feature (word) and probability, respectively; in unigram, it is assumed that the successive features are not dependent on one another. The features of the feature string have no mutual information. Hence, the conditional probability of unigram is given as follows. P (f 1/w1) =



 p

fi w1

(4)

In bigram, two adjoining features provide language information. Its conditional probability is written as follows. P (f 1/w1) =



 p

fi + 1 w2

(5)

Various n-gram techniques must be integrated to exploit its entire ability. Several n-gram techniques can be used for analyzing an individual sentence, and then the results obtained are combined. Thus, the relationship among n-gram feature is expressed as an analysis at word level, which is given as follows. P (f 1, f k + 1) =



p(f 1)p (f k + 1, f k, . . . . . . f k − m, wi) p(wi)

Algorithm Input ← Training data Dtr, testing data Dts, and defective rate σd Output → Class label cj prediction of every instance in testing data For every instance of Dtr and Dts Delete every duplicate instance from Dtr and Dts.

(6)

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Fill the missing instance value in Dtr and Dts with the mean of the corresponding instance value2. Normalize both the Dtr and Dts using min-max method3. Input ← SDA for generating a deep representation of aik, where kth metrics of ith instance of class label ci. Output → Deepaik is the deep representation of aik. End for for every base learner bl EL phase l=1, 2,...10. Train Dtr; According to bl, a cross-fold validation is performed on Dtr. Calculate average MCC(AvGMCCi) and average F-measure(AvGF) End for tuning xk with yk yk = ak + f × (bk − ck) {np, f , cr} = {10n, 0.8, 0.9} Data Point Clustering: Initially, the k-mean clustering technique performs the clustering operation on the new data points in training which separates the data points into k clusters. In this method, depending upon the dataset structure and total data points, the selection of clusters has been carried out. The clustering technique’s accuracy is based on two things, i.e., initial centroids and the k values. In the clustering method, singular clusters are formed with the help of only one data point class. The nonsingular clusters are formulated where more than one data point class are present. Using k-mean techniques, five cluster groups are formed on the data points. It contains four “singular” clusters and one “nonsingular” cluster in the dataset. Convex Hull Construction: Using quickhull technique, the convex hull is constructed for every cluster. Then, the convex hull is calculated. It contains the singular and nonsingular cluster. V1 and V2 denote the vertices set of class label 1 and 2 correspondingly. Redundant Data Points Elimination: In this step, we eliminate the data point, which is not used to form the vertices in step 2. The data points in the dataset are used to form the vertices and are defined as “Rem” data points. Here, 41 data points are used to perform the next step of the naïve Bayes classifier. The system model of this work contains two TelosB mote sensors and one desktop computers, which is accumulated to make the measurements. This model contains three stages.

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Stage 1 TelosB sensor is used to take the sensed data readings. These readings are utilized to form the data preparation block with new data measurement V_t. It makes a new observation vector. The measurement of temperature (T_1 and T_2) and humidity (H_1 and H_2) is combined to form the dataset. The new observation vectors are formed by the three sequential data measurements V_t, V_(t-1), and V_(t-2). Stage 2 In the second stage, the faults are included in the training dataset. From [1], four different faults are taken (offset, gain, out of bounds, and stuck-at). In addition to that, data loss fault and spike fault are taken in this system model. Stage 3 In this third stage, the many cluster nodes are connected to form the WSNs. To make the communication between other nodes and the network layer, each cluster contains one cluster head. In each cluster head, the naïve Bayes algorithm is incorporated to classify the fault. To form the decision function, the observation vectors are used. This process is less expensive because the decision functions are incorporated in each cluster head along with the classifiers. By using the decision function, the fault may be classified. Classifiers classify the dataset into positive (fault) and negative (normal).

3.2 Attribute-Based Encryption Attribute-based encryption is a form of public key encryption that depends on the attribute of the user’s secret key and the cipher text. In such a framework, the unscrambling of a cipher text is conceivable if the arrangement of the trait of the cipher text is an urgent security part of property-based encryption resistance. An individual getting numerous keys may have the choice of obtaining data if, at any rate, one individual key awards is received.

3.3 Symmetric-Key Algorithm Executions of symmetric key encryption can be especially successful to ensure that consumers do not encounter any significant time delay due to encryption and unscrambling. Likewise, symmetric-key encryption offers a degree of confirmation as data mixed with one symmetric key cannot be decoded with some other symmetric key. Therefore, if it can be used by the two gatherings to scramble correspondences and keep the symmetric key private, each gathering will ensure that it communicates to the other as long as the decoded messages become consistent and pleasant.

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3.4 Cipher Text Attribute–Based Encryption A collection of descriptive attributes will define private keys. In our construction, a party that wants to scramble a message will suggest a method that private keys must follow to unscramble through an entrance tree structure. Every inside hub of the tree is an edge door and the leaves are related with qualities. We utilize similar documentation to depict the entrance trees, even though for our situation, the credit is utilized to distinguish the keys (as restricted to the data) and specified in the private key.

3.5 Performs the Naïve Bayes Classifier on the Remaining Data Points In the last step, the remaining 41 data points are used to perform the naïve Bayes classifier. From 85 training data points, only 50% of the data are utilized to obtain the accurate naïve Bayes classifier technique [38]. Here, the data points can be reduced requiring fewer mathematical formulation steps to classify data points. In addition to that, the computation times are reduced and obtain higher accuracy. Proposed AEL-HO Algorithm 1. Choose the cluster value K. 2. Perform the k-means clustering techniques. 3. Where k varies up to K (k ≤ K) for each cluster do. 4. Based on cluster k, check the data points class label. 5. If the cluster data points are a single class. 6. Allocate the cluster label as “Singular.” 7. Else, allocate the cluster label as “Nonsingular.” 8. End. 9. End. 10. For “Singular” cluster, do the following: 11. Perform quickhull techniques. 12. Estimate the convex hull (V1 ), which denotes the class-1 label vertices points. 13. Estimate the convex hull (V2 ), which denotes the class-2 label vertices points. 14. Set of vertices points are formed. 15. Eliminate each clusters sample not related to the group. 16. End. 17. For “Nonsingular” cluster do the following: 18. Choose each cluster data points and form in a single set. 19. End. 20. Remaining samples are structured as “Rem” dataset. 21. Perform naïve Bayes classifier to the “Rem” values.

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4 Experimental Results The experimental results of the proposed system are evaluated. Here, the dataset used in this work is explained and the performance analysis parameter and their equations are presented. A final comparison of the proposed method with the other three classifier algorithms is discussed. System configuration: • • • •

Operating System: Windows 8. Processor: Intel Core i3. RAM: 4 GB. Platform: MATLAB.

4.1 Dataset In WSNs, measurements of the sensor and the different fault types are combined to form the labeled dataset. This dataset is used based on the existing dataset proposed by the investigators in the North Carolina University at Greensboro in 2010 [13]. From the single hop, multi-hop, and two outdoor multi-hop sensors WSNs, the data are gathered in TelosB motes. The sensed data contain temperature and humidity measurements. Each vector is formed from the three successive instances t_0, t_1, and t_2. By using temperature T_1 and T_2 and humidity H_1 and H_2 measurements, the construction of each instance has been carried out. Here, six different faults (stuck-at, data loss, offset, out of bounds, gain, and spike) are taken at different rates (10%, 20%, 30%, 40%, and 50%), which is introduced in the dataset. From 9566 observations, 40 datasets have been prepared: each has 12 dimensions. Each dataset contains the measurement values and target values (1 for normal and 2 for fault). The naïve Bayes classifier is used to classify the whole dataset into two labels, i.e., normal case and fault case. Table 1 summarizes the various types of fault results. Table 1 MCC of SVM, CNN, RF, and proposed AEL-HO

Matthews correlation Techniques SVM CNN RF Proposed AEL-HO

coefficient (MCC) 0.65 0.32 0.46 0.73

Rank 2 4 3 1

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Table 2 Performance comparison of accuracy and response time Classifiers RF [12] CNN [9] SVM [1] Proposed AEL-HO classifier

Data accuracy (%) 95.22 95.33 95.45 96.42

Response time (s) 1.99 1.76 1.22 0.97

4.2 Performance Evaluation Parameters DA is the first metric [1, 7], which is represented in Eq. (7): DA = (Number of faulty observations detected)/(Total number of faulty observations) (7) TPR is the second metric [15]. It is defined by actual positive quantity that is identified as correct. The corresponding expression is given in Eq. (8). TPR = TP/ (TP + FN)

(8)

where, true positive (TP) denotes the estimation of fault capable of identifying true positives. The false negative (FN) denotes the estimation of fault which is wrongly requested as negative. MCC is the third metric [17–25], which ranks the classifiers based on the accuracy values. It ranges between −1 and 1. The expression of MCC is given in Eq. (9): √ MCC = (TP × TN-FP × FN) / ((TP + FP) (TP + FN) (TN + FP) (TN + FN)) (9) where, true negative (TN) denotes the estimation of non-faulty nodes correctly and false positive (FP) denotes the estimation of faulty nodes incorrectly. F1-score is the fourth metrics [16], which is the mean of harmonics precision and recall. Table 1 shows the ranking of the proposed AEL-HO classifier with existing CNN, SVM, and RF classifiers based on the MCC score. The AEL-HO classifier is considered to perform well based on MCC values. A classifier with an MCC value of 1 means the classifier is the best. Here, the MCC value of the proposed naïve Bayes classifiers is close to 1 as compared to SVM, CNN, and RF classifiers. Therefore, the naïve Bayes classifier is proven to be the best classifier (MCV value, 0.73) followed by SVM classifier (MCC value, 0.65) [26–31]. Table 2 shows the accuracy and response time of the proposed AEL-HO classifier compared with CNN, SVM, and RF classifiers [32–37]. The analysis shows that the proposed classifier exhibits a higher accuracy of 96.42% and response time of 0.97 s.

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5 Conclusions and Future Work The clustering is performed on the data points, and then the convex hull algorithm is used to find the vertices of the data points that belong to each cluster. The performance of the classifiers is analyzed based on the metrics such as F1-score, DA, MCC, and TPR. Based on the values of DA and TPR, it has been concluded that the proposed algorithm has performed better than the existing methods. For future work, the same dataset can be applied to different new data that appear in WSNs. In addition, identification of WSNs data is found to be accurate in the network layer and the sensor nodes.

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DSPPTD: Dynamic Scheme for Privacy Protection of Trajectory Data in LBS Ajay K. Gupta and Sanjay Kumar

1 Introduction Location-aware service [1–3] is a type of context-aware services in which location is provided as input to the system. The system takes location as input and provides services to the user. The location may be geometric, i.e., latitude and longitude form, or it may be semantic, i.e., near and within. User querying the services provides his or her location to service providers believing that the correct location would improve the quality of services (QoS). However, it led to a risk of disclosure of private and confidential information [4]. It is highly challenging to design efficient trade-off between the QoS and privacy of the mobile user. In location-based services, the user provides his current location to the third party for a service request. An attacker (or untrusted service provider) may make an inference attack [5] through these live locations of the user may infer the personal confidential information regarding his health or lifestyle by observing location, duration of stay, and habits of activity performed by him. So, this is a security and privacy problem. The aim here is to reduce the privacy leakage risk as well as to provide the quality of service. The general architecture of the cellular mobile environment [15, 16] consists of mobile units (MU), fixed hosts (FHs), and base stations (BSs). The BS has its fixed location, functions with two-way radio, and has some data processing capabilities. The basic function of data and transaction management is done by the database server (DBS). Many BSs and FHs are linked via a high-speed network. Each cell has a limited radio coverage area and a BS to manage mobile clients. The cell A. K. Gupta () Indian Institute of Information Technology, Pune, India S. Kumar United Services Automobile Association (USAA) – AADC Project Technical Lead (HCL America Inc., America), San Antonio, TX, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_4

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can be seen as a limited bandwidth radio coverage area and normally represented by the shape of a hexagon. In wireless local area networks, it can be treated as a high bandwidth network within the area of the building. The wireless channel is splitted in two channels known as uplink and downlink channels. Here, the first one is utilized for the submission of the mobile client’s queries, and the second is used to answer the mobile client queries by the mobile switching stations (MSSs). The base station controller (BSC) is used to control the various BSs. The mobile switching center (MSC) gives commands to BSC to control an appropriate BS. Unlimited mobility in personal communications service, global system for mobile communication, and reachability to any BS or FH facilitate many services being easy to deploy in the real world. The public switched telephone network and MSC connects the databases available for a mobile environment to the outer world. The mobile transactions run in the frequent disconnection mode. Due to mobility and frequent disconnection behavior of these mobile transactions, they are longlived. The data and/or user may also move in a mobile environment. Therefore, the mobile transaction may have their associated sub-transactions (cohorts). Among those, some may run on the MSS and some may run on mobile nodes. Due to the disconnection and mobility nature of the transaction, it shares its information of states and also partial results with other transactions. Also, the mobile transaction should fulfill some prerequisites to work well in the environment of mobility. With the mobility nature of nodes, the state of the data object being accessed and the corresponding location information must also move. There should be the availability of the techniques to deal with concurrency, frequent disconnection, and consistency between replicated data objects residing at different locations. The mobile transactions are also executed in a distributed manner, which may be subjected to further restrictions such as limited bandwidth. Evolving commit protocols [17] for a distributed transaction in the presence of mobility is the most challenging task in comparison with the generic environment. Here, the mobile transaction may need to deal with the forced wait or forced abort, if wireless channels (uplink or downlink) are not available at any instant of time, and this could be delayed due to hand off randomly. The mobile transactions might not be in a position to complete its implementation due to the unavailability of full database management system (DBMS) capability [18]. This is the reason why conventional transaction control strategies are not well suited to the mobile environment. If the connection is not possible to mobile nodes or due to high expenses in continuous connection, the mobile host can decide to work in disconnected mode also. Based on the locations of initiation and execution of the mobile transaction, it can be classified into three types. The first category is of those mobile transactions, which are both initiated and executed by a mobile host (MH). The second category is of those mobile transactions [19], which are initiated by fixed host (FH), but executed by the MH. The third category is of those mobile transactions, which are initiated by MH, but executed by both MH and FH. In the mobile transaction environment, where MH initiates transactions but executed completely by FH, the MH requires no record retrieval capability.

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The location-dependent information system (LDIS) is an application of contextaware computing in the mobile environment where the transaction is initiated and/or executed by MH. Moflex is an example of this type of scenario. This model primarily stood on dependencies set, suitable goals, and also on rules. Sub-transactions for location-based services are also supported by this model. A further version of the same scenario is pre-serialization, which permits the cohorts of the global transactions to commit independently. The serialization technique permits in releasing the nearby resources in a well time-stamped way. There is need of integrating revised data visualization and indexing technique of data item in LDIS for user-friendly response and faster access rate, respectively. A number of mobile devices together with the personal digital assistants has very small screens. Therefore, the requirement for potential future research work is to consider the mobile system screen size and computational limits when developing lightweight simulation methods for desktop and handheld apps applications. Indexing in LBS is used to get faster search results. Before one can search through the LBS, he has to create a search engine index. It facilitates power saving mode to the client until queried records arrive on the requested channel. Index overhead induced by an LBS implementation certainly affects indexing approach selection. The proportional frequency of queries vs. updates especially favors either queryoptimized or update-optimized indexing approaches. The scope of future research is toward an investigation of trajectory and filtering approaches to further enhance the efficiency of these indexing approaches in terms of updating and querying.

1.1 Problem Statement The past trajectory privacy protection approaches mostly rely on obfuscation of the trajectory locations and add more uncertainty to preserve privacy. However, it is challenging to monitor the trade-off between the efficacy of trajectory privacy security and the usefulness for spatial and temporal behavior, and this problem has not been thoroughly explored or measured in past strategies [6, 10]. The recent analyses concentrate predominantly on the spatial component of trajectory details, whereas other semantics such as thematic and temporal attributes are seldom addressed. In comparison, existing methods depend extensively on manually crafted procedures. If the process is revealed, the initial trajectory details can be recovered. To this end, this study intends to investigate the feasibility of deep learning methods to overcome the above mentioned privacy security challenges in trajectory. The following points can describe the primary contributions of this work. 1. The edge-based distance measure has been introduced in proposed DSPPTD for k-path trajectory clustering of deep neural network processed trajectory to achieve differential privacy before publishing it. The work discusses an end-toend solution of deep learning to produce trajectory data supporting differential

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privacy. A Gaussian mechanism for synthetic trajectory preparation has been described in this work. 2. The two functions, namely mutual information and Hausdorff distance, are used to measure the intensity of privacy protection and utility of the trajectory data with training deep learning approach. 3. Analysis of the trade-off between privacy protection effectiveness and the usefulness of the new model are made utilizing real-world LBS details. The rest of this paper is organized as follows: Sect. 2 gives an overview of related work. The deep-learning-based differential privacy protection approach has been described in Sect. 3. We discuss the factors affecting privacy protection effectiveness to verify the utility and privacy trade-off of the proposed policy in Sect. 4. Finally, Sect. 5 concludes this paper.

2 Related Work With the advancement in mobile technologies, smartphones allow peoples to access numerous LBSs and provide interactive information depending upon location of the user. The study of user’ positions and associated confidential information not only enables more sophisticated and reliable user information to be created but also inevitably leads to security and privacy problems. Therefore, this domain needs more research works for the development of location-based technologies to resolve such burning issues [11–13]. There are various reports on the privacy security of dummy-based trajectories. Kido et al. [14] were the first who used the concept of a random move to create dummies. Lu et al. [15] suggested a confidentiality-conscious, dummy-based strategy for preserving consumer data. However, the history details were overlooked by these systems. Niu et al. [16] established a Dummy-T effective privacy security system for the route. It employs the minimum cloaking area and context details to ensure each dummy produced on the trajectories is just like the real one. However, it lacks the actual mobility trend and spatiotemporal association, which leads to the deterioration of the degree of privacy. The definition of k-anonymity was first introduced for relational databases [17]. If the position of the recipient is indistinguishable from the position of certain k-1 persons, then the query is said to be location k-anonymous. Zhang et al. [18] also suggested caching and spatial K-anonymity (CSKA) policy to improve safety through k-anonymity and caching. This system, though, is not well suited to protection for trajectories. Moreover, past policies are based on user-clustered or centralized architectures. Hence, the workload of the network is high, and the anonymizer could lead the bottleneck performance. To make sure the optimal distribution of the selected dummy locations, the authors in [19] also provided an enhanced decay lengths (DLs) approach that could expand the cloaking region while retaining a degree of privacy near to the

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DLs algorithm. In [15], two approaches of dummy creation were suggested by the authors, notably grid-based and circle-based methods, which take into account the privacy criteria. In [8], the authors developed dual dummy-based techniques to guarantee the k-anonymity of privacy-conscious clients in LBS, recognizing that opponents would exploit side details. The previous approaches have also not understood the information on the side that attackers may exploit while picking dummy locations. While some approaches have taken the side information into account, they have a high processing cost. However, the effective selection of dummy locations in IoT remains a research problem. In K-anonymity systems, Hu et al. [5] applied a credit-incentive framework to maximize the efficiency of selecting dummy roles. Based on the fuzzy reasoning, credit rating contributes to a certain maximum level of probability for each customer. A client can still get help from specific users on the condition that his credit rating passes a certain likelihood threshold amount. It motivates people to assist others in building Kanonymity actively. In a sense, all the above solutions originate directly from the single time LBS position privacy policy [20] and therefore ignore the following two issues: (a) Protection of communication messages in user’s LBS request. (b) Exposure of the users’ real location details due to the continuous importance of query position. Present findings on the evolution of privacy protection concentrate primarily on two sources of study. One is the hierarchical solution to privacy to combine and mix trajectories from various users such that the detection of person trajectory data is turned into an issue of k-anonymity [19, 21]. Here, the spatial cloaking method utilizes k-anonymous cloaked spatial regions to combine trajectory locations between k-objects and renders these trajectories k-anonymized [22]. The mix-zone strategy often anonymizes trajectory locations in a mix-zone using aliases. It removes the link between the former section and the latter section of the mix-zone trajectory [23]. Additionally, the positions of k trajectories are divided into k-anonymized separate regions first by the generalization-based method and then uniform selection and reassemble k new trajectories by connecting points of each k-anonymized region [24]. A further analysis medium is termed geo-masking, which blurs the positions of actual trajectory details by using spatial dimension interference to cover or change the original positions. However, spatial trends might not be substantially affected [25, 26]; for example, Zandbergen [27] discussed the need to preserve privacy and the spatial usefulness of many forms of geo-masks. Kwan et al. [28] tested the efficacy of three independent arbitrary geo-masks of perturbation on lung cancer cases in space research. Seidl et al. [29] introduced grid masking and random disruption to data sets from GPS and measured the efficiency of privacy security. Gao et al. [26] studied the efficacy of Twitter data aggregation, Gaussian disruption, random disruption, exploration of the complexity, degree of anonymity, and analytics of each process.

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Users may access preference details of the actual position in the implemented system without revealing their location data to the service provider. Beresford et al. [30] proposed anonymous communication techniques, who are first to introduce mixed zones concept. A mixing zone applies to a geographic area where no call back activity has been recorded by any users. The researchers in [31] allow users to swap the pseudonyms if they met in mix zone and also care for user to avoid the use of pseudonym for a larger time. The association of app positions and pseudonyms may, therefore, be disrupted by pseudonyms exchange. Finally, it may be claimed that these days scientists are energetically researching the privacy concern of query processing [5, 32]. A few worthy survey articles have emerged in recent years addressing privacy problems in LBS—difficulties and probabilistic scope connected with it [7, 33].

3 Our Proposed Scheme We follow the system approach based on fog computation, as seen in Fig. 1. It is made up of three entities: handheld device, LBS server, and fog server. The fog system is operated by the consumer and installed with enough hard space in the user’s spare devices. In the proposed approach, the fog server receives the background information. It applies the DSPPTD policy for protecting trajectory and dependent confidential information from the attacker while providing maximum QoS for the user’s query request by the LBS server. The LBS server scans the POIs of users, and it returns the output of the applicant to the fog server after this fog server delivers the relevant results to the customer.

Fig. 1 LBS system structure

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Table 1 Key notation used in proposed scheme Terms T

Description Trajectory

E

Description Skewness parameter based on zipf access distribution Differential privacy

DNN_Datat K CmaxH n

DNN processed trajectory data Anonymity degree Optimized set using entropy The total number of snapshots

qjt−1

A query probability for j dummy location at time t−1

HCRti

Path entropy

m

Randomly selected m’ locations set from 2 k optimized set

pjt

Terms Theta

Location point at time t of set j Datat Trajectory original data K Number of clusters E(S) Entropy of set S N System defined variable N>2k drt A real location from trajectory at time t  T R dJt |djt−1 Transition probability from time t−1 to time t for j dummy locations, where 2>j≥k-1 M Randomly selected m locations set from N dummy locations, where m ≤ C(2 k-1, N) Dt

Anonymous set at time t

Disttmax

Separation length from the current position to the next Mutual information

MI



 q djt−1 (x, y) HD

Time-dependent query probability at location djt−1 Separation angle between x and y Hausdorff distance

The concept of cloud fog computing makes server computation resources available in the ground nearer to end-users. In comparison with clustered data centers, these nodes are physically much closer to smartphones, which leads to fast communications between entities. It has the remarkable ability of edge nodes to process and measure large amounts of data under their own, without submitting it to distant servers. Fog computing is an intermediary between external servers and mobile devices. It controls the details that the server can obtain, which can be accessed locally. For this sense, fog is a smart portal that offloads clouds making for more effective data collection, retrieval, and analysis. Table 1 summarizes the notations used in the proposed scheme. The DSPPTD approach is a trajectory privacy protection that incorporates the deep neural network and structure of the Gaussian system to build privacypreserving synthetic trajectories as substitutes to actual trajectories for the exchange and publishing of trajectories. In this paper, we propose a new approach consisting of four main components. The four main components that are implemented by the system include processing, generation, optimization, and release of trajectories. A detailed summary of each unit is given below.

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A. Trajectory processing model uses the user’s moving scene to generate corresponding high-dimensional data items. B. Trajectory generator uses a deep neural network, which takes random noise and original location points of trajectories as inputs to generate synthetic trajectories as outputs. The processed trajectory consists of position points in actual timestamps that can shield the original collection of data. C. Apply k-means clustering for k subregions division of the location trajectory data region with common data points. D. The trajectory release step involves comparing each clustered “synthetic trajectories datum” to corresponding “real” trajectory and merging accordingly. The process also involves a prejudging mechanism to ensure at least one actual trajectory record can be seen in processed trajectory.

3.1 Trajectory Processing Model The trajectory is a sequenced series of user movement points where the interval period between two user location points does not reach a fixed threshold Th . It is represented by T : p1 → p2 → · · · → pm , where, Th > pi+t t >0 with (m > i ≥ 1) and pi ∈ P ⊂ L. The |T| is the number of samplings (|T| = m), and t is defined as the interval of the sampling point. P = p1 , p2 . . . , pm are the arrangement of points known as user movement log, where each point pi ∈ P contains pi .lat, pi .lng, pi .t, and pi .v as latitude, longitude, timestamp, and velocity, respectively. pi = {pi .lat, pi .lng, pi .t, pi .v} Also, the location coordinate can change as time passes. Figure 2 provides a distinctly unpredictable glimpse of the initial trajectory data collection. These nodes are related as per the time – series data and thus shape a trajectory. In the equation, a general representation of a record is given below:

Fig. 2 Log and trajectory for moving person

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Original Data : Datat : (p1 .lat, p1 .lng, p1 .t, p1 .v) → (p2 .lat, p2 .lng, p2 .t, p2 .v) → → (pi .lat, pi .lng, pi .t, pi .v) The Gaussian method is used to attain differential anonymity by applying random noise to the time parameter t of the client behavior predicted trajectory results. The Gaussian method can be described using a data set D = {x1 , x2 , . . . , xN }, privacy parameter ε, global sensitivity Δf of given function f. In the differential privacy mechanism, with the given sibling data set D and D , the function f sensitivity is represented by Δf as given below:

  Δf = max f (D) − f D  DΔD 



DΔD is the set of each pair data sets that differs in at most one record. Theorem 1 For a given output function f : Dd → Rd , the following function M have 2 (ε, δ)-differential privacy if δ > 45 exp − εσ2 and ε < 1. M (f, D) = f (D) + (Y1 , Y2 , . . . , Yd )

The likelihood of differential privacy is represented by probability δ. The parameter δ bounds the differential privacy level, and its value is smaller than  1 |D| . The parameter ε is inversely proportional to privacy protection. The Gaussian distribution draw in the form of Yi (i = 1, 2, . . . , d) has 0 as the value of mean and Δf σ as the value of standard deviation, i.e., Y(0, (Δf σ)). Trajectory data given in the below equation is the trajectory data post-processing  Δf the Gaussian noise function value, Gaus ε to all-time attribute, that can resist an attack through context awareness.   Processed Data : Pro_Datat : p1 .lat, p1 .lng, p1 .t + Gaus Δfε , p1 .v →   p2 .lat, p2 .lng, p2 .t + Gaus Δfε , p2 .v →   → pi .lat, pi .lng, pi .t + Gaus Δfε , pi .v

3.2 Trajectory Generator DSPPTD’s essential purpose is to increase the performance of trajectory data reporting statistics as well as the scheme’s productivity based on maintaining

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the differential privacy. Differential privacy frameworks and deep neural network (DNN) deep learning algorithms are the core methodologies applied in this paper. DSPPTD uses differential privacy to offer protection and privacy functionality to LBS apps and uses DNN to efficient trajectory data processing from complex time series. DSPPTD is built for a dynamical object movement, which defines the dynamic model of four components correlated with the speed, latitude, longitude, and time of the users.

3.3 Multilayer Perceptron and Deep Neural Network A “perceptron” is a known “artificial neuron,” forming the “neural” system. This paper first discussed the simplest single hidden layer multilayer perceptron before deep learning-based multilayer perceptron. In general, the multilayer perceptron has the structure in which every location might be represented by way of a single input and a single output neuron and having one hidden layer. Positive weights are typically considered to be excitatory in neural network, whereas negative weights are known to be inhibitory. Training is the method of weight change to build a network that performs some task. The basic architecture of artificial neural network consists of the three components, namely presynaptic connections, which input xi , synaptic influence, which is modeled using real weights wi , and neuron reaction, which is a nonlinear weighted inputs function f. As shown in Fig. 3, x1 , x2 , and x3 are given as inputs to the perceptron, which produces a single binary output. Piecewise linear and sigmoid are examples of output or response function. The equation for sigmoid and piecewise linear is given below: 1 f (x) = 1 + e−λx

Fig. 3 Perceptron in ANN

 f (x) =

x, if x ≥ θ 0, if x < θ

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The functioning of the human brain is imitated by employing neural network technology for understanding pattern recognition rather than passing the input through the different layers of simulated neural connection. “Artificial neural networks” have an “input layer,” at least one “hidden layer” in-between and an “output layer.” In “feature hierarchy,” specific sorting and order types are carried out in each layer. To deal with unlabeled or unstructured data is among the practical uses of these neural networks. Figure 3 shows the perceptron in artificial neural network (ANN). The leftmost layer refers to “input neurons” present in the “input layer.” The rightmost layer refers to the “output neurons” present in the “output layer.” The middle layer refers to the “hidden layer,” which does not contain the “neurons” of input or the output. One of the downsides of the “neural network” is cost work slope processing. One of the quicker ways to deal with slope processing is “error back propagation,” which gives an in-depth knowledge of changing the metrics toward the system’s behavior. The “deep neural network gives the hierarchical composition of the “linear” and “nonlinear” activation function. We propose using “deep neural networks” or “deep learning.” In this proposed work, the system considers an input layer, two hidden layers, and a final output layer. The former layers and output layer have been evolved the activation function sigmoid. The three steps involved in back propagation preparation are listed below: 1. Training set: Neural network uses a collection of input–output patterns for training. 2. Test set: For assessment of neural network performance, another collection of input–output patterns are used. 3. Learning rate: It is a scalar parameter used to determine the change rate, which is similar to phase size in numerical integration. Network error is used as termination criteria or as an indicator for desired training of the neural network. Root mean square error (RMSE) and sum squared error (SSE) are the two most important indicators commonly used in most of the neural network applications. The equations for root mean square error (RMSE) and total sum squared error (SSE) are given below:  RMSE =

TSSE =

2∗ TSSE #patterns∗ #outputs

1   (desired − actual)2 2 patterns outputs

The deep neural network processed trajectory data is a new trajectory that may mask the original data set for the trajectory. Using this training model, we can only get more anonymous data according to specific points in the complex trajectory. The

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model facilitates the avoidance of loading complete data sets inside the standard procedures and leads to running time reduction. The trajectory data given in the below equation is the trajectory data after deep neural network processing of the trajectory data: DNN_Datat : (p1DNN .lat, p1DNN .lng, p1 DNN .t, p1DNN .v) → (p2DNN .lat, p2 DNN .lng, p2 DNN .t, p2 DNN .v) → → (piDNN .lat, pi DNN .lng, pi DNN .t, pi DNN .v) The trajectory data generation procedure can be described by Algorithm 1 as given below: Algorithm 1: Differential Privacy Generation of Trajectory Data Input: Trajectory Original Data (Datat) Output: DNN processed Trajectory Data (DNN_Datat) Begin For_ALL Datat For_ALL t ≠ 0 in Datat ∆f ∆f = Gaus = max   −   D∆D′ ε tdnn = t + ∆f latdnn = DNNlat(Pro_Datat, tdnn) londnn = DNNlon(Pro_Datat, tdnn) vdnn = DNNv(Pro_Datat, tdnn) (latdnn, londnn, tdnn, vdnn) → DNN_Datat End_For End_For Return DNN_Datat End

3.4 K-Paths Trajectory Clustering Partition-based approaches are more like clustering techniques that are categorized before processing by the count of clusters (or centers). A parameter k (k ≤ n, n is the data point count in the data set) is needed to set the count of final data partitions. The cluster is represented by partitions, which must require at least one data point. Partition-based approaches involve techniques of k-medoids and k-means. In [] and [], two improved variants of k-means and k-medoids are described. The k-means algorithms have been utilized in several clustering projects. The central concept is to locate k cluster centers randomly and then in an iterative manner, a grouping of the piece of data according to the divergence to the nearest clustering center until all clustering centers converge.

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This step involves the k subregions division of the location trajectory data region of common points. Here, the positions data with the same timestamp t is first segmented. Then k subregions or groups are identified with similar data points, and initial centroid corresponding to each subregion is chosen. If, at any instance, the area covers a more significant number of mobile users than the threshold, then k needs to be revised accordingly. In clustering, the location data with closer trajectories are merged into a common cluster. The k-paths trajectory clustering process can be defined as given below: Given a set of trajectories T: p1 → p2 →···→ pm , the goal of the k-paths is to divide the n trajectories into k (k ≤ n) clusters groups C = {C1 , C2 , . . . , Ck } to minimize the below objective function: O = arg min C

k  

Dist (pi , μx )

j =1 pi ∈Cx

where each clusters Cx have their centroid path μx, which is an element of the set of paths in road network directed graph G [34], and Dist is the measure of the Euclidean distance between two trajectories. The k-means and k-paths can be differentiated based on the following four points: (a) In a Euclidean space, trajectories can differ in length rather than fixed-length vectors. (b) A trajectory length estimate “Dist” must be specified for two trajectories. (c) We cannot locate the centroid direction μx by merely measuring the average value with each trajectory throughout the cluster. Analogous to a version of kmeans named k-medoids [35], it is possible to use a current trajectory as the centroid path. Let EH, ALH are the edge histograms and accumulated length histograms, respectively. The terms ub(i) and lb.(i) be the Ti to its nearest cluster upper bound distance and the Ti to its second nearest cluster lower bound distance, respectively. The terms cd(x) and cb(x) be the centroid drift and centroid bound of μx , respectively. The formula for edge-based distance measure used in Algorithm 2 is given below: Edge-Based-Distance (T1 , T2 ) = max (|T1 |, |T2 |) − | T1 ∩ T2 | |T1| and |T1| be the travel length of the total trajectory T1 and T2 , respectively. In k-path trajectory clustering, the trajectory distance measure “Dist” is replaced by edge-based distance. Therefore, the applied objective function has been revised as given below: O = arg min C

k   j =1 pi ∈Cx

Edge-Based-Distance (pi , μx )

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Algorithm 2: K-Paths Clustering (K, DNN_Datat ) Input: Number of clusters (k), DNN processed Trajectory Data (DNN_Datat) Output: k centroid paths: {μ1, . . . , μk}. Begin Centroid paths μ = {μ1, · · · , μk} initialization, t ← 0; Repeat If (t = 0) For Each Ti ∈ DNN_Datat do mini ← +infinity; For Each path centroid μj do lb(i, j) ← Edge-Based-Distance(pi, μj ); If (mini > lb(i, j)) then a(i) ← x mini ← lb(i, x) End For UpdateHistogram(pi, ALH, EH, a(i)); End For Else For Each cluster Compute and make changes to centroid bound cb and centroid drift cd End For For Each trajectory Ti ∈ DNN_Datat do Compute and make changes to lb and ub; If (ub(i) < max(cb(a’(i))/2, lb(i))) then a(i) ← a’(i) \\Ti remain in same cluster: Else mini ← +infinity; For Each path centroid μx do If (lb(i, x) < ub(i)) then lb(i, x) ← Edge-BasedDistance(pi, μx ); If (mini > lb(i, x)) then a(i) ← x mini ← lb(i, x); End If End If End For End If If  (i) ≠ ( ) UpdateHistogram(pi, ALH, EH, a(i)); End If End For For Each centroid path μj do Compute =  min Edge Based Distance( , μ) and update μx ;

End For t ← t + 1; While (t = 0 or μ changed) Return {μ1, . . . , μk}

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The trajectory path-k clustering procedure can be described by Algorithm 2. To adjust the centroid direction in iteration and to determine the objective function system manages two histograms of trajectory for each cluster. (a) Edge histogram: The edge histogram (EHj ) for given trajectories in cluster Cj has the graph edges frequency information in sorted order. EHj (e) stands for the edge e frequency, i.e., EHj (e) =|e|, and EHj [l] stands for the l-th most considerable frequency. In any iteration system, no need to reconstruct the histograms; instead, it holds one histogram progressively for every cluster and refreshes it only as a trajectory passes through in or goes out of this cluster. Many trajectories would continue in the same cluster for further iteration, although there would be few changes to the histogram. (b) Accumulated length histograms: The critical point is the size in a meter of the trajectories for each entry. This histogram measures the number of trajectories that have this defined size. ALH is ordered by key in ascending order; ALHx [l] gives the trajectories count in cluster Cx that have a size l.

3.5 Trajectory Release Trajectory release is the last step, which involves comparing each clustered “synthetic trajectories datum” to corresponding “real” trajectory and merging accordingly. The process also involves a prejudging mechanism to ensure at least one actual trajectory record can be seen in processed trajectory. So when the count of records is zero, it means that the produced trajectory data is a null trajectory and is considered to be irregular. The probability of issuing a null trajectory is further minimized due to the inclusion of the decision process of an irregular course, the reliability of the orbiting assignment is increased, and better data availability has been assured.

4 Performance Analysis of Privacy Protection Scheme To check the feasibility of our proposed approach and the data availability, we performed specific tests based on TDrive pre-project data from Microsoft research [38], which includes the trajectory details of 10,357 taxis for a week duration. The cumulative points count is about 15 million, for a cumulative trajectory size of nine million kilometers. The evaluation was conducted on Octa-core 3.2 GHz, RAM of 64 GB, Windows 8 operating system, and Intel i7 processor. The processing time overhead of the query and service schedule is assumed to be negligible in the proposed model. Location-based services have drawn millions of users and their digital footprints are massively contained. The query process and interval process are the two modules executed for the simulation of the proposed model.

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A location-dependent k-nearest neighbor query (e.g., nearest hospital profile info) is continuously generated by the query process with the exponential distributed query interval. Driven by the assessment process surrounding anonymity, modeling, and uncertainty [36], we are examining the relationship between the efficacy and usefulness of data security. Past policies are based on user-clustered or usercentralized architectures. Hence, the workload of the network is high, and the anonymizer could lead the bottleneck performance. Different from existing work, our suggested methodology integrates the fog server, which processes the data in an IoT gateway or fog node, as it is nearer to the consumer and can be partly managed by the user [37]. For the safety of trajectories, we assume in our system the timedependent mobility trend, probability of query, and spatiotemporal connection. It produces k − 1 dummy positions and trajectories with full entropy, which can render offline and online original trajectory security. Here, we have undertaken two measures, namely, mutual information and Hausdorff distance, to establish this relationship and evaluate the proposed policy. We have a belief that consideration of these measures may assist in choosing and implementing acceptable methods of privacy security for particular situations on the pathway. The two measures, i.e., mutual information and Hausdorff distance, can be defined as given below. Mutual information: Mutual information (MI) is a measure of privacy protection intensity of a given privacy protection scheme. It is directly proportional to the differential privacy parameter (ε) and inversely proportional to privacy protection intensity. The differential privacy budget is represented by ε, which is also known as the differential privacy parameter. Hausdorff distance: Hausdorff distance is a method for calculating the difference in a metric space between two sets of points and has been commonly used to calculate the spatial dissimilarity of two trajectories. We measure the Hausdorff distance from each pair of initial trajectories to the synthetic ones. A higher value of Hausdorff distance between trajectories pair represents high dissimilarity of two trajectories, and so it has a reduced set of POI than original trajectory POIs. Therefore, the higher Hausdorff distance value shows a lower utility of given trajectory data for LBS. From the comparative analysis of past policies such as TSTDA [38], NGTMA [39], and SDD [40] with the proposed state-of-the-art proposed scheme deep neural network-based differential privacy protection policy, it is proven that DSPPTD outperforms the other policy with the highest privacy protection intensity in terms of mutual information (MI) and trajectory data utility in terms of Hausdorff distance (HD) has been computed for all models, which have been depicted in Figs. 4 and 5. The DSPPTD does have the lowest MI level, which shows that RNN-DP has a higher level of privacy security relative to NGTMA, TSTDA, and SDD methods. In this study, we discover that the level of privacy security is directly linked to ε as depicted in Fig. 4. Because DSPPTD uses the Gaussian method in the data collection step in addition to the exponential method in the data release phase; therefore, the dual differential privacy security protocols provide better privacy protection. As depicted in Fig. 5, DSPPTD has the smallest HD of the four systems, so the data set for publishing is identical to the initial data collection. DSPPTD has

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increased the practicability of results. The fundamental explanation for this is the prediction system convergence. The trajectory data redundancy is carried out during the processing of the data. When DSPPTD discovers the data to be incorrect, it removes this data to boost the reliability of the reported trajectory data. The reliability of these data leads to the higher utility of the LBS. As shown in Fig. 6, DSPPTD seems to have the lowest execution time of algorithms within a separate budget for privacy. The algorithm’s execution time comprises of time for generating noise and time for processing trajectories. The execution time of the proposed policy is correlated with the algorithm’s time for

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processing trajectories while the noise generation time is the same for all algorithms. The distinction lies in the computation time of trajectories. DSPPTD has the benefit of utilizing the projected model trajectory data collection for the study and is not the time series data conventional processing. So, it has better execution time efficiency. We concluded, therefore, that DSPPTD ensures computing security and availability of data, along with the high efficiency of the device in terms of the running time.

5 Conclusion In this work, we introduced Dynamic Scheme for Privacy Protection of Trajectory Data (DSPPTD). DSPPTD involve Gaussian framework and double differential privacy requirement focused on deep learning to provide private security and edge computing based on enhanced utility services. For consumer services, a mechanism of dual deep learning-based differential privacy model has been suggested. Via empirical study, we have shown that DSPPTD has more effective privacy security strength, better data efficiency, and overall reliability than state-of-the-art systems currently existing. Our future research will concentrate on improving the trajectory resemblance loss metric model, expanding our system to global trajectory data sets, creating personalized simulated trajectory data for variable lengths, investigating possible attacks on privacy and security techniques, and assessing the efficacy and usefulness of our system in other trajectory data mining and analytics schemes. Competent Interest Declaration On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Part II

Data-Driven Decision-Making Systems

n-Layer Platform for Hi-Tech World R. B. Patel, Lalit Awasthi, M. C. Govil, and Rachita

1 Introduction The online information management systems are in demand of today’s world for easing the life of human beings. The growth for demand of services becomes populous because of increasing use of the Internet. If every citizen of a national is fond of to use technology to fulfill day-to-day needs, then the traffic/network bandwidth will be the crucial for a nation. Democratic increasing participation, accountability, transparency, quality of service, and on time availability of services are challenges for a country [14]. An e-governance system, which caters such type of functions, are partially available in few developed countries [1, 2, 3, viz., Europe, the United States, Australia, and Singapore, etc.]. A complete e-governance system is demand of present generation as on date it is not fully functional in any country around the world. An intelligent e-governance system may be adapted/implemented worldwide by next decade [7, 8]. At present, it will be early to say that e-governance system is available across the world. It gives knowledge to the citizen about the day-

Note: We have used model, platform, and framework interchangeably. R. B. Patel () Chandigarh College of Engineering and Technology, Chandigarh, Punjab, India L. Awasthi National Institute of Technology, Hamirpur, Himachal Pradesh, India e-mail: [email protected] M. C. Govil National Institute of Technology, Ravangla, Sikkim, India Rachita TD Canada Trust, St. Catharine, ON, Canada © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_5

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to-day development of a country. But due to limitation of client/server technology, this system suffers in terms of network bandwidth when number of client increases. The next decade world will be challenging for system managers to fulfill the desire of human beings, result force to create hi-tech world. This development in the mind of people demand a system, which should fulfill requirement of the people, and also it should adapt the new changes/developments. Thus, it is required to model such a system in which every country should be a member of it and work over a common platform in global public interest [9, 10]. A work agent (WA) is a mobile software program, i.e., mobile agent (MA), which moves from nodes to nodes over the global network. It searches for the required resources to accomplish the assigned task to it. When it finds resources, it finishes the assigned task and returns the outcome of the task to its owner and issues death warrant to itself and dies. Death warrant is important to avoid the misuse of code and associated data with a WA. This article presents a novel n-layer platform for hi-tech world. In this platform, agent technology is used. A new and unique naming scheme is used to identify citizen of a country and unique name to the work agent(s) created by its owner. The initial part of this naming scheme provides unique identification to citizen of a country, which is the owner of the agent(s). It provides a unique name for an agent across the global network for everyone using mobile agent technology (MAT). In the proposed platform, value of n may vary from 2 to 10. We have considered India as a case study, and in this platform, n is considered as 7 plus 1. Here, 7 is used to uniquely define identification for every citizen and eighth level to give identification to work agent(s) created by a citizen. This platform is named as neighbor assister framework for mobile agents (NAFMA). Here, mobile agent is interchangeably used for physical mobile agent(s). It may be a citizen of a country. This scheme uses an eight-component-based naming scheme for MAs. In this naming scheme, seven components are arranged in logical hierarchical order. The seven components of the name are contributed by seven layers of the NAFMA and eighth component is contributed by the agent owner itself. Every layer of the NAFMA is integrated with platform for mobile agent distribution and execution (PMADE) [2, 12]. In NAFMA, each layer is integrated with layer intelligent agents (LIAs). This system uses hexadecimal digital identification code for every citizen of a country. The length of code is 17-digit for citizen and 18th digit for defining a WA. NAFMA supports two types of services, internal and external. These services are being useful for the day-to-day work of the persons belonging to a country. External services serve everyone around the world. These are available at country layer of the said system. Thus, system tells one world, one umbrella. There are several other types of services, which are required to be available among the people of a country and are managed by provincial layers, which are known as internal services. Implementation of this system will bring the whole world under one umbrella and make hi-tech world smart and green. This system will also remove the hurdles of carrying documents while traveling across the world. Only using the NAFMA card and finger/face print details can be fetched from the system, which in result will present identification of an individual along with face value.

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This system assigns unique 17-digit identification (ID) code to every citizen and 1-digit ID to their work agent(s), which means that a citizen will be permitted to use 16 work agents concurrently. This system uses the peer-to-peer concept to generate location-dependent identification code for the citizens [2, 4]. Citizens are permitted to share their views and ideas with other citizen and authorities as well as with the government with the help of work agents across the globe. This system will support all kind of applications related to human beings. In the development of this system, we have used a model, platform, and framework interchangeably. Rest of the article is organized as follows: Section 1 discusses the Introduction, and Sect. 2 presents information management issues and challenges. Discussion about Indian administrative system is given in Sect. 3. System model is explored in Sect. 4, and system architecture is given in Sect. 5. Unique costumer identification code is presented in Sect. 6. Implementation and performance study is given in Sect. 7. Discussion about findings is explored in Sect. 8, and finally, this chapter is concluded in Sect. 9.

2 Information Management Issues Easily availability of Internet connectivity fuelled the growth of electronic information. This happened due to the advancement of electronic system. The growth of advancement in electronic system promoted production of economical electronic gadgets and their usages. Because of these, cheap/economical use of Internet services grows drastically around the world. These usages of the Internet services are considerably worsening information management challenges. These information management challenges are prodigious issues for the organizations and governments because of their dependencies on existing rules and resources. Organizations and governments are suffering because of no clear direction for the use of technologies and their integration with disparate information management systems available with them. Policies of organizations and governments are also suffering from internal politics and non-clarity around broader organizational strategies and directions. Thus, information management system suffers from poor quality of information, which leads to inconsistency, duplicate, and stale information. Further, most important factor in changing working practices and processes of staff of the organizations is that it does not want to go for upgradation as per need of demand of time. To handle such issues, researchers proposed several models. Authors [5] explored the opportunities and challenges for the organizations, which were networked. They presented a flexible and efficient information architecture for establishing new values, attitudes, and behaviors to share information and build databases. This system provides integrated customer support on a worldwide basis and protects personal freedoms and privacy. Electronic brainstorming is seemingly suitable and prevalent platform in the twenty-first century. It makes daily public life easy but leaves the issues behind its management and security [6].

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The Unique Identification Authority of India (UIDAI) is mandated to issue an easily verifiable 12-digit random number as unique identification for its residents. The UIDAI issued 12-digit unique identification (UID) number (termed “Aadhaar”). In Aadhar card number, twelfth digits are used for checksum [13]. There are several limitations with this UID. It does not guide straight way about the location/place of actual birth of a person who is using it. This card does not provide day-to-day wealth condition of individuals. One card with all kind of tasks (banking, income tax, vehicle registration, driving license, loan account management, payment, etc.) is not possible with UID. Aadhar card also does not warrant food guaranty to everyone every day. It does not keep records of unemployed citizens of India. Besides, there several other issues which are not addressing real life of human beings of most developing and under developing countries. Poverty is a major issue amongst underdeveloped/developing countries, where the system is not able to reach in time to the common people, resulting in growth of younger generation being hampered due to lack of basic necessities. Thus, there is a need of a common e-governance system, which should address the issues of common people with reduced management cost of overall system of a country [10, 11].

3 Indian Administrative System When the population and area of a system becomes very large, the cost and processing involved in directed communication are prohibitive. A popular alternative to direct communication that eliminates these difficulties is to organize the population and area into a federated system. Citizens of a country do not directly communicate with the higher authority, but locally, they can communicate. A set of people/area has a facilitator, who kept informed about their individual needs and abilities. Citizens/individuals can also send and receive applicationlevel information and requests to these facilitators. Facilitators use the information provided by citizens/individuals to transform these application-level messages and route them to appropriate authorities. A federated system consisting of a group of organizations, countries, regions, etc. have joined together to form a larger organization or government. India is a federated republic, with a civil law system. It consists of 29 states and eight union territories. There are 638 districts in states, 11 districts in Delhi, and 26 districts in union territories. Further, these districts are organized in Tehsil/block/Taluka, which are about 5479 in the states +269 in Delhi and union territories. India at route level divided into villages and wards. There are approximate 638,365 villages and wards across the country. The system proposed in Fig. 1 faces several issues, viz., observation about common people is not possible in time, observation of the higher authority by lower precedence (common) people is not possible, what schemes are for the individual’s welfare never reach in time to everyone, higher authority always being dependent on their subordinates for getting the status of the common people, current election

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system of India utilizes 20–30% of the budget of a complete year, and how the common people earning will be saved and utilized for the society.

4 System Model The proposed hi-tech world model is n-Layer Platform, which uses agent technology in background. In this model, a new and unique naming/identification scheme is used to identify citizen of a country and to provide unique name to the citizen’s work agent(s). This naming scheme is composed of two parts. The first part is composed of seven layers of this naming scheme, which provides unique identification to citizen of a country who are entitled to create their work agent(s). The eighth layer shows how many work agents a citizen allowed to create. The proposed system promises a unique identification to every citizen of a country and name for every work agent within the global network for everyone using agent technology. In the proposed platform, value of n may vary from 2 to 10. We have considered India as a case study, and in this, platform n is considered as 7 plus 1. Here, seven is used to uniquely define identification for every citizen and eighth level to give identification to work agent(s) created by a citizen. The seven-coordinating layers are arranged logically in a hierarchical fashion. Seven components of the name/identification are contributed by seven layers of

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the said system, and eighth components is contributed by the agent owner/citizen itself. This new agent naming/identification scheme will give base to develop hi-tech world as shown in Fig. 2. The coordination among the layers is an important factor for smooth functioning of the system. Further, NAFMAs of different countries’ coordination will be a major factor for the completion of the task of a work agent and of a citizen who is a resident of a country. The proposed system (NAFMAS) accepts the information through registration process. But it also opens channel to accept other format databases. Anyone who wants to become member of this system may register by giving his/her details with valid credentials and documents. Database created through registration process or through integration of databases of other system is partially shared by this system among its layers [2, 7]. It also accepts the Aadhar card database of India for gathering the information about its citizens. This system converts 12-digit decimal numbers into a 17-digit (Hex digit) unique identification for a citizen. A person who registers with this system has the right to decide one-nibble identification code range to his/her work agent. In general purpose, a work agent may be a vehicle, a house, an income tax identification, a passport, a field of land, etc. A registered member with NAFMAS system allowed for both types of services to fulfill dayto-day work. The deployment of NAFMAS e-governance system will allow/permit the services, namely the Internet, E-taxation, E-health schemes, E-education, social

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service, and E-conversation with the persons of other countries. It will also facilitate events, which are based on resource, time, and money constraints, viz., E-voting, Edemocracy, and E- suggestions [14]. NAFMA database is useful for all kind of systems who are focused to work through e-governance. One such example is international law, which is required to be formed for using the database of the said system. By implementation of international law, international police may use this database for identifying the persons/systems, who/which are doing illegal work(s). When an organization/government wants to allocate project(s) to any person/organization, it is not required to collect the information for the same person. Simply by using the identification code of a person/organization, all details can be collected/verified before the allotment of project(s). Further, to mention that, this system may work like a ready-made database at every layer. A person is not required to keep identity proof; only NAFMAS card will be sufficient because every kind of identification marks, viz. snap, finger print, and retina of every person, are collected by the said system just once. This NAFMAS system keeps track of all kind of changes a person may possible make to do the crime. If changes are made by a person same, is reflected at every layer of the system.

5 System Architecture We have developed a neighbor-assisted framework for mobile agent (NAFMA) based on e-governance system. It is a peer-to-peer n-layer architecture. These layers are logically hierarchical in nature. Scalability and communication efficiency is a major achievement of the proposed system. PMADE is background technology, and layer specific intelligent agents (LSIAs) are integrated at each layer. NAFMAS egovernance system uses one LSIA at each layer. Number of LSIAs depends on governance structure of a country that is going to be member of NAFMAS. If governance structure uses n-level federated system, then at list n-LSIA will be required to run the system smoothly. We have considered the province of India as a case study. Figure 3 shows NAFMAS model for province of India. The top layer contains country intelligent agent(s) (CIA) and maintains external linkage with the world. It manages information about a country for it is serving like external affairs. It keeps information about the culture, gender wise population, source of income from agriculture and industry, area and category-developing/developed country, etc. Besides above said information, CIA running at P-1-Server keeps several attributes. The state intelligent agent (SIA) keeps track of state information at the P2 Server, which is at layer 2. Similarly, P3 Server takes care of district intelligent agent (DIA), which lies at layer 3 and is district head quarter. It maintains information about the people of districts. Tehsil intelligent agent (TIA) keeps itself on P4 Server, which is in-charge of layer 4 and keeps records of public of a tehsil. Block intelligent agent (BIA) is being owner by P5 Server. It keeps records of citizens of a block

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at layer 5. P6 Server runs Panchayat/ward intelligent agent (PIA). It is the owner of layer 6 of the system. Bookkeeping about citizens of a Panchayat/ward is done by it. Layer 7 runs P7 Server for village/town. It uses smart and intelligent agent for keeping the records. Actual data maintained at this layer is being replicated across the different layers. Authenticity of records/data of citizens is important for a country. All above discussed agents decides about the unique identification number

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of a citizen. A citizen is permitted to allocate 1-nibble ID to its work agent, which is the eighth layer of NAFMAS. NAFMAS is an intelligent e-governance model, which maintains heterogeneous collection of databases. Accessibility of this database is only possible through NAFMAS members. This system maintains information specific agents (ISAs) for the help of users. An ISA handles user/client tasks with the help of system intelligent agents (SIAs.). Communication may takes place between ISA and SIA(s) for processing queries/exchanging information. System keeps track record of visiting work agent(s), ISA(s), SIA(s), and system resources access during the execution of assigned task(s). It is also required to make distinction between the task(s), information access agent (IAA), and other agent(s). An IAA is permitted to access the databases in read-only mode. This database may belong to government organization/department/private. Securities of the records are important at any stage, which is easily secured by NAFMAS system using the PMADE security features [15].

6 Unique Citizen Identification Code (UCIC) The NAFMAS e-governance system ensures unique costumer identification code (UCIC) for every citizen of a country. It uses down-streamed concept for generating identification code (IDs) of different layers. This process is done at the system boot up time. Higher level layers are responsible to provide IDs toward lower order/level layers. A layer at lower level in the hierarchy is responsible for prefixing the main part of identification code to its own local ID. A combined approach of all the layers in the system contributes for the generation of new ID of a layer. The country being studied in this article is India (as a case study). At primary level, it is the land of villages and secondary level towns. The lowest (layer 7) will be at Province Level 7. A 17-hex digits identification code (Id) is issued by the said system to every person of a country. A citizen itself is permitted to allocate 1-nibble ID to its WA(s). Figure 4 illustrates sample identification code. This identification code consists of 2-nibble country provincial code, 2-nibble state provincial code, 2-nibble District provincial code, 1-nibble to represent tehsil provincial code, 1-nibble for block provincial code, 2-nibble for Panchayat provincial code,1-nibble for village/town provincial code, and 5-nibble for representing the citizen identification number (ID). In code, the first field is priority code, which is sued to represent one for developed country, two for developing country, any other number as per need and will be decided by international body. Here, 0 is used for no priority. A nibble (4-bits of binary digits) is used to form a hexadecimal digit. So for simplicity data, size format nibble is used in the system. Sample format shown in Fig. 4 enables a total population of 1,048,576 in a village/town. Each citizen is allowed to launch simultaneous 16 work agents at 16 sites at a time. This system generates 252 million unique user identification codes. Initially, a citizen is required to register himself/herself through local provinces. It may be done through

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Fig. 4 Citizen ID generation fields using WA Province Level 7, Village/town(VPC); Province Level 6, Panchayat (PPC); Province Level 5, Block (BPC); Province Level 4, Tehsil (TPC); Province Level 3, Districts (DPC); Province Level 2, State (SPC); Province Level 1, Country (CPC)

a ward/village/town layer of the system. NAFMAS system appends citizen ID with the system ID for generating a unique identification code for a citizen. Figure 4 presents 2 (Priority-developing country), 5B (India), 0B (Delhi), 05 (West Delhi District), 2 (Punjabi Bagh, Tehsil), 8 (Nilothi block), 01 (Chander Vihar, Panchayat), 5 (Shani Bazar Town), 0001C (Dr. Munshi Yadav), and 4 (work agent) for Dr. Munshi Yadav’s agent. Dr. Yadav is a citizen of country India; Delhi State, West Delhi District; Punjabi Bagh, Tehsil; Nilothi block; Chander Vihar, Panchayat; and Shani Bazar Town. Here, 5B is a code of India, personal ID of Dr. Munshi Yadav is 0001C, and his work agent ID is 4. Other details are as discussed above. This identification code is hierarchical in nature. Code is generated through upstream toward the root of tree and every component of the code passes through the branch(s) of the tree. This process reflects availability of identification to each of its parental ancestor layer. A WA moving across the global network possesses a unique identification code and permitted to make conversation to any ancestor layer through its local village/town layer agents. In case of failure, corruption, and maliciousness citizen, a work agent is permitted to directly approach to its next higher ancestor in the branch. A citizen work agent is not allowed to route through the branch to which it does not belong, because the identification of that citizen work agent will be supported only by branch to which it belongs. In case of roaming of the citizen work agent, the system may also be enhanced to provide all privileges, viz., create work agent assign to the work agent.

7 Implementation and Performance Study Implementation of NAFMA-based e-governance system tested on the networks of 60 machines. These machines are divided into 40 networks. To implement all the state provinces, 38 networks are established, one for each state/union territory. Two state networks are completely implemented, and all layers are equipped with 1/2 machines. Each network has a gateway to work as a province server.

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Remaining four machines are working as country servers for different countries. Every machine is equipped with PMADE secure mobile agent platform, and on the top of it, NAFMAS system is executed. Each machine possesses configure as follows: Intel(R), Core(TM), i7–8700, CPU @ 3.20GHz, 3.19 GHz, 8.00 GB RAM, 64-bit operating system, x64-based processor, and Windows 10 Pro. The arrival rates of WAs on different sites/servers are function of poison distribution. Further, to mention that, registration process of 1 million citizens is done randomly. System is also updated with some UDAI database. More than 5000 WAs were launched from different users/clients at different traffic load (high/peak and medium and low) on the network. These WAs allowed for working on different location of the network. In the implementation of the said system, different performance metrics were used, viz., fault tolerant, security, network delay, and failure of different layers. This system kept continuously running for few weeks in different conditions. Performance measurement of NAFMA e-governance system depends on the various implementation factors. Case study implementation of said system inherently consists of eight layers. Country layer sits at first level and a citizen at eighth level. The network delay (ND) may occur in WA transportation. Movement of WA may be upward (from layer 8 to 1) or downward (from layer 1 to 8) in system. This movement takes time accordingly. Per record registration time is 10 minutes for entering a fresh record into the database. In the implementation of said system, it is assumed that minimum number of record per village/town is 500 citizens, and maximum is 10,000. In city wards, population is more and is assumed that is in 5000 minimum and maximum 50,000, respectively. Record processing time (RPT) depends on the network traffic. Figure 5 shows time required for registration of all the citizens of country (India). It shows that if system will run, fault free about 100 days will be required to complete the registration process. When random network failure occurs, maximum registration time increases, and it is about 119 days. RPT also increases in maximum time in processing of records, which is 876 ms. But without failure, it is 776 ms. In the implementation of the system, 500 minimum and 800,000 maximum numbers of queries were generated to study the processing time of the system. Figures 6 and 7 show the query processing time (QPT) with and without network failure. It is observed that network failure affects system performance but its effect is very less.

8 Discussion NAFMAS-based e-governance system warrants food guaranty for every poor in the locality of every province. This system will realize duty of every public-elected official. The said system is fully distributed. One-time initial implementation cost of system will be about Rs. Six hundred cores for India, like huge country. UDAI

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Fig. 5 Registration and record processing time

Fig. 6 Query processing time (QPT) without fault

system of the government of India does not warrant food guaranty for every poor in the locality of every province. Further, it also does not warrant weekly unemployment record like the United States. But NAFMAS governance system warrants all kind of citizen-oriented applications.

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Fig. 7 Query processing time (QPT) with fault

9 Conclusion and Future Work This chapter gives a look of n-layer NAFMAS system for promoting e-governance toward a hi-tech world. NAFMAS layers are working in hierarchical fashion. Both internal and external services are available to the citizen of a country. PMADE is playing key role in the deployment of the said system. Citizens are allowed to use 16 WAs simultaneously to accomplish their task across the world. A WA is a mobile and intelligent agent and is enough for the dissemination of work/accessing the information. NAFMAS integrates fault tolerant and reliability attributes from PMADE for the implementation of successful e-governance system. NAFMASbased governance system warrants most of the citizen-oriented applications, viz., daily food for the poor in the vicinity of a province. Weekly unemployment record of citizens maintains at provenance level. NAFMAS card warrants every humanrelated application identities to food guaranty, income tax to voting, etc. But UDAI only warrants identity to the citizens of India. In future, more rigorous properties of the said system will be tested.

References 1. West, D. M. (2004). E-government and the transformation of service delivery and citizen attitude. Public Administration Review, 64(1), 15–27. 2. Patel, R. B. (2004). Design and implementation of a secure Mobile agent platform for Distributed computing. PhD Thesis. Department of Electronics and Computer Engineering, IIT Roorkee.

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3. Bagchi, S., Srinivasan, B., Kalbarczyk, Z., & Iyer, R. K. (2000). Hierarchical error detection in a software implemented fault tolerance (SIFT) environment. IEEE Transactions on Knowledge and Data Engineering, 12(2). 4. Azar, Y., Kutten, S., and Patt Shamir, B. (2003). Distributed error confinement. In ACM PODC (pp. 33–42). 5. Jarvenpaa, S. L., & Ives, B. (2015). The global network organization of the future: Information management opportunities and challenges. Journal of Management Information Systems, 10(4). 6. Maaravi, Y., Heller, B., Shoham, Y., Mohar, S., & Deutsch, B. (2021). Ideation in the digital age: literature review and integrative model for electronic brainstorming. Review of Managerial Science, 15(6), 1431–1464. 7. Habermas, J. (1996). Citizenship and national identity between facts and norms, contributions to a discourse theory of law and democracy (pp 491–515). Translation by William Regh. MIT Press. 8. Tsuchiya, T., Sawano, H., Lihan, M., Yoshinaga, H., & Koyanagi, K. (2009). A distributed information retrieval manner based on the statistic information for ubiquitous services. Progress in Information, 63–77. 9. Tryfonopoulos, C. (2008). P2P information retrieval and filtering (pp. 607–611). Springer. 10. Jung, J. (2009). Consensus-based evaluation framework for distributed information retrieval system. Knowledge and Information System, 18(2), 199–211. 11. McLean, M., & Tawfik, J. (2003). The Role of information and communication technology in the Modernization of e-Government, pp. 237–245. 12. Patel, R. B., & Garg, K. (2004). A new paradigm for mobile agent computing. WSEAS Transaction on Computers, 1(3), 57–64. 13. K-13011/26/2012-DD-I, Gazette of India. Retrieved 7 July 2015. 14. Vaid, R., & Patel, R. B. (2009, October). A 7-layer model for modernizing the World: a step towards a Hi-Tech World. ARTCOM’09: Proceedings of the 2009 international conference on advances in recent technologies in communication and computing, pp 840–843. 15. Patel, R. B., & Garg, K. (2005). A Flexible Security Framework for Mobile Agent Systems. Control and Intelligent Systems, 33(3).

A Comparative Study of Machine Learning Techniques for Phishing Website Detection Mohammad Farhan Khan, Rohit Kumar Tiwari, Sushil Kumar Saroj, and Tripti Tripathi

1 Introduction The Internet has become one of the integral parts of our life in recent years due to the availability of various services in online mode like online banking, social media, entertainment, etc. These online services have caused an exponential increase in Internet users, which in turn has given an opportunity to cyber criminals for cyber fraud causing huge financial loss to users every year. Cyber criminals use various techniques to harm the users’ system or steal their sensitive information like username, password, email ID, and other credentials by deceiving the users as a trustworthy entity [1]. Phishing is one of the techniques of cybercrime where the attacker presents himself as a trustworthy entity to the users to collect sensitive information through email or websites. In a phishing website, the attacker creates a fake website by cloning the legitimate website and sends an email to target users to update their information. Once a user goes through the email and clicks on the URL of the website, it redirects the target users to a fake website where the users enter their credential to update the information. The attacker gets the credentials of the users and uses them for financial or any other type of fraud. Phishing website attack has become one of the main challenges in cyberspace. It is causing huge financial loss to users every year with the increase of Internet users. According to the RSA quarterly fraud report for the period of 1st of January to 31st of March 2018, phishing is responsible for 48 percent of all cyberattacks [2]. The report says that Canada, United States, India, and Brazil are the most victim countries of the phishing attack. Figure 1 shows the statistics of phishing attacks of various countries in the above period.

M. F. Khan · R. K. Tiwari () · S. K. Saroj · T. Tripathi Department of Computer Science & Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_6

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Fig. 1 Percentage of phishing attack in different countries during January to March 2018

Due to the increasing number of cyber frauds through phishing websites, it has become necessary to develop some techniques to prevent it. Many techniques have been proposed to detect phishing websites with the help of black and whitelisted databases like PhishTank. In this approach, whenever a user visits a website, its URL is checked in the database. If the URL is present in the blacklist label, then the website is phishing. However, these techniques are not sufficient to detect phishing websites as new phishing websites are created every minute and their addition to the database takes time. Therefore, there is a need for an intelligent phishing website detection system that can detect phishing websites automatically. In this paper, we have developed intelligent phishing detection systems based on machine learning techniques that use structural features of the website to detect whether a website is phishing or not. The features used for training the detector are based on the structure of the webpage and URL. We have used various machine learning algorithms to train a detector using a standard dataset consisting of phishy and non-phishy websites. We have also compared them in terms of various metrics to show the usefulness of the machine learning algorithm for phishing website detection. The remaining part of this paper is organized as follows: Section 2 discusses the categorization of phishing website detection techniques with a detailed review of them. Section 3 discusses the various structural features, which are being used to train the phishing website detector. Section 4 explains the working of the proposed method of machine learning-based phishing detector followed by result and discussion in Sect. 5. At last, the conclusion is presented in Sect. 6.

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2 Literature Review Phishing website detection is one of the major challenges in cybersecurity. There are various methods that exist in the literature to detect phishing websites. Figure 2 presents the taxonomy of phishing website detection systems. In user awarenessbased techniques, a user recognizes a phishing website from its experience or knowledge whereas software detection-based techniques use automated techniques to detect phishing websites. Vision and web page structure-based techniques use website design and structure to detect phishing. The vision-based technique detects a phishing website based on the visual comparison of a legitimate and illegitimate websites. They use interest point detector techniques of computer vision to locate a phishing website. The web page structure-based technique detects a phishing website using a structure of web page like referencing, HTML structure, URL, etc. Various authors have proposed phishing website detectors based on the above categorizations. Some of them have been discussed below. Rao et al. [3] have proposed an approach to detect phishing websites based on machine learning techniques. They trained eight machine learning algorithms to detect phishy websites out of which random forest-based technique was more accurate. Sönmez et al. [4] also proposed a machine learning-based phishing website detector. They initially extracted features of the website and used them to train the classifier. They used support vector machine, naive Bayes, and extreme learning machine (ELM) as machine learning techniques out of which ELM has the highest accuracy. Sharmin et al. [5] have proposed a supervised learning technique in which they discussed the problem of spam detection in social media platforms. They worked on the comment section of YouTube to filter out spam comments. They tried to solve the phishing problem by applying various methods. Ensemble classifier has the best response among them. Altaher et al. [6] have proposed a hybrid algorithm by combining KNN and SVM methods. They first applied the KNN method to remove noisy data followed by

Phishing Detection Technique

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the SVM method that is used to classify the phishy website. This hybrid approach has 90.04% accuracy. Karnik et al. [7] proposed the phishy website detector using the SVM algorithm to solve the phishing attack problem. They used features like webpage contents, DNS information, link structure, textual properties, and network traffic of webpage to train the detector. The proposed approach has 95% accuracy. Abunadi et al. [8] presented a review of features used in machine learning-based techniques to detect phishy websites. They also added some new features, which are helpful in phishing websites detection and experimentally show that new features are more helpful in phishy website detection. James et al. [9] proposed machine learning based to prevent phishing attacks. They used three more features like host properties, page importance properties, and lexical features to train the detector. Xiang et al. [10] proposed an extension of CANTINA called CANTINA+ to detect the phishy website. In CANTINA +, they added new features with previous features to get better accuracy. The proposed system filters website without entering the login form in the first step to reduce the false positive rate. The proposed approach utilizes 15 attributes like URL, HTML document object model, search engines, other services to train SVM to identify phishing websites. The true positive rate of CANTINA+ is 92%, and the falsepositive rate is 0.4%. Aburrous et al. [11] presented a method using data mining techniques to search and identify the Internet banking system to prevent phishing attacks in the banking system. They used 27 phishing characteristics divide into six categories like source code and JavaScript, protection and encryption, URL and domain identity, content and page style, the web address bar, and social human factors to train the detector. Wenyin et al. [12] proposed a method to detect phishy websites in two stages. The first stage detects the keywords and suspicious URLs on the local email server. After detection of the URLs or suspicious keywords in the email, the second module compares the layout, block-level, and style equality for the suspicious webpage to detect a phishy website.

3 Phishing Websites Features The phishing website follows some patterns related to web page structure and URL. There are many features to identify phishing websites. These patterns and features are used to categorize the websites as legitimate or illegitimate websites. Figure 3 shows the classification of phishing websites features.

3.1 Address Bar Features Address bar features correspond to features obtained from the URL or address of a website. There are various patterns in the address of a website that indicate the website is phishy or not. Figure 4 shows an example of the address of a website

A Comparative Study of Machine Learning Techniques for Phishing Website Detection Fig. 3 Phishing websites features

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Fig. 4 URL and its components

with its various parts. A typical URL consists of a domain, a protocol, and a file path. Some important address bar features used for detecting phishing websites are discussed below. • IP address: An IP address uniquely identifies a computer on the Internet. The websites are hosted on the server, and they are accessed through URL. Sometimes, the URL consists of an IP address that normally does not exist. So, if the website consists of a URL, then users perceive that it is phishy. The rule used to identify a phishy website is given in Eq. 1:  Rule : If

IP address exits in URl → Phishy Otherwise → Feature = Legitimate

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• URL Length: URL length also indicates whether a website is phishing or not. The rule for classifying a website as phishing, legitimate, or suspicious is given in Eq. 2: ⎧ ⎨

URL length < 54 → Legitimate Rule : If URl length ≥ 54 and ≤ 75 → Suspicious ⎩ Otherwise → Phishy

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• URL with @ symbol: If a URL consists of an @ symbol, it is categorized as a phishing URL otherwise legitimate. The rule is shown in Eq. 3:  Rule : If

URl has@Symbol → Phishy Otherwise → Legitimate

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• Prefix or suffix: To make users perceive that they are working with a legitimate website, phishers use suffixes or prefixes separated by “−“in the area name. Therefore, the users think that they are working with a valid webpage with a domain name. The rule used to categorize a website based on suffix and prefix is given in Eq. 4:  Rule : If

Domain has’ − ’ symbol → Phishy Otherwise → Legitimate

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• Subdomain and multisubdomains: A website is classified as legitimate, suspicious, or phishy based on the number of subdomains in its URL. The rule used to classify it is shown in Eq. 5: ⎧ ⎨ Dots in the domain part < 3 → Legitimate Rule : If Else if dots in domain part = 3 → Supicious ⎩ Otherwise → Phishy

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3.2 Abnormal Features Abnormal features are the information or features obtained from a website. We have examined different abnormal features. Some important abnormal features used for identifying phishing websites are presented below. • Request URL: Websites use different application programming interfaces (API) to access some resources. The API is identified by URL and request data. The website accessing API with the same domain name is legitimate. The ratio of request URL with same domain name to another domain name is used to identify if a website is phishy or not. If the web page has a ratio of request URL less than 22%, then it is legitimate. If request URLs are between 22% and 61%, then it is suspicious; otherwise, the website is a phishing website. The rule to detect phishing websites is given in Eq. 6:

Rule : If

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Request URL% < 22% → Legitimate Request URL% ≥ 22%and < 61% → Suspicious ⎩ Otherwise → Phishy

(6)

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• Anchor tag URL: A website is classified as phishy based on the URL of the anchor tag. An anchor is defined by HTML element tag. A website can be classified as phishy based on the percentage of URLs pointing to another domain. The rule used to classify a website as phishy is given in Eq. 7:

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Anchor URL% < 31% → Legitimate Anchor URL% ≥ 31%and ≤ 67% → Suspicious ⎩ Otherwise → Phishy

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3.3 HTML and JavaScript Features These features are extracted from the HTML and JavaScript files of the websites. The various features that can be extracted from HTML and JavaScript files of a website to classify a website phishy or non-phishy are discussed below. • Redirect page: Page redirection is a situation where when we click on a URL to reach page x but it redirects to page y. The rule used to classify a website as phishy is given in Eq. 8:

Rule : If

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page redirect ≤ 1 → Legitimate page redirect > 1 and < 4 → Suspicious ⎩ Otherwise → Phishy

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• Hide link: JavaScript supports various functions to make a website responsive based on user mouse click. One of the functions provided by it is onMouseOver(), which hides the text when the mouse hovers over it. Phishers use this feature to hide phishy links. The rule used to categorize a website phishy is given in Eq. 9: ⎧ ⎨ status bar change on nmouseover → Phishy Rule : If no status bar change → Supicious ⎩ Otherwise → Legitimate

(9)

• Right click disable: JavaScript is used by phishers to block the right clicks on a webpage, which makes users unable to view source code and helps them to do cyber fraud. The rule used to classify a website as phishing or legitimate based on this feature is given in Eq. 10: ⎧ ⎨ disabled right click → Phishy Rule : If alert on right click → Suspicious ⎩ Otherwise → Legitimate

(10)

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3.4 Domain Features Domain features are the information or features extracted from the domain of a website. We have discussed various domain-based features following based on which we can categorize a website as phishy or legitimate • Domain age: The domain age of a website is obtained from the WHOI database. The domain age information helps us to identify a phishing website. If a website is very old, it indicates that it is valid and not created for phishing purpose. The rule used to classify a website phishy on domain age is given in Eq. 11:  Rule : If

domain age ≥ 6 months → Legitimate otherwise → Phishy

(11)

• DNS record: A website is categorized as phishing or not based on its domain name system (DNS) record. The rule used to classify it is given in Eq. 12:  Rule : If

no DNS record → Phishing Otherwise → Legitimate

(12)

4 Proposed Method The proposed method used to detect a website is phishy or non-phishy is shown in Fig. 5. It aggregates the different website features and provides them as input to a trained machine learning classifier to classify it as phishy or non-phishy. The classifier is trained on a standard dataset. We have used six machine learning algorithms such as k-nearest neighbor (KNN), logistic model tree, support vector machine, naive Bayes, multilayer perceptron, and decision tree machine learning algorithms to classify a website as legitimate or non-legitimate. The details of the machine learning algorithm are discussed below. • K-nearest neighbor’s algorithm: KNN is a simple and most commonly used algorithm. It is a type of supervised learning method that classifies a new website based on similarity measures. It uses distance measures to find the distance of the new website from the phishy and non-phishy websites available in the dataset, and based on a similarity measure, it predicts whether the website is phishy or not. The distance measure generally used in KNN is Euclidean; however, hamming distance is also used in some cases. • Logistic model tree: The logistic model is a supervised learning classification model built by combining logistic regression and decision tree. It uses the concept of both decision tree and logistic regression tree. The decision tree classifies the problem as a tree where logistic regression generates the result as a discrete

A Comparative Study of Machine Learning Techniques for Phishing Website Detection Fig. 5 Flowchart of proposed method

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value such as yes or no, 0 or 1, true or false, or high or low. So, we can say that the logistic model tree works on combining two methods into a model tree and generates a tree with nodes containing a logistic regression function. • Support vector machine: Support vector machine (SVM) is one of the most effective machine learning classifier that is used in various fields such as face recognition, cancer classification, and many more. It is a supervised classification method that separates data using a hyperplane where a hyperplane acts like a decision boundary between the various classes. It is a representation of training examples as points in space such that the points of different categories are separated by a gap as wide as possible. It can also perform nonlinear classification and work well with large datasets. • Naive Bayes: Naïve Bayes is a simple probabilistic classifier based on the Bays theorem with an assumption of independence among training cases. It assumes that the quantity of interest is governed by probability distributions and the optimal decision can be made by reasoning about these probabilities together with observed data. Bayes theorem provides a way to calculate the probability of a hypothesis based on its prior probability of a hypothesis. • Multilayer layer perceptron: A multilayer perceptron is a perceptron with multiple layers. It is a type of feed-forward artificial neural network. It has an input layer, output layer, and hidden layer with perceptron. The perceptron consists of weights, the summation processor, and an activation function. A perceptron takes a weighted sum of inputs and outputs a single value. From the input layer, input signals are taken, and all the computations are performed at the hidden layers, and the final output is reflected on the output layer. If the predicted output is the same as the desired output, then the performance is considered satisfactory, and no changes to the weight are made. However, if the output does not match the desired output, then the weights are changed to reduce the error.

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• Decision tree: A decision tree is a graphical representation of all the possible solutions to a decision based on certain conditions. It is a decision support tool that arranges datasets in the form of a tree-like structure. It is also called the tree-like model, in which each internal node represents the test attribute and all the branches represent the outcome of the test. It built the decision tree based on the training examples using computing entropy and information gain of samples. Once the decision tree is constructed, a new sample is classified into a category based on the decision rule of each node of the decision tree.

5 Results and Discussions We compare the different machine learning algorithms for phishing website detection using a publicly available dataset available at UCI Machine Learning Repository collected by organizations Phish Tank, MillerSmiles, and Google [13]. The dataset consists of a total of 11,055 entries of phishy and non-phishy website features; out of which 4898 are non-phishy, and the rest are phishy websites. There are total 30 features of each website, which is used for classification purpose. Some of them are IP address, URL length, right click, etc., which are discussed in Sect. 3. We have used Windows 8 operating system and Weka tool to train and compare the accuracy of machine learning algorithms like multilayer perceptron, support vector machine, decision tree, logic model tree, random forest, and k-nearest neighbor machine learning algorithm for phishing website detection. We used tenfold cross-validation during training to remove the biases. The confusion matrix obtained during the training and validation phase for different machine learning algorithms is shown in Fig. 6. It can be observed from Fig. 6 that random forest has the highest value of true positive while k-nearest neighbor has the highest value of false negative. We further compared the accuracy of different machine learning algorithms used for phishing website detection in the proposed approach. The accuracy of different algorithms is shown in Fig. 7. It can be observed from it that the random forest is efficient in terms of accuracy to detect phishing websites. The accuracy of random forest is 97.20%. K-nearest neighbor has an accuracy of 97.2% while logistic model tree and multilayer perceptron both have an accuracy of 96.9%. The decision tree has an accuracy of 95.9% while the support vector machine has the least accuracy of 94%.

6 Conclusions Phishing is one of the important challenges for today’s era in cybersecurity. The cases of phishing are growing exponentially and causing many cyber frauds, which result in the loss of money of business organizations or individuals. In this paper,

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we have proposed a machine learning-based approach to detect phishing websites. We used various website features to train a classifier to detect a phishy website. We used six machine learning like the random forest, multilayer perceptron, naïve Bayes, support vector machine, decision tree, and logistic model tree algorithms to train the classifier. It was found that the random forest is the most efficient algorithm to detect phishing websites while other methods detect phishing websites with less accuracy. A comparison of machine learning methods to detect a phishy website in terms of accuracy is given at last.

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Accuracy 98 97 96 95 94 93 92

Machine Learning Algorithms Fig. 7 Accuracy of various machine learning algorithms for phishing detection

References 1. Singh, P., Maravi, Y. P., Sharma, S. (2015, February). Phishing websites detection through supervised learning networks. In 2015 international conference on computing and communications technologies (ICCCT) (pp. 61–65). IEEE. 2. HeidiBleau. RSA quarterly fraud report: Q4 2018. URL: https://www.rsa.com/en-us/offers/rsafraud-report-q4-2018. Accessed 15 Sept 2021. 3. Rao, R. S., & Pais, A. R. (2019). Detection of phishing websites using an efficient feature-based machine learning framework. Neural Computing and Applications, 31(8), 3851–3873. 4. Sönmez, Y., Tuncer, T., Gökal, H., & Avcı, E. (2018, March). Phishing web sites features classification based on extreme learning machine. In 2018 6th international symposium on digital forensic and security (ISDFS) (pp. 1–5). IEEE. 5. Sharmin, S., & Zaman, Z. (2017, December). Spam detection in social media employing machine learning tool for text mining. In 2017 13th international conference on signal-image Technology & Internet-Based Systems (SITIS) (pp. 137–142). IEEE. 6. Altaher, A. (2017). Phishing websites classification using hybrid svm and knn approach. International Journal of Advanced Computer Science and Applications, 8(6), 90–95. 7. Karnik, R., & Bhandari, G. M. (2016). Support vector machine-based malware and phishing website detection. International Journal of Computer Applications in Technology, 3(5), 295– 300. 8. Abunadi, A., Akanbi, O., & Zainal, A. (2013, December). Feature extraction process: A phishing detection approach. In 2013 13th international conference on Intellient systems design and applications (pp. 331-335). IEEE. 9. James, J., Sandhya, L., & Thomas, C. (2013, December). Detection of phishing URLs using machine learning techniques. In 2013 international conference on control communication and computing (ICCC) (pp. 304–309). IEEE. 10. Xiang, Y. A. N. G., Li, Y. A. N., Bo, Y. A. N. G., & LI, Y. F. (2017). Phishing website detection using C4. 5 decision tree. DEStech Transactions on Computer Science and Engineering. 11. Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2010). Intelligent phishing detection system for e-banking using fuzzy data mining. Expert Systems with Applications, 37(12), 7913–7921.

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12. Fu, A. Y., Wenyin, L., & Deng, X. (2006). Detecting phishing web pages with visual similarity assessment based on earth mover’s distance (EMD). IEEE Transactions on Dependable and Secure Computing, 3(4), 301–311. 13. Mohammad, R., Thabtah, F., & McCluskey, T. L. (2015). Phishing websites dataset.

Source Camera Identification Using Hybrid Feature Set and Machine Learning Classifiers Ankit Kumar Jaiswal and Rajeev Srivastava

1 Introduction In today’s era, with the growth of the digital world in communication technologies, where one can freely take images and videos without the consent of the third party without giving access to its location and time, felonies have become a big concern for our society. Digital images are used in different applications in areas such as entertainment, social networking, and security systems. With the development of new image editing tools, these images can be manipulated and forged, which can cause harm to public credence and can also question the result of the forensic because of the manipulated evidence. Source camera identification (SCI) is used to identify the source camera of the images/photos as shown in Fig. 1. SCI has a wide range of applications in the department of forensic and judicial systems. In felonies like tampering of images [1–5], terrorist-act scenes, video voyeurism, and sharing with a third party without consent, or any distribution of illicit content, it helps forensic investigators to extract relevant information about the culprit by detecting information about the camera device, like brand and model of camera. Many techniques were introduced by researchers involving correlation-based and feature-based models [6–8]. Some techniques used manufacturing defects such as

A. K. Jaiswal () Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India e-mail: [email protected] R. Srivastava Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_7

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Fig. 1 Given image belongs to which camera model?

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lens distortion and noise filtering as their identification criteria. In 2006, Lukas et.al [9] uses SPN-based approaches and wavelet filtering approach implemented in [10] for taking an average of SPN from images of the same camera model in finding the average SPN for a particular camera model. Some advanced it [11] by implementing two-preprocessing steps using zero mean (ZM) and Wiener filter (WF), but it suffers from the drawback of image contaminations. After that several other papers worked on sensor pattern noise (SPN) such as SPN enhancement and SPN extraction, but due to the high rate of complexity, it is not an efficient approach to work on further. With the growth in machine learning and advancement in techniques, featurebased methods are way better than correlation-based methods. In this chapter [12–14], statistical features are trained on multi-class classifiers such as SVM, which results in better detection accuracy. Considering all these methods as motivation, this project comprises feature-based methods but with techniques to improve feature-vector extraction and to overcome the drawback of limited data size. To overcome the problems based on correlation-based techniques and previous feature-based techniques, this chapter introduces a machine learning-based SCI technique using a combination of frequency and spatial features, where data augmentation on the images is performed first and DWT and LBP features are extracted to overcome the limitations of existing techniques. Then, different classifiers such as SVM, KNN, and LDA are used to classify these features. Images are classified into five different camera models, namely, Sony, Samsung, Apple, Motorola, and Nexus. In the feature-based approach using machine learning, this work majorly contributes the following: • To increase the number of feature vectors available for training, data augmentation is being performed over the dataset by rotating, resizing, gray scaling, and brightness and contrast adjustments. This overcomes the limitation of the small size of the dataset. • Since spatial features are not sufficient in the case of different properties of images in the same class, discrete wavelet features and local binary pattern features are extracted from the augmented images. • This results in insufficient amount of feature vectors, which gives better results as compared with other correlation-feature-based techniques.

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This chapter comprises five sections: Section 1 gives the introduction to the problem, motivation behind work, and major contributions. Section 2 talks about theoretical background and literature review. The proposed method is discussed in Sect. 3. Section 4 demonstrates the experimental results on the publicly available dataset followed by its discussion. The presented work is concluded with future scope in the fifth section.

2 Literature Review Source camera identification methods proposed by various researchers in literature are divided into two categories:

2.1 Correlation-Based Method The hardware defects while manufacturing leaves ineradicable marks on camera models, which then can be helpful for the research to identify it uniquely. This makes the researchers use the sensor pattern noise (SPN) method [12–14]. The main component of SPN is photo-response nonuniformity (PRNU) noise. The wavelet filtering technique is used to extract the SPN from images, and camera reference was obtained by taking the average of SPN of multiple images of the same camera model. These methods are based on three approaches – SPN enhancement, SPN extraction, and correlation calculation. Researchers always try to increase the SPN to increase the identification accuracy, by the relation, the higher the SPN, the more the identification accuracy, but still, SPN’s accuracy and the huge complexity of matching techniques concern the researchers.

2.2 Feature-Based Method This method uses statistical features, which make this approach a classification problem. Various classifiers such as support vector machine (SVM) and ensemble are used. High-order wavelet statistics (HOWS) and sequential forward feature selection (SFFS) are different algorithm approaches used for feature extraction. These methods are mostly based on extracting first-order or higher-order statistical features by identifying overall variations in the imaging process of different camera models. These methods are discussed in the following Table 1 on different parameters. The various research articles are reviewed above to manifest different approaches and techniques to achieve the desired goal; all of them have their advantages and limitations. Let’s discuss it in detail. In the method [7], the authors used wavelet

This methodology is based on the hypothesis that the attenuation in signal is required for those having high n value as they are less trustworthy components and generally fluctuate. This is tested by giving greater weight to the smaller SPN elements and performing the correlation-based method.

[18]

[16]

Method Based on photo-response nonuniformity (PRNU) that provides features for classification with SVM and applying two-level DWT wavelet transform and PCA for reducing-edge effects and de-noise effects in PRNU noise pattern. It uses CNN for classification by using linear SVM as a classifier with feature-extraction and dimensionality reduction techniques.

Ref. [15]

Dresden image dataset [19]

Flickr dataset [17]

Dataset 130 random images per model and a total of four models

Accuracy: 80.8%

Accuracy: 93%

Result Accuracy: 89%

Table 1 Discussion of the state-of-the-art techniques of SCI on different parameters

To resolve the issue of complexity and contamination of noise in images in a correlation-based method, this method provides a faster approach for enhancing SPN.

It is prone to less error because of its simplistic assumptions. Can work on small-image patches with high accuracy.

Merits This method achieves better results as compared with a correlation-based method, which is time-inefficient.

Since the model needs to be trained from scratch, it increases its computational burden. For camera models having a smaller number of images, it shows poor accuracy. This method is often time-consuming and involves analysis on a larger dataset of images having a smooth surface for averaging.

Demerits This method performs poorly when the number of classes increases and also training on a very small size of the dataset.

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This methodology involves averaging sensor pattern noise by different camera models using de-noising filters and then estimating false alarm rates and false rejection rates.

Summarizes all the existing feature-based methods and gives a unified method to determine not only the camera model but also the brand as well as individual with high accuracy. Six steps: image preprocessing, residual calculation, feature extraction, reduction, and classification.

[9]

[20]

Dresden image dataset [19]

Three hundred twenty random images per model total nine camera models

Accuracy: 89.9%

Good values of correlation without JPEG or gamma correction

The proposed framework achieves higher accuracy as compared with other state-of-art methods due to the LBP feature extraction and bilinear classification model.

This chapter introduced the idea that sensor pattern noise can be used in identifying unique camera models.

(continued)

Since the identification process requires proper alignment, geometric operations, such as cropping, enlargement, rotation, digital zoom, result in desktop separation and protect the correct camera identification. In this case, the brute force search will need to use a powerful search. The combination of wavelet and contourlet transform shows slightly worse performance as compared with when we use only wavelet transform.

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[22]

[21]

This work focuses on small-scale training samples by introducing a concept of megatrend diffusion (MTD) and combined with reading together. This method generates visual samples with a uniform distribution of the distribution distance of the samples calculated from the practice distribution method. This method uses a multi-classifier instead of a binary classifier for testing images taken from different camera models. The main idea is to develop a CNN architecture that can extract characteristic features on its own.

Table 1 (continued)

Dresden image dataset [19]

Dresden image dataset [19]

Accuracy: 86.22%

Accuracy: 53.93% in five on samples and 83.28% with ensembles

JPEG compression and noise do not affect the image resolution.

Through ensemble learning, we increased the identification accuracy of the model, which was not achieved by using only MTDBOX and MTDRELATION.

The accuracy gets affected by the rescaling attack.

Not efficient on large training samples Ensemble models are difficult to interpret as compared with other models. Ensemble learning is expensive in both time and space.

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[15]

[23]

This work considered each noise pattern as its fingerprint to identify its source camera sensor where SPN is used to classify the images further characterized by wavelet-based feature vector noise images obtained by PRNU extraction and further 81 features were exacted using the extracting feature. The RBF kernel in SVM performed the classification. An algorithm by extracting color models and color channels and using them in image texture features. It can even differentiate between the images taken from the same model and source device.

Dresden image dataset [19]

A random sample of 100 images was used for training and 100 different for testing.

Accuracy: 93.2% and 87.2%

The road is high on both gaining accuracy and durability. Images remain unaffected even after rescaling, adding noise, or JPEG compression.

This method can be considered for a large number of different cameras and hence can be utilized in forensics and mining.

We cannot detect the accuracy based on resizing.

The decrease in the accuracy of experiment two indicates that the performances decrease when the number of classes is increased.

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transform on photo-response nonuniformity (PRNU) to provide the features, which were further classified by using support vector machine (SVM). It gives a satisfying accuracy when tested on level-1 decomposition but performs poorly when the number of levels increases. Authors in [8] first trained the algorithm through CNN and SVM and then used this trained algorithm to further classify the images; it was only performed on original JPEG images without any manipulation, but this model is not of any use when it comes to performing on manipulated images. Authors of [15] worked on the hypothesis that attenuation in signal is required for those having high SPN value as they are less trustworthy components and generally fluctuate. Method [9] includes finding false alarm values and false disposal rate using denoising filters but, due to geometric operations such as cropping, reuse, digital zoom, can cause desynchronization and predicting incorrect camera models to use a powerful search detection. The authors of [20] proposed a method that worked on a feature-based method. Image preprocessing and residual image calculations were done to extract specific artifacts. Then, image transformation was performed followed by a dimensionality reduction process to reduce its complexity. The concerning part is that its accuracy is lesser than hierarchical model accuracy. A method [24] talks about using high-order wavelet features and SFFS algorithm to improve the accuracy of multi-class SVM; this method didn’t discover any new idea and instead worked on the existing method. In work [21], a method to deal with small training samples was proposed; a combined mega-trend-diffusion method (MTD) with ensemble learning is used to generate visual sample images. It fails for large training samples, and ensemble learning is inadequate in both time and cost. In a research article [22], a multi-classifier is used to extract features automatically instead of handcrafting them. Rescaling attacks affect its accuracy. In [23], the authors used the same feature-based extraction method but with the RBF kernel SVM for classification. They experimented by taking a different number of classes and concluded that the accuracy decreases with the increases in the number of classes. In the method [24], color channels and color model methods are used to extract image texture features even if they are taken from the same camera source. Though it cannot analyze accuracy on various parameters such as resizing. After reviewing the literature, it can be concluded that there is a need to implement a model that can overcome the above limitations. We need to implement a model that can perform well even on unseen testing datasets and if there is a distribution gap between training and testing images and whose accuracy remains unaffected by various parameters, such as double JPEG compression, rescaling, and resizing.

3 Method and Model A source camera identification model is proposed using a combination of spatial and frequency-based features. The main concept behind it is that every camera model has its unique property, so when one clicks a picture then these properties

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such as color channels interpolation, focusing light rays through lenses, complex operations at acquisition time, an adjustment in brightness, and many others act as footprints to identify the specific camera model. Techniques based on sensor pattern noise (SPN) and lens radial distortion have been used to identify the source of the camera where one used manufacturing defects of the camera as its unique property to identify the particular camera model. Earlier, correlation-based methods [25, 26] were used by taking the average of multiple SPN taken by different camera models to identify them uniquely, but it involves a lot of complexity in the matching process, and the images severely contaminated by scenes make it difficult to reach a proper conclusion. In this chapter, a machine learning classification model is given based on spatial as well as frequency domain-based features. To overcome the limitation of a small size dataset, data augmentation is performed on images of each class. The local binary pattern (LBP) features and DWT features are extracted from the augmented image to make a feature vector. Then these feature vectors are trained in classifiers such as support vector machine (SVM), linear discriminant analysis (LDA), and KNN to detect a camera model. This methodology is divided into three steps – the first is image preprocessing, the second is feature extraction, and the third is classification.

3.1 Image Preprocessing The dataset used for evaluation purposes consists of diverse images varying from close-ups, indoor to nature, and outdoor. Since we don’t require classification on the nature of these images, instead we want to extract frequency component features of these images, which would be useful in classifying a unique camera model so we converted color images into gray scale (see Fig. 2). Furthermore, this conversion is necessary as color image processing requires more computational cost. The sample output and the conversion formula is as follows: Gray scale = 0.289∗ R + 0.587∗ G + 0.114∗ B To overcome the limitation of a small-size dataset, data augmentation is performed on these images. Augmentation techniques such as brightness enhancement, contrast adjustment, rotation, and resizing are done as shown in Fig. 3. Images are rescaled into 1500 × 1500 of high-resolution sizes with performing two types of brightness enhancement one by increasing in the intensity values by Fig. 2 Color conversion of image

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10 and then again by 20. Images are also rotated by 5 and 10%, and contrast adjustments are done through three ways – first by adjusting the intensity values to increase the contrast of output image, second by contrast-limited adaptive histogram equalization, and third by histogram equalization where it transforms the input image into an output image, which will have 64 bins and approximately flat.

3.2 Feature Extraction In source camera identification, features extracted from the wavelet feature domain can perform better than spatial features (such as image color, IQM, and CFA features). The reason being that wavelet transform algorithms reduce the edge effect and remove noise while preserving perceptually important features whereas spatial features often smoothens edges and affect image quality. Wavelet Features Discrete wavelet transform (DWT) is a digital filtering process used to process images into tiny wavelets into its four subfrequency bands low–low (LL), low–high (LH-vertical), high–high (HH-diagonal), and high–low (HL-horizontal) as shown in Fig. 4. LL is part of approximation coefficients (generated from low-pass filters), and the rest three are part of detailed coefficients (generated from high-pass filters). Local Binary Pattern Features The LBP feature output is used to achieve feature vectors that may be slightly involved in the conversion and conversion of gray matter. LBP features are extracted by comparing the pixels to their neighboring pixel cells. It is computationally simple and gives high accuracy. After image preprocessing, a local binary pattern (LBP) is performed to extract 59 features.

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Applied 2d DWT ll 4th level

Mean of each row

Median Absolute deviaon from 4x4 matrix of HH4

Fig. 4 Extraction of frequency domain-based features (discrete wavelet transform)

From augmented image, first spatial features (LBP features) are extracted to make a feature vector. From one image, 59 feature descriptors are extracted as LBP. Similarly, for frequency-domain features, four-level DWT features are extracted from the augmented image using Daubechies (db1) wavelet [6]. This four-level transformation is performed because we want enough frequency component features for classifiers. The steps are as follows: • From each level of wavelet decomposition, one approximation coefficient and three-detailed coefficients are produced. The second level decline is made from the first level coefficient, and the process continues until the fourth level of equity and detailed coefficients. • From this fourth level-detailed coefficient, the diagonal component is selected and divided into 4 × 4 size distinct blocks. • From every 4 × 4 distinct block median absolute deviation is calculated. • Further, feature reduction is done by calculating the mean for each row, which results in 23 feature vectors. This results in 82 feature vectors (59 from LBP and 23 from DWT).

3.3 Classification To perform classification among different camera models, multi-class classification models are used. These classifiers use extracted feature vectors to train the model. SVM (Support Vector Machine) It is a supervised classification model that has good generalization ability. It is one of the machine learning algorithms, which analyzes the training data and generates

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a classifier function. It takes feature vectors after feature extraction as input and produces tags that define one of the introduced ten camera models as output. It performs best for small observations. In this chapter, a medium Gaussian SVM and one-vs-one multiclass method are implemented. Linear Discriminant Analysis (LDA) LDA is mostly used for the supervised classification model. It is also used to project higher dimension features into lower. Here, the linear discriminant is used as a linear classifier that performs multi-class classification and performs dimensionality reduction. KNN (k-Nearest Neighbor) KNN is the easiest algorithm known so far. It relies on the fact that similar datasets are near each other, i.e., if two similar data points are close enough, then they are classified into the same class. In image classification, input is taken as N images, which are classified into K classes and the classifier is trained onto these image datasets. After that, a set of testing images is taken and compared with every single one of the training images and predicts the label of the closest training image.

4 Experiment and Result Analysis To evaluate the given model there is a need for an experimental result. The experiment of the proposed model is performed on MATLAB R2017b on a Windows 10 operating system having Intel Core i5 8th Gen processor and 8GB RAM. For the evaluation purpose, a publicly available dataset is used. The source of the dataset used for implementation is from – IEEE’s signal processing society – camera model identification dataset [27]. The dataset consists of images taken from five popular mobile devices namely Sony, Samsung, Apple, Motorola, and Nexus. This dataset is having 495 images per class. In total, there are 2475 images in the dataset. In the proposed approach, data augmentation is performed first. Using these operations, a total of 3960 images are created per class. Hence, a total of 19800 images are used for training the model. From each image, a total of 82 features are extracted using DWT and local binary pattern (LBP). This section gives details about the results obtained by performing experiments on different classifiers. For the performance measure, different evaluation metrics are used. These evaluation metrics are calculated using a confusion matrix. These measures are accuracy, precision, recall, and f1-score value. Since the number of images is equal for each class (balanced dataset), these performance measures are sufficient for evaluation purpose. The confusion matrix is given as:

Source Camera Identification Using Hybrid Feature Set and Machine Learning. . .

TP: Correctly predicted the positive class FP: Incorrectly predicted the positive class

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TN: Correctly predicted the negative class FN: Incorrectly predicted the negative class

By using these instances, performance measures are calculated as follows: precision =

recall =

accuracy =

tp tp + f n

tp + tn tp + tn + fp + f n

specificity =

f 1 − score =

tp tp + fp

tn tn + fp

2 × precision × recall precision + recall

In this way, three different classifiers are used namely support vector machine (SVM), K-near neighbor (KNN), and linear discriminant analysis (LDA). The dataset is divided into two parts – one is for the training of the model, and another is for validation of the model. A tenfold cross-validation test is performed on the dataset. Classes are represented in numeric values (i.e., 1-Apple, 2-Sony, 3Samsung, 4-Motorola, and 5-Nexus). SVM Medium Gaussian SVM is taken to train our dataset. The method that we implemented has an overall accuracy of 89.7% with average precision and recall/sensitivity rates of 90.2% and 89.7% and an average specificity rate of 97.3% on the dataset. The quantitative result of the proposed model with an SVM classifier on different performance measures is shown in Table 2. LDA In this method, a linear discriminant analysis classifier is used for training our dataset, and the classifier gives an accuracy of 80.5% with average precision and recall/sensitivity rates of 81.6% and 80.04% and an average specificity rate of 94.76%, respectively, on our dataset. The figure mentioned below shows the

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Table 2 Quantitative result of the proposed model with medium Gaussian SVM on different performance measures Camera model Apple Sony Samsung Motorola Nexus

Precision (%) 89.4 95.1 91.0 84.6 91.0

Recall (%) 91.0 89.7 89.0 88.6 90.5

Specificity (%) 96.9 99.4 97.5 95.4 97.4

F1 score (%) 90.2 92.3 89.9 86.5 90.7

Table 3 Quantitative result of the proposed model with LDA on different performance measures Camera model Apple Sony Samsung Motorola Nexus

Precision (%) 77.4 88.6 84.7 75.2 82.1

Recall (%) 84.1 77.0 76.4 82.0 80.7

Specificity (%) 91.9 98.6 96.0 92.4 94.9

F1 score (%) 80.6 82.3 79.6 78.4 81.3

Table 4 Quantitative result of the proposed model with KNN on different performance measures Camera model Apple Sony Samsung Motorola Nexus

Precision (%) 81.6 88.2 85.7 85.4 88.4

Recall (%) 88.3 89.9 86.0 79.4 86.3

Specificity (%) 94.3 98.5 95.9 96.1 96.7

F1 score (%) 84.8 89.0 85.8 82.3 87.2

Table 5 Comparison of the different classifiers on the proposed feature set Model SVM LDA KNN

Accuracy (%) 89.7 80.5 85.6

Precision (%) 90.22 81.6 85.86

Recall (%) 89.76 80.04 85.98

Specificity (%) 97.32 94.76 96.3

F1 score (%) 89.93 80.44 85.8

confusion matrix of the experimental result of LDA. The quantitative result of the proposed model with an LDA classifier on different performance measures is shown in Table 3. KNN In this method, a Fine KNN classifier is used for training our dataset, and the classifier gives an accuracy of 85.6%. The average precision and recall/sensitivity rates come out to be 85.86% and 85.98%, respectively, and the average specificity rate 96.3%. The quantitative result of the proposed model with KNN classifier on different performance measures is shown in Table 4. Table 5 compares the average values of accuracy, precision, recall, specificity, and F1-score on different classifiers.

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From the above table, it can be concluded that SVM (medium Gaussian) performs better for a given dataset. So, SVM can be considered for further comparison with the other state-of-the-art techniques mentioned in Sect. 2. The implemented method works better than other state-of-the-art techniques (as accuracy comes out to be better than most of the papers mentioned in Sect. 2) because we extracted a significant amount of feature vectors, which helped the classifier to train the model better. However, we noticed that as the number of classes increased, the accuracy decreased, due to the limited number of photos of each class.

5 Conclusion In this chapter, a framework based on features for source camera identification is proposed that has three steps – image preprocessing, feature extraction, and classification. Image preprocessing includes data augmentation and color conversion from RGB to gray scale. A total of 82 features were extracted using DWT (23 features) and LBP (59 features). On performing experiments on these 19800 × 82 feature vectors using different classifiers, SVM shows an overall accuracy of 89.7% for five camera models, whereas LDA performed with an accuracy of 80.5% and KNN with an overall accuracy of 85.6%. After comparing all three SVM, LDA, and KNN, SVM has shown comparatively better results than other art form techniques mentioned in Sect. 4. However, the accuracy of the proposed model is limited to five classes because of the small size of the dataset available till now. In the future, the accuracy and method can further be improved by performing experiments on the required large number of images by generating a dataset containing more classes.

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Analysis of Blockchain Integration with Internet of Vehicles: Challenges, Motivation, and Recent Solution Manik Gupta, R. B. Patel, and Shaily Jain

1 Introduction Due to the increased number of road vehicles, the demand for effective transport networks has grown considerably. The growth of the urban population has made the management of traffic more difficult and the management of severe traffic problems more difficult. However, road conditions, including traffic congestion and other factors, like poor public transportation, are all problems that are important to take into consideration. Additionally, cities using the smart city approach will need traffic control systems to support municipal administrations and new car ownership applications. Consequently, smart services will only be provided by using technological and unique solutions, and thus, the significance of these solutions is critical for road authorities and driver satisfaction. Traditionally, transportation management systems have relied on the use of vehicular ad hoc networks (VANETs) to provide various applications and services. Since there is a lot of available data and improved connections provided by the Internet of Things, the Internet of Vehicles (IoV) approach [1] improves technology by connecting vehicles together (IoT). The Internet of Vehicles (IoV) is actually a sophisticated, traffic-efficient ad-hoc network. IoV apps not only have many features like traditional IoT (Internet of Things) applications, but they also have significant differences. It is used in a network environment which is accessible to wireless networks, and the topology of

M. Gupta () · S. Jain Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India e-mail: [email protected]; [email protected] R. B. Patel Department of Computer Science & Engineering, Chandigarh College of Engineering & Technology, Chandigarh, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_8

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the network is continuously changing, which complicates the processing of mobile IOV services [2]. The IoV produces a variety of data kinds, including extra data like trajectories, road traffic statistics, and multimedia data, continuously via moving vehicles. The vehicles may interact with one another and with the surroundings under this paradigm: individuals, telecommunication networks, gadgets, indications of transport, etc. This will lead to the development and implementation of effective road safety and worldwide traffic efficiency apps that will reduce the causalities of traffic [3]. But drivers and passengers who use ridesharing services may be at risk of physical injury or property damage as a result of security and privacy concerns like data manipulation, identity counterfeiting, and sensitive information exposure. Many distinct variables are responsible for the inherent danger of privacy breaches. One of the ways that people’s privacy may be compromised is dependent on the kind of data that is being gathered by various organisations on a network. Additionally, even for apps that are capable of providing accurate data, the collected data is kept on devices that are meant to be reused, increasing the possibility of data reuse without permission. Blockchain technology has recently risen to prominence, bringing with it the benefits of decentralisation, privacy, and reliability [4–6] that it provides. Blockchain technology is a new craze in computer science for safeguarding information resources between networked devices. This has been widely suggested as a promising solution to IoT’s trust-related issues [7–9]. It is possible for Internet of Things devices to safely exchange energy or resources with some other unknown counterparts with the help of blockchain [10–12]. Blockchain technology is appropriate with distributed consensus properties for decentralised applications, in particular when vehicles do not have mutual confidence in complicated road transport settings. The initial implementations of blockchain technology were as a distributed ledger for the Bitcoin [13–15] system, with the goal of solving the cryptocurrency’s double-spending issue. As a result of the immutability of the distributed ledger, one of the most important characteristics of the blockchain is that it enables transacting parties and stakeholders to build trust among previously untrustworthy organisations in a decentralised way [16– 17]. The blockchain-based architecture is decentralised, open, and implemented by many dispersed nodes, each with a copy of the Bitcoin transaction records linked from a cryptographical perspective, structured into blocks, which certain consensus procedures between the blockchain nodes agree on [18–21]. The increasing number of smart cars is anticipated to generate and interchange a large quantity of data, and the network traffic to be handled will be considerably large, thanks to the fast development of vehicular applications and services. Significant problems will also be encountered when using conventional cloud services and administration explicitly with the great speed, low latency, contextual complexities, and heterogeneous features of IoV. It is also challenging for IoV companies from various service providers to guarantee good interoperability and compatibility. As a result, blockchain technology, in conjunction with current cryptographic methods and edge computing, has already offered significant possibilities in a variety of IoV applications. It is anticipated that the combination of blockchain technology with the

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Internet of Things will significantly enhance the security, intelligence, large-scale data storage, and efficient administration of the Internet of Things, making it an attractive study subject. Because the communication connection between vehicles or vehicles and roadside units (RsUs) is insecure or unreliable, it’s indeed critical to address the issue of designing a blockchain-based car network and modelling the accessible portion of the vehicular network.

1.1 Organization and Reading Map Figure 1 shows the organization of the chapter. Section 1 mentions the basic introduction of the chapter followed by the abstract. Section 2 describes the basic architecture of the Internet of Vehicles followed by the challenges with which IoV deals. Section 4 details the overview of the blockchain technology, and Sect. 5 focuses on the types of blockchain networks. In Sect. 6, the motivation for using blockchain in IoV is highlighted, and Sect. 7 briefs the recent solutions for IoV integration with blockchain. Section 8 highlights the applications of blockchained IoV from three major perspectives: incentive mechanism, trust establishment, and security and privacy. Some common use cases are shown in Sect. 9 with the future scope of blochained IoV conclusion of the chapter in Sects. 10 and 11, respectively.

2 Architecture of IoV The Internet of Vehicles (IoV) enables individuals to manage their vehicles from a distance via the connectivity of people, vehicles, and things, as well as the intelligent provision of services by vehicles to people. Figure 2 depicts the organisational structure of the IoV. Taking into account the lifespan of data in IoV, we split the IoV structure from bottom to top into four layers, with the first layer being the sensing layer, the second layer being the communication layer, the third layer being the computation layer, and the fourth layer being the application layer.

2.1 Physical Layer It has a wide range of sensors on board and sensors that are situated in noteworthy locations and portable devices. Take real-time vehicle and road environmental conditions into consideration while collecting real-time vehicle operational parameters [27]. It may include, for example, velocity, geolocation, engine RPM, distance, and the like. For uniform visualization and interpretation, all gathered data will be transmitted to the cloud server to get business data needed by clients and to offer accurate IoV data support [28–31].

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Section I - Introduction Organization and Reading Map

Section II - Architecture of IoV Physical Layer

Communication Layer

Computation Layer

Application Layer

Section III - Challenges of IoV Privacy Leak

High Mobility

Complexity in Wireless Networks

Latency-Critical Applications

Scalability and Heterogeneity

Section IV - Overview of Blockchain Technology Blocks

Miners

Nodes

Section V - Types of Blockchain Network Public Blockchain

Private Blockchain

Consortium Blockchain

Hybrid Blockchain

Section VI - Motivations of Using Blockchained IoV Section VII - Recent Solutions for IoV Integration with Blockchain Section VIII - Applications of Blockchained IoV

Incentive Mechanisms

Trust Establishment

Security and Privacy

Section IX - Use Cases of Blockchained IoV Section X - Future Scope of Blockchained IoV Section XI - Conclusion

Fig. 1 Organization of chapter

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Application Layer Geo-Location Service On-board Entertainment

Computation Layer

Auto Pilot

Navigation Planner

Data Analysis

Vehicle Fault Detection

Safety Measures Calculation Traffic Flow Calculation Cloud Server

Communication Layer

NFC Bluetooth Wi-Fi

Physical Layer

Satellite Near Field Communication

Vechicles

Cellular Network

Smart Phones Human

Traffic Signals

Fig. 2 Architecture of Internet of Vehicles (IoV)

2.2 Communication Layer There are two types of communication: near-field communication and far-field communication. Bluetooth, RFID, and other technologies are used for near-field communication to construct VANET in order to share information between cars, vehicles, and the environment. Far-field communication uploads data to cloud servers via a variety of wireless communication protocols, including cellular networks, satellite links, and other wireless connections. Following that, the server calculates the information in order to offer matching services for each car [27, 29, 32].

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2.3 Computation Layer The data is summarised, analysed, and processed once it has been deployed on cloud servers to offer powerful computing services [33]. Carry out a combined data analysis in order to establish different connections between automobiles. Data computation services that are effective, correct, and quick are provided to user groups.

2.4 Application Layer The layer is the topmost layer of the IoV, and it may offer consumers a range of vehicle services [34]. The network layer processes the vehicle’s real-time information, which is then sent to the on-board system after being calculated. It enables customers to access location and navigation-related travel services, such as assisted trip planning, on-board entertainment systems, and self-driving cars.

3 Challenges of IoV The progress in the Internet of Vehicles (IoV) sector is greater than ever before, as new car equipment and Internet technologies join forces. In principle, the aim of this potential IoV area is to increase driver safety in the near future, while at the same time improving vehicles, transport infrastructures, and people’s lifestyles. The introduction of an enormous quantity of data, as well as that which is derived from cars and vehicle services, will occur in the cloud and on edge storage devices due to IoV. In addition, the futuristic vehicles will be equipped with very powerful computing and storage capabilities. These resources and services will be exchanged with each other in order to provide a broad variety of application services. With the development of IoV connections, the problems will be exacerbated. Because of this, a variety of issues are intricately linked to ITS. When IoV technologies are integrated with current Internet technologies, numerous problems [24–26] arise, including security, confidentiality, trustworthiness, openness and connection issues, and performance and connectivity issues.

3.1 Privacy Leak Privacy leaking is defined as the unauthorised acquisition and the use of a person’s private information by a foreign entity without their knowledge or consent. Because of the economic value of the information, the owner’s life will be disrupted as a

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result of the privacy breach. While the owner’s mobile phone number or licence plate number may have little commercial worth, the driving track and car pricing may have advertising potential. In addition to allowing personal itineraries to be leaked, sensitive travel information may also lead to the disclosure of additional personal information. The leak of personal information has three different pathways to the IoV architecture:

3.1.1

Leakage in the Communication Layer

When there is no authentication method in the near field communication, it is not possible to ensure the security of the other party. The sharing of private information may lead to the loss of certain confidential information. During the data transmission of far-field communication, information may be watched or captured by other parties.

3.1.2

Vulnerabilities in the Computation Layer

An attack on the computational layer, which is responsible for executing the computing job, may result in private information about the user being exposed. Additionally, since the data is kept on devices that are not within the user’s control, the service provider may acquire and utilise the data unlawfully.

3.1.3

Vulnerabilities in the Application Layer

Vehicle-embedded operating systems, as well as vehicle-mounted software and hardware, may be compromised via a variety of technological methods that are difficult to understand. When the vehicle information storage system, infotainment system, or navigation system is compromised, malicious hackers will steal private data from users, and they will be watched for an extended period of time [22, 23].

3.2 High Mobility In IOV situations, both drivers and self-controlled cars (AVs) are regarded as highly moving items that normally travel along the roadways, as opposed to other IoT smart devices. Likewise, the speeds at which cars operate may vary, leading to varied mobility, especially for hand-operated vehicles. As a result, while cars are able to make effective use of computing and communication resources when they are connected to a large number of peers, the vehicles’ connectivity will be challenging to sustain because of the highly mobile and varied nature of the network. In particular, strong mobility qualities may cause additional difficulties.

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3.3 Complexity in Wireless Networks A diverse wireless connectivity system, in which a variety of wireless technologies compete, serves as the basis for the IoV ecosystem. In this environment, vehicles communicate with neighbouring vehicles, humans, and fixed RSUs through a wireless network. The technology used is often a combination of Bluetooth, mm Wave, and dedicated short-range communication (DSRC), which makes different wireless network-related services possible. This is an example of Bluetooth and mm Wave’s relative coverage areas: Bluetooth has coverage under 100 m, while mm Wave has coverage under 10 m. Unlike the two, DSRC often has a wide coverage of communications. Furthermore, when the vehicles move, the topologies of their networks are altered. As a result, the effects of network complexity on IoV situations are substantial.

3.4 Latency-Critical Applications Many IoV applications need more efficient network methods to share data with local peers rather than far-off centralised cloud nodes. In fact, such applications are frequently sensitive to delays and usually have relatively short lengths of propagation. As a result, the maximum time between source and destination should be as short as feasible for them. For instance, emergency and safety-related automotive applications, where communication must occur within a certain time frame in order to avoid unforeseen circumstances such as collisions. To guarantee that prospective Internet-assisted technologies do not introduce excessive transmission delays in Internet transmission, place a delay restriction on IoV implementations.

3.5 Scalability and Heterogeneity Vehicles that often travel over a large geographical region offer a potentially handy alternative for achieving scalability through roadside edge computing nodes, VANETs, and wirelessly linked Internet technologies. Additionally, by taking into account heterogeneity, IoV components with diverse devices, protocols, and platforms may anticipate smooth integration with cutting-edge information and communication technologies. Furthermore, this variability among IoV components makes achieving interoperability an additional difficulty. Interoperability, in fact, refers to the capacity for the use of information by IoV components and the interchange of information across sectors, centres, and systems, including hardware and software. Nonetheless, the majority of the aforementioned characteristics are virtually universal in automotive ad hoc network situations. In such instances, with IoV

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applications, difficulties must be overcome owing to these particular features, and how to overcome these issues will also be distinct and unique.

4 Overview of Blockchain Technology A blockchain, which facilitates trustless cooperation and coordination between institutions who are not entirely trusting of one another, is a decentralised computing and information exchange platform. The blockchain is a kind of database that is built using blocks and chains. Blockchains, in contrast to conventional databases, store data in blocks, which are subsequently linked together. When new information arrives, a new block is inserted. For instance, a Bitcoin block includes information about the originator, the recipient, and the total amount of Bitcoins being sent. Blockchain is a synthesis of three cutting-edge technologies: cryptographic keys, often known as cryptographic hashes, a peer-to-peer network with a shared ledger, and a computer system to store the network’s transactions and records. In a nutshell, blockchain technology is a decentralised, distributed ledger that tracks the origin of a digital item. Because the data on a blockchain can’t be changed, it’s a genuine disruptor in sectors like finance, cybersecurity, health, and the Internet of Things. Blockchain is a particularly promising, revolutionary technology, since it contributes to risk reduction, eradication of fraud, and scalable transparency to a multitude of applications. Blocks, nodes, and miners are the three main elements of the blockchain.

4.1 Blocks A chain is made up of many blocks, each of which has three fundamental elements: data in the block, a nonce (a 32-bit random number used once), and a hash (a 256-bit integer produced by hashing the nonce), which becomes the block header. The cryptographic hash of the first block in a chain is generated when a nonce is applied. Unless the block is mined, the data in it is deemed signed and will remain permanently associated with the nonce and hash.

4.2 Miners Through a process known as mining, miners add new blocks to the chain. A blockchain not only does have its own distinct nonce and hash for every block but also links to the preceding block’s hash for every block in the chain, which makes mining a block very difficult. Miners use specialised software to tackle the extremely difficult mathematical challenge of generating an approved hash. Due to

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the fact that the nonce is only 32 bits long and the hash is 256 bits long, there are about four billion nonce-hash permutations, which must be mined before the correct one is discovered. When this occurs, it is claimed that the miners have discovered the “golden nonce,” then the block is added to the blockchain. To make a modification to a block earlier in the chain needs not just reminiscence of the block, but of all the blocks that follow. Therefore, the manipulation of blockchain technology is very hard. This may be seen as “securing mathematics” since it takes a great deal of time and computer power to discover golden nonces. Upon successfully mining a block, all of the nodes in the network acknowledge the change, and the miner is monetarily compensated.

4.3 Nodes Decentralization is one of the most significant ideas in blockchain technology as the chain can’t be owned by a single device or entity. Rather, it is dispersed among the nodes linked to the chain. Any technological device capable of maintaining clones of the blockchain and facilitating the proper operation of the network is called a node. Each node has its own blockchain clone, which must allow the network to algorithmically update, retain, and verify each freshly mined block of the chain. Due to the transparency of blockchains, any transaction on the ledger can be readily verified and seen. A unique alphanumeric identifier is issued to each participant to ensure transactions are accurate. Using a constitutional framework in conjunction with publicly available information helps to preserve integrity and foster confidence among the network’s users. Blockchains may thus be described as the technological scalability of trust. As shown in Fig. 3, the transactions recorded in a blockchain are organised into blocks, with each freshly produced block referencing the block before it using a unique identification number known as a “hash.” These blocks form a chain, which is why the term “blockchain” was coined. This sequence of events may continue forever. Here, trust is established via technical characteristics, including the reality that all blocks are publicly visible. Without first being confirmed by a miner, no transaction is placed into a block-a specific computer type inside the network. In this manner, the community keeps an eye on transaction integrity to guarantee there are no false records on the blockchain. As a result, parties that do not necessarily trust one another to do business may utilise a blockchain since they are certain that their transactions are secure and cannot be tampered with.

5 Types of Blockchain Network It was with blockchain technology that the concepts of a public blockchain and a cryptocurrency were first presented to the world. The developers’ intentions are

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Blockchain validation and new blocks addition by miners

Fig. 3 Transaction flow in blockchain

unclear, but the concept of decentralised heading technology was presented. This has altered our way of resolving problems. It provided the opportunity for groups to function without relying on a centralised body. Not only distributed technologies address centralization disadvantages, but also they bring with them many additional solutions to various situations when it comes to blockchain technology. For example, Bitcoin uses inefficient proof of work, a consensus algorithm. It necessitated the use of energy by the nodes to perform mathematical computations. As long as the problem was simple, it did not take much time or effort to answer those equations. However, as soon as the complexity rose, the time and effort needed to solve those equations likewise increased. Due to its inefficiency, it is unsuitable for any system that must remain efficient regardless of the circumstances. Banks, for example, deal with a large number of transactions on a daily basis. It will not be appropriate for the kind of blockchain this is built on. With the initial generation of the blockchain, additional difficulties arose, like scalability, no automation, and other issues. Next, let us just look at it from a different perspective. Blockchains are not suitable for all entities. If entities must keep some aspects of their operations private, they will not be able to utilise a public blockchain. Their company does have some

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important data that sustains their performance. There could be rivals who use it if it becomes public. A private or federated blockchain was created to address the aforementioned use cases. Private blockchains enable organisations to have total control over who participates in the network. This gives them the opportunity to benefit from blockchain-based capabilities without having to expose everything. With this knowledge in hand, we may summarise that the first-generation blockchain has numerous shortcomings, including inefficiency and scalability. A public blockchain is suitable for everybody’s objective or requirements, but is not suitable for certain business interests. The main causes of development in the various kinds of blockchain technology may be seen as built on these two aspects. Public blockchains, private blockchains, consortium blockchains, and hybrid blockchains, each of which is unique, are the primary kinds of blockchain networks [35]. Each of these platforms has a number of advantages, disadvantages, and optimal applications.

5.1 Public Blockchain A public blockchain is a distributed ledger system that is not restricted by permission and does not need any additional permission from its users. The blockchain network may be accessed by anybody with an Internet connection who registers on a blockchain platform to become an authorised node and become a member of the blockchain network. Public blockchain nodes or users are permitted to view current and previous data, perform verification of transactions, and provide proofof-work for an arriving block. Public blockchains are the most essential use for mining and cryptocurrency exchange. Litecoin, Ethereum, and Bitcoin are the most popular public blockchains. As long as users adhere to rigorous security rules and procedures, public blockchains are generally safe. But it is dangerous only if the participants do not genuinely follow the security procedures.

5.1.1

Benefits

Because public blockchains are entirely independent of organisations, even if the organisation that created them goes out of business, they will continue to function as long as there are computers linked to them. This is one of the main benefits of public blockchains. Public blockchains provide another benefit in that they allow for more network openness. While individuals take precautions, public blockchains are generally safe as long as users strictly follow security rules and techniques.

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Drawbacks

On the other hand, networks may be painfully sluggish, and businesses cannot limit usage or access. If hackers acquire 51% or more of a public blockchain network’s processing power, they may change it unilaterally. Public blockchains are not wellsuited for scalability. As additional nodes enter the network, the network slows down.

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Applications

For the most part, public blockchains are used for cryptocurrency mining and the trading of digital currencies like Bitcoin. Nevertheless, it is often used to certify permanent documents, such as affidavits and government documents of property ownership, using an auditable chain of custody. A blockchain that focuses on being transparent and trustworthy is excellent for organisations like social assistance groups and non-governmental organisations. Due to the network’s public character, private companies will almost certainly wish to avoid it.

5.2 Private Blockchain A private blockchain restricts or provides access exclusively to an organization’s internal network. Private blockchains are often utilised inside an organisation or business where only a small number of individuals are allowed to participate in a blockchain network, as opposed to public blockchains. This arrangement gives the governing organization exclusive discretion over the degree of security, authorization, approval, and access. As a result, private blockchains are identical to public blockchains in terms of functionality, but they have a smaller and more restricted network. For example, private blockchain networks are used for a variety of purposes, including e-voting, supply chain, digital identification, asset ownership, and more. Multichain and hyperledger projects like Fabric and Sawtooth, Corda, and others are typical instances of private blockchains.

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Benefits

This company determines who is granted access to a certain application or section of a programme. A blockchain network setup for a particular company, for example, may control which nodes have permission to access, contribute, or modify data. Additionally, it may prohibit other parties from gaining access to specific information. Due to their small size, private blockchains may be extremely rapid, and transactions can be processed considerably faster than public blockchains.

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Drawbacks

The inconvenience of private blockchains is the disputed assertion that they are not real blockchains because decentralisation is the fundamental concept of blockchain. Moreover, complete confidence in the information is more difficult to establish since centralised nodes decide what is genuine. Additionally, a limited number of nodes may imply poorer security. The consensus process may be undermined if a few nodes are misguided. Furthermore, because the source code for private blockchains is usually proprietary and locked, it’s difficult to mine on such platforms. Auditing or verifying its veracity is not possible for users to independently do, which may lead to reduced security.

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Applications

Blockchains built specifically for private use are more suitable for situations where confidentiality is required but a controlling organisation doesn’t want the information to be accessible by the general public. Supply chain management, asset ownership, and internal voting are just a few among all of the applications for private blockchain technology.

5.3 Consortium Blockchain A blockchain consortium is a partnership type in which a blockchain network is managed by many organisations. Unlike a public blockchain, which is maintained by a variety of organisations, a private blockchain is managed by a single company alone. Multiple organisations may serve as nodes in this kind of blockchain, exchanging data and doing mining. Banks, government agencies, and other institutions often utilise consortium blockchains. Several well-known instances of consortium blockchains include the Energy Web Foundation, R3, and others.

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When using a consortium blockchain, security, scalability, and efficiency all increase. As is the case with private and mixed blockchains, it also incorporates access restrictions.

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With the exception of consortium blockchains, public blockchains are more transparent. If a partner node is violated, the blockchain’s own rules may still affect the functioning of the network.

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Applications

This kind of blockchain is used in banking and payments, to name a few applications. In certain cases, several banks may collaborate and create a consortium, and this consortium will choose which nodes verify transactions. A comparable approach to organisations wanting to monitor food may be developed by research groups. It is excellent for food and medical applications, especially supply networks. However, there are other consensus methods to take into consideration. Aside from PoW and PoS, anybody intending to build up a network should also examine the other kinds, which are accessible on various platforms, such as Wave and Burstcoin. This concept has many different examples, such as the use of leased proof of stake to let users earn money without the node having to mine itself. To show the relevance of users, proof of importance combines transactions and balances.

5.4 Hybrid Blockchain It is possible to create a hybrid blockchain by combining elements of both private and public blockchains. They utilise both kinds of blockchain, which may include a private system based on permission and a public system without permission. Users may restrict access to which information is kept on the blockchain using such a hybrid network. Only a portion of the blockchain’s data or records may be made public while maintaining the remainder inside the private network. The hybrid blockchain solution is versatile, enabling users to connect with many public blockchains and a private blockchain simply. When doing a transaction on a private network of a hybrid blockchain, the transaction is validated on the private network. However, users may also publish it to be validated on a public blockchain. The public blockchains, as a result, have increased hashing power and need a greater number of nodes for verification. This improves the network’s security and openness. The Dragonchain is a well-known example of a hybrid blockchain.

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Benefits

One of the great benefits of the hybrid blockchain is that attackers outside of the system cannot launch a 51% network assault since it operates inside a closed ecosystem. It also secures personal information while allowing for contact with

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other parties. The network has more scalability than a public blockchain network, according to the developers.

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The main issue with this kind of blockchain is that information may be hidden. Upgrading may also be a difficulty, as consumers are not encouraged to engage or participate.

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Applications

Real estate is one of the many compelling applications for hybrid blockchains. With the use of a hybrid blockchain, companies may keep the majority of their business running secretly, while allowing open access to specific information, such as listings. Retail can also simplify its operations using the hybrid blockchain, as well as the advantages of utilising highly controlled sectors such as financial services and medical record maintenance. Users may get access to their information via the use of a smart contract, which prevents third parties from seeing the information. It may also be used by governments to keep data secret from citizens or to securely exchange information across organisations.

6 Motivations of Using Blockchain in IoV There is significant potential for creative solutions in nearly all IoV use cases with the introduction of blockchain technology. Therefore, nearly all of the IoV simulations have the property of being real time and dynamic, and they create and interchange a substantial quantity of data. In IoV situations, it is very doubtful that many traditional methods would be appropriate and successful. In addition, increased connectivity may offer new attack avenues for hostile actors in these situations. In addition to enhancing security, privacy, and trust, the incorporation of blockchain into IoV increases system speed and automation. Therefore, blockchainlike robust technology should be used to allow flexibility and manage large amounts of data. Due to its characteristics, BC may be an appealing method of solving these problems.

6.1 Decentralization The blockchain’s design is decentralised and is less dependent on a single body; thus, it may prove to be an effective method for deploying secure solutions.

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Blockchain enables decentralised IoV networks to be established and includes additional distributors, such as RSUs, vehicles, and people. Simultaneously, these dispersed entities are capable of managing their own activities autonomously. The present IoV network’s operating principles, which are heavily reliant on central decision-making, will be simplified and moved to a decentralised architecture. The ultimate effect of decentralisation will be to improve the transportation user experience.

6.2 Availability Due to the decentralised nature of BC, there is no one point of failure. The safety and availability of the system are thus improved. This is because all linked peer nodes replicate and synchronise blockchain data. If one or more nodes are hacked, the services can still work efficiently. Blockchain technology, on the other hand, is based on current cryptographic methods to guarantee that basic confidentiality and anonymity characteristics are maintained. Furthermore, the greater the anonymity and confidentiality, the better it is for the Internet of Value (IoV) networks.

6.3 Transparency The BC resources are available to all the nodes that have linked into it and have access to the BC content. The system itself is open and transparent, which eliminates the need to create trust relationships among nodes. Nodes cannot deceive one another within the constraints of the system’s rules.

6.4 Immutability Blockchain technology offers a high level of immutability for IoV services and situations, since blocks in a blockchain link to each other using hash values that represent the chain of blocks. The blockchain ledger contains information that cannot be changed. BC offers a straightforward and fast method of storing protected data. This immutability characteristic of blockchain possibly eliminates data manipulation and alteration, as well as aids in correct auditing. With the assistance of a smart contract, it is also possible to install and enforce any preset rules or scripts. Thus, the blockchain makes it easy and safe to store confidential data.

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6.5 Exchanges Automation Autonomous transactions between gadgets and automobiles may be automated using smart contract technology. As a result, services such as data interchange or resource sharing may be automatically implemented without the need for human involvement. The decentralised nature of the blockchain is especially useful for peer-to-peer (p2p) trade, sharing, and interactions between two parties. The service requests and suppliers are able to communicate directly with each other through a p2p network. For IoV situations, this p2p functionality is very helpful for securely transferring data and resources between cars and RSU. Due to the fact that no middleman is required in the peer-to-peer network, it eventually leads to low-latency services and applications.

7 Recent Solutions for IoV Integration with Blockchain In this part, based on our observations and research, we highlight recent solutions for the integration of IoT with blockchain, and the major difficulties that must be tackled when blockchain is included in the IoV scenarios. Based on the review of recent research contributions from the year 2019, this section focuses on the perspective of these research contributions, the challenges and their outcomes in brief. In addition, we have emphasised the possible remedies in various literature for these difficulties. [36] concentrates on uncertain key management and proposes a new network model based on safe broadcasting groups. The suggested model enables more efficient distributed key management by shortening key transfer times. The energy and transaction loads are increased while updating distributed ledgers and performing blockchain transfer operations [37]. uses a technique known as distributed clustering that makes it possible to control the number of transactions in the most efficient way. When compared with the Bitcoin model, the suggested approach uses much less energy and necessitates a significantly lower number of transactions. [38] focused on the difficulties associated with centralised privacy solutions. It employs a security mechanism based on remote attestation. The suggested approach satisfies the decentralised characteristics, user anonymity, and traceability. [39] aims to compromise vehicle identification privacy via monitoring attacks and the dissemination of falsified communications from inside vehicles. It employs methods such as an anonymous reputation system, which calculates reputation based on past interactions as well as views, and pseudonym addresses rather than actual names. This approach can create a model of trust and also meet the needs of anonymity, transparency, and stability. [40] is specifically designed to address security and authentication issues in consensus algorithms. It employs a byzantine consensus method in conjunction

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with a gossip protocol and a time sequence mechanism. The suggested consensus outperforms the conventional method in terms of consensus effectiveness, security mechanisms, scaling, and fault tolerance. Due to the absence of penalty systems for sending misleading signals, [41] relies on monitoring and enforcing trust values on vehicles. This method provides a safer and more trusted network while limiting the transmission of misleading messages. Due to the lack of decentralisation in current authentication methods, [42] has chosen to concentrate on mutual authentication using elliptic curve cryptography (ECC). The model suggested a lightweight, scalable, decentralised, and anonymous authentication and key-exchange system. There is currently no incentive system in place for backup miners in general blockchain solutions. The suggested method by [43] improves and secures data exchange by using contract theory. Scalability has always been a major problem when trying to handle large amounts of IoV data on the blockchain. The suggested framework by [44] can maximise throughput while simultaneously guaranteeing low latency and decentralisation, and deep reinforcement learning is used to achieve it. Another significant obstacle to electric vehicle participation in energy distribution is the absence of an adequate incentive programme. The suggested system by [45], which is based on price and reputation theories, not only encourages cars to join the blockchain network in order to create a balanced grid but also allows vehicles to optimise their utility. For VANETs, [46] suggested block-SDV, a permissioned blockchain-enabled software-defined architecture. The authors suggest using the consensus method known as Redundant Byzantine fault Tolerance (RBFT) to guarantee that all consensus-required transactions, including executing and writing transactions, are performed properly. A Markov decision process with three functions, state space, action space, and reward functions, is used to represent a joint optimization issue. Security concerns regarding the delegated PoS consensus mechanism, as well as the possibility of collusion between miner candidates and attacked high-stake vehicles when selecting miner candidates via stake-based voting, are considered another aspect of the challenge when dealing with IoV using blockchain technology [47]. offers reputational mining selection among candidates, two-stage verification and auditing by active and stable miners, and the theory of the contract to propose an improved mechanism for security. It provides excellence in the defence of internal collusion, a high detection rate for compromised candidate vehicles, and a better reputation system than the existing systems. Due to restricted resources, vehicles are unable to engage in competitive PoW and PoS-like consensuses to earn incentives. Based on the theories of satisfaction module, a suggested brokerage technique with decision-making capacity has been developed by [47]. The suggested method generates much more profit and consumes significantly less energy when mining and validating operations are uploaded. The risk of exposing vehicle location privacy, such as location tracing and sensitive information leaks, while using location-based services has always remained high. For the purpose of managing trust, the Dirichlet distribution is used by [48].

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The suggested data structure is capable of storing trustworthiness. This suggested system is capable of defending against assaults that target trust models, protecting location privacy, and detecting hostile vehicles. There has been a dearth of effective decentralised keyword search methods. By including smart contracts and searchable encryption [49], the suggested method may significantly enhance privacy protection with forward and backward privacy. Smart contracts have taken the role of centralised searches. [50] offers an SDN-enabled blockchain-based system for IoVs in fog computing and 5G communication networks for the efficient and effective management and control of the vehicular network in order to ensure the safety of a standardised vehicle communications architecture. As part of the shared management process, IoVs integrate blockchain and SDN. On the other hand, the blockchain fulfils the requirement for trust among linked peers. However, SDN ensures efficient network administration and a smooth control procedure across the network. Furthermore, fog computing addresses the handover issues that arise when a large number of vehicles are linked to the RSUs, which are a concern with SDN. Low-latency communication services help to improve network performance with 5G. The authors propose the network trust model, which may reduce harmful actions and stop users from being deceived by peers in-network by determining if the information supplied by those peers is reliable. Permissioned blockchain users, in contrast to public blockchain users, are subject to certain restrictions. In [51], authorization based on the policy, signatures that are based on attributes, and cryptography without the need for certificates have been assigned. In addition to having a small signature size, this suggested signature method also has a minimal computational cost.

8 Applications of Blockchain in IoV BC technology has many advantages, such as decentralization, immutability, anonymity, and exchange automation. Its potential to transform the vehicular environment is evidenced by the various applications that it has already been considered. As shown in Fig. 4, there are three primary applications of blockchain in IoV.

8.1 Incentive Mechanisms The concept of vehicle cooperation is the reason why the V2V networks are so important. In order to improve the safety of the vehicles on the road, data forwarding is very important. Due to the nature of the networks, they could be affected by various factors such as improper storage and bandwidth consumption. Current incentive mechanisms could be adapted to incentivize vehicles to share their

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Fig. 4 Applications of blockchain in IoV

network and computing capabilities. The BC technology could be used to design incentive mechanisms that are secure and scalable. The concept of Vehicular Driver Reinforcement Networks (VDRNs) is based on the idea of storing and carrying forward data between connected road devices. In [52, 53], the authors proposed a method that incentivizes vehicle operators to improve their cooperation by using Bitcoin. Vehicle drivers can be rewarded if they transport data from one base station to another. However, in a normal Bitcoin transaction, the money is taken from the sender and is sent to the recipient. Here, the authors present a method that enables vehicle-to-car sharing services to use Bitcoin BC in exchange for conditional rewarding. This method overcomes the limitations of Bitcoin BC and could be extended to other scenarios. Vehicle-to-car sharing services could also be considered as clients. Despite the advantages of vehicular cloud computing, its existence is still dependent on the vehicles’ cooperation [54]. proposes a credit-based incentive approach to encourage them to cooperate in this field. The goal of this chapter is to develop a conditional rewarding system that would allow a vehicle to collect data without being incentivized. However, this system should not be implemented as it involves the deployment of Bitcoin BC in a complex environment. The authors in [52, 53] introduce a framework for establishing trust in vehicular networks. They also introduce an incentive mechanism that rewards a vehicle that contributes to the proper functioning of the networks. This chapter aims to provide an ambitious goal, which is to implement a secure and private key management system based on hash and public/private keys. However, this approach is not yet fully implemented and the system’s security is not yet guaranteed.

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Legends: Blockchain Network

Road Side unit

Cloud Storage

Blockchain Communication

Blockchain Nodes

Node Communication

Link Connectivity

Fig. 5 Blockchain-enabled Internet of Vehicles

One of the most important factors in optimising an incentive system is the protection of privacy. Although it can be guaranteed that a basic level of privacy is maintained, unlinkability is also an essential component of a system. The authors of [55] propose an incentive mechanism that allows vehicles to inform the public about driving conditions. An incentive mechanism is also proposed to incentivize the users of the system. For instance, if a vehicle wants to find out about a certain traffic condition, it will provide a reward to the users who provide the information. Since the BC technology was initially designed to enable cryptocurrencies’ exchange, it has been proposed to be used in the vehicle environment for various incentive mechanisms. Some of these include the sharing of information and computational resources among vehicles, establishing collaborations in vehicular cloud computing and intersection management, and improving the efficiency of BC ledgers through different mechanisms. This can be taken by considering vehicles as the blockchain nodes [54], presented in Fig. 5. The feasibility of deploying BC ledgers in an unstable environment should be studied. Also, the various use cases for BC should be defined. The performance of BC technology will be an important challenge in order to improve its reliability. The concept of decentralised cloud computing is proposed in [56]. Due to the increasing computational capabilities and storage requirements of vehicles, it has been proposed that the development of vehicular cloud computing should be conducted in the vehicle environment.

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8.2 Trust Establishment The various types of nodes in a vehicular network can be composed of various kinds of vehicles such as mobile platforms, roadside units, and fixed base stations. These nodes can be easily connected to each other using various communication technologies. Establishing trust between these components is very important to ensure the safety of road users. BC technology can help to create trust models that are based on the mutual data and immutability of the ledger. It can also identify non-believers and trusted users. Vehicle networks are mainly used for providing road safety services. These systems operate by broadcasting messages to alert the surrounding vehicles about certain road conditions. However, these systems are also prone to various types of attacks. In order to improve the trust between vehicles and the environment, a system [57, 58] that uses a Bayesian inference model was proposed. This method is used to determine the credibility of the messages that are sent by other vehicles. The messages sent by each vehicle are analysed by the BC network to determine if they are credible. The goal of this system is to maintain a trust index of the vehicle [57, 58]. proposes a method that will allow a vehicle to determine which of the surrounding vehicles are trusted. It will also collect the trust index of the vehicles and their surrounding areas. There is no mechanism to ensure that the data collected by the system is valid. As a result, the proposed solution should be improved to establish a secured solution [59]. proposes a system that enables local BCs to control the behaviour of the nodes within a given area. This method not only could provide a better security measure but also requires the evaluation of the system’s complexity. To improve the trust level of messages, [60] introduced the proof-of-event algorithm. To accelerate the dissemination of data, the authors of this chapter propose a two-phase continuous transaction in BC. This method allows the vehicles to share their messages with the surrounding RSUs, which in turn simplifies the data dissemination process [57–60]. introduce a vehicle-centric system that will allow the use of RSUs to control and store data. The system will only store and manage the trust establishment and governance of the data. Another component of the system is a reputation management mechanism. The objective of this system is to determine which vehicle provides the most reliable data. The data collected through the BC ledger is then stored in a secure environment and is easily verifiable. Each vehicle can then select a trusted data provider. [61] describes a system that aims to provide security in networks without RSUs. It stores the BC ledger inside the vehicles and updates it when the vehicles are exchanged. Even if this system is attractive, it should not be considered as a way to establish trust within a platoon without any external help. This chapter proposes a more complex approach to manage the global trust index. In [62], the authors present a decentralized system that enables vehicles to send and receive emergency messages through a BC ledger. They also provide a secure and trustbased environment for the users. In [63], the authors introduce the benefits of permissioned BC for Content Centric Networks. They show how it can improve

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the trust in vehicular environments by allowing users to control the behaviour of the various nodes. Message transmission between vehicles is handled through the BC ledger. The vehicle’s surrounding details are then controlled by the vehicle’s owner. If the transaction is valid, a trusted validator can be added to the ledger. Since the position of the message sender is not verified, the message is not sent to all the vehicles in the vicinity. This method could require different security measures and energy consumption. In a virtual environment, such as a software-defined IoV [64], applications can request and modify the behaviour of the controllers. With the SD-IoV technology, it will allow applications to request resources from different controllers. This will enable them to control the behaviour of the controllers and is very useful for monitoring and controlling the usage of resources and can be done by modifying the data plane’s configuration (reserving resources and modifying communication path) [65]. introduced the concept of application trust index and application identity and BC as a public key infrastructure. When a controller discovers an abnormal behaviour, it will share this information with the other SDN controllers. This method prevents the exploitation of the network by malicious nodes [65]. aims to introduce AI-based techniques to evaluate the behaviour of SDN applications. The systems described here rely on the BC ledger to store details about the vehicles. This method avoids the possibility of exploitation by hackers. A decentralized version of this system called the anonymous reputation system is proposed. The authors in [66] created an architecture composed of various entities, such as the law enforcement authority, the registry authority, and the vehicle securing system. This system will allow each vehicle to request a new key. Instead of tracking a vehicle, it is impossible to monitor its privacy. Its reputation score is attached to its certificate, which enables the surrounding vehicles to identify which ones are trusted. The proposed system in [66] should be able to provide the vehicles with the privacy they deserve. It should be based on an efficient certificate management mechanism [59]. presents a more complete and robust BC-based mechanism that enables vehicle authentication and control. It can be improved in terms of its complexity and deployment. BC aims to enable secure exchange of data between nodes. For vehicles, this will be very important since they will be able to share data with the surrounding cars. In order to control the messages’ trustworthiness [60], various authors have proposed systems that allow users to modify the messages’ trustworthiness. These systems can also protect the privacy of the users. In terms of fully decentralized systems, this concept is not yet clear. Instead, vehicles would be able to act as BC nodes and update their own BC ledger. However, this method requires various steps to be successful. Vehicle networks are designed to enable trust establishment by controlling the behaviour of the vehicles. These networks can then check if the information provided by the vehicles is correct. These systems can help driverless cars operate by controlling the behaviour of the surrounding vehicles. Aside from this, they can also detect the location of vehicles and provide useful data. In these systems, the performance of the multilayered BC should be evaluated to determine

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its relevance. Then, the behaviour of the RSUs should be controlled by the BC network to ensure their security.

8.3 Security and Privacy Regardless of the type of application, securing a network is an essential component. There are various security services that can be utilized to protect your data. Aside from having the necessary permissions, a BC system should also allow the users to authenticate and read private content. It should also authenticate and protect the integrity of the data. Due to the decentralized nature of the network, data availability is guaranteed, and control mechanisms are easily defined. This eliminates the need for manual intervention and complex encryption methods. Vehicle-based authentication is a challenging task, especially since it requires the development of new solutions that can solve the issue of fast-moving vehicles. For this reason, it is often necessary to develop solutions that are specifically built for vehicular environments. Due to the various limitations of centralized PKIs [67], they are not able to provide their users with the same level of security. In [68], the authors introduce a BC-based PKI system that is designed to address these issues. Through this system, a BC ledger can be shared between the revocation Authority and the RSUs, allowing for mutual authentication between the vehicles and the RSUs [69]. introduces an approach that enables users to quickly share new revocation notifications with their surrounding users. This method works by storing the new certificates and other details in a secure environment. This method could improve the system’s latency and provide better storage capabilities. It can also reduce the verification overhead. However, this approach should be evaluated to ensure that the security level is still secure. The Security Credential Manager System is a PKI system that secures vehicular communications [70]. It is a system that can be used to establish and maintain mutual trust. Its main goal is to provide a transparent and authenticated system that can be used by both parties. Each vehicle that has been certified to use BC must agree to share its abnormal behaviour data with a designated RSU. The data will then be shared with the global BC network. This proposal mainly addresses the issue of revoking lists. However, it also addresses the various aspects of implementing it. Some of these include establishing a cluster and implementing security measures [71]. studies the key management of heterogeneous vehicular communication systems. In this chapter, the authors introduce a method that enables key transfer between central and local security managers. Due to the complexity of the distributed key management concept, it is necessary to implement a simple and secure method for transferring key information between different service managers. This chapter presents a distributed key transfer handshake scheme that takes advantage of the BC ledger’s efficiency and transparency. This chapter proposes a method for establishing a handshake scheme in a BC environment. Although it can reduce the complexity of the transaction, it still has the potential to provide high

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computational time and overhead. To overcome the limitations of the existing public key infrastructure (PKI), various BC-based solutions have been proposed. These include: protecting against unauthorized access to the networks of certification authorities, recusing communication exchanges between network devices and the authorities, and improving the performance of existing systems. With the increasing volume of data generated by the IoV and the vehicular networks, securing the confidentiality of these pieces of information is an important point. The data collected by the vehicles will be very useful for various applications. However, accessing and storing these fragments in a fast and efficient manner is an important challenge. The RSU allows the applications to request bits of information from a given RSU. The system can also handle the load between the different RSUs. With the permissions being controlled, the applications can be authenticated and secured. However, the complexity of the access control mechanism should not be ignored. The concept of BC-based access control has many advantages, such as transparency, low-cost deployment, and distributed audit ability [72]. However, the idea of implementing this technology in vehicular networks is still in its early stages [73]. describes a BC-based access control system for Internet of Things devices, which could enable users to own and control the data they consume. This concept could be used in new applications. One of the main advantages of a BC ledger is the ability to provide consistent and reliable data availability. However, in the event of a network failure, this benefit can be prevented by ensuring that the data is always available. In addition, in order to minimize network downtime, it is important that the system is capable of handling V2V communications. Non-repudiation and integrity are intrinsic in BC-based applications. This means that these transactions are verifiable through the BC ledger and are not prone to being invalidated. Even though it’s not considered a major issue, a high level of integrity could still have a negative impact [74]. For instance, if a huge amount of data is stored in a BC ledger, it will not be able to be easily erased or modified. BC technology could provide the integrity of the data stored in BC ledger, but its correctness and relevance could not be guaranteed. This issue could be solved by combining BC technology with other secure computing platforms. Different privacy preserving mechanisms have been designed to help prevent attacks and minimize the exploitation of users’ privacy. However, these mechanisms can be useful for many applications. For example, carpooling could expose sensitive information such as a person’s location and identity [75]. proposes a privacypreserving scheme that uses BC technology. As per the system requirements, RSUs should also be involved in the system’s operations. Ideally, they should be able to detect when a car is driving and prevent it from accessing certain services such as carpool. This system eliminates the need for intermediaries and the user himself to protect his privacy. It allows the vehicle to modify its pseudonym at any time. However, this method is not secure and has non-traceability. To improve scalability, regions are defined, which is a set of service managers that are responsible for maintaining the BC ledger. This system allows users to authenticate and access credentials with a list of authorized pseudonyms. It does so by creating a new alias

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for each new connection. This approach is not ideal for handling the registration of vehicles. Instead, it uses a centralised registration service, which is typically used to manage the entire process. Due to the large amounts of data that are generated in the vehicular environment, it is important that the users’ privacy is protected [76– 78]. show three similar approaches that enable users to generate pseudonyms for their privacy and also discuss the possibility of protecting localisation based on BC technology.

9 Use Cases of Blockchained IoV Blockchain is a potential and revolutionising technology for the motor sector. Mobile devices and other gadgets around the car (road signs, smart phones, etc.) communicate information with BC, and this communication is authorised and protected. Certain typical instances of usage may be seen below:

9.1 Supply-Chain Management BC may be a method of facilitating transparent communication between suppliers, transporters, and manufacturers as well as of coordinating their activities in the future.

9.2 Manufacturing and Production All those processes may be enhanced with blockchain transparency through inventory management, ownership problems, and product traceability and quality check records.

9.3 Settlements of Insurance Claim It may be able to handle insurance claims in an effective way by storing various pieces of information in a protected BC header, such as location, acceleration, and braking, and vehicle speed.

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9.4 Management of Fleet With blockchain, members of the fleet management, owner, operator, and driver community may securely share critical information. The technology may automate tasks such as route planning, payment processing, and vehicle maintenance, among other things.

9.5 Tracking of Vehicle Blockchain technology enables the safe management of transparent vehicle information, automobile title transfers, and car leasing.

10 Future Scope of Blockchained IoV In the previous section, we discussed the various applications of blockchain in the IoV. Some of these are related to improving the vehicular network environment, while the others are focused on developing new technologies. Some of these could contribute to improving the network environment in near future and are listed under.

10.1 Off-Chain Data Trust Although the use of blockchain technology has been widely used to address various security issues in IoVs, there are still many concerns regarding the quality of offchain transactions. In [83], the authors proposed a method that aims to provide secure and privacy-oriented incentives for off-chain data.

10.2 Evaluation Criteria Most of the proposed solutions are based on independent evaluation and simulation. As a result, there is no comparison between the advantages and disadvantages of these solutions. Evaluations could be interesting for different research directions.

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10.3 Management of Resources Due to the high number of transactions on each server, it consumes a huge amount of energy. The current consensus mechanism also has the issue of resource waste. Although DPoS and PoS can reduce resource consumption, they have the same problems that come with weak supervision and inadequate security. The use of lightweight cryptography algorithms is necessary to avoid these issues.

10.4 Data-Centric consensus Designing consensus mechanisms for IoV is an important challenge [79] as it involves ensuring that the data sent by users are verified correctly. This is because there are many factors that can affect the integrity of the data sent by users. The traditional consensus mechanism for validating data has not been designed to handle the issue of invalidating or validating data sent by a vehicle. Instead, it has been suggested that various algorithms be used to guarantee the correctness of the data. Due to this, consensus algorithms must be improved to provide secure and resilient solutions. However, they are still not deployable.

10.5 Blockchain for the Environment The current BC and consensus mechanisms require a high level of computational capabilities to perform their intended functions. This limitation could be solved by reducing the computational capabilities of existing consensus protocols. The security level of the BC ledgers is still an area of concern [80]. Another issue that concerns the system is the amount of storage that the BC ledgers can handle. Designing a lightweight BC system is a step towards realising the full potential of this technology. This process involves improving the existing approaches [81, 82] and developing new ones.

10.6 Administration of Blockchain Platform In order to successfully implement BC-based applications in vehicles, the requirements should vary depending on the environment and the complexity of the task. Some of these include: bandwidth constraints, storage capabilities, and latency constraints. Due to the complexity of the vehicular environment, various solutions are being developed to enable the deployment of Bitcoin on mobile platforms. These include enabling devices with limited processing capabilities to access BC services

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or side chains that improve the network’s scalability. It is also important that the deployment of BC networks in vehicles is carried out in a way that is efficient and secure.

10.7 Evaluation of Performance The integration of various technologies such as SDN, NFV, edge computing, and BC has been proposed to improve the overall performance of vehicular networks. However, in order to get the most out of these new features, it is necessary to develop a proper software that can allow the evaluation of their performance in an approaching environment. The current evaluation of the proposed solutions mainly relies on independent and specific simulation methods that are not very meaningful. It is also possible to determine which kind of application would be supported by each technology. For instance, for some applications, such as high-speed web surfing, BC-based approaches might not be suitable.

10.8 Design of New Services Through the use of BC technology, connected cars could be enabled to operate seamlessly, allowing the users to collect and share their data in exchange for a financial contribution. This concept is very beneficial for the vehicle economy as it will allow the users to store and manage their data without having to establish a financial institution.

10.9 Future Architecture Integrations There are a number of technologies that should be integrated in IoV: artificial intelligence, edge computing, and software-defined networking. BC could be used to improve these technologies or even secure them. Its immutability could allow it to be used to improve various aspects of AI. This chapter, inspired by paper [84], proposes using blockchain technology to improve the security and privacy of a hybrid vehicular framework. This chapter tackles one of the main challenges of the integration of various technologies into one vehicle architecture. In order to get the full benefit of blockchain technology, this chapter proposes to implement blockchain in a hybrid framework that combines various technologies such as 5G, fog computing, and SDN.

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11 Conclusion Each and every car will be linked to the Internet in the future vision of the Internet of Vehicles, and blockchain technology is anticipated to offer credit support for the fundamental information of vehicles at a cheap cost. The distributed ledger (blockchain) provides an effective way to circumvent the centralised Internet of Value (IoV) architecture. As a result, our research has begun with preliminary background covering the fundamental architecture of IoV, IoV’s difficulties, and short explanations of blockchain technology, as well as IoV’s reason for using blockchain technology. We also addressed the study’s motives by highlighting the difficulties connected with IoV and the realisations of decentralisation, great flexibility, accessibility, and trustworthiness, which were followed by some significant use cases of blockchained IoV. Based on this analysis, many significant difficulties in the IoV (including unfulfilled expectations and challenges) seem to be just around the corner. It is anticipated that the combination of blockchain technology with IoV will substantially enhance the functionality of transportation networks. We believe this chapter will be useful as a starting point for further study.

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Reliable System for Bidding System Using Blockchain N. Ambika

1 Introduction Blockchain [5, 18] innovation that starts from Bitcoin, the first digital currency framework propelled in 2008, can give a viable answer for IoT protection and security. The e-sell-off [7] is one of the well-known web-based business exercises and enables bidders to legitimately offer the items over the Internet. Concerning the fixed offer, the additional exchange cost is required for the middle people because the outsider is the significant job between the purchasers and the merchants help to exchange both during the sale. It never ensures whether the outsider can be trusted. To determine the issues, the blockchain innovation [3] with low exchange cost is utilized to build up the brilliant agreement of open offer and fixed offer. The savvy contract, proposed in 1990 and executes through the Ethereum stage, can guarantee the bill secure, private, non-reputability and inalterability inferable from every one of the exchanges are recorded in the equivalent however decentralized records. Bitcoin is a rule that sequences events into collections called segments. The procedure targets a section generation interlude of ten moments with the highest capacity of 1 MB. The ultimate 100 blocks had a 0.99 MB midpoint block volume and a 9.8-min mean interim. The circuit order executes a peer-to-peer arrangement based on flooding block and activity reporting. The peer-to-peer system is created by point-to-point connections. To make a joint, customers authorize a TCP attachment and complete a protocol-level three-way handshake. The protocol-level handshake transfers the status of individual clients, such as the slope of the blockchain [3, 4] and a transcription sequence amalgamated with the software being administered.

N. Ambika () Dept. of Computer Science and Applications, St. Francis College, Bangalore, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_9

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Fig. 1 Principles of blockchain

When a consumer finds or takes a new segment, it overwhelms the system with the jumble of the intersection. If an adjacent customer requires the block, it demands the block based on the hash content. Blockchain [5] innovation that starts from Bitcoin, the first digital currency framework propelled in 2008 [5], can give a viable answer for IoT [25] protection and security, because of its three fundamental principles: • Information in the blockchain is put away in a common, appropriated, and deficiency-tolerant database that each member in the system can share the capacity to invalidate foes by saddling the computational abilities of the genuine hubs and data traded is versatile to control. • Blockchain is a decentralized engineering to make the designs strong against any disappointments and assaults. • Blockchain depends on an open key framework which enables the substance to be encoded in a manner that is costly to split. Figure 1 portrays the same. Figure 2 represents the interplay of other factors.

1.1 Features of Blockchain 1.1.1

Decentralization

The features allude to the procedures of information confirmation, stockpiling, support, and transmission on the blockchain, which depend on a disseminated framework structure. The trust between appropriated hubs is worked through numerical strategies as opposed to the concentrated associations. The presentation position [20] is the Jean Jaurès primary academy with a wood-fired evaporator. It is a temperature generator. The customers and sustaining workers follow-up on their recovery steps to increase power administration. Virtual nodes are added to the framework. The performance of supplementary estimates the scaling potential for an eco-district. The green documents are allocated to regional generators. The credentials are bought by energy consumers. The transaction is certified by

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Social Economic Aspects - business ethics - fair trade - worker’s benefits

SOCIETY

ECONOMICS

- Standard of living - Education - Jobs - Equal opportunity

- Growth - Profit - Cost saving - R&D

SUSTAINABILITY

Social Environmental: - Conservation policies - Environmental justice - Global stewardship

ENVIRONMENT - Natural resource use - Pollution prevention - Bio-diversity

Environmental Economic - energy efficiency - renewable fuels - subsidies, incentives - green technology

Fig. 2 Interplay of other factors [35]

the blockchain controller node. It plays the role of central memory of certificate transactions. Three client interfaces have been received and driven on the Predix principles. All the segments of the blockchain store construction framework [6] with their functionalities are distinct. It gives ease, pace, and effectiveness. It has two components. The customer segment is in the end-user tier. The framework is a blockchain store having three tiers. The end-user tier assists in the communication between the end users. It is implemented by the management of the graphical interfaces or workflow administration. The end users can use assistance from the current production methods on the structure via blockchain technology as the P2P method is preferred between the possible cooperations. The focus zone supervises the organization by achieving all the additional courses on conformity. Its main competency is to produce a blockchain interface and sustain the P2P arrangement. The sub-responsibility describes warehouse providers, scheduling of the regularity, administration, and subsistence of the operation. The NIST design [2] characterizes characters, administrations, and thoughts into seven distinct regions and their subareas. These regions have buyers, businesses, assistance providers, enterprises, formation, communication, and relationships. The customer specialty consists of the ultimate power customers in the energetic framework. The demand areas are formed of current business associates. Assistance providers are the items that present store and end computation assistance to

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clients, prosumers, and benefits. The employment region consists of autonomous operation workers that promote intelligent framework utilization to manage edge and store computing assistance and administer diverse market principles. The volume production, communication, and administration areas are the generators and transporters. The underlying P2P systems and dispersed record technology of the blockchain arrangements are obliged to guarantee consensus-based power expenditure info participating and collaborative utilization of strength supplies between users, prosumers, and services. The cryptocurrencies and influence tools are wanted to produce new decentralized trading paradigms among prosumers and uses. It [17] has seven positions. Electors do a modification for polling. Certification server establishes the voter’s identification and presents qualified taxpayers with voting documents. The authorization host checks the record. The polling method is the administration of the appointing executives. Recording center stocks the certification. Shared info hosts save the encrypted coordinates of features of the chosen amount. Intelligent engagement is a productive season to repair the functionality of the proverbial summary provisions. It can calculate the tickets to improve the trustworthiness and authenticity of the voting. In the initial frame [15], the agencies start redundancies. The tools convey an introductory assemblage of knowledge. It assists their opinion for their allocation in the business solvent. The collection of learning is a resolution package. The answer container is fashioned of arbitrary measures but in the capacities of the original suggestions. The representatives renew the supplies that participated in the clarification parcels in all degrees according to the pheromone tiers. In the initial repetition, every agency determines the accurate purpose concerning its possible period and the answer packages they collected from all additional representatives. Every ant’s journey describes a potential answer to the business puzzle. After all, insects make their opening travels, and the health of every voyage is assessed. The robustness is assessed based on two principles. The benefits of the weighted normal of the health measures are used to decide the ablest journey. This metric recognizes the suspension with the highest human progress and least positive net surplus. The refusal standard is introduced, which excludes warehouse answers with net power excess that exceeds a permitted negligible failure of ±5%. The DACO uniquely proposes two tiers of pheromones updating. The initial tier is a regional pheromone updating based on the achievement tier of the ants to their assignments. Every ant determines behind its traces and deposits pheromones on the advantages it inflicted. The purpose is to recognize the ants to investigate new listings with the low significance of pheromones congregation. The suggestion [32] combines a Turing comprehensive programming conversation with intelligent engagement computing functionality. An answer is developed that authorizes the establishment of structures. The shareholders conserve realtime inspection by adding the necessary components. The governance arrangements are formalized, automatized, and inflicted utilizing the software. The necessary regulations for good agreement are composed to make a decentralized autonomous establishment on the Ethereum blockchain. It creates a conceptual arrangement

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course with a built-in Turing-complete programming communication. It authorizes any character to draft intelligent agreements and decentralized importance. They compose their own discretional rules for the control, performance setups, and status development agenda. Ethereum Virtual Machine Code is low-level, stack-based bytecode semantics. Ethereum contracts are addressed in EVM principles. Every byte in the regulations communicates for a purpose. When the law accomplishes, it begins in an unending circuit that consists of many terms achievement of the gathering at the prevailing schedule board. It starts with a void value and increases by one till the code is stopped.

1.1.2

Traceability

This feature implies that all exchanges on the blockchain are masterminded in sequential requests, and a square is associated with two contiguous squares by the cryptographic hash work. In this way, every exchange is identifiable by looking at the square data connected by hash keys. Produce suppliers and retailers utilize for traceability assistance [24] for various determinations. Suppliers want to get documents to determine their output origin and feature to customers and comply with ordinances. Retailers need confirmation of the product source and essence. Each result supplier that practices the partner assistance has on medium 20 outcomes to be traced. The traceability knowledge granularity is generous. It replies to results from groupings rather than single outputs. This information’s measurement isn’t simple to predict because many reports currently aren’t digitized, such as licenses announced by traceability assistance providers. The traceability helps the provider and confirms an application from a product supplier or retailer based on paperwork. The two parties sign a legal agreement. It generates a clever arrangement that serves the contractual transaction. The intelligent understanding arranges the organization of cooperations and other limitations described in the correspondence. It also examines whether all the learning needed by the ordinance is provided. It enables automatic regulatory compliance checks. The practice [34] relies on RFID methodology to perform knowledge recovery, distribution, and experience in the creating, processing, warehousing, delivery, and selling sections of the agri-food equipment series. The feed protection disaster occurred; administration activities could take crisis steps promptly to limit the spreading of the danger. The contribution [9] is a fully decentralized traceability system for the agri-Food supply series administration. The tiered structure can rely on the blockchain and IoT technologies. It manages the clarity, audibility, and immutability of the collected recordings in treacherous circumstances. It takes benefit of the growing abilities granted by advanced edge tools. It completes connections of coursed blockchain scheme. It prolongs the stability, distribution, safety, and confidence of the complete arrangement. API is a REST utilization programming interface proving the abilities of AgriBlockIoT to additional reinforcements. It has a level of distraction,

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permitting a smooth combination with actual software methods. The controller is segmentable of changing the high-level purpose commands into indistinguishable low-level signals for the blockchain panel. The principal ingredient of the practice includes all the transaction inferences. It implements by intelligent contracts. It is a hub to the chain itself. It will change in complexity according to the plan capacities of the selected and the skills of the customer. The stock series [8] consists of various characters and details the method from the beginning till the end outcome in the stocks obtained and employed by a customer. The traceability of players is managed via private traceability among the characters of inner arrangements. Central in the structure is that all characters in the feedstock succession provide knowledge to and recover learning from the Food Safety Information System via diverse technology. It includes different data necessary to complete clearness and support of excellence among the feed store’s connection characters. It comprises the protection and feature management of the stock series players. Traceability data that reflect compliance with these arrangements are collected in the FSIS. A clever agreement is network regulations in the blockchain that is administered once requirements are satisfied. The determining process of operation is automated and inevitable as predefined in the philosophy of the processor principles. The case analysis examines the characteristics of the farm stores connection methods of four various trading situations. The outcomes show features among the events and disagreements. The surface efficacy presents discussions in businesses with comparable industry conditions. It provides to induce the decisions concerning frame situations.

1.1.3

Immutability

There are two reasons that blockchain innovation is changeless. From one perspective, all exchanges are put away in hinders with one hash key connecting from the past square and one hash key highlighting the following square. Messing with any exchange would bring about various hash esteems and would along these lines be distinguished by the various hubs running a similar approval calculation. Then again, blockchain is a shareable open record put away on a huge number of hubs, and all records keep on adjusting continuously. Effective altering would need to change over 51% of the records put away in the system.

1.1.4

Currency

The quintessence of blockchain innovation is highlight point exchanges; no outsider is included, which implies that all exchanges don’t require the support of outsiders. The course of computerized cash dependent on blockchain innovation is fixed. In particular, in Bitcoin, the money base is set at 21 million tops, so the age of computerized cash is made by utilizing a particular mining calculation and is limited by a precharacterized recipe. Consequently, there won’t be the issue of

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Fig. 3 Features of blockchain

expansion, breakdown, etc. In Blockchain 2.0 and 3.0 applications, the mix of different exercises, for example, government exercises, instructive exercises, and money-related exercises, can make these nonmonetary exercises have the property of cash. Figure 3 portrays the features of blockchain. The e-auction [33] is one of the well-known web-based business exercises, which permits bidders to legitimately offer the items over the Internet. With respect to the fixed offer, the additional exchange cost is required for the middle. The outsider has significant job between the purchasers and the venders helping to exchange both during the bartering. Also, it never ensures whether the outsider can be trusted. To determine the issues, the blockchain innovation with low exchange cost is utilized to build up the brilliant agreement of open offer and fixed offer. E-sell-off has two principle issues. Initially, an incorporated mediator is required in offering a framework to help correspondence among bidders and salespeople. The charge expenses for the concentrated go-between to build the exchange cost. Also, the individual information and exchange records are put away in a database that may cause security spillage. Furthermore, in a fixed envelope, bidders have no real way to guarantee that lead bidder never releases their offering cost. The work [11] applies the blockchain method into the e-sale to determine the two issues. The blockchain is a shared access structure with the end goal that focuses on the structure that can confide in one another focuses. Every area can safely convey, verify, and move information to any of different destinations. Therefore, in the decentralized structure, the brought together go-between can be expelled to lessen the exchange cost. With respect to the subsequent issue, the brilliant contract is utilized to evade the offer value spilled by the lead bidder. A few standards are

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composed inside the shrewd arrangement, which cannot be opened before the cutoff time. The proposal [11] is made out of the location of auctioneer, the beginning closeout time, cutoff time, the location of the current winner, the current most significant expense. The keen agreement is a lot of codes and digits actualized by means of the Ethereum stage. In an astute understanding, the agreement will begin if the time or occasion is activated, for example, communicating something specific, managing exchanges, and ending the agreement. Prior to the cutoff time, all the legitimate bidders can send the fixed envelope to restore the cost. All the fixed envelopes are opened when the time is expected. The most significant expense on the fixed envelope is the last champ. During public bidding, bidders can offer a few times; in this way, open offer is additionally called multi-offering sell-off. Fixed offer is that bidders scramble the bill and send the bill once. On the off chance that the time is expected, the salesperson looks at all of the bills. The bidder who offers at the greatest expense is the victor of the fixed offer. The proposed system embeds blockchain technology. The procedure is divided into three phases. In the registration phase, the user is to register himself with the server by providing the details of the device and himself. In the broadcast phase, the server broadcast the auction details to all the registered users. In the auction phase, the user is provisioned to transmit his auction value. This methodology aims in bringing reliability to the system by using the device identity and biometric extract as the parameters. Following the introduction, literature survey is summarized in Sect. 2. The notations are listed in Sect. 3. The proposed work is described in Sect. 4. The work’s security is evaluated in Sect. 5. The work is concluded in Sect. 6.

2 Literature Survey Many contributors have used the blockchain methodology in different applications. The same is briefed in this section. Shaikh and Iliev [31] introduce an exchange preparing framework for e-trade by utilizing blockchain innovation and zero-information verification, and changed elliptic curve cryptography encryption is proposed. A strategy sharing the database among the members is given by the blockchain innovation regardless of whether they don’t confide in one another. Based on the distributed system, it produces a commercial center to move resources without a focal position. At that point, the zero-information confirmation strategy is handled; given this solitary, a blockchainbased TPS (Bb-TPS) is dealt with and exhibits its functionalities of nonstop observing, bookkeeping, and consent the executives in the constant applications. At last, the altered elliptic bend cryptography is utilized to scramble the information by utilizing the improvement strategy cuckoo search (CS) calculation. The private key and open key are the two keys utilized in ECC. Current general well-being data innovation frameworks, for example, qualification, enlistment, and electronic wellbeing records, have archived issues with

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interoperability and are delayed to adjust to changing project and innovation requests. We recommend that blockchain can unravel these issues that can be understood. The Medicaid Management Information Systems (MMIS) program [28] burned through $3.7 billion out of 2015, and complete organization and other innovation spend on qualification frameworks, electronic well-being records, and innovation related to the organization were over $25 billion every 2015. The guarantee of blockchain can conceivably fathom these issues at a decreased expense because of the relative simplicity of sending versus conventional equipment and programming foundation. Ranganthan et al. [29] introduce an application that cures each of the three disadvantages using the Ethereum blockchain stage. The application was created utilizing the Truffle advancement system. The application’s capacities were contained inside an Ethereum keen agreement, which was then moved to the Ethereum organize. An Ethereum arrange is made out of a lot of hubs running an Ethereum customer. Every one of these hubs has a duplicate of the blockchain, which contains a rundown of all tasks performed on the system. This empowers hubs to forestall fake exercises, for example, forging and copying digital forms of money, and also containing an auditable record of all exchanges performed on the system. Because of the decentralized idea of the system, the Ethereum structure jam client pseudonymity, as every client’s character, is given by a public credential. This empowers clients on the stage to perform capacities, for example, moving cash, purchasing, and selling. To take care of the twofold spending issue [21], every calculation hub in the blockchain arrange needs not only to store each exchange to empower the dispersed confirmation of the exchanges but also to follow an appropriated timestamp system to figure out which exchanges ought to be acknowledged and which ought to be dismissed. An extra advantage of the evidence of work accord convention utilized in the blockchain is the capacity to determine contradiction of the chains and hence lets blockchains be changeless review trails. That is the point at which an aggressor alters a square; all the squares after that square are recomputed because each square contains the hash estimation of the past square’s header, and the computational expense of such change ought to be sufficiently high to preclude assaults. Huh et al. [19] Ethereum is used as the blockchain stage, utilizing its brilliant agreement. They compose their very own Turing-complete code to run over Ethereum. In the work of [12], each brilliant home is furnished with a constantly on the web, high asset gadget, known as “excavator” that is answerable for taking care of all correspondence inside and outside the home. The digger likewise saves a private and secure BC, utilized for controlling and evaluating interchanges. The authors show that our proposed BC-based keen home structure is secured by completely investigating its security as for the central security objectives of classification, uprightness, and accessibility. Dorri et al. [1] is a lightweight BC-based design for IoT that takes out the overheads of exemplary BC, while keeping up the vast majority of its security and protection benefits. IoT gadgets profit by a private unchanging record, that demonstration like BC, however, is overseen midway to enhance vitality utilization. High asset gadgets make an overlay system to execute a freely open circulated BC that

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guarantees start-to-finish security and protection. The proposed engineering utilizes circulated trust to diminish the square approval preparing time. We investigate our methodology in a brilliant home setting as a delegate contextual analysis for more extensive IoT applications. An architecture for scalable access management in IoT by Novo [26] is another design for refereeing jobs and authorizations in IoT. The new design is a completely circulated access control framework for IoT dependent on blockchain innovation. The engineering is upheld by proof of idea usage and assessed in practical IoT situations. The plan works in a solitary brilliant agreement, streamlining the entire procedure in the blockchain system and diminishing the correspondence overhead between the hubs. Furthermore, the entrance control data is given to the IoT gadgets in ongoing. Zhang and Wen’s [36] IoT electric business model is an e-business engineering structured explicitly for IoT, which is based on the convention of the Bitcoin. The creators have embraced conveyed self-sufficient enterprises (DACs) as the exchange element to manage the paid information and keen property. DACs can offer paid administrations with no human contribution under the influence of an honest arrangement of business rules. These guidelines are executed as freely auditable open source programming conveyed over the PCs of their partners. In the proposed e-business design, individuals can exchange with DACs to acquire IoT coins through P2M. Liang et al. [22] suggest a trusted and versatile engineering for IoT administration dependent on blockchain, which gives the capacity to self-trust, information uprightness review, and information flexibility, just as adaptability. Drone is a runof-the-mill microcosm of IoT, where automatons gather information from inserted sensors and cameras and get the directions from remote control frameworks. Each control directly from the control framework or the cloud server is responsible for transferring the activity records to the blockchain arrange. This gives every activity a unique finger impression, which makes each activity discernible. The circulated idea of blockchain hubs adds to the accessibility of the two information and information approval, making it an on-request administration with no personal time. Ouaddah et al. [27] suggest the utilization of SmartContract to express finegrained and logical access control strategies to settle on approval choices. The structure uses the consistency offered by blockchain-based digital forms of money to tackle the issue of concentrated and decentralized access control in IoT featured at the start of this paper. In FairAccess, the creators have settled on approval tokens as access control component, conveyed through developing cryptographic money arrangements. They use blockchain right off the bat to guarantee to assess access approaches in dispersed conditions where there is no focal power/executive and assurance that arrangements will be appropriately authorized by all interfacing elements and to guarantee token reuse identification. Shafagh et al. [30] suggest a blockchain-based structure for the IoT that brings appropriated access control and information to the executives. The structure is customized for IoT information streams and empowers secure information sharing. They empower a protected and versatile access control for the executives, by using

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the blockchain as an auditable and circulated get to control layer to the capacity layer. They encourage the capacity of time-arrangement IoT information at the edge of the system by employing a territory-aware decentralized stockpiling framework leading to blockchain innovation. The framework is freethinker of the physical stockpiling hubs and supports also usage of distributed storage assets as capacity hubs. Blockchain innovation can be applied to training from numerous points of view past just recognition of the board and accomplishments appraisal [10, 11]. For the two students and instructors, blockchain innovation has an incredible potential for more extensive application prospects on developmental assessment, learning exercises structure and execution, and continue following the entire learning forms. The shrewd agreement among instructors and understudies can be applied to the instructive situation. Continuous honors can be given to understudies through some straightforward snaps by the teachers. Understudies will get a specific number of computerized cash as indicated by shrewd agreements as remunerations. This sort of cash can be put away in the instruction wallet, utilized as educational cost, even traded with genuine monetary standards. Dujak and Sajter [13] intend to present the idea of blockchain and its present applications in coordination and supply systems. Blockchain innovation guarantees overwhelming trust issues and permitting trustless, secure, and confirmed frameworks of coordination and inventory network data trade-in supply systems. The absolute most significant current execution territories of blockchain in coordination and inventory network are following the item starting point just as the following item move through stockpile arrange and request gauging, diminishing of fake and extortion chance, open access to data in the production network, lessening the negative effect on the surroundings, and exchange atomize through keen agreements. Liang et al. [22] is an imaginative client-driven wellbeing information-sharing arrangement. It uses by a decentralized and permissioned blockchain to ensure protection utilizing channel development plan and improves the personality of the board. A versatile application is conveyed to gather well-being information from individual wearable gadgets, manual information, and clinical gadgets and synchronize information to the cloud for information imparting to medicinal services suppliers and health care coverage organizations. To save the respectability of well-being information, inside each record, proof of honesty and approvals is for all time retrievable from the cloud database. This record is then submitted to the blockchain arrange, which is trailed by a few stages to change a rundown of records into an exchange. A rundown of exchanges will be utilized to shape a square, and the square will be approved by hubs in the blockchain arrange. After a progression of procedures, the respectability of the record can be saved, and future approval on the square and the exchange identified with this record is accessible. Each time there is a procedure on the individual well-being information, a record will be reflected in the blockchain. This guarantees each activity close to home well-being information is responsible.

176 Table 1 Description of work complexity

N. Ambika Contribution [31] [21] [19] [1] [26] [36] [22, 23] [27] [30] [10, 11] [13] [22, 23] [11] [11]

Work complexity N*O(N2 log N) (N) O(N3 ) N* O(logN) (log N) O(N5 ) O(N*log N2 ) N*O(2N ) O(Nα ) O(2N ) O(log N!) 2*O(log N) O(log N2 ) O(N)*N2

The work of Chen et al. [11] applies the blockchain method into the e-sale to determine the two issues. The blockchain is a shared access structure with the end goal that focuses on the structure that can confide in one another focuses. Every area can safely convey, verify, and move information to any of different destinations. Therefore, in the decentralized structure, the brought together go-between can be expelled to lessen the exchange cost. With respect to the subsequent issue, the brilliant contract is utilized to evade the offer value spilled by the lead bidder. A few standards are composed inside the shrewd arrangement, which cannot be opened before the cutoff time. The proposal [11] is made out of the location of auctioneer, the beginning closeout time, cutoff time, the location of the current winner, and the current most significant expense. The keen agreement is a lot of codes and digits actualized by means of the Ethereum stage. In an astute understanding, the agreement will begin if the time or occasion is activated, for example, communicating something specific, managing exchanges, and ending the agreement. Prior to the cutoff time, all the legitimate bidders can send the fixed envelope to restore the cost. All the fixed envelopes are opened when the time is expected. The most significant expense on the fixed envelope is the last champ. During public bidding, bidders can offer a few times; in this way, open offer is additionally called multi-offering sell-off. Fixed offer is that bidders scramble the bill and send the bill once. On the off chance that the time is expected, the salesperson looks at all of the bills. The bidder who offers at the greatest expense is the victor of the fixed offer. Table 1 is the representation of the contributions complexity.

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Table 2 Notations used in the study Notations Ui N As Ri Ad Uid Ul Ubio Aid Al Abio Tb Te Tc Bu Tu h(..)

Description ith user of the network Network in consideration Auctioneer server ith request of the user Ui Auction details transmitted by the auctioneer server User’s device identity User’s location User biometric extract Auctioneer’s device identity Auctioneer’s location Biometric extract of the auctioneer Beginning time of the auction Close time of the auction Cutoff time of the auction Bidding value of the user Timestamp of the user Hashing algorithm (blockchain algorithm)

3 Notations Used in the Study Table 2 lists the notations used in the proposal.

4 Proposed Work Online bidding aims in providing flexibility to the user. The user is provided with the timeslot to provide his bidding value. This received value is evaluated, and the winner is concluded. To achieve this process, the user is to get registered with the server. The server will validate the user and make an entry into the system. To make the system better, blockchain can be utilized to enhance security to the system. The proposed system embeds this technology. The procedure is divided into three phases. In the registration phase, the user is to register himself with the server by providing the details of the device and himself. In the broadcast phase, the server broadcasts the auction details to all the registered users. In the auction phase, the user is provisioned to transmit his auction value. (a) Registration Phase The respective users get registered with the auction system using the device unique identity, biometric extract, and location information. In the notation (1), the user Ui transmits requests Ri , its identity Uid , user biometric extract Ubio , and

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location information Ul to the auctioneer server As .



Ui → As : h Ri ||Uid || Ul Ubio

(1)

Both the communicating parties have to undergo mutual authentication before starting to bid. As the system is sharing the hashed values, the confidentiality is maintained. In Eq. (2), the auctioneer server As is calculating the hash value using the received message – device identity Aid , location of the user Al , auction details Ad , and biometric extract Abio :

As → Ui : h (Ad ) h (Aid | |Al | |Abio ) (2) This hashed value is used as identification by both the communicating parties by identifying themselves during auction time. (b) Broadcast Phase At the time of auction, all the devices are provisioned to give their options. The auction server broadcasts the cutoff time to its clients. In the Eq. (3), the auctioneer server As is transmitting cutoff time Tc , beginning time Tb , closeout time Te , and hashed identity to the auctioneer to the network N:

As → N : h (Tc ||Tb || Te ) h (Aid | |Al | |Abio )

(3)

(c) Auction Phase The user inserts the hashed identity, hashed bidding value, hashed timestamp and transmits the same to the auction server. In Eq. (4), the user Ui is transmitting the hashed bidding value Bu , timestamp Tu , and hashed identity to the auction server As : Ui → As : h (Uid , Ul , Ubio ) | |h (Bu )| | h (Tu )

(4)

Using the received data from various users, the auction server concludes with the winner of the auction. The server applies the auction rules to conclude the winner, and the same is broadcasted to all the registered users.

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5 Security Analysis The proposal [11] is made out of the location of auctioneer, the beginning closeout time, cutoff time, the location of the current winner, and the current most significant expense. While the suggested proposal aims in enhancing reliability to the system. The proposed system embeds this technology. The procedure is divided into three phases. In the registration phase, the user is to register himself with the server by providing the details of the device and himself. In the broadcast phase, the server broadcast the auction details to all the registered users. In the auction phase, the user is provisioned to transmit his auction value. Comparing to the previous work, the proposed work uses device identity and biometric extract of the user in the work to improve reliability to the system. Table 3 lists the parameters used in the study. (a) Reliability The previous system uses the location details while the proposed system provisions the system to use device identity and biometric extract to enhance the reliability of the system. The auctioneer will be able to trust the user better in the proposed system. The proposed system enhances reliability by 11% compared with Chen et al. [11]. The same is represented in the Fig. 4. Table 3 Lists the parameters in the study

Fig. 4 Comparison of work w.r.t Reliability

Parameters No of users used No of auctioneer Time duration Beginning time Close time Cutoff time Length of the biometric extract Length of location information Length of device identity Length of timestamp

Description 5 1 60 ms 0 ms 30 ms 28 ms 32 bits 8 bits 16 bits 12 bits

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6 Conclusion Online bidding aims in providing flexibility to the user. The user is provided with the timeslot to provide his bidding value. This received value is evaluated, and the winner is concluded. To achieve this process, the user is to get registered with the server. The server will validate the user and make an entry into the system. To make the system better, blockchain can be utilized to enhance the security of the system. The proposed system embeds this technology. The procedure is divided into three phases. In the registration phase, the user is to register himself with the server by providing the details of the device and himself. In the broadcast phase, the server broadcast the auction details to all the registered users. In the auction phase, the user is provisioned to transmit his auction value. The proposed system enhances reliability by 11%.

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Security Challenges and Solutions for Next-Generation VANETs: An Exploratory Study Pavan Kumar Pandey, Vineet Kansal, and Abhishek Swaroop

1 Introduction Wireless communication and Cloud computing [1] has evolved and gained significant popularity due to their applicability and several recent technical advancements in the area of communication. Mobile ad hoc networks (MANETs) [2] and wireless sensor networks (WSNs) [3] [4] are a few popular examples of wireless communication-based networks. Vehicular ad hoc networks (VANETs) [5] and flying ad hoc networks (FLANETs) [6] are subclasses of mobile ad hoc networks. By utilizing fixed roads and roadside units (RSUs), VANETs provide infrastructure less communication framework for vehicles and other infrastructure nodes. Figure 1 presents vehicular networks based on different factors such as used components, types of communication, and their applications. Participating vehicles in VANETs are equipped with onboard units (OBUs), a global positioning system (GPS), and other sensors. Widespread RSUs in VANETs work as traffic authorities for facilitating registration, tracking, and monitoring of vehicles. Several OBUs and other sensors can communicate for the Internet of Things (IoT) applications through wireless communication. Substantial enhancements in the vehicle’s capabilities and evolved communication technologies contrive the design of an intelligent transportation system (ITS) [7].

P. K. Pandey () Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India V. Kansal I.E.T. Dr. A.P. J. Abdul Kalam Technical University, Lucknow, India A. Swaroop Bhagwan Parshuram Institute of Technology, New Delhi, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_10

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Communication Range Types of

 Short

Communication

Challenges

 Wide area  Efficient

 Vehicle to

Routing

Vehicle (V2V)

 Security

 Vehicle to

 Power

Infrastructure (V2I or I2V)

Management

VANETS

Applications Entities  Comfort Driving  Traffic Efficiency  Traffic Management  Infotainment

 Vehicles  RSU  Central Authority  Data Network

Fig. 1 Graphical taxonomy of VANETs

VANETs possess characteristics such as dynamic topology, high mobility, and variant network size. Consequently, numerous challenges are associated with VANETs such as efficient routing [8], security [9], and power management. By considering these challenges, traditional routing strategies have been proposed in [10]. ITS supports short-range vehicular communication among vehicles known as vehicle-to-vehicle (V2V) communication. Moreover, communication between vehicles and other fixed infrastructure nodes called vehicle-to-infrastructure (V2I) communication also exists. Figure 2 presents V2V and V2I communication in VANETs. The further evolution of cellular technology led to the integration of 5G technology [12] with VANETs to improve flexibility, scalability, and mobility management. Load distribution challenges [13] are also associated with high-speed VANETs. Software-defined networks (SDN) [14] play an important role in the integration of 5G by decoupling control functions from the data plane. SDN is a logical network paradigm to manage networks in a centralized manner.

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Fig. 2 Types of vehicular communication [11]

SDNs [15] are three-tier networks that have layers of application, control, and infrastructure. They provide a centralized view of the networks and their associated hardware. SDN is a technology that enables network programmers to develop new services without requiring the usual hardware or software updates. Therefore, SDN eliminates the need for manual intervention and allows networks to run smoothly. SDN-enabled communication standard is different from the traditional networking paradigm based on various factors such as configuration, performance, and features. The function of each layer is explained below: • Application layer: The network services that are defined within this layer consist of path reservation, network configuration, and network topology. These services use the service of control layer and infrastructure layer. SDN applications can be designed by using network virtualization (NV), network function virtualization (NFV), and information content networking (ICN) [16]. • Control layer: The control layer is a network device that can install and modify the flow rules according to the running applications. It also keeps the flow table up to date with the changes in network topology. • Infrastructure layer: This is also known as a data plane. A data plane is a forwarding entity in a network that forwards packets by following instructions and messages from a controller. A communication standard, which is known as open flow [17], is used for sending flow instruction instructions and messages between controller and switch.

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Fig. 3 5G VANETs integrated architecture [18]

In NG-VANETs, evolved node base controllers (eNBCs) play an important role in providing intelligence for policy management and traffic control. At the same time, the road-side controller (RSC) is inducted for sharing the load with eNBC on traffic management. RSC is key to the 5G-VANETs that manage the control plane with eNBC. In addition to that, RSC handles data plane and security plane in 5GVANETs with vehicles. 5G-based VANETs architecture is presented in Fig. 3. Numerous ITS applications [19] such as traffic efficiency, comfort driving, infotainment in vehicles, and traffic management have attracted lot of researchers and industry personal in recent years. Therefore, several researchers have discussed the security challenges, attack models, and their respective solutions in VANETs so far. This chapter focuses on the security perspective of NG VANETs. Our major contributions in the current exposition are listed below: • Security requirements for NG-VANETs are investigated and discussed. • Several security threats and attacks are classified based on their ultimate impact. • Few best-suited security solutions have been discussed for targeting security services for NG-VANETs. • Comparative analysis of discussed security solutions has been presented based on their applicability in VANETs. The remaining chapter consists of three more sections. Section 2 explains security requirements in VANETs with details of possible security threats and

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attacks. The next section presents some recently proposed security methods to secure the NG-VANETs. We conclude our discussion in the last section.

2 Security Requirements The specific characteristics of VANETs such as high mobility, low trust management, and inefficient key distribution lead to several security challenges in vehicular communication. The next-generation VANETs are more vulnerable to security threats. Therefore, proper treatment of security attacks is required to make sure that no alteration in messages and traffic information is exchanged among vehicles.

2.1 Security Services As part of security requirements, several security services [20] are used to measure security imposed on VANETs. Some of the security objectives in VANETs are: Authentication This is to make sure the correct identification of the sender of received messages in terms of identity, location, etc. It can be achieved using certificates and pseudonym methods. Confidentiality Confidentiality intends to restrict the access of messages for sender and receiver only. Some predefined rules and keys are used to ensure confidential communication. Integrity It ensures the correctness of messages and makes sure that the transmitted messages are not altered or dropped in communication. Privacy Nondisclosure of the identity of vehicles and RSUs etc. against unauthorized access is known as privacy. Availability Availability demands the system always be in the operative mode, and a wireless interface should be always available for communication. The availability is enhanced by resisting DoS attacks etc.

188 Table 1 Security services for different vehicular communication mode

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VANET communication mode V2V communication V2I communication V2V and V2I communication

Security services Content verification Access control Traceability Privacy Authentication Availability Confidentiality Integrity

Content Verification This is to avoid false messaging in the system by verifying the content of messages. Data verification must be done to check data consistency in multiple messages for ensuring content correctness. Access Control Access control derives some protocol and mechanism which is to be followed for accessing critical information and resources. Every entity must act in a network according to rules and role privileges. Traceability It provides a mechanism that can verify the location or history of any entity by using some recorded identification. Since the real identity of vehicles should not be revealed, some other mechanism must be used to obtain the real identity of the vehicle for tracking. After discussing several security services required in vehicular communication, Table 1 presents a further classification of discussed services based on their applicability in different communication modes. All discussed security services are classified in three communication modes, namely V2V, V2I, and both V2V and V2I.

2.2 Security Attacks Deployment scenarios in VANETs are highly vulnerable to several types of attacks. The incorporation of recent technologies caused increased security concerns for future generation VANETs. Sometimes optimization [21] techniques can help in securing the system. However, proper security solutions are required against security attacks. Several researchers have investigated attacks [22] and classified them into subcategories based upon different parameters. Relevant attacks for NG-VANETs are listed and classified in different subcategories in Fig. 4.

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User Based

Content Based

• Repudiation

• Malware & Spamming

• Masquerading

• Bogus Information

• Location Tracking

• Message Alteration

• Brute force

• Repression

• Illusion

• Sybil Attacks

• Identity & Location

• Forgery

Revealing

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• Message Delay

Security Attacks in NG-VANETs

Channel Based

Network Based

• Jamming

• DoS / DDoS

• Blackhole

• Unauthorized Access

• Man in Middle (MiM)

• Eavesdropping

• Grayhole Attack

• Clone Attack

• Wormhole

• Session Highjack

• Replay

• Timing Attack

Fig. 4 Classification of attacks in NG-VANETs

As evident from Fig. 4, several attacks are filtered by considering high bandwidth utilization, centralized control (cloud), and powerful sensors used in vehicular communication. These attacks are further categorized based on their impact on corresponding participating entities in communication. Therefore, these attacks are divided into four different categories: user-based, content-based, channel-based, and system-based attacks. User-Based Attacks These attacks target the identification and personal details of vehicles and RSUs for reducing the effectiveness of communication in several ways.

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• Repudiation – Any participated nodes including vehicles or RSUs deny communication. • Masquerading or impersonation or spoofing – To get some additional privileges, attackers take the identity and location of some other legitimate user and participate in the communication. • Location tracking – By launching this attack on GPS, the attacker tracks the location of any vehicle to misuse that information. • Brute force attack – By using this method, the confidential details of users such as identity number, user ID, and password are stolen and misused. • Illusion attacks – Tampering of sensors and software used by communication entities to broadcast incorrect and misleading details into a network. • Identity and location revealing – By attacking some common servers of the system, disclosing the details such as identity and location of any participant.

Content-Based Attacks Attacks discussed under this section affect the transmitted message directly and tempered the communicated data. • Malware or spamming – Sending spam messages into a network for affecting QoS of the network such as latency and bandwidth consumption. • Bogus information – Attackers float fake information into the network intentionally that affects the behavior of vehicles and RSUs in traffic. • Message alteration or repression – The act of dropping or modifying the messages by adversaries comes under this category. • Sybil attack – Sending the same message from different senders to the same receiver is known as Sybil attack that reflects on the receiver side that the same messages are received from different sources. • Forgery – Attackers make it possible by sending fake warning messages and alerts (e.g., accident alerts and poor road conditions) into VANETs. • Message delay – Attackers introduce a significant time delay in messages so that these may be discarding them on the receiver side. Channel-Based Attacks These kinds of attacks pick out channels and paths established for vehicular communication and affect that particular session. • Jamming – Attackers intentionally put disturbance in the channel established between nodes and interfere in their conversation. • Black hole – Adversaries receive messages and intercept conversation by falsely indicating that they have the shortest routes to the destination. • Man in middle (MiM) – Attackers sit between sender and receiver to listen to their conversation and pose as a responder for each one of them. • Gray hole attack – Malicious node pretends as forwarding node in the network. However, the same node drops the packet on receiving the messages.

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• Wormhole – In this attack, attackers are strategically placed at two places and create a tunnel between both places. Then, attackers start receiving packets from one place and forwarding them toward another place using that tunnel. • Replay – Malicious node repeatedly and fraudulently forward valid messages toward one destination node. Network-Based Attacks These attacks are liable to degrade the performance of the complete vehicular system. • Denial of services (DoS) – Malicious nodes keep the system resources and services unavailable for vehicles and other users in VANETs. • distributed denial of services (DDoS) – These are DoS attacks in the system from several locations. • Unauthorized access – Attackers try to use some network resources and services without proper permissions and privileges. • Eavesdropping – The malicious nodes intend to intercept information transmitted over the vehicular network using some connected devices into an unsecured network. • Session hijacking – Interception of complete conversation by hacking a particular channel established between two or more entities. • Clone attack – Adversaries create several devices similar to legitimate devices in a network to compromise other genuine devices in the network. • Timing attack – Malicious nodes introduce additional time slots into critical messages toward infrastructure nodes such as RSUs; this may down the complete network. So far, several security requirements and security attacks are discussed, which are applicable in NG-VANETs. For better interpretation of security in NG-VANETs, mapping between security services and corresponding attacks is required. Table 2 depicts discussed security services with some security attacks, which compromise that particular security service in vehicular communication.

3 Security Mechanisms Several researchers have proposed solutions for different types of attacks. Based on different attack scenarios, the security approaches are designed to achieve security requirements. The design of effective security schemes for future VANETs becomes more critical due to demanding specifications such as increased network bandwidth, enhanced sensors, and high processing devices in next-generation VANETs. Some of the designed security approaches to target different types of attacks for NGVANETs are discussed below.

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Compromised services Privacy

Confidentiality

Authentication

Integrity

Availability

Content verification Access control Traceability

Security attacks Impersonation Identity/location revealing Repudiation Anonymity Eavesdropping Replay attack Spamming Black hole Gray hole Packet analysis Sybil attack Password attack Illusion attack Clone attack Timing attack Bogus information Man in middle (MiM) Session hijacking Dos DDoS Jamming Sybil attack Message alteration Message tempering Brute force attack Unauthorized access Vehicle tracing Packet tracing

3.1 Hybrid Device to Device (D2D) Message Authentication (HDMA) Scheme HDMA [23] approach uses a group signature algorithm for authentication and precomputed lookup table to reduce authentication overhead. This approach divides the network into two different logical groups: global group and local group. The global group contains all network entities such as trusted authority (TA), a roadside base station (RSBS), and vehicles. On the other hand, the communication range of RSBS and vehicles creates a logical boundary and forms the local group. The global group uses the pseudonyms authentication method, whereas signature-based authentication is used in a local group. HDMA functions in four different phases, namely, initialization, authentication, tracing, and revocation. As part of initialization, TA generates system parameters for global and local groups. Moreover, TA generates identities and pseudonyms for RSBSs and vehicles. The authentication phase follows different authentication methods for V2V and V2I authentication. The tracing phase is used for revealing the

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real identity of the nodes in communication to prevent the system from malicious activities. If TA identifies malicious vehicles, then certificates for those malicious vehicles get revoked and get stored in the certificate revocation list (CRL) for future reference. By using effective authentication methods, the HDMA approach prevents VANET’s from several attacks such as impersonation, message alteration, replay, identity revealing, etc. In addition to adding several security features, this approach reduces overall computation overhead by reducing the authentication overhead of messages signing and verifying the users. A predefined lookup table is suggested to be used for increasing the computation speed of modular exponentiation.

3.2 Blockchain-Based Secure and Trustworthy Approach The Internet of Things (IoT) plays a key role in future VANETs. Therefore, the blockchain-based approach [24] is specifically designed for trust management and privacy for IoT services in VANETs by using decentralized and inflexible properties of the blockchain. The use of a blockchain in the proposed framework improves the security and efficiency of vehicular systems. In this approach, all participating nodes including vehicles, roadside units (RSUs), and traffic authorities (TAs) are suggested to prepare a P2P network to maintain blockchain. Vehicular services of trust management, real-time video report of traffic situations, and message exchanges among vehicles are discussed with the proposed vehicular architecture. This approach provides user privacy, secured data, and trust management for preventing the system from several types of attacks and message tampering. The two-step process is suggested for real-time video reports and messagesharing services. The first step is the vehicle registration, and the second step is the road condition report. There are separate algorithms designed for both processes. Each vehicle needs to be registered first using the subscriber number and device number. At the time of registration, symmetric key (SKE) is generated after verifying identity details. After the registration process, the video file gets recorded using a camera, and the message digest of video content is calculated. Thereafter, the vehicle broadcast that video toward neighbors after encrypting hash value and signing the content. Trust management is four steps process that consists of traffic collection, trust computation, miner election, and credibility assessment. After receiving traffic information from vehicles, RSU classifies the scores of messages received from forwarding vehicles and calculates the trust value of vehicles. In the next step, the difficulty level of RSU is calculated using RSU’s trust value. Then, RSU gets elected as a miner, and a new block is added. Credibility assessment is used to see an uploaded video for checking any suspicious activity observed on traffic recording.

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3.3 Searchable Encryption with Vehicle Proxy Re-encryption-Based Scheme An efficient and secure routing scheme based on searchable encryption with vehicle proxy re-encryption (ESSPR) [25] was presented to provide privacy preservation of messages for vehicular peer-to-peer social networks (VP2PSN). Security scheme functions in six steps: system initialization, peer registration, document generation, document forwarding, proxy re-encryption, and document receiving. ESSPR provides authentication, privacy, and data integrity based on public-key encryption, aggregate signature, proxy re-encryption, and quality of services (QoS)– based clustering. This scheme prevents VANETs from multiple attacks such as eavesdropping, wormhole, packet analysis, packet tracing, and replay packets. The first phase of the approach starts with the generation of several parameters as part of system initialization. Peer registration makes sure that joining of any new vehicle into a cluster with the corresponding private key, public key, and certificate. After peer registration and authentication, the vehicle picks encryption algorithm and public–private keys pair and generates public–private keys pair for peer vehicles. Thereafter, the vehicle generates a chipper of the document with its keywords. Document forwarding algorithm specifies procedure when a destination is not in range of source vehicle. It is suggested to encrypt cipher again on a proxy of destination vehicle as part of proxy re-encryption phase. In the last phase, a cipher is decrypted again to an original document after receiving on destination vehicle.

3.4 Secure and Efficient AOMDV (SE-AOMDV) Routing Protocol Multipath on-demand routing protocols are more exposed to multiple types of security attacks such as man in middle (MiM) and black hole attacks. Therefore, SE-AOMDV [26] designed to provide security in a multipath on-demand routing approach has a more challenging task ahead. This scheme introduces authentication and integrity for a route-reply packet that is used to fetch the best and secure routes. Authentication of vehicles is introduced as a mandatory step in AOMDV to provide trust ability in participating vehicles. This step helps in discarding malicious activity by differing the malicious behavior and legitimate behavior of vehicles in VANETs. Few new parameters have been added by the authors in routing information format to support authentication in vehicular communication such as AUTH, DETECT, and R BIN. Information check value (ICV) field is added into the RREP packet for capturing the hash value of the source, destination, sequence number, and hop counts. This approach captures details of the authentication process with misbehavior detection and integrity check.

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In addition to the traditional authentication process for generating certificates by a third party, the certificates for both vehicles and RSUs are a major contribution to this approach. The use of certificates assures a relationship between identity and used key pair. The authentication process takes place in two steps: initialization and authentication. The initialization process generates all required parameters and certificates whereas the authentication process is performed through TA using generated keys and certificates. An algorithm for misbehavior detection was proposed with an approach that considers suspicious behavior of vehicles such as duplicate packets, drop packets, change in vehicle’s speed, and increased interrupted connections. Additionally, ICV is introduced in the RREP packet for providing data integrity and the R Bin field, which is used to handle node disconnection. R Bin field is used to verify, whether packets need to be forwarded or not before broadcasting the packet further. Possible node disconnection is handled in AOMDV by selecting disjoint routes only.

3.5 Socially Aware Security Message Forwarding Mechanism To prevent the system from privacy attacks, trust-based socially aware security message forwarding (SASMF) [27] strategy was proposed. In this approach, pseudonyms are used to protect privacy. This security scheme is used to prevent the system from some important attacks such as anonymity, location privacy, message authentication, traceability, and edge information attacks, although this scheme suggests two different strategies namely privacy protection and forwarding control. Privacy protection strategy is designed using three steps, namely, key generation, pseudonyms updating, and message protection. Traffic authority (TA) generates system parameters and derives private and public keys from generated parameters. Moreover, TA chooses a hash function for protecting messages from replay attacks during transmission. After that, all parameters except the private key are publicly available on the system. Based on these global parameters, RSU application provider (AP) and other vehicles generate their set of keys. In the next step, TA generates pseudonyms identity by utilizing the registration identity of vehicles. In certain intervals, pseudonym’s identity needs to be updated, otherwise the use of the same identity for a long time is a risk of disclosure of the identity. Exchange entropy is suggested as an exchange control tool. Therefore, this should be directly proportional to the privacy of the vehicle, i.e., greater entropy tends to higher privacy of vehicles. In the last step, message forwarding is suggested by using a fragmentation scheme based on Shamir’s secret sharing algorithm. Apart from privacy, security is also associated with this approach. For enhancing security, the original message is divided into several fragments based on traffic situations, and fragments are transmitted to AP through RSUs. For the forwarding control strategy, the trust management approach is used to provide security and reliability. There are two separate algorithms discussed for trust forwarding decisions and message forwarding mechanisms. In the trust decision

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phase, every vehicle is evaluated based on their trust degree and trust degree can be assessed in several ways like direct trust, indirect trust, and comprehensive trust. After that, based on the vehicle’s trust values, forwarding vehicles are selected and messages are forwarded through those high trust vehicles.

3.6 Puzzle-Based Co-authentication (PCA) Scheme Pseudonym schemes are significantly exposed to DoS attacks due to the cost of initial authentication in the pseudonyms scheme. Therefore, the puzzle-based coauthentication scheme (PCA) [28] was proposed by the authors for preventing the system from DoS attacks against pseudonyms authentication using a hash puzzle. In the proposed scheme, it is assumed that attackers will use the cost of first-time certificate verification to forge a huge number of fake verification requests for DoS attacks. Authentication of pseudonyms is a time-consuming operation and total time taken for frequent changes in pseudonyms identities of vehicles is too high. Therefore, any vehicle can verify only a few pseudonyms identities in case a huge number of fake identities are inducted into the system by attackers. Consequently, any vehicle can verify only a few pseudonyms identities in case of DoS attacks. PCA scheme is divided into two sections: Hash puzzle designing and mutual trust-based co-authentication. Property of one-way function is used to design computational puzzle and hash puzzle consisting of two parameters messages and answer. The value of the puzzle can be calculated based on these two parameters. Generation of the puzzle is finding the answer of hash function with an assumption of message, and value is given for that hash function. Three different roles for generator, verifier, and beneficiary are also associated with the puzzle. In the next phase of the scheme, a trust-based cluster is defined as a group of vehicles for collaborative authentication. A trust cluster is defined as a strongly connected component from a derived undirected graph of VANETs. It is recommended that member vehicles from mutual trust clusters can generate puzzles together and cooperate in certificate verification.

3.7 Intelligent Drone-Assisted Security Scheme This scheme [29] proposes an assistant-based communication protocol that uses an intelligent drone. This method will allow vehicles to securely communicate with other vehicles while protecting the privacy of the individual. The current approach proposes anonymous authentication and key agreement protocol for 5Gbased vehicular networks. It introduces an authentication scheme that uses drones for remote areas that do not have adequate base station coverage, and this method is mainly aimed at rural regions with poor signal.

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The complete approach has been divided into five subsections. First, system initialization where the control center (CC) chooses a random number-based master key and calculates the respective public key and several one-way hash functions. Subsequently, each vehicle gets registered with CC in offline mode as part of the vehicle registration phase. Once vehicles are registered, the drone also gets registered with CC to get a secret key as part of the third phase of the approach. Vehicles and drones both follow three steps registration process. In the fourth phase of the approach, the login phase has been discussed. During the login phase, the drone broadcasts messages at a regular frequency for vehicles that require services from drones. In the last phase of the approach, authentication and key agreement mechanism have been discussed. This approach uses a hybrid encryption algorithm such as elliptic curve, hash functions, and AES.

3.8 Efficient Privacy-Preserving Anonymous Authentication Protocol This approach proposed in [30] has been designed for targeting multiple security services such as privacy, authentication, confidentiality, integrity, etc. in a single solution. In this approach, anonymous authentication scheme with an efficient privacy-preserving mechanism has been discussed. Identity-based signature is used for designing authentication protocol. This approach suggests vehicles send authenticated messages to nearby RSUs. Furthermore, the protocol proposes a key exchange mechanism for generating session keys for secure communication between vehicles and RSUs. The first phase of the approach focuses on identity-based signature, which contains four algorithms: setup, key extract, sign, and verify. In the second phase of the approach, authentication protocol has been described, and the complete authentication protocol further contains three steps. The three steps discuss system initialization, registration, and authentication. In system initialization, TA generates several public parameters, which are to be used in the next steps of the approach. These parameters include prime numbers, random numbers, hash functions, and AES-based MAC. The next step is known as registration, which suggests that each vehicle should register its identity with TA before communicating to nearby RSU. The last step of the approach discusses authentication where a vehicle with a unique identity authenticates to the nearby RSU. This approach makes authenticated communication possible between vehicles and RSU.

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4 Comparative Study of Security Solutions In previous sections, several relevant attacks for next-generation (NG) VANETs have been discussed. Those attacks have been classified into four categories based on impacted vehicular entities. Additionally, some recent security approaches have been discussed for the prevention of these attacks. The comparative analysis of discussed strategies is summarized in Table 3. Security approaches are compared for their features such as provided security services and applicability of the abovementioned approaches. All the discussed approaches are suitable for tackling security attacks in NGVANETs, since these are specifically designed by considering the architecture of 5G-VANETs. These approaches are suitable for high-bandwidth vehicular networks. However, the suitability of discussed approaches varies based upon the used methodology and their targeted applications. Among discussed security solutions, each approach is designed for a specific goal to achieve in a certain environment. HDMA and PCMA are signature-

Table 3 Comparison chart of discussed security approaches Security approaches HDMA

Targeted security services Privacy Confidentiality

Blockchain based

Privacy Confidentiality Trust Management Privacy Authentication Integrity Authentication Integrity Availability

ESSPR

SE-AOMDV

SASMF

Privacy Authentication

PCA

Privacy Availability Privacy Authentication Privacy Authentication Confidentiality

Drone assisted Privacy preserving

Applicability High bandwidth VANETs for V2V communication Vehicular IoT environment in SDN enabled VANETs Vehicular P2P social networks (VP2PSN) High-speed vehicular environment (highway/expressway) Informal vehicular communication (not suitable for critical alerts/warnings) High bandwidth VANETs A rural or mountainous area Military VANETs

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based and puzzle-based mutual authentication protocols, respectively. Both of these protocols provide authentication and confidentiality in high bandwidth vehicular communication. On the other hand, blockchain-based security framework and SEAOMDV detect malicious vehicles and messages in vehicular communication, where blockchain-based algorithms are specifically designed for IoT services such as real-time traffic monitoring. Furthermore, ESSPR and SASMF both provide privacy to vehicles in a vehicular network using searchable encryption and pseudonymbased algorithms, respectively. Based on use cases of security approaches, these are more efficient in different environments. SASMF is more suitable for communication of noncritical messages, blockchain-based algorithms for IoT services and ESSPR can be effectively used in P2P social networks. Apart from these approaches, HDMA, SEAOMDV, and PCA are effectively used for high bandwidth V2V or V2I vehicular communication for providing several security services such as privacy, confidentiality, etc., where PCA is specifically designed for providing availability and SEAOMDV provides integrity in message communication.

5 Conclusion and Future Work VANETs play a key role in designing intelligent transportation systems (ITS) to enhance our traffic experience. However, recent advances in VANETs keep vehicular communication more exposed to several types of attacks by some greedy drivers and other selfish users. Therefore, the security perspective of VANETs must be focused upon before starting to use it. In this chapter, we investigated possible security threats for NG-VANETs. The various attacks have been categorized into four different categories based on influenced entities by attackers that give an extensive analysis of attack scenarios. In addition to that, some recently proposed solutions to prevent the network from various types of attacks have also been discussed. Moreover, we have summarized security strategies with their features and suitability for various application areas. The presented security analysis in the current exposition may enable other researchers and users to select appropriate security mechanism for using vehicular communication in a secured way.

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24. Xie, L., Ding, Y., Yang, H., & Wang, X. (2019). Block-chain-based secure and trustworthy internet of things in SDN-enabled 5G-VANETs. IEEE Access, 7, 56656–56666. 25. Ferrag, M. A., & Ahmim, A. (2017). ESSPR: An efficient secure routing scheme based on searchable encryption with vehicle proxy re-encryption for vehicular peer-to-peer social network. Telecommunication Systems, 66, 481–503. 26. Makhlouf, A. M., & Guizani, M. (2019). SE-AOMDV: Secure and efficient AOMDV routing protocol for vehicular communications. International Journal of Information Security, 18, 665– 676. 27. Yang, P., Deng, L., Yang, J., & Yan, J. (2020). SASMF: Socially aware security message forwarding mechanism in VANETs. Mobile Networks and Applications, 25, 660–671. 28. Liu, P., Liu, B., Sun, Y., Zhao, B., & You, I. (2018). Mitigating DoS attacks against pseudonymous authentication through puzzle-based co-authentication in 5G-VANET. IEEE Access, 6, 20795–20806. 29. Zhang, J., Cui, J., Zhong, H., Bolodurina, I., & Liu, L. (2020). Intelligent drone-assisted anonymous authentication and key agreement for 5G/B5G vehicular ad-hoc networks. In IEEE transactions on network science and engineering. 30. Zhang, X., Wang, W., Mu, L., Huang, C., Fu, H., & Xu, C. (2021). Efficient privacy-preserving anonymous authentication protocol for vehicular ad-hoc networks. Wireless Personal Communications.

iTeach: A User-Friendly Learning Management System Nikhil Sharma, Shakti Singh, Shivansh Tyagi, Siddhant Manchanda, and Achal Kaushik

1 Introduction Electronic learning or e-learning [5] refers to gaining knowledge through leveraging the Internet and computer network across the globe. E-learning consists of various forms of learning methodologies that are electronically supported [29]. The process of automation has made the lives of people a lot easier. Therefore, incorporation in education has also contributed to enabling students to learn in different styles altogether by making the process more accessible and empowering educators by developing a set of automation tools to create content and teaching significant [4]. E-learning in the current COVID-19 pandemic situation has proven to be a boon. It provides a learning platform for the students of all the classes. The e-learning offers an instant solution to the COVID-19 outbreak, where the authorities have forced countrywide lockdowns, including educational institutions. In this gloomy scenario, e-learning systems prove to help content makers, educators, and students. Such online software allows people to study from home and get online learning materials and guidance from the teachers online, without their physical presence. The e-learning systems offer many features such as online doubt sessions, online tests and quizzes, assignment submission help in isolation and protection from the virus, and learning in an altogether holistic approach challenging to simulate in a physical schooling environment. In the twentieth century, the change was from the industrial age to the age of knowledge and technical know-how. E-learning or electronic learning refers to the concept of utilizing the Internet, providing a platform for the students to learn via accessing content available on the Internet such as notes, videos etc., making

N. Sharma · S. Singh · S. Tyagi · S. Manchanda · A. Kaushik () Bhagwan Parshuram Institute of Technology, Affiliated To GGSIPU, Delhi, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_11

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learning more effective and performant. One of the pivotal purposes of e-learning is that every actor should know the technology’s core know-how and understand how it can be utilized to reach a specific goal or objective [4]. In today’s world, competition among the industries regarding their product efficiency, features, and performance plays an essential role in retaining the customers and tackling their fellow competitor products. The cut-throat competition requires skillful employees who can update themselves with the latest technology at a demanding pace. An efficient learning management system plays a pivotal role in helping learners learn the technologies beyond the time frames and physical boundaries. Also, the e-learning systems can be a great way to document and reference this knowledge for referring to and planning accordingly, which can help automate the learning tasks [4]. In our work, we have developed a platform with an inbuilt scribble pad for simulating the effect of blackboard teaching. This feature consists of a paint-like drawing area where an external device like an electronic pen is used to write on it, draw shapes, and even import pictures and explain them visually. The application also consists of a video editor that enables content creators to create and edit videos on the fly. Cloudinary is used as an online hosting cloud service for hosting their videos. Another application feature is a screen recorder for recording full or a custom part of the screen while teaching via webcam or through a scribble pad. The content creators can publish playlists of their courses available for purchase at the student marketplace. Each video lecture comprises an online discussion forum where students could discuss what concepts they need clarity on and the teachers’ assignment answers as an attachment in the video lectures. One of the platform’s significant features is an online one-to-one doubt session with the teacher after the video lecture. Other learning management systems do not offer an online doubt session with the same instructor present in the video. The student can avail this facility by performing well in examinations, lecture revision tests, and assessments that earn them iTeach [17] passes. These iTeach passes would help the students encash live doubt session access and discounts on video content purchases. Apart from that, the application has an extensible and easy-to-use user interface, making it super easy to access the application. The platform has a rich session and role-right management done at its backend, which normalizes the database. The paper’s outline is as follows: Section 2 briefs the related work by surveying the literature, and Sect. 3 details the formulation of the problem and the proposed model showing all the features of the model. The quantification of various parameters and their comparison is in Sect. 4, and feedback analysis is demonstrated in Sect. 5. Concluding remarks appear in Sect. 6.

2 Literature Review Various learning management strategies [3, 32, 33] have been implemented, which have significantly contributed to a different approach to learning. But if the users

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cannot utilize their full potential or face problems due to lack of accessibility, it defeats the whole purpose. Therefore, an LMS is successful if the students can make the most out of it and fulfill their purpose. However, recent studies [16, 26] have shown that e-learning implementation is a technical solution and have many different factors such as social factors and individuals. There is a process of factors facilitating conditions in addition to organizational, such as behavioral and cultural factors [4]. Absorb [27] is a learning management system that empowers organizations to teach their employees the required skills to stay in this modern and competitive world and change according to the technology demands. The LMS offers content libraries to provide an instant return on investment via thousands of pre-built online courses. An e-commerce is a marketplace for selling their courses and applying monetization and competent administration for the teachers and learners to automate various processes. The LMS consists of 12.9M users across 129 countries with 11,000 customers. One of the disadvantages of this application is its accessibility, a dead-simple user interface, and various modern web optimization approaches. Also, it does not provide automation in the context of content creation. Unacademy [37] is another learning management platform that offers features like an accessible UI, live lecture streaming, course subscriptions, etc. It uses React [11] as its frontend and Nginx and Node [36] as its backend service, along with some management and automation tools like CMake and Google tag manager [10]. But it also does not provide features like an inbuilt text editor and a scribble-pad and software for video creation and content editing on the fly. Moodle [8] is one of the most famous learning management systems in the market, providing features like learner progress tracker, quick activity, and course setup with ease. It uses Nginx and PHP heavily under its platform layers [18]. Google Classroom [28] is a product backed by one of the learning tech brands called Google, a web platform for creating classrooms to enable the teachers to distribute, collect, and manage classwork. It utilizes Google suite and Kotlin for building the android version of the app, providing features like distribution of resources to platforms like YouTube videos, Google Drive links, and tools like GeoGebra Classic, and Activity learn Hiver under the hood [2]. Easyclass [22] is an LMS that provides a shared digitized environment to content creators and the students for uploading and delivering content to students in videos, digital notes, etc. The students can also submit quizzes and assignments along with tests on the platform. The platform provides a secure environment for teachers and students so that the content is safe from any external or unauthorized access. Zoom classroom [6] is another such platform that provides the synchronous teaching mode. The platform allows a host and students to connect to a meeting room where teachers connect with them in real time and deliver their content. It also enables platform versatility since a person can connect from a Windows machine or a Mac or even connect via mobile devices. Backed by Office365, Microsoft Teams (MS-Teams) [21] is another learning platform. MS-Teams allows meetings through a virtual meeting room where approximately 10,000 people can connect at the same time. It performs text chats and shares documents and files all in one place and its

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accessible user interface, making it a platform to teach many audiences. Hypersay [13] is an online platform that brings presentations with a new perspective. The students can interact with presentations in real time and support features like live subtitle changes. It is not suited to cater to a broad audience since it only allows 20 participants for each classroom session. Nearpod [30] is another online learning management strategy that provides an interactive learning environment to the audience, where instructors and content creators can deliver interactive lessons. Some of the platform’s features are polls, 3-D objects, open-ended questions, and field trips through virtual reality. BrainPOP [9] provides online access to resources to students in interactive class sessions, quizzes, and study materials when access to schools is not feasible. Therefore, it has been one of the favorite tools for such types of closures. Eduflow [25] is an online platform that allows content creators to create online content. It provides a simulation environment for students to submit quizzes and assignments, ask live doubts, track their performance, conduct teamwork activities, and many more. YouTube [7] has been one of the most popular sources to learn online. This platform allows content creators to create education-specific channels and upload a series of videos in the form of playlists. The students have free access to the content and can save a playlist to access it offline. Many screen recording applications are present, which do not act as a separate learning management system but as tools to create and deliver content online. Applications like Camtasia, screen hunter [38], ice cream screen recorder [34], and windows screen recorder provide the facility to create audio, video, and screen recordings. They further provide an option to edit them, customize the videos through intro and ending screens, add animations, etc. The main drawbacks of these systems are their inability to provide automation in terms of video creation. Their approaches are not very friendly and encourage educators to create videos with ease rather than restricting them in the barrier of content creation software, discouraging them from creating content and sticking to their straightforward old approaches. Therefore, iTeach [17] offers features like accessible UI, online video recorder, and editor for content creation on the fly, hosting through Cloudinary, and a rich video editor. The application usage is updated to the demands of the modern era. It uses the latest and in-demand technologies like React.js [11] as the frontend, Node [36] for API creation and exposing those endpoints, and Mlab [15] for saving and managing the contents of the users.

3 Proposed Model One of the critical goals of LMS [23] is to provide learning facilities to students in the best possible manner. They allow the students to connect with the best teachers worldwide, clear their doubts online, and conduct regular exams to track their knowledge and learn better. It also empowers educators to create content without

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getting stuck with the cumbersome buying process and video-making software, which block many enthusiastic instructors willing to create content. Although the most prevalent e-learning models are available, they fail to provide a simple interface and access for students and teachers. For example, a person who wants to teach students at YouTube or Unacademy [37] should spend money on recording and editing software and hardware. A teacher cannot teach students to use images on the screen and make direct edits. He cannot conduct live interactive doubt sessions. One of the pivotal purposes of building an LMS is to make learning accessible to students, connecting them with the best teachers worldwide, clearing their doubts online, and conducting regular tests to track their knowledge and learn better. Summarizing the following points indicate how the present systems pose to be blocker among the potentials users: The present systems include a cumbersome and painful process for the instructors to make content for the students, for example, if a person wants to create educational content on platforms like Unacademy [37] YouTube, Scrimba, etc. He has to go through the painful process of buying and setting up video editing software, renderers, and editing videos that require some knowledge and sound technical know-how. The tedious process discourages many such teachers from making content. In many platforms like Unacademy [37], Moodle [8], and Absorb [27], the feature of a live scribbler, live video editor on the fly, and live doubt sessions with the teachers is not present. Our model has incorporated new features with pre-existing e-learning systems and pre-existing infrastructure, which makes learning easier. Our learning management systems have provided various following features shown in Fig. 1 to make learning easier and help automate the learning tasks. 1. Webcam teaching [24] – This utility allows teachers to teach from anywhere worldwide through video live video sharing. A webcam enables a teacher to simulate the behavior of live teaching. This feature utilizes the browser APIs to record videos using a webcam and host the video sessions on a cloud service (Fig. 2). 2. Screen recorder – It allows teachers to teach via broadcasting their screen to the students. It serves many applications like teaching through code, explaining through pictorial representations, etc. The instructor can start the screen recorder with just a click. He would be given a set of options to load the whole desktop or a custom screen. Saving the recording makes an API call where a storage management utility called Cloudinary stores and hosts it to its server (Fig. 3). 3. Scribble pad [20] – Another application feature includes an inbuilt scribble pad. The pad is provided to simulate a blackboard’s behavior more interactively and handily, which would enable educators to teach on a virtual board with a hardware pen attached to their computing device. The device would talk to the OS and would trigger drawing strokes on the provided area. Usually, the existing

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Fig. 1 Lecture creation

Fig. 2 Embedded video

mechanisms only handle and upload the instructors’ videos but don’t offer the tools. An online scribble pad would prove to be a great boon for the teachers, which would allow the following features. (a) Pen – The feature offers the teachers to write using a hardware device to construct strokes on the screen to simulate a blackboard’s behavior. Apart from a pen, other handy features like drawing squares, circles, or even panels are also provided, aiding in geometrical learning.

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Fig. 3 Live screen

(b) Image upload – The image upload feature allows the user to upload an image and explain it using scribbles, which would enforce a better learning experience. The teachers would be free from old school methods of writing everything on the board and explaining afterwards or building the whole diagram first and then explaining. The image upload feature works by inserting an image link either from Google or any other jpeg link or importing from the computer and clicking the upload button. The corresponding image will be on the display screen through which the instructor can teach seamlessly. (c) Undo/redo – The scribble pad also opens the options for undoing and redoing strokes and other actions like adding images or changing the stroke colors, which proves to be useful for effortless teaching and a smooth experience (Fig. 4). (d) Color palette – The pad also has a rich set of color palettes for colorful teaching and highlighting the essential parts or indicating proper texts. 4. Lecture live streaming – This feature allows teaching to live stream their lectures, directly interact with the students, and improve their doubts (Fig. 3). The live sessions help enhance mutual communication between the two parties [1]. 5. Student marketplace – This section of the application offers a complete marketplace where students can select a set of different courses or teachers worldwide. 6. One-to-one doubt sessions – This feature offers live one-to-one doubt sessions with the teachers who undertook the lectures. Teachers would be able to come live and interact with students launching their doubts and questions. This feature allows instructors to directly interact with the students after discussing the students’ queries. This feature is missing from many prevalent systems and would improve accessibility and user experience significantly.

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Fig. 4 Playback class

7. A blazing fast video editor – The teachers would be saved from the hassle of extensive manual video editing. The features include providing video presets to automatically enhance sound quality and an easy-to-use GUI for video editing on the fly. 8. Teacher ratings – According to student feedback, it offers an anonymous user feedback forum by which a teacher could understand the students’ learning strategies and mold their teaching methodologies [19] (Fig. 5). Following are some of the screenshots of the applications:

4 Comparison There are various LMS available in the market based on different technological stacks and platforms. These available LMSs offer multiple features ranging from tracking learner progress and setting up the course, offers live classes, doubt sessions, assignment management, distribution of resources, planner for teachers, and other feature like in-line scribble pad, video editor, student discussion forum, etc. Table 1 indicates the comparison between various learning management platforms on the parameters like the technology used, features and platforms, etc. [28]. From the literature, we have identified various LMSs offering different feature sets. The following table demonstrates the feature comparison of the applications based on factors like SCORM, course content creation, LTI support, etc. (Table 2).

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Fig. 5 User interface

5 Users Feedback Analysis We have tested our application during the COVID-19 pandemic. This section describes the questionnaire distributed among 104 teachers and 600 students to test the application and provide their valuable feedback by answering the questionnaire. Two questionnaires (for teachers and students) were prepared according to their respective application usage sections. Table 3 specifies a set of generic questions for both teachers and students. In our feedback evaluation, we have used the Likert scale, which ranges from strongly disagree (1), disagree (2) and neutral (3) to agree (4) and strongly agree (5). For the feedback analysis, we have used the divergent stacked bar chart. These graphs consider the dual-axis charts that measure positive and negative sentiments and visually help us understand the feedback’s polarity. From Fig. 6, it is very evident that there is positive feedback on the feature set. The evaluation of the questionnaire set provides the following insight about the application: it offers a smooth user experience without lagging on its interface (Fig. 5). Further, it is working fine even with a low bandwidth network. The student’s feedback, shown in Table 4, on the application usage provides the effectiveness on various parameters. From Fig. 7, we can identify that the application proves to be effective, where nearly 40% of students agree that the

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Table 1 Comparison of the various learning management platforms LMS name Moodle [18]

Technology used PHP, Nginx

Unacademy [37]

JavaScript, React [11], Ruby, C#, Typescript PHP, Amazon Web Services, JavaScript, TypeScript, React

Absorb LMS [27]

Google Classroom [12]

Google Suite, Kotlin

Edmodo [31]

jQuery, NGinx, Handlebars, Node.js

iTeach [17]

React, JavaScript, Node.js, MongoDB, Mongoose

Features Learner progress tracker, quick activity and course setup Live classes, doubt sessions, student subscriptions E-commerce, content libraries, analytic reports, AICC/SCORM support Assignment management, distribution of resources Planner for teachers, Edmodo badges, publisher communities In-line scribble pad, video editor, student discussion forum

Platform and tools Mandrill, GSuite, RequireJS for DevOps

CMake, Google Tag Manager [10] Google Analytics

GeoGebra Classic, Activity learn, Hiver

Optimizely, Google Analytics, Google Tag Manager [10], Bugsnag, New Relic Mlab [15], Cloudinary

Table 2 Feature comparison SCORM import Bundled course content Google app integration Single sign-on E- commerce Developer API available LTI support Native web hosting Scribble pad Screen recording LMS Name

Yes No

Yes No

Yes Yes

No Yes

No Yes

Yes

Yes

Yes

Yes

No

Yes Yes Yes

yes Yes Yes

Yes No Yes

Yes No Yes

Yes No No

No No

Yes No

Yes Yes

Yes Yes

No Yes

No No Absorb LMS [27]

No No Moodle LMS [18]

No No Infrastructure Canvas LMS [35]

No No Schoology LMS [14]

Yes Yes iTeach [17]

S. no. 1. 2. 3. 4. 5. 6.

Questions The application has a smooth user experience The application is easy to operate and find on the Internet No personal data leaked from the application The application works even on slow connections The interface does not get stuck and responds effectively The application replies to bugs and patch update issues on time

Table 3 General feedback ratings 1 (Strongly disagree) 66(10%) 22(3%) 20(3%) 23(3%) 50(8%) 73(11%)

2 (Disagree) 47(7%) 19(3%) 47(8%) 45(7%) 62(9%) 79(12%)

3 (Neutral) 82(12%) 100(15%) 419(70%) 111(17%) 61(9%) 97(15%)

4 (Agree) 262(40%) 200(30%) 95(16%) 336(51%) 76(12%) 281(43%)

5 (Strongly agree) 203(31%) 319(48%) 20(3%) 145(22%) 411(62%) 130(20%)

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Fig. 6 General feedback stacked bar chart

application provides a good learning platform. Almost 70% of students settled upon the smooth test experience, with more than 90% appreciating the result evaluation and representation for pinpointing the scope of improvement and learning gap. The application offers better adaptability and understanding of the topic as the same teacher takes the doubt session. The students also approved that the application was not bulky, and they didn’t observe any connectivity issues. The application doesn’t take too much RAM for mobile/desktop devices, and the scratchpad feature is handy for visual learning. Over 80% of students are willing to recommend the application platform to other students. From Fig. 8, the teacher’s feedback (Table 5) on various usability parameters also suggests that the application serves a good purpose and easy to use interface. The platform is user-friendly in creating content as approved by over 80% of faculty. Also, nearly 70% found that the application doesn’t take too much video rendering time, which is otherwise an issue with any LMS system. The platforms allow bulk entry to questions through excel sheets, which is a good help for the evaluators to save their time. The platform is time-saving to effectively evaluate tests and assignments, provide correct visualization data, and correctly depict students’ weak points. More than 75% of teachers agree with the effectiveness of the application and are ready to recommend the application to other teachers.

11

10

9

8

7

6

5

4

3

2

S. no. 1

Questions The application helps me learn efficiently The course material is useful and covers all concepts The application has a smooth experience for taking tests The application is sound with no security vulnerabilities (like hacking the test timer, etc.) The application shows the correct test result data after the test The application shows the graphical representation of tests results taken over time The doubt sessions involve the same teacher taking your classes, with no connectivity issues The application doesn’t take too much RAM for mobile/desktop devices The scratchpad feature is handy for visual learning You wish to recommend the platform to other students The courses provide a sufficient number of assignments to cover the topic

Table 4 Students feedback ratings

70(12%)

2(0%)

32(5%)

68(11%)

77(13%)

5(1%)

15(3%)

31(5%)

15(3%)

67(11%)

83(14%)

43(7%)

24(4%)

88(15%)

93(16%)

7(1%)

12(2%)

70(12%)

46(8%)

124(21%)

Student feedback rating 1 (Strongly disagree) 2 (Disagree) 32(5%) 112(19%)

196(33%)

36(6%)

69(12%)

115(19%)

70(12%)

14(2%)

20(3%)

363(61%)

58(10%)

172(29%)

3 (Neutral) 221(37%)

155(26%)

300(50%)

163(27%)

267(45%)

200(33%)

56(9%)

27(5%)

96(16%)

339(57%)

203(34%)

4 (Agree) 145(24%)

96(16%)

219(37%)

312(52%)

62(10%)

160(27%)

518(86%)

526(88%)

40(7%)

142(24%)

34(6%)

5 (Strongly agree) 90(15%)

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Fig. 7 Students feedback stacked bar chart

6 Conclusion and Future Scope The learning management system has been successfully built with easy-to-use features. The model has implemented new features with pre-existing e-learning systems and pre-existing infrastructure, which makes learning easier. The model provides a better teaching–learning environment suited well to students and teachers. Students can appealingly visualize the content, and teachers can create the content with great ease. A few of the critical components that make the application smooth and easy to use are as follows: • Fast video editor allows teachers to create and edit their videos without using external resources easily. • There is a new feature called a Feedback system, in which the student can complete a short survey of what they understand, which requires more clarity.

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Fig. 8 Teachers’ feedback stacked bar chart

It allows online quiz sessions with the teacher, not just those students, to write questions in forums that are never fun. • It offers a built-in, powerful Scratchpad that mimics the board experience with the added feature of importing images and videos and interpreting concepts with visual learning. • Teachers can periodically conduct tests that show what concepts the children have understood and what elements need repetition. • The product enables the instructors to create content without requiring any prior knowledge of video editing, content rendering, etc. They now need to focus on what they are going to teach with the utmost focus. In the application’s future scope, we need to provide more advanced software for e-learning management systems. The teletyping features, where the teacher’s words could be written when they are speaking, give the students notes for greater accessibility and a better provision for storing, saving videos, and removing noise.

11

10

9

8

7

6

5

3 4

S. no. 1 2

Question Students can understand the content delivered Students happy with audio/video quality The application provides correct visualization data and correctly depicts the weak points of students Video saved have no audio/video issues You would recommend the application to other teachers The application doesn’t take too much video rendering time The platform is user-friendly in creating contents The platforms allow bulk entry to questions through excel sheets The platform is time-saving to evaluate tests and assignments The discussion forum helps understand the doubts of the students The application correctly depicts the weak points of students

Table 5 Teachers feedback ratings

14(13%)

5(5%)

1(1%)

0(0%)

1(1%)

7(7%)

5(5%)

5(5%) 2(2%)

19(18%)

23(22%)

3(3%)

4(4%)

13(13%)

10(10%)

9(9%)

7(7%) 17(16%)

Teacher feedback rating 1 (Strongly disagree) 2 (Disagree) 13(13%) 11(11%) 12(12%) 14(13%)

15(14%)

9(9%)

10(10%)

0(0%)

6(6%)

16(15%)

9(9%)

17(16%) 8(8%)

3 (Neutral) 12(12%) 6(6%)

30(29%)

61(59%)

46(44%)

26(25%)

38(37%)

22(21%)

15(14%)

57(55%) 49(47%)

4 (Agree) 18(17%) 16(15%)

26(25%)

6(6%)

44(42%)

74(71%)

46(44%)

49(47%)

66(63%)

18(17%) 28(27%)

5 (Strongly agree) 50(48%) 56(54%)

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The above items are improvements that can be made to increase the applicability and utilization of this model. Hence, effective management of student and assignment records and a strategy to utilize the cloud space to get better space at a low cost. Also, the players are as versatile as they can see now. It is possible to introduce a method to manage e-learning management systems with student, administrator, and teacher improvements like quizzes and assignments. A significant role-right management system is necessary to provide a normalized implementation so that the real data can be handled without any security leaks and the users’ reliability can be retained.

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Part III

Data-Intensive Systems in Health Care

Analysis of High-Resolution CT Images of COVID-19 Patients A. Joy Christy and A. Umamakeswari

1 Introduction Corona virus, the deadliest pandemic, was first identified in Wuhan, Hubei Province, China, in late December 2019 [1]. The virus, formerly called 2019-nCov, is a mutated virus that emerged from the family of severe acute respiratory syndrome coronaviruses (SARS) and termed as SARS-Cov-2. Later, World Health Organization (WHO) officially named the disease Coronavirus 2019 (COVID-19) on 12 February 2020 [2]. The main symptoms of the disease include pneumonia fever, dry cough, shortness of breath, fatigue, sore throat, and severe respiratory illness in the later stages [3]. The disease is highly infectious and has spread across 210 countries all over the world. The asymptomatic characteristic of the disease leads the infected individual as virus carrier or transmitter which results in the fastest spread of the disease. Despite the fact that the disease is highly contagious, the virus has also claimed more than 461,715 lives so far and contracted to 8,708,008 people in the entire globe as on 22 June 2020. The pandemic not only poses threat to human lives but also has advertent effect on social, economic, and political crisis among the nations. Countries are racing to slow down the spread of the virus through rapid tests and treatments with the intention to reduce high case of fatality rate and to get back to normal life from the worldwide shutdown. The initial screening of the symptomatic individuals starts with the testing of nasal and throat swabs. The high false negative results of these tests increase the difficulty in controlling the COVID-19 outbreak with misdiagnosed patients who might miss the golden hours for proper treatment and cause the disease’s spread. Analysis of computed tomography (CT) high-resolution scan images is proposed as the main diagnostic

A. Joy Christy () · A. Umamakeswari School of Computing, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_12

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method, suggested by the “Pneumonia diagnosis and treatment guideline for SARSCoV-2 infection (trial version 5),” issued by the National Health Commission of the People’s Republic of China. Specifically, CT scan images of the chest are found to be effective in detecting the abnormalities in the lungs and help the early diagnosis of the disease it claims. The use of machine learning approach for descriptive analysis of various diseases have recently gained focus and are intended to be designed as an assistive tool for physicians. Image segmentation is one of the popular applications of machine learning approach. The goal of image segmentation is to represent an image into something that is easier to analyze. Image segmentation methods in machine learning approach typically help to locate objects and boundaries in images. In image segmentation, every pixel in the image is labeled, wherein the pixels with same label share similar characteristics with respect to color, intensity, or texture. The outcome of image segmentation methods is a set of segments, or set of contours extracted from the image, which would be aiding the medical practitioners to locate tumors and pathologies, measure the volume of tissues, and study the anatomical structure, surgery simulation, and surgery planning. This work aims at segmenting the chest CT scan images of patients admitted with corona virus symptoms and analyzing the abnormality in the lungs from the segmented images, in order to have more comprehension and understanding of patients with COVID-19. The proposed work would facilitate the quantification of lesion regions with more emphasis on the survivability of the patients. The clinical data has been acquired from the Kaggle data repository as a dataset for 80 MB. The dataset contains a database of COVID-19 cases with chest x-ray and high-resolution CT images. It is mentioned that the images are taken from the costophrenic angle with the patients’ supine, head-first position in a breadth-holding manner. In addition to the images, the dataset also holds metadata information about the patients with patient ID, offset (number of days since the start of symptoms or hospitalization of each image), gender, age, finding (which type of pneumonia), survival (whether the patient will survive or not), view (x-ray or CT image), date (date the image was acquired), location (hospital name, city, state, and country), source (the source file name of the image), DOI (DOI of the research article), URL (URL the research article or website resource of image), clinical notes (radiograph information), and other notes (other information related to credit). An image segmentation algorithm is employed over the CT scan images to analyze the distribution features and the shape of abnormal attenuation involved in the lungs. Each pixel in the CT scan image is represented as gray scale image as the pixel values ranging from 0 to 1. In this work, the gray scale image is converted into binary image having only two classes, 0 or 1, where 0 represents black and 1 represents white color. Image segmentation groups the pixels with value 0 into one segment and the remaining in another. The binary regions segmented by image segmentation algorithms are distorted by noise and texture and may contain numerous imperfections. To overcome the issues, morphological image processing is used to remove the imperfections by accounting for the form and structure of the

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image. The segments are then processed to extract the regions of interest from CT images. The regions of interest from the segmented images are to be independently reviewed in the following aspects: (i) (ii) (iii) (iv)

Lesion distribution: Left lobe or right lobe of the lung Lesion location: Central and peripheral Lobes involved: Superior, middle, and inferior lobes Lesion characteristics: Ground glass opacity (GCO) and margins

2 Review of Literature Recently, many predictive analysis-based diagnostics tools have been proposed to detect the existence of COVID-19. Many of these works use deep learning approaches. The methods have analyzed the CT scan or x-ray images to determine whether a person is infected by COVID-19 or not. The literature focuses on the recent models of COVID-19 disease. Ozturk et al. [4] have proposed an automated diagnostic tool to detect COVID19 cases using deep neural network with x-ray images. The authors have used Darknet convolution neural network model as a predictive model. The authors have developed models for binary classification with COVID vs. no-findings cases and multiclass classification with COVID vs. no-findings vs. pneumonia cases. The authors have obtained the images from public image data sources and have processed 1127 images. Among the experimental images, 500 images represent nofinding cases, 500 images denote pneumonia cases, and 127 images illustrate the COVID-19 positive cases. The authors have claimed that the classification accuracy of the proposed model is 98.08% for binary classification and 87.02% for multiclass classification. The authors have stated that their work does not include feature selection. Prathak et al. [5] have proposed a deep transfer learning-based classification model for COVID-19 disease. The authors have also used a top-2 smooth loss function with cost-sensitive attributes to handle noisy and imbalanced images. The authors have taken CT scan images of COVID-19 cases and implemented ResNet-50 convolutional neural network model as a knowledge prediction model. The authors have used transfer learning to tune the initial parameters and deep transfer learning to train the classification model. The authors have built a multiclass classifier that categorizes 413 COVID-19 positive cases and 439 normal and pneumonia infected cases with 96.22% accuracy. The authors have claimed that the tenfold crossvalidation of the classifier is used to prevent overfitting issues and have stated that the optimal selection of hyper-parameters is not considered in their work. Togacar et al. [6] have proposed deep learning models that exploit social mimic optimization (SMO) and structured chest x-ray images using fuzzy color and stacking approaches for the detection of COVID-19. The authors have implemented

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MobileNetV2 and SqueezeNet deep learning models with the feature sets obtained by the models using the SMO method. The authors have used SVM algorithm as a classifier for diseased and non-diseased cases of COVID-19. The authors have accessed publicly available COVID-19 x-ray image dataset with three classes. The authors have collaborated images from multiple sources and have processed 458 images with 295 COVID-19 images, 65 normal images, and 95 pneumonia images. The authors have claimed that their proposed model achieves 99.27% accuracy in COVID-19 disease prediction. RahimZadeh and Attar [7] have introduced a modified deep convolutional neural network model for detecting COVID-19. The authors have proposed a concatenated Xception and ResNet50V2 neural network models that help to detect COVID-19 disease. The authors have taken images from two open-source datasets: ieee8023 and Kaggle, where the prior contains three classes of x-ray images and the posterior contains two classes of x-ray images. The dataset encompasses 118 COVID-19 cases, 42 pneumocystis cases, 25 streptococcus cases, 6012 pneumonia cases, and 8851 no-finding cases. The authors have claimed that the proposed model correctly classifies the COVID-19 disease with the accuracy of 99.50%, with overall accuracy for classes at 91.4%. The authors have stated that the unbalancing of images in various classes is chaotic. Zhang et al. [8] have constructed a large CT dataset on novel coronavirus pneumonia (NCP) and other common types of pneumonia and normal controls. The authors have developed an AI diagnostic system for assisting junior radiologists in the epidemic area and two non-epidemic areas in China. The authors have also provided prognosis indications for patients with NCP by using a combination of CT scan images and clinical attributes. The dataset contains 617,775 CT images from 4154 patients. The authors have built the knowledge prediction model as multiclass classification with four classes as NCP, viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal control class. The authors have claimed that their system is able to differentiate NCP from other classes with 92.49% accuracy. Wu et al. [9] have developed a deep learning-based method to assist radiologists to identify patients with COVID-19 cases by CT images. The authors have collected the CT images of 495 patients from three hospitals in China. The dataset contains two classes such as COVID-19 and other pneumonia class. The authors have built a binary classifier as a knowledge prediction model. The authors used a multiview fusion model using deep learning network to screen patients with COVID-19 with maximum lung regions in axial, coronal, and sagittal views. The authors have claimed that the proposed multi-view deep learning fusion model has achieved 81.9% accuracy. The authors have also computed the risk score for each patient based on the morbidities and comorbidities. Most of the articles published in the existing literature on COVID-19 are concerned with the detection of COVID-19 disease using deep learning models. These models are varying with image size, classes, neural network model, classifier, and accuracy. Disease prediction comes under the predictive analysis of machine learning. There is only a little effort made in the descriptive analysis of COVID-19.

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Hence, in this work, we will analyze the CT scan images of COVID-19 cases to perform the following: • To segment the patchy ground-glass opacities with clear margins and highlights septal thickening inside the lesions • To exhibit disfigured and abnormal shape of the lung • To quantify the lesion region

3 Materials and Methods This work examines the lung-CT images of 21 COVID-19 patients and reviews their clinical data such as age, survival, and the conditions of respiratory organs during the admission of patients. The images are obtained from Kaggle’s ieee8023-covidchestxray dataset. The data set also consists of the clinical notes of each patient that describes the signs, symptoms, and technical complications of the patients. The features of the lung-CT images encompass GGO, mixed GGO, and consolidations. The objective of the study is to assess the lesion region segment by segment so as to find the correlation between the patient’s survivability and percentage of lesion region. The study is also extended to analyze the relationship between patient’s age and lesion region. To obtain this, we need to extract the affected lung regions from CT images. Thus, in this work, a novel thresholding-based image segmentation method called percentage split distribution (PSD) is used for segmenting lesion regions. The method is a multilevel thresholding-based image segmentation method and segments an image based on the distribution of image pixels. The method intends to identify the region that develops the GGO, which is considered the benign lesion region of COVID-19 disease. The images are preprocessed to remove the background noise. The processed images are segmented into different groups to extract the GGOs using the notion denoted in Eq. 1: PSDn−1 i=1 = ((Maximum Pixel Value − Minimum Pixel Value) ∗ (i ∗ sp)) − Minimum Pixel Value

(1) sp refers to the segment percentage and is calculated using the formula denoted in Eq. 2: sp =

100 n

(2)

Equation 1 generates n−1 thresholds that will assign the pixels in the relevant group. The number of pixels in each group is counted, and the fraction with overall image pixels is calculated to quantify the lesion and normal regions. The formula depicted in Eqs. 3 and 4 defines the quantification of normal and lesion regions:

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Fig. 1 Graphical abstract of the proposed work

Number of Pixels in Gray Region Total number of pixels in the Image

(3)

Number of Pixels in Black Region Total Number of Pixels in the Image

(4)

GGO =

NR =

Figure 1 denotes the graphical abstract of the proposed work. The correlation between quantitative imaging data and survivability of the patient is tested using Pearson’s correlation. The method measures the linear dependency between two variables with a value ranging from −1 to 1, where 0, 1, and −1 represents a positive, perfect, and negative correlations, respectively [10]. The ρ value in Pearson’s correlation refers to the probability of finding the current result. If the probability is lower than 0.05, the linear relationship between the two variables is statistically significant. Equation 5 denotes Pearson correlation measure:   − X − Y X Y i i i=1

n

r=  n 

i=1

 Xi − X

2

 Yi − Y

2

(5)

The quality of the segmented images is analyzed using the performance metrics such as MSE, PSNR, SSIM, and computational complexity. The mean-square error (MSE) is the summation of squared error between the original and segmented image [11]. The lower the value of MSE is, the lower the error. Equation 6 is used to compute the MSE:

Analysis of High-Resolution CT Images of COVID-19 Patients

 MSE =

X,Y

231

[O (x, y)) − S (x, y)



2

(6)

X×Y

Where O refers to the original image S refers to the segmented image X and Y refer to the rows and columns in the original and segmented images PSNR metric computes the peak signal-to-noise ratio (PSNR) between original and segmented images in decibels. The higher the PSNR is, the better the quality of the segmented image [12]. PSNR is a measure of peak error, derived from MSE. The formula for computing PSNR is denoted in Eq. 7:  PSNR = 10 log10

F2 MSE

(7)

Where F refers to the maximum fluctuation in the image data type Structural similarity index measure (SSIM) is a multiplicative combination of luminance, contrast, and structure [13], as denoted in Eq. 8, followed by the expanded notions from Eqs. 9 to 11. SSIM(O,S) = [l (O, S)]α .[c (O, S)]β .[St (O, S)]γ

(8)

Where l (O, S) =

2μO μS + C1 μ2O + μ2S + C1

(9)

c (O, S) =

2σO σS + C2 σO2 + σS2 + C2

(10)

σOS + C3 σO σS + C3

(11)

St (O, S) =

4 Results and Discussion Figure 2 shows the segmentation of lesion regions from lung-CT images using PSD method. The method correctly segments the patchy ground-glass opacities with optimized margins that highlight the lesion region. The results of the PSD method clearly exhibit the disfigured and abnormal shape of lungs. It is evident that

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Fig. 2 PSD image segmentation results

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Analysis of High-Resolution CT Images of COVID-19 Patients

Fig. 2 (continued)

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Table 1 Image quantification of lung-CT images

S. no 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Lesion region (%) 12.805 17.77 62.695 28.095 33.26 12.205 9.63 16.195 15.05 47.11 40.16 65.505 37.19 19.795 15.29 14.58 28.325 20.56 15.37 32.23 45.65

Normal region (%) 87.195 82.23 37.305 71.905 66.74 87.795 90.37 83.805 84.95 52.89 59.84 34.495 62.81 80.205 84.71 85.42 71.675 79.44 84.63 67.77 54.35

Left lung lesion region (%) 2.48 21.36 60.18 33.07 27.11 11.19 7.48 14.46 13.84 48.37 39.53 63.41 41.86 21.15 16.28 14.85 31.24 27.32 14.83 29.34 43.91

Left lung normal region (%) 97.52 78.64 40.1 66.93 72.89 88.81 92.52 85.54 86.16 51.63 60.47 36.59 58.14 78.85 83.72 85.15 68.76 72.68 85.17 70.66 56.09

Right lung lesion region (%) 23.13 14.18 65.21 23.12 39.41 13.22 11.78 17.93 16.26 45.85 40.79 67.6 32.52 18.44 14.3 14.31 25.41 13.8 15.91 35.12 47.39

Right lung normal region (%) 76.87 85.82 34.51 76.88 60.59 86.78 88.22 82.07 83.74 54.15 59.21 32.4 67.48 81.56 85.7 85.69 74.59 86.2 84.09 64.88 52.61

the GGO pattern is the most common finding in COVID-19 infections. The GGO patterns are most commonly of multifocal, bilateral, and peripheral regions. GGO is presented as a multifocal lesion in 11 out of 21 experimental lung-CT images and involves all lobes. GGO is presented as a bilateral lesion in 6 out of 21 images and most commonly located in the inferior lobes of both the left and right lungs. In the remaining four images, GGO is exposed as a unilateral lesion and most commonly found in the inferior lobe of the right lung. Table 1 denotes the quantification of lesion and normal regions of the lungs. The first two columns in the table denote quantification of the lesion and normal regions of both the right and left lungs. The third and fourth columns illustrate the quantification of lesion and normal region in left lung. Finally, the fifth and sixth columns show the quantification of lesion and normal regions of the right lung. Figure 3 illustrates the linear dependency between the quantified lesion region and survivability of COVID-19 patients using Pearson’s correlation. The correlation value r is 0.83, and ρ is 0.05, denoting that there is no or less correlation between patient’s age and quantified lesion region. The result signifies that the patient’s age does not impact lesion regions, i.e., irrespective of the patient’s age the lesion region is growing across the lungs and the variables do not have any statistical relations. Finally, Fig. 6

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Fig. 5 Lesion region vs. survivability

Fig. 6 Normal region vs. survivability

explicates the correlation between normal region and age. The results show no or less negative relationship between the attributes with r value as −0.23 and ρ value as 0.31 > 0.05. Hence, it has been proved that the patient’s age and normal lung are two independent variables with no linear relationship. From the results, it has been observed that the analysis of the lung-CT images promises a higher sensitivity but lower specificity and can play a role in the descriptive analysis of COVID-19 disease. Though the severity of COVID-19 disease can be estimated by the visual assessment of lung-CT images, the software assistance provided in this paper is more supportive for the quantification of lesion region. Based on the involvement of the lobes and GGO patterns, the severity of the disease can be computed. Table 2 denotes the performance of the PSD image segmentation algorithm with respect to PSNR, SSIM, MSE, and time analysis. The results obtained from the

Analysis of High-Resolution CT Images of COVID-19 Patients Table 2 Performance analysis of PSD image segmentation method

S. no 1

2

3

4

5

6

7

8

9

10

Metric PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms)

PSD 17.16858 0.993961 0.127365 0.146008 21.77537 0.998667 0.0598 0.154008 23.16629 0.999119 0.043412 0.169009 16.75699 0.993321 0.139908 0.35302 16.75818 0.993495 0.189856 0.086004 17.1571 0.993971 0.173193 0.134008 22.42507 0.998859 0.051491 0.101006 27.02776 0.999672 0.017842 0.006001 28.09448 0.999749 0.013957 0.044002 17.51284 0.995754 0.159572 0.092005

237 K-means 14.63592 0.992213 0.137552 0.694038 13.10178 0.981128 0.195831 0.642037 12.1858 0.893495 0.241813 0.573032 13.99115 0.936215 0.159567 0.555031 13.78368 1 0.167375 0.20601 15.26202 0.985491 0.119085 0.556032 21.07038 1 0.031262 0.425024 15.24397 0.970221 0.119581 0.099006 19.18688 0.98294 0.048236 0.090005 11.62889 0.783696 0.274898 0.206012

Fuzzy 9.37694 0.98507 0.461706 0.131007 15.19089 0.987263 0.121051 0.395021 7.698778 0.952042 0.679488 0.117006 8.672593 0.963403 0.543001 0.234013 8.242791 0.981396 0.599489 0.192011 9.289661 0.943923 0.471079 0.141008 9.770341 0.978357 0.421721 0.105006 12.65338 0.992091 0.21713 0.18601 13.02038 0.992957 0.199536 0.099006 7.462082 0.962826 0.717549 0.159009 (continued)

238 Table 2 (continued)

A. Joy Christy and A. Umamakeswari S. no 11

12

13

14

15

16

17

18

19

20

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Metric PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms) PSNR SSIM MSE Time(ms)

PSD 24.87536 0.999432 0.029289 0.043003 19.4277 0.997376 0.102676 0.044002 23.7363 0.999245 0.038072 0.123007 19.8939 0.997598 0.092226 0.131008 22.55313 0.999693 0.05057 0.264015 20.19215 0.998391 0.072288 0.077004 18.49671 0.989972 0.153847 0.127007 21.65835 0.998636 0.061434 0.16001 21.4594 0.998314 0.056597 0.17401 23.20182 0.979198 0.030587 0.103006 24.9786 0.998483 0.046011 0.015007

K-means 17.06113 0.972831 0.074474 0.127201 11.72732 1 0.268737 0.0624 16.12227 0.983357 0.197686 0.470001 22.11123 1 0.0246 0.468402 13.00766 0.896225 0.200122 0.1248 12.27778 0.889713 0.236746 0.136008 12.73913 1 0.212886 0.437025 22.15041 0.999531 0.024379 0.593034 9.271233 0.75446 0.473082 0.605035 13.01902 0.99078 0.199599 0.418024 13.35937 0.963549 0.184554 0.309018

Fuzzy 9.047227 0.975545 0.498124 0.104006 8.223032 0.987717 0.602222 0.082005 6.658499 0.940081 0.863396 0.206012 9.386661 0.976044 0.460674 0.122007 8.14966 0.984885 0.612483 0.081004 7.905123 0.985079 0.647959 0.076004 8.019295 0.982554 0.631147 0.109006 14.29212 0.996478 0.148884 0.139008 6.420316 0.984929 0.91207 0.125007 7.05006 0.979232 0.788958 0.088005 7.317229 0.983924 0.741886 0.137008

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PSD method are compared with K-means image thresholding and adaptive image thresholding methods for analyzing the quality of segmented regions. Almost in all images, the PSD method has obtained higher PSNR and SSIM values and lower MSE value with reduced time complexity. Hence, it has been proven that the segments created in lung-CT images of COVID-19 patients using the PSD method are valid and seem to be better than the other two experimental methods by creating quality segments.

5 Conclusion In this work, we have used PSD, a novel thresholding-based image segmentation method to segment the lung-CT images of COVID-19 patients for the quantification of lesion regions. The segments created by PSD method are qualitative and correctly elucidate the lesion regions from the lung CT-images than the other experimental methods. The quantitative analysis on lesion regions could precisely quantify not only the whole volume of infected regions COVID-19 disease but also the proportion of GGO in the right and left lungs. The method highlights the abnormality and shape of the lungs due to the disease. The results clearly specify that a patient admitted with less than 20% of infection may be survived. The complexity of the disease is increased with the growing infected regions. The quantitative segment regions also well correlated with the survivability of the patients. In this work, we have taken 21 images for the quantification analysis. The efficiency of the method can be proven with more number of images. A predictive model may also be implemented for predicting the survivability of a patient based on infected regions.

References 1. Wang, W., Tang, J., & Wei, F. (2020). Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. Journal of Medical Virology, 92(4), 441–447. 2. Zu, Z. Y., Di Jiang, M., Peng Peng, X., Chen, W., Ni, Q. Q., Guang Ming, L., & Zhang, L. J. (2020). Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology, 296, 200490. 3. Chen, Z. M., Fu, J. F., Shu, Q., Chen, Y. H., Hua, C. Z., Li, F. B., Lin, R., Tang, L. F., Wang, T. L., Wang, W., & Wang, Y. S. (2020). Diagnosis and treatment recommendations for pediatric respiratory infection caused by the 2019 novel coronavirus. World Journal of Pediatrics, 16, 1–7. 4. Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792. 5. Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., Singh, S., & Shukla, P. K. (2020). Deep transfer learning based classification model for COVID-19 disease. IRBM, 43, 87.

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6. To˘gaçar, M., Ergen, B., & Cömert, Z. (2020). COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine, 121, 103805. 7. Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19, 100360. 8. Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., & Ye, L. (2020). Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell, 181, 1423. 9. Wu, X., Hui, H., Niu, M., Li, L., Wang, L., He, B., Yang, X., Li, L., Li, H., Tian, J., & Zha, Y. (2020). Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study. European Journal of Radiology, 128, 109041. 10. Cheng, Z., Qin, L., Cao, Q., Dai, J., Pan, A., Yang, W., Gao, Y., Chen, L., & Yan, F. (2020). Quantitative computed tomography of the coronavirus disease 2019 (COVID-19) pneumonia. Radiology of Infectious Diseases (Vol. 7, pp. 55–61). 11. Deshmukh, A. B., & Rani, N. U. (2019). Fractional-Grey Wolf optimizer-based kernel weighted regression model for multi-view face video super resolution. International Journal of Machine Learning and Cybernetics, 10(5), 859–877. 12. Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study. Journal of Computer and Communications, 7(3), 8– 18. 13. Ho, Y. H., Cho, C. Y., Peng, W. H., & Jin, G. L. (2019). Sme-net: Sparse motion estimation for parametric video prediction through reinforcement learning. In Proceedings of the IEEE international conference on computer vision (pp. 10462–10470).

Attention-Based Deep Learning Approach for Semantic Analysis of Chest X-Ray Images Modality Rishabh Dhenkawat, Snehal Saini, Shobhit Kumar, and Nagendra Pratap Singh

1 Introduction Computer-vision-based diagnosis provides an automatic classification and suggestions for reference to improve accuracy and efficiency of diagnosis. Since the past few years, many deep learning and machine learning algorithms are used for the classification of medical images; SVM, K-nearest neighbors, random forest, and other techniques are included. They can be used in a variety of medical image processing applications. Using old machine learning methods poses two major difficulties. First, the inaccurate results are due to the limited processing of large input. Second, the use of manual feature extractions instead of learning valid features. Thus, deep learning methods are preferred for medical image processing. Deep learning technology has a wide variety of applications in healthcare image processing, such as diagnosis and organ segmentation. The convolution neural network CNN has been used extensively in several pieces of research that include reading and interpreting CT images for medical applications. Deep learning is a representation learning technique that connects different layers and nonlinear components efficiently to obtain various representation levels. Deep learning algorithms have two essential characteristics: local connectivity and shared weights (CNN). Deep learning is widely used in image analysis because of all these features, which make it much easier to handle complex data processing

R. Dhenkawat · S. Saini () · N. P. Singh NIT Hamirpur, Hamipur, India e-mail: [email protected]; [email protected]; [email protected] S. Kumar Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_13

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tasks. Convolution layers, pooling layers, and fully connected layers are the three layers that make up the CNN architecture. Convolution layers extract features from the previous layer, pooling layers minimize computational complexity, and completely connected layers, eventually, are used to extract features from the previous layer. A recurrent neural network (RNN) is used to process sequence data to recognize things. Since words in a sentence are semantically related, word generation uses previous word knowledge to predict the next word in the sentence. In RNN, the current output of a sequence is related to the previous output, enabling word relationships to be determined. It is used to model temporary sequences and their long-range dependencies because of the property of feedback connections. In this chapter, we propose a CNN–LSTM chest X-ray image semantic analysis focused on an attention process to produce a description of the chest X-ray images. In the deep learning model, we used the idea of the attention to highlight the infection regions in the lungs. Two types of attention mechanisms in deep learning are local attention and global attention. In our pour model, we used local attention, also known as additive attention or Bahdanau attention. As a result, the model assists in the analysis and clarification of chest X-ray images, automatically supplying doctors with valuable knowledge about the input X-ray image. Two types of chest X-ray images available are frontal and lateral sides. Using these two types of images as data, our model generates a report for these chest X-ray images. To construct a deep learning model, we present a predictive model that uses both image and text processing. This chapter uses chest X-ray images from Indiana University’s large chest X-ray dataset to describe the model’s architecture and detection efficiency.

2 Literature Review 2.1 Image Captioning Various studies in computer vision were initially determined on generating descriptions for visual data from videos [1, 2]. However, these models were relatively brittle, complex, hand-designed, and were limited to applications in very few domains. The problem of image captioning gained popularity after the advancement in object recognition systems. Farhadi et al. [3] converted a triplet of scene elements into text descriptions using templates. Li et. al [4] detected objects and their relationships and put together the descriptions for them to form phrases. Kulkarni et al. [5] used a template-based model to generate text from complex detection graphs beyond triplets. Aker et al. [6] used dependency relations in documents from the web that had information about the location of an image to summarize that image automatically and generate its caption. Elliott et al. [7] proposed visual dependency representations to find out the relationships within the objects of an image to improve image captioning.

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Kuznetsova et al. [8] used a tree-based design for image caption generalization and generation. They used web images and their captions to generate natural language image descriptions that are more expressive. These approaches were however much rigid in terms of text generation. Oriol et al. [9] combined the techniques of computer vision and machine translation to propose a model that produced captions or sentences that would describe a particular image. The model uses a supervised learning approach for training purposes and learns individually for each image by encoding the image first using a convolutional neural network and then generating a description of the image in natural language using a recurrent neural network. Karpathy and Fei-Fei [10] proposed a model to generate captions for images and their regions using the image and description pairs. The model uses multimodal embedding to align the modalities for a convolutional neural network on images and a bidirectional recurrent neural network on sentences. Anderson et al. [11] proposed an image captioning model that is trained using partially specified sequence data from labeled images and object detection datasets. Their algorithm utilizes finite state automata to describe partial sequences for training the recurrent neural networks. This method solved the problem of training the image captioning models on image–sentence pairs that was a requirement for previously proposed models. Chen et al. [12] proposed an adversarial training model for exploring crossdomain image captioning on unpaired image–sentence data. Despite achieving improved accuracy, the novel algorithm still requires image–sentence paired data for training purposes. Gu et al. [13] used the technique called language pivoting by capturing the attributes of an image captioning module first in a pivot language and then translated it into another target language instead of using the traditional image–language pairs. However, this method was still dependent on pivot-target parallel language translation corpus. Feng et al. [14] proposed an unsupervised technique to generate captions for images by utilizing a visual concept detector, a set of images, and a sentence corpus without requiring any paired image–sentence data during the training phase.

2.2 Attention Mechanism Image captioning models for a large time could not accurately extract all the features present in the image. This problem was solved by a new technique called the attention mechanism proposed by Bahdanau et al. [15] in 2015. They proposed a model for neural machine translation that automatically “soft-searched” for relevant parts in a sentence to predict a target word without taking each word into consideration explicitly. This method of soft searching and focusing only on the specific parts of the input source was then named attention mechanism or Bahdanau attention. Since then, it has been widely used in the application of computer vision and image captioning domains [22].

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You et al. [16] combined the top-down and bottom-up approaches for image captioning via employing a semantic attention module that selectively focuses on semantic concepts and fuses them into outputs of RNN thus forming feedback connecting the two approaches. This novel approach for image captioning technique achieved outstanding results compared to the pre-existing methods. The attention mechanism for image captioning in most cases forces specific words to correspond to a particular region. However, words such as articles and conjunctions cannot correspond to the image region. Deng et al. [17] solved this problem for image captioning by using adaptive attention to formulate the decision of using a specific image feature to generate a corresponding word for description. They introduced DenseNet to extract all the features of the image and simultaneously employed a sentinel gate for adaptive attention. The decoding phase utilizes LSTM for word generation tasks. Using this new technique, they were able to improve the BLEU and METEOR evaluation criteria. Yan et al. [18] further improved this problem by introducing diversity regularization for adaptive attention so that it does not only focus on visual features while generating image captions instead generates words with much more expressivity.

2.3 Medical Report Generation Writing reports from medical imaging can be a time-consuming and tedious task for physicians. For this purpose, many studies in computer vision and the natural language processing domain have tried to formulate artificial methods to automatically generate such medical reports. However, this poses various problems for the system such as identification of abnormal regions in the medical scans, generation of multiple long sentences for describing the medical image accurately, and including all sorts of multiple heterogeneous pieces of information such as findings and tags. To solve these problems, Jing et al. [19] introduced a multi-task learning framework to jointly predict tags and generate paragraphs for medical images fed as inputs. They employed a co-attention mechanism to focus on abnormal regions in the scanned images and developed a hierarchical LSTM network model for producing long descriptive findings of the images in the form of medical reports. Xue et al. [20] proposed a CNN–LSTM model to generate radiology reports from chest X-rays involving high-level conclusive findings and detailed descriptive findings. They also used an attention input to maintain the coherence between the generated sentences. The model was evaluated on Indiana University’s chest X-ray dataset. Yuan et al. [21] used a generative encoder–decoder model and pre-trained it using chest X-ray images to discover 14 radiographic observations. They employed a late fusion attention mechanism on a sentence level to extract visual features. Further, they enhanced the model by fine-tuning the encoder to bring out the most common medical concepts present in the X-ray images and fused these concepts at every step

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of the decoding using the word-level attention mechanism. This allowed the model to be more expressive and generate much more descriptive semantics for the image under consideration that increases the accuracy of the radiology report obtained in this fashion.

3 Methodology 3.1 Overview We have used an encoder and a decoder architecture with an attention mechanism (Fig. 1) and compared it with encoder and decoder architecture without attention. Here convolution neural network is taken as an encoder to extract visual features; this encoder will output image feature vectors. The resulted feature vectors will be taken as input to an additive attention-based LSTM decoder. LSTM decoder took image feature vector and sequence vector to process reports. An image classification using InceptionV3 model over chest dataset is used; with this classification model, the weights were saved over the training and later used in encoder feature extraction by using the saved weights to InceptionV3.

3.2 CNN Encoder The convolution neural network is popular in deep learning due to its ability to learn and represent image feature vectors. Many frameworks such as VGG16, ResNet, Inception, and DenseNet are trained on ImageNet Dataset containing 1.3 million

Fig. 1 Model flow

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natural images. Due to the difference between medical chest images and natural images, InceptionV3 is again trained on labeled chest X-ray images to improve transfer efficiency. The encoder is a single linear model that is fully connected. The input X-ray image is fed to InceptionV3 that extracts the features of two images or adds them. Then they are input to the FC layer, and an output vector is obtained. The encoder’s last hidden state is connected to the decoder. To deal with datasets that are too big to fit into memory, rather than shuffling the entire dataset, it keeps a buffer of buffer size elements and picks the next element at random from that buffer (replacing it with the next input element, if one is available). Hence, buffer size is taken as 1000. To allow us to turn each word into a fixed-length vector of a predetermined size, embedding layer is used with size 256. The resulting vector is dense, with real values rather than just 0s and 1s. The constant length of word vectors allows us to better represent words while reducing their dimensionality. Here we have used rectified linear activation function that gives the same output as input if it is positive; otherwise, it will give zero. A concatenation layer is used, which gives the output to the dense layer, and further a dense layer is used whose output is passed through ReLU activation function. The final output from the activation function is further passed to the decoder.

3.3 LSTM Decoder Recurrent neural networks model non-static behavior of sequences through connections between different units. LSTM is a type of RNN that has 3 added states such as forget state, input state, and output gates. Hence, the LSTM layer is present in the decoder that does language modeling up to word level. The first step receives encoded output from the encoder and the vector. The input is passed to the LSTM layer with additive attention. The output consists of two vectors: one is the predicted label and the other is the previous hidden state of the decoder; this feedback goes again to the decoder on each time step. Here, bidirectional LSTM layers are used with the ReLU activation function. One embedding layer is used with the bi-LSTM layer. The output of the 2 concatenation layers is passed to additive attention, and the final output is given with one flatten layer and 2 dense layers. In Fig. 2, LSTM model is shown with attention.

3.4 Attention Mechanism Recurrent neural networks model the non-static behavior of sequences through connections between different units. LSTM is a type of RNN that has 3 added states such as forget state, input state, and output gates. Hence, the LSTM layer is present in the decoder that does language modeling up to word level. The first step receives encoded output from the encoder and the vector. The input is passed to

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the LSTM layer with additive attention. The output vectors are two vectors: one is the predicted label and the other is the previous hidden state of the decoder; this feedback goes again to the decoder on each time step. Here bidirectional LSTM layers are used with the ReLU activation function. One embedding layer is used with the i-LSTM layer. The output of the 2 concatenation layers is passed to additive attention, and the final output is given with one flatten layer and 2 dense layers.

3.5 Model Architecture The model proposed as per Fig. 2 in this chapter contains five components: input layer input labels are given to the model and then summed with the image feature vectors. The embedding layer is then used to map each label to a low-dimensional vector. LSTM layer is used to get high-level features, and from step, these LSTM layers are repeated twice to understand features in more depth. A weight vector is provided by the attention layer, and it also merges word-level features from each time step into a sentence-level feature vector, by multiplying the weight vector. Finally, the sentence-level feature vector in the output layer is finally used for relation classification.

4 Experiments 4.1 Dataset We have used Indiana University’s vast chest X-rays dataset provided by the Open-i service of the National Library of Medicine. The dataset contains 7000 chest X-rays

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from various hospitals along with 3900 associated radiology reports. Each report is associated with two different views of the chest, i.e., frontal and lateral views. The associated tags contain the basic findings from the X-ray images that are used to train the model so as to generate image captions later on.

4.2 Exploratory Data Analysis Before jumping to the main code, we analyzed the dataset to visualize some of its important characteristics. For eg, by performing text analysis, we obtained the bar plot of the most unique sentences indicated in the x-ray reports and the frequency of their occurrences (see Fig. 3). By generating a word cloud, we can see the most occurring words in the sentences present in X-ray reports (Fig. 4). Some of these words are chest pain, shortness, breath, male, female, dyspnea, and indication. The word cloud in Fig. 4 is used to represent the words having the maximum word count in the impression column target variable. Further, we visualize the word count distribution plot in Fig. 5 for the impression column target variable. This plot offers better insights to see the minimum and maximum word count. From the plot, we conclude that the minimum word count is 1, the maximum word count is 122, and the median word count is 5.0. Further, we analyze the distribution of image count per patient using a bar plot, and we see that the minimum image count is 1 and the maximum image count is 5 in Fig. 6. Since two types of chest X-ray images are available to us, which are frontal

Fig. 3 Bar plot of unique words for indication

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and lateral views, by selecting a sample data point, we find out the total number of images present for that particular patient, its findings, and impressions. From here, we analyze that there are multiple images associated with every patient.

4.3 Pre-processing and Training The transfer learning method is used for the image to feature vector conversion, and text data tokenization is used for dataset preparation. InceptionV3 model trained on

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ImageNet is used. A classifier to detect which type of disease the person is suffering from was made. Once classification was done, weights of the trained model were saved in hd5 format.

4.4 Model without Attention Mechanism 4.4.1

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Single fully connected layer linear output is used. Before we pass to the FC layer, two image tensors were added, and we pass to the FC layer. This layer outputs the shape of batch size and embedding dimension.

4.4.2

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It contains an embedding LSTM layer and dense layer that outputs shape (batch size, vocab size). Here bi-LSTM layer is used with two dense and one flatten layers (Fig. 7).

4.4.3

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In the training phase, the teacher forcing is used, for training recurrent neural networks that use the output from a previous step as an input. In training, a “start” token is used to start the process, and the generated word in the output sequence is

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used as input in the subsequent time step along with other inputs such as an image or a source text. Until the end, the same recursive output as the input method is used till better results are generated.

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The encoder part is the same as the previous model architecture and summed image vector with a single fully connected layer. In the decoder, part LSTM with attention is used. Here additive attention (local or Bahdanau attention) is used.

4.4.5

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Beam-search-based teacher forcing method is used to find the resulting sentence. Beam search is used here, as it chooses the most probable next step when the sequence is made. Beam search is a heuristic search algorithm that investigates a graph by extending the most promising node in a small set. Beam search is a

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heuristic search approach that grows the W number of optimal nodes at each level at all times. It travels downhill exclusively from the best W nodes at each level as it develops level by level. Beam search constructs its search tree using breadthfirst search. Beam search constructs its search tree using breadth-first search. It generates all the successors of the current level’s state at each level of the tree. However, at each level, it only evaluates a W number of states. Other nodes are not taken into account. It uses all possible next steps and takes most likely k. Here k is user-specified and controls the number of semantic analyses of X-ray images and other medical scanned images to generate reports. To evaluate the model for various chest X-ray inputs, it is first checked upon a sample data point, and the results as shown in Fig. 8 are observed. The model accuracy for pre-processing is also plotted against every epoch of training and can be visualized as shown in Fig. 9. Figures 10 and 11 show the accuracy and loss curves obtained for the model without using attention technique. As it can be clearly seen, the accuracy increases after utilizing the attention mechanism to a greater extent from Figs. 12 and 13. Also, the loss is decreased after employing the attention mechanism, which shows how the attention mechanism proves to be more effective for image captioning tasks and can be used for medical searches. The argmax function is widely used in mathematics and machine learning. However, there are some specific situations where you will see argmax used in applied machine learning and may need to implement it yourself. The most common situation for using argmax in applied machine learning is in determining the index of an array that yields the largest value. The probabilities show how likely a sample is to correspond to each of the class designations. The predicted probabilities are sorted into classes, with the predicted probability at index 0 belonging to the first class, the anticipated probability at index 1 to the second, and so on.

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4.5 Results Model accuracy and loss of the architecture with attention and without attention have been calculated and plotted in Figs. 10 and 13. Accuracy of CNN–LSTM without using attention mechanism on training set comes out to be 84%. But accuracy of CNN–LSTM with additive attention comes out to be 91%, which is

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Fig. 11 CNN–LSTM without additive attention accuracy curve

Fig. 12 CNN–LSTM with additive attention loss curve

much better than the model without using attention on the training data (Fig. 13). Actual and predicted captions of various chest X-ray images have been generated and compared. On testing data, additive attention gives the accuracy of 87.5, and without additive attention, testing accuracy comes out to be 80.5%. In Figs. 11 and 12, loss without attention and loss with attention mechanism are plotted. From Figs. 15, 16, 17, 18, 19, and 20, actual and predicted captions of chest X-ray

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images are given with attention mechanism. The results are evaluated prominently using two types of searches, namely argmax search and beam search. The actual description associated with Fig. 20 is also printed, i.e., “no acute cardiopulmonary process,” while the model after performing an argmax search predicts “no acute cardiopulmonary abnormality” as the caption. For the same set of input, using beam search, the predicted caption is “no cardiopulmonary abnormalities.” Similarly, the model is tested upon various other chest X-ray images, and the predictions are compared for both argmax and beam search methods. For another such test input as seen in Fig. 14, the actual caption is “no acute cardiopulmonary abnormalities,” while argmax search provides output caption “no acute cardiopulmonary abnormality” and beam search provides an output “no acute cardiopulmonary disease.” To better understand how different types of search methods yield different results for similar input, we have tried to analyze them via cases discussed below.

4.5.1

Case 1

The actual caption is “right-sided chest in without demonstration of an acute cardiopulmonary abnormality,” while the predicted caption using argmax search comes out to be “no evidence of acute cardiopulmonary disease,” and using beam search, it comes out to be “no evidence of the same (Figs. 15 and 16).”

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Case 2

The actual caption is “heart size is normal lungs are clear no nodules or masses no adenopathy or effusion stable slightly sclerotic posterior inferior of one of the mid-thoracic vertebral bodies seen on the lateral radiograph only this most represents overlying degenerative spurring than metastasis,” while the predicted caption using argmax search comes out to be “no acute cardio low lung volumes no pneumothoraces is normal heart size and normal and clear lungs are grossly within normal limits no acute cardiopulmonary abnormality identified,” and using beam search, it comes out to be “no acute finding (Figs. 17 and 18).”

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Case 3

The actual caption is “no acute cardiopulmonary disease,” while the predicted caption using argmax search comes out to be “no acute cardiopulmonary disease,” and using beam search, it comes out to be “no acute abnormalities (Figs. 19 and 20).”

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Case 4

The actual caption is “comparison no suspicious appearing lung nodules identified well expanded and clear lungs mediastinal contour within normal limits no acute cardiopulmonary abnormality identified,” while the predicted caption using argmax search comes out to be “no impression nodules of size normal and posterior inferior degenerative changes without superimposed pleural based suspected,” and using beam search, it comes out to be “no evidence for disease (Figs. 21 and 22).”

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predicted caption using argmax search comes out to be “no acute abnormality noted stable previous and appearance of atelectasis,” and using beam search, it comes out to be “low lung features negative (Figs. 23 and 24).”

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The actual caption is “no active cardiopulmonary disease left humeral head is positioned anterior and inferior to the glenoid concerning for anterior shoulder subluxation this is related to the muscular dystrophy and decreased shoulder muscles support postoperative changes from the spinal placement,” while the predicted caption using argmax search comes out to be “no active disease no evidence for contour no degenerative spurring of the prominent head of symptoms from no acute tuberculosis since mediastinal contour no acute abnormalities since patients symptoms of right volumes and l this may be an artifact of the previous the exam is recommended no typical findings of pulmonary edema,” and using beam search, it comes out to be “no acute cardiopulmonary disease (Figs. 25 and 26).”

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Conclusion

By comparing the results, model architecture of attention-based long short-term memory networks for relation classification worked well in classification tasks than without attention. Loss is converged to 0.3 with an accuracy of 89% train and 92% validation; from the result, we can see there is a similarity between each predicted and actual output. Thus, by using the attention mechanism, along with conventional deep learning methods, we can improve the accuracy of the model. Acknowledgments This paper and the research behind it would not have been possible without the exceptional support of my supervisor, Dr. Nagendra Pratap Singh. His enthusiasm, knowledge, and exacting attention to detail have been an inspiration and kept my work on track from our coding to the final draft of this paper. The magnanimity and proficiency of one and all have enhanced this study in innumerable ways and saved us from many errors.

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References 1. Gerber, R., & Nagel, H.-H. (1996). Knowledge representation for the generation of quantified natural language descriptions of vehicle traffic in image sequences. In ICIP. IEEE. 2. Yao, B. Z., Yang, X., Lin, L., Lee, M. W., & Zhu, S. C. (2010). I2T: Image parsing to text description. Proceedings of the IEEE, 98(8), 1485–1508. 3. Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. (2010). Every picture tells a story: Generating sentences from images. In ECCV, 2010. 4. Kulkarni, G., Premraj, V., Dhar, S. Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2011). Baby talk: Understanding and generating simple image descriptions. In CVPR, 2011. 5. Aker, A., & Gaizauskas, R. (2010). Generating image descriptions using dependency relational patterns. In ACL, 2010. 6. Elliott, D., Keller, F. (2013). Image description using visual dependency representations. In EMNLP, 2013 7. Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016). Attention-based bidirectional long short-term memory networks for relation classification (pp. 207–212). https://doi.org/10.18653/v1/P16-2034 8. Kuznetsova, P., Ordonez, V., Berg, T., & Choi, Y. (2014). Treetalk: Composition and compression of trees for image descriptions. ACL, 2(10), 351–362. 9. Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015). Show and tell: A neural image caption generator. In CVPR, 2015 10. Karpathy, A., & Fei-Fei, L. (2015). Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3128–3137). 11. Anderson, P., Gould, S., & Johnson, M. (2018). Partially-supervised image captioning. In NeurIPS, 2018. 12. Chen, T.-H., Liao, Y.-H., Chuang, C.-Y., Hsu, W.-T., Fu, J., & Sun, M. (2017). Show, adapt, and tell: Adversarial training of cross-domain image captioner. In (ICCV, 2017). 13. Gu, J., Joty, S., Cai, J., & Wang, G. (2018). Unpaired image captioning by language pivoting. In (ECCV, 2018) (Vol. 61). 14. Feng, Y., Ma, L., Liu, W., & Luo, J. (2019). Unsupervised image captioning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4120–4129). 15. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 16. You, Q., Jin, H., Wang, Z., Fang, C., & Luo, J. (2016). Image captioning with semantic attention. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4651–4659). 17. Deng, Z., Jiang, Z., Lan, R., Huang, W., & Luo, X. (2020). Image captioning using DenseNet network and adaptive attention. Signal Processing: Image Communication, 85, 115836. 18. Yan, C., Hao, Y., Li, L., Yin, J., Liu, A., Mao, Z., Chen, Z., & Gao, X. (2021). Taskadaptive attention for image captioning. IEEE Transactions on Circuits and Systems for Video Technology, 32(1), 43–51. 19. Jing, B., Xie, P., & Xing, E. (2017). On the automatic generation of medical imaging reports. arXiv preprint arXiv:1711.08195. 20. Xue, Y., Xu, T., Long, L. R., Xue, Z., Antani, S., Thoma, G. R., & Huang, X. (2018). Multimodal recurrent model with attention for automated radiology report generation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 457–466). Springer. 21. Yuan, J., Liao, H., Luo, R., & Luo, J. (2019). Automatic radiology report generation based on multi-view image fusion and medical concept enrichment. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 721–729). Springer. 22. Zhou, P., et al. (2016). Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers).

Medical Image Processing by Swarm-Based Methods María-Luisa Pérez-Delgado

and Jesús-Ángel Román-Gallego

1 Introduction There are several techniques to obtain medical images, such as x-ray, magnetic resonance (MR) imaging, computed tomography (CT), positron emission tomography (PET), ultrasound (US) or single-photon emission computed tomography (SPECT). In general, each technique is applied to specific parts of the body and generates a different type of image (Fig. 1). The images obtained by all these methods provide very useful information to help experts in making medical decisions. For this, it is necessary to process the images to extract useful information. Currently, there are several different methods available to apply each processing. It must be taken into account that many image processings have a high computational cost due to the dimensionality of the data. This makes it necessary to use rapid techniques that allow obtaining good results. Among such techniques, swarm-based algorithms have been successfully applied in various image processing operations. This chapter shows the application of swarm-based methods for medical imaging. In general, these methods are combined with others to define a system that addresses various aspects of image processing. Although there are many articles related to the subject, the description focuses on analyzing recent works that present interesting proposals. Many image processing operations are closely related and are often applied sequentially to an image. For example, feature extraction and feature selection are two operations that are usually applied to an image consecutively. The first operation extracts a set of features from the image, which allow representing the image and at the same time reducing the dimensionality of the data to be treated. Subsequently,

M.-L. Pérez-Delgado () · J.-Á. Román-Gallego University of Salamanca, Escuela Politécnica Superior de Zamora, Zamora, Spain e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Pandey et al. (eds.), Role of Data-Intensive Distributed Computing Systems in Designing Data Solutions, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-15542-0_14

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Fig. 1 Medical images obtained by different techniques

a subset is selected that includes only the most interesting features for the next processing that must be applied to the image. Among the operations applied to medical images, the chapter focuses on four interesting cases: feature selection, segmentation, classification, and registration. A section is included to describe interesting works that use swarm algorithms to apply each of these processings. As already indicated, the processing of an image includes several operations that are carried out by applying different methods. For example, it is necessary to perform feature selection before applying a classification operation. Therefore, although all the operations described in the subsequent sections are related, it is easier to analyze them separately.

2 Swarm-Based Methods Swarm-based methods apply a bioinspired approach to solve complex problems [1]. These algorithms try to imitate the intelligent behavior observed in several natural systems formed by a set of individuals that cooperate to face problems. Although everyone in the population can only perform simple tasks, the cooperation established among all individuals enables the population to perform complex tasks.

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Swarm-based algorithms simulate this collective behavior and apply it to solve optimization problems. These methods have been applied to solve a variety of complex problems, generating good results compared to other existing methods [2–4]. Although there are various swarm-based algorithms, they all share the same basic structure. A population of individuals that represent solutions to the problem is considered, and an iterative process is applied in which the population shares information to move toward better positions in the search space. The initial solution represented by each individual is defined in the initialization step. This step generally associates each individual with a random solution of the search space. Then, an iterative process improves the current solutions associated with the individuals (some or all). To perform this improvement, it is necessary to compute the quality or fitness of the solutions. This value is computed by applying the objective function of the problem (or a modification of said function) to the solution represented by each individual. The solution with the best fitness of the current iteration represents the solution to the problem in that iteration, while the final solution of the problem is the best found throughout the iterations. Once the fitness of the solutions has been determined, the population shares information to try to move the individuals to better areas of the search space. The computations applied to perform this operation are different for each swarm-based method. Nevertheless, in all cases, some or all the individuals move to new positions (generally more promising positions) in the search space. The iterative process continues for a predefined number of iterations or until the solution converges. The first swarm-based method proposed in the literature mimics the foraging behavior of ants. Several ant-based algorithms have been proposed over the years, [5]. The first one, called ant system, was applied to solve the well-known traveling salesman problem. To solve this optimization problem, the associated weighted graph is considered, and the algorithm looks for a minimum cost path on the graph. With this purpose, a set of ants is used that move on the graph. The ants share information through the pheromone that they deposit on the connections of the graph that they traverse. Each ant traverses the graph to define a path that passes through all the nodes once, choosing more likely connections that have low cost and large amounts of pheromone. When an ant has built its solution, it shares information with other ants by updating the pheromone of the graph’s connections. The amount of pheromone that an ant contributes is proportional to the cost or quality of the solution it has found. As a result of this update, the connections that are part of the best solutions become more desirable to the ants in the next iteration of the algorithm. The solution to the problem is the lowest cost path found throughout the iterations. Algorithm 1 shows the basic steps that have been described above.

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Algorithm 1: Ant-Based Algorithm Initialize the pheromone of the graph connections REPEAT Define a closed path for each ant Compute the cost of the solution defined by each ant Update the pheromone of the graph connections Update the best solution to the problem UNTIL stop criterion is met The particle swarm optimization (PSO) algorithm proposes a different approach to that proposed by the ant-based algorithm [6]. PSO is applied to solve an optimization problem that has an associated objective function. The solution to the problem is a vector whose size is equal to the number of dimensions of the solution space. To solve this problem, a set or swarm of particles is considered, and the position of each particle is a feasible solution to the problem. In addition to a position, each particle has a velocity and remembers the best position it has found throughout the iterations of the algorithm. The quality or fitness of a solution is calculated by applying the objective function of the problem. The algorithm begins by giving initial values to the particles in the swarm. Then, it applies an iterative process that allows the particles to move within the search space, to find a good solution to the problem. Each particle adjusts its position, based on both the best position reached by itself and the best position reached by the swarm. The best position found by the swarm throughout the iterations will be the solution to the problem. Algorithm 2 shows the basic steps of PSO.

Algorithm 2: PSO Initialize the particles in the population REPEAT Update the velocity of each particle Update the position of each particle Update the personal best position of each particle Update the best solution of the swarm UNTIL stop criterion is met The length of this chapter precludes detailing the operations of other swarm algorithms. However, the description given for PSO shows a general scheme followed by many of these algorithms. For example, this can be seen in the steps of the firefly algorithm (FA) [7] and the shuffled-frog leaping algorithm (SFLA) [8], shown in Algorithms 3 and 4, respectively. To complete the information associated with the algorithms that appear in this section, this chapter includes an appendix that shows the flowchart of each method along with the equations associated with the basic operations.

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Algorithm 3: FA Initialize the population of fireflies REPEAT Sort the fireflies by brightness (fitness) Update all fireflies except the brightest one Update the brightest firefly Update the best solution to the problem UNTIL stop criterion is met

The following sections of this chapter refer to some other swarm-based methods that cannot be described in this section due to space limitations. However, they are listed below, and a reference is cited where they are clearly described. The indicated methods are as follows: artificial bee colony (ABC) [9], bacterial foraging optimization (BFO) [10], bat algorithm (BA) [11], cat swarm optimization (CSO) [12], crow search (CRS) [13], cuckoo search (CUS) [14], flower pollination algorithm (FPA) [15], and gray wolf optimization (GWO) [16].

Algorithm 4: SFLA Initialize the population of frogs REPEAT Sort the frogs by fitness Create the memeplexes FOR each memeplex Improve the worst frog in the memeplex END-FOR Recombine the frogs of all memeplexes Update the best solution of the population UNTIL stop criterion is met

3 Feature Selection An image can be represented by a set of features drawn from it. They are obtained as a result of a feature extraction procedure, which is usually applied before other image processing operations, such as classification. Once the set of features that represent the image has been extracted, different operations can be applied to said image. In general, these operations do not use the entire feature set, but only the most suitable subset for the task to be performed. Therefore, a feature selection operation is applied to the initial feature set. The objective of feature selection is to reduce the initial set of features to a small subset, by selecting those that are the most relevant for the processing to be applied to the image and reducing the redundancy.

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The proposal of Jona and Nagaveni defines a feature selection method that is applied to mammograms to detect breast cancer [17]. This method applies an antbased algorithm called ant colony optimization (ACO) and uses the CUS algorithm to perform the local search of ACO. The method considers an initial set with 78 features. When the first iteration of ACO is applied, each ant randomly selects a subset of features. However, in subsequent iterations, the ants can only select features from the subsets used in the previous iteration to update the pheromone. CUS is used at each iteration to select the best features. Sudha and Selvarajan described a feature selection method for breast cancer classification based on mammograms that uses a modification of the CUS algorithm [18]. The image is first segmented to extract the region of interest that contains the suspicious mass. The mass is then represented by a set of 123 features, and the CUS method is applied to select the most suitable subset of features to classify the image. Since the final objective of the feature selection process is to classify the images, the fitness function used for CUS is computed based on the classification accuracy of the nearest neighbor classifier. Jothi combined FA with tolerance rough set to define a feature selection method for MR brain images in which the features are used for the detection of brain tumors [19]. The tolerance rough set is a feature selection method that can operate on real values [20]. The method described by Jothi first performs image segmentation. Then, feature extraction is applied to obtain 28 features (including shape, intensity-based features, and texture-based features). After this, the feature selection operation is applied by executing FA. This algorithm uses the similarity measures defined in the tolerance rough set to compute the similarity among fireflies. The research reported in [21] describes a system for brain tumor grade identification based on the analysis of MR images. The system applies successive methods for image segmentation, tumor isolation, feature extraction, feature selection, and classification. The feature extraction operation obtains textural, non-textural, shape, and intensity-based features. Then, SFLA is applied to said features in order to select the best subset of features to perform the classification. Sahoo and Chandra describe a system for classifying cervix lesions as benign and malignant [22]. This system applies a modified version of GWO to perform the feature selection operation. Since the original GWO was defined to solve single objective optimization problems, this article describes two variants for applying GWO to the multi-objective problem associated with feature selection. Shankar et al. described a system for Alzheimer detection from MI brain images [23]. After identifying the region of interest in the image, the features of such region are extracted. The feature selection is then performed by applying the GWO algorithm that uses the classification accuracy as fitness function. Tan et al. describe a method for the diagnosis of skin cancer applied to dermoscopic images, where a modified PSO is used for feature selection [24]. The PSO-based method is applied to the general set of image features to identify the most significant features of benign and malignant skin lesions. The main modifications of the PSO are the use of two subpopulations and a new equation to update the velocity

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of the particles, which considers the best particle of a sup-population and discards the worst particle. In addition, some updates are applied to selected subdimensions, while others are applied to all subdimensions. A feature selection method to classify MR images of brain tumors is described in [25]. Said method is based on the Fisher criterion and a variant of BA. The modification introduced in BA tries to improve the exploration capacity of the basic algorithm. Many feature selection methods measure the importance of the feature subset by using the metric of classification accuracy. When the classification accuracy is used as the fitness criteria, the feature subset selected depends on the classifier considered. To avoid this limitation, the method proposed in this article uses the trace obtained via the Fisher criteria as a fitness function, instead of using the classification accuracy to define said function. The system described in the article completes the operation by applying a support vector machine (SVM) to perform the classification. The proposal of Dandu et al. describes a method for the detection of brain tumors and pancreatic tumors where CSO is used for feature selection [26]. After performing image segmentation, scale-invariant feature transform is applied to extract features. CSO then selects the features that allow to distinguish the objects of different classes. After this, the classification is performed by applying a back propagation neural network. The method was applied to MR images and CT images.

4 Image Segmentation Image segmentation consists of decomposing an image into regions that do not overlap. This operation makes it possible to identify interesting parts of the image for further analysis. Image segmentation is an important operation in the analysis of medical images, since it allows identifying areas of tissues, bones, or organs affected by different problems (Fig. 2). Segmentation makes it possible to determine the shape or volume of the affected area, and this information helps experts in making medical decisions. Various approaches can be applied for image segmentation, such as clustering, thresholding, edge detection, or region identification. Clustering algorithms are commonly used as segmentation techniques. These methods divide the pixels of the image into clusters or groups of similar pixels. The research presented in [27] proposes a model for blood vessels segmentation that combines the matched filter method with the ant-based method called ant colony system [28]. Matched filter is a method commonly used for blood vessel detection, but the combination with the ant-based clustering method increases the accuracy of the results. In this case, the matched filter algorithm and the ant-based algorithm are applied in parallel, and the results of both methods are combined. Hancer et al. describe an image segmentation method that applies ABC to extract brain tumors from MR images [29]. Segmentation is carried out by ABC, which is applied as a clustering method. In this case, each food source used by the

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Fig. 2 Original brain image (a) and segmented suspicious area (b)

algorithm represents the centroid of each cluster. After this, the segmented image is converted into a binary image by applying thresholding, and finally, the brain tumor is extracted by applying connected component labeling. Mostafa et al. describe a liver segmentation method that applies ABC [30]. This proposal uses ABC as a clustering method that identifies regions with different intensity in abdominal CT images. The initial liver area segmentation obtained by this method is then refined by a region-growing approach. Fuzzy c-means (FCM) [31] is a clustering method that has been widely applied to image segmentation. This method is the fuzzy version of K-means [32]. Certainly, K-means and FCM are two very popular clustering methods. K-means separates a set of items into a predefined number of groups or clusters. Each item is assigned to the most similar group. Similarity is calculated by comparing the item and the cluster centroid, which is the mean value of the elements associated with that cluster. The process is applied iteratively to refine the centroids. In the case of FCM, each item can be associated with several groups. There is a membership value that determines the degree of association of each item with each cluster. It should be noted that the results of both methods are influenced by the initial centroids used. The method described by Taherdangkoo et al. in [33] combines ABC with FCM to segment MR images. This proposal considers the method described by Shen et al. in [34] as a starting point. To improve the results obtained for noisy images, Shen et al. introduced two new parameters in FCM (the feature difference between neighboring pixels in the image and the relative location of the neighboring pixels) and computed them by a neural network. Since this operation is time-consuming, Taherdangkoo et al. proposed using ABC to compute these parameters. The proposal of Forghani et al. is also based on the method of Shen et al. but uses PSO to calculate the two new parameters [35].

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PSO was used in [36] to select the optimal cluster centers for the FCM method that performs segmentation. Then, FCM was applied for MR brain images segmentation. The authors use a variant of FCM described in [37] and improve it by including three main modifications. First, PSO is used to initialize the FCM cluster centers. Second, the membership function of FCM considers outlier rejection. Third, the method considers spatial neighborhood information by using a square window around the pixel being processed. The proposal of Alagarsamy et al. combines CUS with a variant of FCM (called type-2 FCM) to define a method for MR brain image segmentation [38]. In this case, an iterative process is defined where CUS and the FCM variants are applied sequentially until the solution converges. The same authors proposed another similar method where BA is used instead of CUS [39]. Kavitha and Prabakaran describe a method for the early detection of lung tumor on CT images [40]. In this case, PSO is used to select the initial cluster centers for the FCM clustering method that performs the segmentation. Before applying PSO, the filtered image is divided into five horizontal equidistant strips, and the second strip is taken to apply segmentation. Thresholding methods are frequently used techniques for image segmentation. They separate the pixels in the image into two or more classes, based on their intensity, and determine the boundaries between classes. The methods used to calculate the thresholds can be divided into nonparametric and parametric, the former being more precise. Nonparametric methods determine the thresholds by optimizing a specific criterion. For example, the Otsu criterion selects optimal thresholds by maximizing the between-class variance [41]. On the other hand, entropy-based criteria maximize the sum of entropy for each class. The Kapur entropy [42], the Tsallis entropy [43], the minimum cross entropy [44], and the fuzzy entropy are very popular entropy-based approaches. Several articles use swarm-based algorithms to find optimal threshold values for the cited criteria. In this case, the fitness function of the swarm is defined based on one of the thresholding criteria described above. The proposal described in [45] adapts the food-searching behavior of ants to define a thresholding method for medical image segmentation. This method was applied to MR brain images. The ants move on the image looking for food (similar pixels) and can memorize the food they found during this process. When an ant finds a new target, a fuzzy measure is used to evaluate the similarity between the target and the previous position. When the operation of the ants is completed, the pheromone deposited by the ants during their movement generates the segmentation results. The segmentation method described in [46] is the same as that described by [45], and it is also applied to the same type of images. Menon and Ramakrishnan apply ABC to segment MR brain images and then use FCM to process the segmentation result [47]. The segmentation method is based on the use of gray levels and considers the entropy method for the threshold estimation. ABC is applied to determine the global threshold. In this case, the authors use an ABC-based method previously applied to satellite image segmentation [48]. Then,

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FCM is applied to cluster the segmented image, which allows identifying the brain tumor. The proposal of Li et al. uses a variant of PSO to optimize the parameters for the Otsu criterion that is applied to perform image segmentation [49]. The PSO variant was previously proposed by the same authors in [50], using quantum uncertainty and cooperation mechanisms to prevent PSO from being trapped in local optima. The new article of the authors improves on this method by making better use of contextual information, which is evaluated after each particle is processed. The article shows the results of the method applied to CT images of a human stomach cavity. On the other hand, the research described in [51] proposes an improvement of the method presented in [49]. In this case, a set of auxiliary swarms is used to initialize the particles in the main swarm. To reduce the effect of local minima, the search space is partitioned into several regions, and each auxiliary swarm is associated with a region. Rajinikanth et al. describe a method to extract a tumor from a two-dimensional gray scale brain MR image [52]. The method includes two stages. First, a multilevel thresholding operation is performed by applying the FA method with a fitness function that uses the Tsallis entropy. This operation enhances the tumor region by grouping the similar pixels. To conclude the first stage, the skull region is eliminated. The resulting image is then segmented into different partitions using the Markov random field model combined with an expectation maximization algorithm, which is a common method for gray scale image segmentation [53]. As a result, three image segments are obtained: white matter, gray matter, and tumor mass. The proposal discussed in [54] uses CUS to define a segmentation method applied to microscopic images. The CUS method was applied considering three different objective functions: Otsu criterion, Kapur entropy, and Tsallis entropy. The article includes results that determine the efficiency of each of the variants in terms of the execution time and the quality of the final solution. The proposal of Want et al. applies multi-threshold image segmentation by using FPA [55]. They use the Otsu criterion to define the objective function of the swarmbased method. In addition, they modify the basic method to increase population diversity. On the one hand, the article proposes a new mutation mechanism for FPA in which the solution vectors are selected in such a way that each vector represents a different region of the search space. On the other hand, a crossover operator is used to increase the population diversity in the local search process. The method was applied to medical images of several types, most of them corresponding to CT and MR images. Edge-based methods used for segmentation attempt to detect edges in the image. This requires finding local intensity changes in the image. On the other hand, regionbased methods try to identify groups of neighboring pixels with similar intensity. The method described by Pereira et al. applies ACO to segment the optic disc in retinal images [56]. The pixels in the image are considered as the nodes of the graph that the ants can visit and the ACO algorithm is used as an edge detector. The ants move over the image driven by the local variation of the intensity values of the image. They then update a pheromone matrix with the same size of the image, which

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represents the edge information at each pixel of the image. At the end of the process, the pheromone matrix is analyzed, and a binary decision is made for each pixel, determining whether it is edge or not. The same authors used a similar approach to define a method for automatic identification of diabetic retinopathy lesions in fundus images [57]. In this case, the ACO algorithm was applied to segment exudates. Another approach commonly used in image segmentation is that defined by active contour models. These models typically use energy-based segmentation techniques, thus attempting to minimize the energy associated with the active contour as it evolves to fit around the desired object. Therefore, it is necessary to solve an optimization problem whose objective is to minimize the total energy, to guarantee that the active contour is located at the limits of the object. An active contour problem is usually solved by the gradient descent method, but some swarmbased methods have also been applied. PSO was applied in [58] for image segmentation based on active contours. This solution uses an active contour model method described in [59], which is a popular region-based model. The authors improve the results of said method by using PSO to solve the fitting energy minimization problem. The article shows the results obtained for various types of medical images. The proposal of Ilunga-Mbuyamba et al. describes an active contour model approach for image segmentation that uses a CUS variant [60]. The method is applied to MR brain images to detect tumors. CUS is used to help control points converge toward the global minimum of the energy function. With this purpose, the method defines a local search space (window) for each control point from the current contour. Then, the control points are placed randomly inside each window, in order to obtain new ones by applying CUS. The proposal presented in [61] describes an intensity-based statistical method that extracts the three-dimensional cerebrovascular structure from time-of-flight magnetic resonance angiography data. This segmentation method combines a new finite mixture model with an improved PSO variant. The information is modeled by a Rayleigh distribution function and two Gaussian distribution functions. In addition, the finite mixture model is used to fit the intensity histogram of the images. In this case, PSO is used to estimate the parameters of the finite mixture model that fits the intensity histogram of the image. The PSO variant uses a modified method to update the velocity of the particles and also considers that each particle can only share information with the neighbors that are within a ring around its position.

5 Image Classification Medical imaging classification is generally used to identify suspicious areas. This operation allows identifying the images that correspond to healthy people and those that correspond to people with some disease (Fig. 3).

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Fig. 3 A classification method can be used to differentiate between normal and abnormal images (images showing a health problem)

In general, a part of the image is selected, and the classification process is applied only to that part. Therefore, the classification operation is usually preceded by a segmentation operation, which identifies the region of interest. There are several methods frequently referenced in the literature related with the classification of medical images, such as clustering methods, artificial neural networks, SVM, or FCM. The quality of the result obtained by any classification technique depends on the proper selection of its parameters. To aid in this task, swarm methods have been combined with these techniques to set the corresponding parameters. Neural networks are trainable systems that learn to solve a problem from examples of that problem. The training process adjusts the weights associated with the network connections. Several research articles apply an artificial neural network to classify medical images and use a swarm-based method to train the network. In this case, each individual in the population represents the set of weights of the neural network. A method that applies a forward neural network to classify MR brain images as normal or abnormal is proposed in [62]. The system applies principal component analysis for feature selection and uses the selected set as input for the neural network. The weights of this network are optimized by a PSO variant. The main difference of this variant with respect to the original algorithm is in the definition of the weights of the equation used to update the velocity of the particles. The fitness function used in this case is the mean squared error. A method that combines PSO and ABC to classify MR brain images is described in [63]. The method classifies the images as normal and abnormal. It applies principal component analysis for feature selection before applying the swarm-based methods. The selected set is used as input of a feed-forward neural network that is optimized with a combination of two swarm methods. The article investigates the application of three different combinations of ABC and PSO previously proposed by other authors. The results show that the best combination is the one described in [64], which applies PSO and ABC in parallel and, at each iteration, recombines the best solution obtained by both methods.

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Dheeba et al. defined a system to detect breast abnormalities on digital mammograms [65]. The method classifies mammograms into normal and abnormal. The feature extraction stage applied to the images allows obtaining texture information that is used in the classification stage. The classification is carried out by means of a neural network that uses the wavelet activation function, combined with the PSO method that is used to tune the initial network parameters. The method described in [66] considers the same problem and uses FA instead of PSO to optimize the parameters of a neural network that also uses the wavelet function. A method that analyzes skin images to detect melanoma was proposed in [67]. This method combines GWO with a neural network to process cancer images. In this case, GWO is used to define the initial weights of a multilayer perceptron neural network. The method identifies two areas for classification (cancer and healthy) and classifies each pixel in the image into one of the two categories. A classification method to identify brain tumors based on MR brain images is described in [68]. The images are classified as normal or abnormal by a supervised neural network that is combined with the GWO method to optimize the network parameters. The proposal described in [69] combines swarm-based methods and deep learning to define a model for the detection and classification of lung cancer nodules from CT images. The model uses a convolutional neural network trained using a swarm-based method. The article analyzes the results obtained for seven swarm methods, including PSO, ABC, BFO, and FA. Computational experiments show that the best results are obtained when PSO is considered. The method described in [70] for lung cancer diagnosis combines deep learning and a variant of the CRS algorithm. The objective is to find lung nodules in CT images and classify them as benign or malignant. The modified CRS is used to update the weights of the neural network during the training phase. The CRS-variant combines the original algorithm with the sine cosine algorithm proposed in [71]. This is a population-based method that creates a set of random initial solutions and requires them to fluctuate outward or toward the best solution by applying a mathematical model based on sine and cosine functions. Each individual in the resulting CRS-variant can select to update its location according to the CRS method or according to the sine cosine method. SVM is a useful classification technique that has also been applied to classify medical images. The objective of the SVM algorithm is to find a hyperplane in a multidimensional space that clearly classifies a set of data points. When considering a nonlinearly separable problem, SVM can use a kernel, which is a function that takes a low-dimensional input space and transforms it into a higher-dimensional space, so as it turns a nonseparable problem into a separable problem. For the results obtained by SVM to be good, it is necessary to give adequate values to the parameters. Several researchers have applied swarm-based methods to set these parameters. Zhang et al. proposed a method to classify MR brain images as normal or abnormal (abnormal images correspond to 17 different types of diseases) [72]. They apply a kernel SVM that replaces the dot product of the original SVM method with

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the radial basis function kernel. In addition, the method applies PSO to optimize the parameters of the SVM classifier. ABC was used in [73] to analyze CT images in order to detect cervical cancer. The method classifies the input images as normal or abnormal. The system first segments the images to obtain the region of interest and then extracts textural features from that region. After this, three methods are proposed to perform the classification, which combine ABC with the k-nearest neighbor, SVM with linear kernel, and SVM with Gaussian kernel, respectively. The computational experiments reported in the article show that the best results are obtained with the third method. Zhang et al. describe a system that classifies three-dimensional MR brain images and can distinguish images corresponding to Alzheimer’s disease, mild cognitive impairment, and normal cases [74]. Although other methods initially determine the region of interest and then focus on it, this method considers the entire brain, so it is not necessary to apply a segmentation operation. The article analyzes the use of several SVM variants whose parameters are defined by the PSO algorithm with time varying acceleration coefficient. This PSO method modifies over time the weights of the components used to update the velocity of the particles (it gives more weight to the cognitive component at the former stage and gives more weight to the social component in the latter stage). In the solution proposed by Ahmed et al., the classification is carried out using a method that combines GWO and SVM [75]. In this case, GWO is used to select the SVM parameters, and the kernel function used by SVM is the Gaussian radial basis function.

6 Image Registration and Fusion As indicated in the introduction section, medical images can be of different modalities since they can be obtained using different techniques (x-ray, PET, SPECT, etc.). It is common to use different types of images when evaluating a patient, to obtain more information related to pathologies and decide the appropriate treatment. In other cases, several images of the same type taken at different times are used. For images to provide reliable and useful information, they must be properly combined or fused (Fig. 4). Before images can be fused, they must be geometrically and temporally aligned. This alignment operation is called registration. Therefore, image fusion is a general operation that includes image registration as an initial step. Different approaches have been applied to tackle the image registration problem. One of these approaches is defined by the intensity-based techniques. These techniques use image intensity values (color or gray level) to calculate similarity measures between the images. This information is used to calculate the transformation that maximizes the value of a similarity metric by searching a certain transformations space and comparing intensity patterns. An advantage of these methods is that they do not require the prior application of a feature extraction

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Fig. 4 Fused image obtained from two brain images

or image segmentation operation. These methods use a similarity metric that determines the match between the features or intensity values of two images. There are several similarity metrics that have been used successfully in multimodal image registration, such as mutual information [76], normalized mutual information [77], or Renyi entropy [78]. On the other hand, the methods apply a search strategy to optimize the similarity metric. Powell’s method and the conjugate gradient [79] are two local methods commonly used in image registration to optimize the similarity metric. Several global methods have also been applied with this purpose, including genetic algorithms, simulated annealing, and swarm-based methods. In summary, intensity-based image registration methods include three important elements: finding a transformation that aligns an image with another taken as a reference, choosing a similarity metric that measures the similarity between these two images, and using an optimization technique to find the optimal transformation parameters that maximize the similarity measure. The following describes several articles that apply swarm-based methods for medical image registration. PSO was applied for registration of medical images in [80]. Said method was used as a search strategy in a solution that is applied to images obtained from different modalities. Specifically, PSO was used to maximize the similarity metric for registering single slice medical images to three-dimensional volumes. The article analyzes three PSO variants. The first one includes crossover operators to update the position and velocity of the particles. The second variant is based on the first proposal but considers five subpopulations that are initialized using the well-known K-means algorithm. The third variant includes three main modifications. Powell’s method is applied to the initial position of the particles, and then particles are generated around the position defined by said method. In addition, this PSO variant includes a constriction coefficient in the expression that updates the velocity of the particles and a relaxed convergence criterion. Once the PSO operations have been completed, the three PSO variants apply Powell’s local optimization method to the best particle in the swarm. In the case of the second variant, which considers five subpopulations, this method is applied to the best particle of each subpopulation. Talbi and Batouche adapted PSO for multimodal medical image registration, [81]. With this objective, they defined a differential evolution operator to improve the best solution of each particle. Differential evolution is an optimization technique that solves a problem by iteratively improving a candidate solution using an

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evolutionary process [82]. The solution proposed in this article alternately applies the basic PSO operations and the differential evolution operation; that is, one iteration applies the equations to update the position and velocity of each particle, and the next iteration applies the differential evolution operator. Abdel-Basset et al. applied PSO as search strategy and used modified mutual information as similarity metric [83]. The modified mutual information includes spatial image information by using a linear combination of image intensity and image gradient vector flow intensity. Dida et al. compare the use of GWO and PSO for multimodal registration of human brain using CT and MR images [84]. In this case, the normalized mutual information is used as similarity metric. The results included in the article indicate that GWO is the best method. Xiaogang et al. define a multi-resolution medical image registration method based on a wavelet transformation that combines FA with Powell’s method [85]. This proposal uses normalized mutual information as similarity measure. The image registration process based on the multi-resolution strategy using the wavelet transform includes two parts: the rough registration with low sampling resolution and the fine registration with high sampling resolution. In the proposed method, both images are decomposed using wavelet transformation, thereby obtaining the associated low-level resolution images. Then, the registration operation is applied. To do this, FA is first used to obtain the approximate registration result from the low-resolution images. The results of this method are used as the initial solution to apply Powell’s method, which is applied to the high-resolution images to obtain a better registration result. Yang et al. described a method for nonrigid multimodal image registration [86]. Image registration methods can be classified as rigid and nonrigid. The main difference is that the transformations applied in rigid methods do not change the shape of the objects, while those applied in nonrigid methods do. The solution proposed by Yang et al. uses CSO as optimization technique and the normalized mutual information as similarity criterion. CSO imitates two behaviors of the cats, called seeking mode and tracing mode. The modified CSO used in this article includes the limited memory Broyden–Fletcher–Goldfarb–Shanno into the seeking mode, which is a commonly used method for optimizing the parameters of the deformation model in the nonrigid image registration. In addition, it includes the roulette wheel method in the tracing mode. The registration method described in [87] uses a similarity metric called enhanced mutual information, defined in [88], and applies an optimization strategy that combines CUS and Powell’s method. This solution combines local and global optimization to improve the results. CUS is applied first to perform a global search. Then, Powell’s method is applied to perform a local search around the best solution obtained by CUS. Several methods have been proposed that combine the pulse-coupled neural network (PCNN) [89], with swarm-based algorithms to perform medical image fusion. This neural network is efficient to perform this operation but uses a set of parameters that are difficult to configure.

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The research described in [90] presents a method that applies artificial ants for fusing multimodal medical images. The method first applies artificial ants for edge detection and optimization and then uses this information as input for a simplified PCNN that generates the fused image. The method was applied to brain images. Xu et al. defined a method to fuse multimodal medical images based on the use of an adaptive PCNN that is optimized by a modified PSO, called quantumbehaved (QPSO) [91]. A basic difference between PSO and QPSO is that in the second method, the state of a particle is not defined by its position and velocity, but by a wave function [92]. Xu et al. used QPSO to set the PCNN parameters and defined a fitness function for QPSO that combines three evaluation criteria: average gradient, image entropy, and spatial frequency. The proposal described in [93] combines PCNN with SFLA for the fusion of CT and SPECT brain images. SFLA was used to optimize the PCNN parameters. First, the intensity-hue-saturation (IHS) of each original image is decomposed using a nonsubsampled contourlet transform (NSCT). This operation generates lowfrequency and high-frequency images for each original image. The method that combines PCNN and SFLA is used to fuse both high-frequency images, resulting in a high-frequency fused image. The same method is applied to fuse the lowfrequency images to generate the low-frequency fused image. The final fused image is obtained by applying the reversed NSCT and reversed IHS transforms. Scaling-based techniques are commonly used in multimodal image fusion. Daniel et al. describe a mask-based technique for multimodal image fusion that uses GWO to select the optimal scale values [94]. Mask-based techniques are controlled by the gain factor called scale value. In general, mask-based methods use static scale values, regardless of the input images considered. Rather, the purpose of this article is to dynamically adjust the scale value by GWO. The mutual information metric is used to define the GWO fitness function. The method first transforms the two original images into Fourier space. Then, the Fourier spectrum of the input images is optimally scaled using scale values obtained by the GWO algorithm. The resulting spectrum mask corresponding to each image is fused using pixel-based averaging rule. The resulting fused image is obtained in the Fourier domain, so the inverse Fourier transform is used to obtain the spatial domain fused image. The method described by Daniel et al. in [95] proposes another mask-based method that shares some characteristics with the one described above. In this case, GWO is also used to select the optimum scale values, but in addition, CUS is used to select the random control parameters of the GWO algorithm. On the other hand, GWO uses the same fitness function as in the previous case. Unlike the previous solution, in this case, each original image is filtered by two masking filters (wavelet filter and Laplacian filter). The filtered input images are scaled using optimal scale values selected using GWO. Then, the Laplacian and wavelet mask corresponding to each original image are fused, generating a mask for each original image. The last operation fuses these two masks to generate the final image. The method proposed in [96] uses the binary CRS optimization algorithm and discrete wavelet transform. The method was applied to MR and CT image fusion. Both images are decomposed using discrete wavelet transform, producing four

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subband images that contain approximation and detailed coefficients. An initial fusion is performed that combines the detailed coefficients of an image with the approximation coefficients of the other image. Then, the final fusion is performed by applying an optimal fusion rule whose parameters are optimally selected by the swarm-based method.

7 Conclusions The popularity of swarm-based algorithms has increased in recent years, and they have been successfully applied to solve complex problems in different fields. These methods use a set of very simple individuals and each of them looks for a solution in the search space of the problem. The final solution to the problem will be the best solution found by the swarm during the search process. Individuals in the swarm share information to guide their search to promising areas of the search space. Another important feature of these methods is that all the individuals perform similar operations and there is no central control. The characteristics of these models make them easy to implement. This chapter shows a review of several interesting applications of swarm-based solutions for processing medical images. Processing these images is not an easy task, due to the large amount of information that must be handled, the different image formats, and the variety of operations that can be applied to an image. As indicated in the previous sections, when applying a processing to an image, successive operations must be carried out on said image. For this reason, image processing usually combines several techniques that are applied to each of these operations. The description in this chapter shows how swarm-based methods can be combined with other methods to define a system that applies certain processing to a medical image. For example, different systems that combine artificial neural networks and swarm algorithms have been described. In this way, the system defined to process the image benefits from the advantages offered by each of the methods integrated in the system. Medical image processing is a very interesting field that offers the possibility of further work on the application of swarm algorithms to improve systems that analyze images and allow diagnoses.

A.1 Appendix A. Flowcharts of Swarm-Based Algorithms This appendix shows the flowchart (Figs. 5, 6, 7, 8, and 9) of the swarm-based methods whose algorithm is outlined in Sect. 2. Several tables (Table 1, 2, 3, 4, and 5) are included that describe the variables used in the flowcharts.

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BEGIN

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Fig. 5 Main operations of a swarm-based algorithm

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Fig. 6 Flowchart of the operation that updates the population in the PSO algorithm

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