Edge of Intelligence: Exploring the Frontiers of AI at the Edge 9781394314379

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Edge of Intelligence

Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

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Edge of Intelligence Exploring the Frontiers of AI at the Edge

Edited by Shubham Mahajan

Amity School of Engineering and Technology (ASET), Amity University, Gurugram, Panchgaon, Haryana

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Sathyan Munirathinam

ASML Corporation, San Diego, California, USA

and

Pethuru Raj

Reliance Jio Platforms Ltd., Bangalore, India

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2025 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep­ resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-­ ability or fitness for a particular purpose. No warranty may be created or extended by sales representa­ tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa­ tion does not mean that the publisher and authors endorse the information or services the organiza­ tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 978-1-394-31437-9 Front cover image courtesy of Adobe Firefly Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1

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Contents Preface 1 A Review on Computational Optimization Strategies and Collaborative Techniques of Vehicular Task Offloading in the Era of Internet of Vehicles and 6G Aishwarya R., V. Vetriselvi and Meignanamoorthi D. 1.1 Introduction 1.1.1 Study of Existing Surveys 1.1.2 Contributions 1.1.3 Survey Organization 1.2 Computational Optimization Strategies 1.2.1 Algorithm-Based Strategies 1.2.1.1 Game Theory 1.2.1.2 Mathematical Optimization Methods 1.2.1.3 Custom-Tailored Algorithms 1.2.2 DRL-Based Strategies 1.2.2.1 Value-Based DRL Algorithms 1.2.2.2 Policy-Based DRL Algorithms 1.2.2.3 MADRL Algorithms 1.3 Collaborative Techniques 1.3.1 Caching 1.3.2 SDN 1.3.3 UAV 1.4 Security 1.5 Challenges and Future Research Directions 1.6 Conclusion References

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xvii

1 2 4 5 6 7 9 9 11 15 18 20 23 25 29 29 31 33 34 39 41 42

v

vi  Contents 2 A Study on EDGE AI Application in Crop Monitoring 51 N.A. Natraj, Pethuru Raj, M. Karpagam and S. Gunanandhini 2.1 Introduction 52 2.1.1 The Dynamic Evolution of Precision Agriculture 54 2.1.2 The Impact of AI on Crop Monitoring 54 2.1.3 AI’s “Edge” and Its Importance in Agriculture 54 2.2 Crop Monitoring AI Basics 57 2.2.1 Using Intelligent Patterns for Maximum Benefit: A Machine Learning Algorithm Study 57 2.2.2 Computer Vision in Precision Agriculture 58 2.2.3 Sensor Integration: Precision Agriculture’s Nervous System 58 2.2.4 Importance of Data: Fuelling AI 59 2.2.5 Real-Time Intelligence for Edge Learning and Adaptation 59 2.3 AI Applications in Crop Monitoring 60 2.3.1 Precision Farming: Nurturing Each Plant with Precision 60 2.3.2 Disease Detection: An Early Warning System for Crop Health 61 2.3.3 Yield Prediction: Anticipating Harvests with Precision 61 2.3.4 Resource Optimization: Balancing Efficiency and Sustainability 62 2.3.5 Edge Computing: Enhancing Efficiency in the Field 63 2.4 Challenges and Possible Future Paths of AI in Crop Monitoring 65 2.4.1 Challenges and Limitations 65 2.4.2 Future Scope of AI in Crop Monitoring 67 2.5 Conclusion 70 References 71 3 A Survey on Reconfigurable Co-Processors Computing Linear Transformations Atri Sanyal and Amitabha Sinha 3.1 Different Linear Transforms 3.2 Reconfigurable Computing 3.2.1 Reconfigurable Computing – An Extension of Configurable Computing 3.2.2 Advantages of Reconfigurable Computing 3.2.3 Features of Reconfigurable Computing

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73 74 75 77 77 77

Contents  vii

3.3

3.4 3.5 3.6

3.2.3.1 On-the-Fly Reconfigurability 3.2.3.2 Partial Programmability 3.2.3.3 Externally-Visible Internal State Field Programmable Gate Array 3.3.1 Advantages of FPGAs 3.3.2 Requirement of Flexibility 3.3.3 Programmable Hardware 3.3.3.1 Advantages of Programmable Logic 3.3.4 FPGA Architecture of Xilinx Virtex IV 3.3.4.1 Structure of a CLB 3.3.4.2 Structure of a Slice Survey of Existing Work Performance Comparison of Different Reconfigurable Co-Processors Implementing Linear Transformation(s) Conclusions and Future Work References

4 Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Based Chatbot on NVIDIA Jetson Nano Kiran Jot Singh, Divneet Singh Kapoor, Amit Singh Bora, Khushal Thakur and Anshul Sharma 4.1 Introduction 4.2 Background 4.3 Literature Review 4.4 Proposed Framework 4.5 Results 4.6 Conclusion and Future Scope References 5 Edge Computing in Educational Technology: The Power of Edge AI for Dynamic and Personalized Learning Ganeshayya Shidaganti, V. Aditya Raj, V.R. Monish Raman and Shubeeksh Kumaran 5.1 Introduction: Unveiling the Potential of Edge AI in Educational Technologies 5.2 Challenges of Traditional Education in the Digital Age 5.2.1 Lack of Personalization 5.2.2 Limited Accessibility 5.2.3 Information Overload 5.2.4 Passive Learning Paradigm

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78 78 78 78 79 79 80 80 80 81 82 82 86 88 89

93 94 100 103 108 112 115 116 121

122 122 124 124 124 125

viii  Contents

5.3

5.4 5.5

5.6 5.7

5.8

5.9

5.2.5 Teacher Workload 5.2.6 Lack of Timely Feedback Edge Computing and AI: Revolutionizing Educational Dynamics 5.3.1 Bringing Intelligence to the Point of Learning 5.3.2 Unlocking New Possibilities for Education Enhancing Education Through Video Lecture Summarization: An Exemplary Scenario 5.4.1 Methodology Benefits of the Edge AI for Learning 5.5.1 Enhanced Learning Efficiency 5.5.2 Improved Accessibility 5.5.3 Personalized Learning Experience 5.5.4 Increased Engagement and Retention 5.5.5 Facilitation of Revision and Exam Preparation 5.5.6 Future-Ready Skills Development Discussions on Edge AI for Education 5.6.1 Case Study: ASUS – Learning on the Edge Ethical Considerations in Edge AI for Educational Settings 5.7.1 Data Privacy and Security 5.7.2 Equitable Access to Technology and Tools 5.7.3 Consent and Potential for Bias in AI Future of Education with Edge AI 5.8.1 Hyper-Personalized Learning Journeys 5.8.2 Engaging, Interactive Classrooms 5.8.3 Democratization of Knowledge and Accessibility 5.8.4 Future Scope in Real-Time Video Summarization and Content Extraction Conclusion References

125 126 126 128 129 130 132 139 140 140 141 141 141 141 142 143 144 144 145 146 147 147 147 148 148 149 150

6 Edge Computing Revolution: Unleashing Artificial Intelligence Potential in the World of Edge Intelligence 153 Saravanan Chandrasekaran, S. Athinarayanan, M. Masthan, Anmol Kakkar, Pranav Bhatnagar and Abdul Samad 6.1 Introduction 154 6.1.1 Motivation for Edge Intelligence 155 6.2 Definitions 157 6.2.1 Edge Computing 157 6.2.2 Challenges in Edge Computing 158 6.2.2.1 Network Connectivity and Reliability 158

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Contents  ix

6.3 6.4

6.5

6.6

6.7

6.2.2.2 Security and Privacy 6.2.2.3 Data Management and Storage 6.2.3 Artificial Intelligence 6.2.4 Edge Intelligence 6.2.4.1 Network Infrastructure Challenges Concepts and Architecture 6.3.1 Comparison Between EI and Cloud-Centric AI Models Algorithms for Artificial Intelligence in Edge Computing 6.4.1 Machine Learning 6.4.2 Deep Learning 6.4.3 Reinforcement Learning and Deep Reinforcement Learning 6.4.4 Evolutionary Algorithms 6.4.5 Model Bias at the Edge 6.4.5.1 Computer Vision 6.4.5.2 Virtual Reality (VR) and Augmented Reality (AR) Optimization of Edge Devices Using a Class of Neural Networks 6.5.1 Strategies for Optimizing AI Algorithms 6.5.1.1 Federated Learning 6.5.1.2 Pruning 6.5.1.3 Localization-Preserving Aggregation Bio-Inspired Algorithms for Edge Computing 6.6.1 Modified Particle Swarm Optimization 6.6.2 Particle Swarm Optimization for Edge Detection 6.6.2.1 PSO in Continuous Domain 6.6.3 BCO-FSS Technique Real-Time Intelligence-Based Edge Device 6.7.1 Smart City 6.7.1.1 Distributed Deep Learning Model-Based Monitoring System 6.7.2 Urban Medical Services 6.7.2.1 Prevention and Management of Infectious Diseases 6.7.3 Management of Urban Energy 6.7.3.1 Challenges 6.7.4 Smart Manufacturing 6.7.4.1 Dynamic Management 6.7.4.2 Equipment Observation

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158 158 158 158 159 160 162 163 163 163 164 164 165 165 165 166 168 168 168 169 169 169 170 171 172 172 173 174 174 175 176 177 177 177 177

x  Contents 6.7.5 Internet of Vehicles 6.7.5.1 Optimizing Allocation of Resources and Task Offloading 6.7.5.2 Enhancing the In-Flight Experience 6.7.5.3 Increasing Autonomous Intelligence 6.7.5.4 Challenges 6.7.6 Practical Challenges and Solutions in Real Life 6.8 Conclusion References 7 Ensuring Privacy and Security in  Machine Learning: A Novel Approach to Efficient Data Removal Velammal B. L. and Aarthy N. 7.1 Introduction 7.2 Related Works 7.3 Objectives 7.4 System Design 7.5 Experimental Results 7.6 Conclusion and Future Scope References 8 Federated Learning in Secure Smart City Sensing: Challenges and Opportunities Monika Gandhi, Sushil Kumar Singh, Ravikumar R. N. and Krunal Vaghela 8.1 Introduction 8.2 Related Work 8.2.1 Preliminaries 8.2.1.1 Smart City Sensing 8.2.1.2 Federated Learning Technology 8.3 Federated Learning-Based Smart Cities Sensing Architecture for IoT-Enabled Smart Cities Sensing 8.3.1 Overview of IoT-Enabled Smart Cities Sensing Using Federated Learning Technology 8.3.1.1 Health Care 8.3.1.2 Fintech 8.3.1.3 Insurance Sector 8.3.1.4 Natural Language Processing (NLP) 8.3.1.5 Smart Devices 8.3.1.6 Autonomous Vehicles 8.3.1.7 Industry 4.0

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178 179 179 180 181 181 182 183 193 193 195 197 198 206 212 213 215 216 218 223 223 229 232 233 233 233 234 235 236 236 236

Contents  xi 8.3.1.8 Virtual Reality and Metaverse 8.3.2 Application Security Issues and Solutions 8.4 Open Issues, Related Challenges and Opportunities 8.4.1 Open Issues 8.4.2 Related Challenges and Opportunities 8.4.3 Service Scenario of Federated Learning for Smart City Applications 8.4.4 Discussion 8.5 Conclusions Acknowledgment References 9 Fusion of Blockchain and Edge Computing for Seamless Convergence Indu Bala 9.1 Introduction to Blockchain and Edge Computing 9.1.1 Defining Blockchain Technology 9.1.2 Exploring the Concept of Edge Computing 9.1.3 Chapter Contributions 9.1.4 Chapter Organization 9.2 Key Components of Blockchain and Edge Integration 9.2.1 Understanding the Blockchain Infrastructure 9.2.2 Components of Edge Computing Systems 9.3 Challenges and Opportunities in Integration 9.3.1 Identifying Integration Challenges 9.3.2 Opportunities for Synergy Between Blockchain and Edge Computing 9.4 Security Considerations in a Converged Environment 9.4.1 Ensuring Data Security in Edge Computing 9.4.2 Blockchain’s Role in Enhancing Security 9.5 Use Cases and Applications 9.5.1 Real-World Applications of Blockchain in Edge Computing 9.5.2 Industry-Specific Use Cases 9.6 Benefits of Blockchain and Edge Integration 9.6.1 Improving Efficiency and Speed 9.6.2 Enhancing Data Transparency and Integrity 9.7 Regulatory and Compliance Issues 9.7.1 Addressing Legal Challenges in Integrated Systems

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237 238 239 239 240 244 245 246 247 247 253 254 254 255 256 257 258 260 261 261 261 262 264 264 265 265 265 266 268 268 268 269 269

xii  Contents 9.7.2 Ensuring Compliance with Data Protection Regulations 9.8 Future Trends and Innovations 9.8.1 Emerging Technologies in Blockchain and Edge Computing 9.8.2 Anticipating Future Developments in Integration 9.9 Recommendations 9.10 Conclusion References 10 Industry Adapting the Machine Learning Scenario in Recruitment and Selection of Employees Megha Ojha, Vinay Kandpal, Archana Singh and Amar Kumar Mishra 10.1 Introduction 10.2 Evolution of Machine Learning in Recruitment 10.2.1 Applications and Advancements in ML for Recruitment 10.3 Methodological Insights and Study Contexts 10.4 Ensuring Reliability and Replicability 10.4.1 Selection and Rationale of ML Algorithms 10.4.2 Functionalities and Suitability of ML Algorithms 10.4.3 Data Analysis Techniques in Recruitment Studies 10.4.4 Importance of Validation Techniques 10.4.5 Bias Mitigation Techniques in Recruitment Studies 10.4.6 Comparative Analysis and Effectiveness of Bias Mitigation Techniques 10.5 Ethical Implications of ML in Hiring 10.5.1 Bias and Discrimination 10.5.2 Transparency and Explainability 10.5.3 Data Privacy and Security 10.6 Addressing Ethical Concerns in Real-World Applications 10.6.1 Algorithmic Auditing and Bias Mitigation 10.6.2 Regulatory Compliance and Ethical Guidelines 10.7 Ensuring Data Privacy in ML Models for Hiring 10.7.1 Anonymization and Pseudonymization 10.7.2 Data Minimization and Purpose Limitation 10.7.3 Consent Management and Opt-Out Mechanisms 10.7.4 Compliance with International Data Protection Regulations

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270 271 271 273 274 276 277 279 280 280 281 281 283 284 285 285 286 286 287 288 288 288 288 289 289 289 289 289 290 290 290

Contents  xiii 10.7.5 Analyzing Practical Implications and Lessons Learned 10.7.6 Actionable Recommendations for ML in Hiring Processes 10.8 Areas for Future Research in ML for Hiring References

292 292 301 302

11 Machine Learning for Nano Process Optimization Manjushree Nayak and A. Sai Satya Narayana Introduction Literature Review Conclusion References

307

12 Quantum Computing for Cryptography: An Extensive Survey Soma Debnath and Avishake Adhikary 12.1 Introduction 12.1.1 Why Quantum Cryptography? 12.1.2 Quantum Cryptography Model 12.1.3 Quantum Superposition 12.1.4 Quantum Key Distribution (QKD) 12.1.4.1 Pre-Processing Before Sending Message 12.1.4.2 Quantum Algorithm 12.2 Related Works 12.3 Statistical Analysis 12.3.1 Research Trends in Previous Years Based on Reviewed Papers 12.4 Comparative Analysis 12.5 Conclusion and Future Scope References

327

13 Role of Blockchain Technology in e-HRM in the Era of Artificial Intelligence: Focus on the Indian Market Archana Singh, Girish Lakhera, Megha Ojha and Amar Kumar Mishra 13.1 Introduction 13.2 Literature Review 13.2.1 How Blockchains Work 13.2.2 Decentralization

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308 309 323 324

328 331 332 333 334 335 335 336 339 340 341 345 347 351 351 354 355 355

xiv  Contents

13.3

13.4 13.5 13.6 13.7

13.2.3 Transparency 355 13.2.4 Cryptographic Security 355 13.2.5 Immutability 356 13.2.6 Consensus Mechanisms 356 13.2.7 Decentralized Identity and Credential Verification 356 13.2.8 Smart Contracts in HRM 356 13.2.9 Blockchain-Based Employee Data Management 357 Blockchains for Business and EHRM 357 13.3.1 Payroll Processing 358 13.3.2 Data Protection and Cyber Attacks 358 13.3.3 Performance Management 358 13.3.4 E-Recruitment System Design 359 13.3.5 Renowned DLT Applications 359 13.3.6 Incident Logging and Reporting 359 13.3.7 Employees Assistance Program 360 13.3.8 Identity Registry 360 Case Studies 361 13.4.1 Barriers to Adoption and Technological Challenges in the Indian Business Environment 361 Integration of Blockchain with Industry 4.0 Technologies in HRM 362 Ethical Implications of Implementing Blockchain in HRM 362 13.6.1 Case Studies and Examples 363 13.6.2 Mitigating Ethical Concerns 363 Conclusion 364 References 364

14 Smart City Innovations and IoT as a Frontier of AI at the Edge of Intelligence Priya Soni Introduction: Smart City Innovations and Internet of Things for Data Analytics Concept of Smart Cities and the Significance of Data-Driven Decision-Making Fundamental Components of Data Analytics in Smart Cities Advanced-Data Analytics Techniques Uses of IoT-Enabled Data Analytics in Smart Cities Challenges and Considerations

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369 370 371 373 375 381 382

Contents  xv



Future Prospects and Emerging Trends of Smart City Innovations and Internet of Things (IoT) for Data Analytics Indian Case Studies: Successful Implementations on Smart City Innovations and Internet of Things for Data Analytics Conclusion References

15 Synergies Unleashed: The Convergence of AI and Edge Computing in Transformative Technologies R. Shobarani, P. Dhivya, G. Savitha, S. Santhi, K. Surya Prakhash and R. Kavitha 15.1 Introduction 15.1.1 Overview of Converging and the Implications of AI and Edge Computing 15.2 Related Study 15.3 Reduction of Latency 15.4 Bandwidth Efficiency 15.5 Privacy and Security 15.6 Real-Time Decision-Making: Decision Made with an Example 15.7 Distributed Architecture: Decentralized Processing Occurs with an Example 15.8 Edge Computing Use Cases 15.9 Challenges and Advancements 15.9.1 Challenges: Resource Constraints 15.9.2 Challenges: Model Optimization 15.9.3 Challenges: Security Concerns in Edge Computing 15.9.4 Advancements: Edge AI Chips 15.9.5 Advancements: Federated Learning 15.10 Future Trends 15.10.1 Future Trends: 5G Integration with Edge Computing 15.10.2 Future Trends: Hybrid Cloud-Edge Architectures 15.11 Conclusion References Index

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384 386 388 389 391 392 393 395 400 401 402 405 407 410 412 412 415 417 419 421 423 423 427 430 431 433

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Preface This book explores the transformative world of Edge AI, examining how artificial intelligence is integrated into edge computing to create innovative and powerful solutions across various domains. As we approach the 6G era, the convergence of AI and edge computing promises to revolutionize our interaction with technology, delivering faster, more efficient, and highly personalized experiences. Edge AI refers to the deployment of AI algorithms and models directly on devices at the edge of the network, closer to where data is generated. This approach reduces latency, enhances privacy, and enables real-time decision-making without heavy reliance on centralized cloud infrastructures. As the demand for intelligent and autonomous systems grows, Edge AI is becoming crucial in sectors ranging from automotive and agriculture to education and smart cities. This book is structured into fifteen comprehensive chapters, each addressing a unique aspect of Edge AI and its applications. A Review on Computational Optimization Strategies and Collaborative Techniques of Vehicular Task Offloading in the Era of Internet of Vehicles and 6G: Explores how vehicular networks and 6G technology enhance task offloading, improving efficiency and connectivity in smart transportation systems. A Study on EDGE AI Application in Crop Monitoring: Discovers how Edge AI is revolutionizing agriculture by providing real-time crop monitoring solutions, leading to more efficient and sustainable farming practices. A Survey on Reconfigurable Co-Processors Computing Linear Transformations: Investigates advancements in reconfigurable co-processors that optimize computational tasks, driving performance improvements across various applications.

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xvii

xviii  Preface Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Based Chatbot on NVIDIA Jetson Nano: Elucidates on developing an advanced chatbot leveraging Edge AI for natural and effective communication. Edge Computing in Educational Technology: The Power of Edge AI for Dynamic and Personalized Learning: Examines how Edge AI transforms educational technology by delivering personalized learning experiences and dynamic content. Edge Computing Revolution: Unleashing Artificial Intelligence Potential in the World of Edge Intelligence: Explains the broader impact of Edge AI across industries, highlighting its potential to drive innovation and efficiency. Ensuring Privacy and Security in Machine Learning: Delves into strategies and technologies designed to protect data privacy and ensure security in AI-driven systems. Federated Learning in Secure Smart City Sensing: Challenges and Opportunities: Explores the role of federated learning in smart cities, addressing challenges and opportunities in creating secure and intelligent urban environments. Fusion of Blockchain and Edge Computing for Seamless Convergence: Investigates how integrating blockchain with edge computing enhances security and transparency across various applications. Industry Adapting the Machine Learning Scenario to Recruitment and Selection of Employees: Shows how machine learning is being applied to streamline and improve recruitment and selection processes in the corporate world. Machine Learning for Nano Process Optimization: Exlpores how machine learning optimizes nanoscale processes, driving advancements in nanotechnology and materials science. Quantum Computing for Cryptography: An Extensive Survey: Examines the potential of quantum computing to revolutionize cryptography, providing enhanced security solutions.

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Preface  xix Role of Blockchain Technology in E-HRM in the Era of Artificial Intelligence: Focus on the Indian Market: Informs about the impact of blockchain and AI on electronic human resource management, with a focus on the Indian market. Smart City Innovations and IoT as a Frontier of AI at the Edge of Intelligence: Explores the role of IoT and AI in developing smart city innovations, enhancing urban living and sustainability. Synergies Unleashed: The Convergence of AI and Edge Computing in Transformative Technologies: Shows how AI and edge computing are driving transformative technologies and shaping the future of industries. As you journey through the chapters of this book, you will gain insights into the latest research, trends, and applications of Edge AI. The convergence of AI and edge computing is not just a technological evolution but a paradigm shift that will redefine how we interact with the digital world. We extend our gratitude to everyone who contributed to this important work, and to Martin Scrivener and Scrivener Publishing for making its publication possible. Welcome to the edge of intelligence.

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The Editors December 2024

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1 A Review on Computational Optimization Strategies and Collaborative Techniques of Vehicular Task Offloading in the Era of Internet of Vehicles and 6G Aishwarya R.*, V. Vetriselvi and Meignanamoorthi D. Department of Computer Science and Engineering, Anna University, Guindy, Chennai, Tamil Nadu, India

Abstract

The Internet of Vehicles (IoV) and emerging 6G communication technology have recently advanced, empowering intelligent vehicles to support pervasive services while also providing an efficient and convenient driving experience. Furthermore, massive amounts of data are being generated by vehicular applications. The i­n-vehicle computing capability is insufficient to meet vehicular applications’ time-sensitive and computation-intensive demands. In such a scenario, task offloading towards other resource-rich computing devices can be considered to process vehicular tasks, thereby improving the application’s Quality of Services (QoS). In this paper, a comprehensive review of task-offloading strategies and collaborative techniques for task offloading is presented. Computational optimization strategies are classified according to the solutions provided for task offloading via various methods such as algorithmic techniques and Deep Reinforcement Learning (DRL) techniques. Collaborative techniques such as caching, Software Defined Networks (SDN), and Unmanned Aerial Vehicles (UAV) along with a vehicular network for task offloading are extensively reviewed. The security aspect of vehicular task offloading is discussed as well. Furthermore, open issues and future directions of vehicular task offloading are highlighted. Keywords:  IoV, vehicular edge computing, task offloading, security, 6G, multiaccess edge computing, VANET

*Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (1–50) © 2025 Scrivener Publishing LLC

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1

2  Edge of Intelligence

1.1 Introduction In the past two decades, there has been a noticeable trend towards the development of intelligent vehicles with substantial developments in communication and computing technologies [1]. It is estimated that the automobile sector will provide the biggest market opportunity for 5G Internet of Things (IoT) solutions by 2023 with the development of intelligent vehicles [2]. IoV, a typical IoT technology application in the Intelligent Transportation System (ITS), is a widely distributed system for information exchange and wireless communication that intelligently supports traffic management, dynamic information services, and vehicle control [3]. Vehicular Technology and Road Side Units (RSU) have progressed rapidly comprising computing units and storage capacities. Utilizing 6G technology, the IoV can achieve seamless connectivity through Space Air Ground Integrated Networks (SAGIN), enabling interoperability between terrestrial and non-terrestrial networks and providing ubiquitous coverage. With the advent of IoV and 6G, vital developments have emerged in vehicular applications by providing global coverage. The vehicular applications include image-aided navigation, online games, intelligent vehicle control, and other social media applications. Those applications are used to improve traffic efficiency, enhance road safety, as well as provide convenient and comfortable user services [4]. Each of these applications requires ultra-low latency, massive connectivity, high mobility, and scalability support that can be provided by Beyond 5G and Next Generation Networks [5, 6]. Incorporating various applications such as advanced driver assistance systems in smart vehicles poses a significant challenge for in-vehicle computing systems, as they grapple with the escalating demand for processing power. Due to space and power constraints, integrating a supercomputer directly into vehicles is impractical. The limited computing resources, including CPU, memory, and storage, may prove inadequate to meet the rising computational requirements. Thus, it necessitates offloading [7]. Initially, Cloud computing was proposed as an effective solution for resource-constrained vehicles to offload tasks to geographically centralized data centers which improves computation performance and resource utilization [8]. However, the cloud computing architecture makes it very hard to satisfy the real-time processing demands of emerging vehicular applications due to the long propagation delay [9]. Hence, to extend the processing capacity of cloud computing to the edge of a network near the vehicles, Multi-Access Edge Computing (MEC) [10] and Fog Computing have been introduced [11]. Vehicular Edge Computing (VEC) provides

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A Review on Vehicular Task Offloading  3 processing and storage resources close to vehicular users by integrating MEC and vehicular networks [12]. Figure 1.1 represents the VEC architecture consisting of smart vehicles and infrastructures. Onboard units with resource capabilities are incorporated in smart vehicles that permit close-range wireless transmission i.e., communicate with each other and an RSU. For network accessibility, RSUs are often dispersed along the roads and connected to the backbone network [13]. The illustration of task offloading is depicted in Figure 1.2. Now vehicles may transfer latency-­ sensitive and computationally heavy tasks to neighboring MEC servers with ­little ­overhead. It can significantly alleviate the overload of resource-­ constrained vehicles. Cellular Base Stations (BS) and RSUs, as well as both, are suitable for placing MEC servers near the network’s edge [14]. Using MEC services with the aid of 6G technology will improve the Quality of Experience (QoE) for vehicular applications. Yet, because of the peculiar features of vehicle networks, particularly the rapid mobility of nodes and the fluctuating channel conditions, it is quite challenging to create an effective edge-enabled task offloading strategy [15]. The MEC servers on distinct BSs may offer a range of services, and the workload on the MEC servers varies over time. MEC servers are less resourceful than cloud servers. To effectively utilize the MEC servers’ resources, consistent load distribution across the MEC servers has to be guaranteed while offloading the tasks from vehicles. The storage and computing capabilities of edge devices are typically constrained. To ensure effective resource usage MEC Server

RSU

Cloud

V2I V2I

Coverage V2I area RSU

V2V

Coverage area

V2V

I

V2

V2I

Coverage area

Figure 1.1  Vehicular network.

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MEC Server

MEC Server I2I link

4  Edge of Intelligence

High end computing platform

BS

Cloud Layer

BS

RSU

Edge Layer

MEC Server

MEC Server

MEC Server

Vehicle Layer Parked Vehicles

Vehicular Cloudlet

Task offloading

Figure 1.2  Task offloading.

of the MEC server, some tasks may still need to be performed either locally or on the cloud platform depending on their QoS. Offloading, therefore, requires absolute cooperation between the cloud and the edge. The tasks can also differ in terms of processing overhead, advance, urgency, and other related factors depending on the necessary QoE criteria. As a result, the issue of selecting the best task-offloading strategy for achieving the best performance of an application while effectively using MEC resources arises [16]. It is conceivable to employ both algorithm-based and DRL-based strategies to address this multi-objective optimization problem.

1.1.1 Study of Existing Surveys The existing survey papers [1, 17–20] were oriented by vehicular task offloading. Ahmed et al. [17] highlighted the classification of vehicular task offloading based on V2V, V2I, and V2X communication models. Liu et al. [1] presented the classification of vehicular task offloading based on DRL methods as value-based and policy-based solutions leveraging MEC servers, nearby vehicles, and both as edge clouds. Hamdi et al. [18] majorly analyzed task offloading in vehicular fog computing and elaborated on the fog node selection for task offloading. Boukerche

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A Review on Vehicular Task Offloading  5 Table 1.1  Existing surveys in vehicular task offloading. Collaborative techniques

Security

Vehicular Communication modes

No

No

2022

Fog node selection

No

No

Boukerche and Soto

2020

Task offloading process

No

No

Liu et al.

2022

RL/DRL algorithm

No

No

Dziyauddin et al.

2021

Optimization objective (QoS, Energy, Revenue)

Caching

Yes

Our survey

2023

Solution of Optimization Strategies for task offloading

Caching, SDN, UAV

Yes

Paper

Year

Categorization criteria

Ahmed et al.

2022

Hamdi et al.

and Soto [19] categorized each step involved in the task offloading process i.e., partitioning, scheduling, and data retrieval as well as analyzed various methods used for these processes. Nevertheless, this survey classifies based on the optimization strategies of task offloading and incorporates associated techniques that enhance vehicular task offloading. Although Dziyauddin et al. [20] have also discussed content caching and security along with computational offloading, there exists a research gap in categorizing optimization strategies applied to task offloading problem and collaborative techniques to improve its performance. Table 1.1 summarizes the existing surveys in vehicular task offloading. This survey systematically categorizes various optimization strategies that are used to address the task offloading problem and other techniques associated with task offloading along with security.

1.1.2 Contributions The key contributions of this review are • We articulated and examined the computational offloading strategies of vehicular task offloading under each subcategory of algorithm-based strategies (i.e., Game Theory,

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6  Edge of Intelligence Mathematical methods, and Custom-Tailored algorithms) and DRL-based strategies (i.e., Value-based DRL methods, Policy-based DRL methods, and Multi-Agent Deep Reinforcement Learning (MADRL) methods) • 6G technology empowers the IoV with interconnected intelligence and widespread connectivity, enabling a variety of intelligent vehicular applications. This heightened demand for task offloading to meet user QoE requirements prompts the integration of collaborative techniques like caching, SDN, and UAV into vehicular task offloading, to enhance its efficiency and performance. We discussed those relevant papers. • The security-related works of task offloading are also analyzed in terms of privacy, and trust management. Open issues, challenges, and future research prospects for vehicular task offloading are discussed.

1.1.3 Survey Organization Figure 1.3 illustrates the structure of this survey. The rest of the paper is organized as follows. Section 1.2 discusses a description of vehicular networks and task offloading, section 1.3 provides the types of optimization

Introduction Game Theory Algorithmbased

Custom-Tailored algorithms

Computational Optimization Strategies

Vehicular task offloading

Value-based DRL algorithms DRL-based

Policy-based DRL algorithms MADRL algorithms

Collaborative Techniques Security Challenges and Future Research Directions

Figure 1.3  Taxonomy of the survey.

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Mathematical algorithms

Caching SDN UAV

A Review on Vehicular Task Offloading  7 strategies of computational offloading and section 1.4 presents the collaborative techniques of task offloading. Section 1.5 discusses the security aspect of vehicular task offloading. Section 1.6 emphasizes the open issues and future works. Section 1.7 depicts the conclusion.

1.2 Computational Optimization Strategies The intelligent vehicles would be laid in the 6G full-coverage communication environment. It can achieve interconnection with peripheral facilities like nearby vehicles, BSs, streetlights, and more [21]. Also, the vehicular network has grown increasingly dynamic, and large-scale as the number of intelligent vehicles rises and the Vehicle-to-Everything (V2X) connections tend to increase. Edge AI plays an important role in achieving an intelligent vehicular network through 6G communications, supporting AI/ Machine Learning optimization approaches [22]. It is challenging for individual vehicles with constrained computing resources to execute rapidly evolving low-latency and computational heavy vehicular applications. Depending on the QoS requirements of the application, vehicles transfer their computation to other resource-rich destinations, which may include MEC servers, neighboring vehicles, or clouds using V2V, V2I, and V2X communication network modes. A task is a fundamental piece of operation that must be carried out to accomplish any vehicular application. Formally, a task can be defined [23–25] using three parameters: S, C, and T. S represents the size of the task’s input data in bits; C represents the computing resources needed by the task in CPU cycles; and T represents the task’s maximum allowable delay; if the time it takes to receive a result is longer than T, the task is timed out and fails. A task also has additional factors, such as priority and degree of reliance. Depending on the complexity and requirements of the vehicle application, a task may be divided into several subtasks. These subtasks may either operate independently of other tasks or depend on them. Depending on the task demands and their characteristics, a decision can be made between binary offloading and proportion offloading. Simple tasks that cannot be broken down into several dependent or independent sub-tasks must be completed as a whole, either locally at the vehicles or through a process known as binary offloading that offloads them to MEC servers. Some tasks can be divided into numerous dependent or independent sub-tasks and various sub-tasks can be executed locally, in the MEC server as well as in the cloud through a process known as proportion offloading. To achieve optimum computation efficiency, the task offloading choice must be made by the

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8  Edge of Intelligence vehicles in dynamic network conditions. The dynamic network topology and rapidly changing channel characteristics make task offloading challenging because of the vehicles’ rapid mobility and the devices’ energy constraints. It is vital to employ a competent computation offloading technique to execute tasks in a wide range of circumstances. The key performance metric and the most challenging QoS constrained among various vehicular applications is the delay which consists of transmission and computation time. The duration of transmission is dictated by the volume of data for transmission and the transmission rate. Similarly, the duration of computation depends on the wait time and processing capacity of the offloading destination. An intolerable delay in vehicular safety applications may cause serious damage to the lives of the people. Hence, effective and expeditious offloading strategies are needed to meet the demands of vehicular applications in the future. The offloading strategies are broadly classified as ­algorithm-based strategies and DRL-based strategies. The existing research work is manifold and varied in terms of optimization objective parameters, assumption of a task model, and task offloading problem formulation. The offloading strategies are broadly classified into two different perspectives, which are further subdivided into various methods based on problem-solving approaches. The main aim of this paper is to investigate and assess diverse computational optimization approaches tailored for addressing the task offloading challenges within the IoV. The IoV ecosystem is rapidly evolving, marketed by the emergence of high-resource-demanding applications and personalized user experiences, particularly in the context of advancing 6G technology. As 6G-enabled IoV intensifies the demand for efficient task-offloading solutions, this study also explores potential collaborative techniques to enhance task-offloading performance.

Game Theory Algorithm-Based Computational Offloading Strategies

Custom-Tailored Algorithms

Mathematical Methods

Figure 1.4  Algorithm-based strategies.

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A Review on Vehicular Task Offloading  9

1.2.1 Algorithm-Based Strategies The research works of algorithm-based strategies for task offloading problem have been summarized in this section. The algorithm-based strategies are classified into Game Theory, Mathematical methods, and Custom Tailored algorithms for solving task offloading problem in IoV as shown in Figure 1.4.

1.2.1.1 Game Theory Many rational players (i.e.,) vehicles with tasks to offload participate in the game, and the problem of decision-making of task offloading to achieve a goal of delay, cost, or energy minimization among multiple players can be effectively solved by designing decentralized mechanisms. The literature focusing on game theory-based decision-making for task-offloading is summarized in Table 1.2 and the details are explained in this section. A distributed game [26] is employed for a distributed computation offloading decision that takes into account energy use, communication costs, and computation costs as the offloading costs to reduce delay and the associated costs. The distributed computation offloading scenario is represented as G = (N, A, U), comprising three elements: N denotes the group of participants. A represents the range of actions the participants can take, and U stands for the utility function. To reduce its own joint cost, the vehicle assumes the role of a player and decides to offload. It is designed as a distributed computation offloading game where players are free to choose computation offloading decisions, to achieve Nash Equilibrium (NE). Here, all of the vehicles have reached a mutually agreeable solution, and none of them are willing to change policy. To make the best possible response decision, each vehicle learns from its data as well as the decision patterns of other vehicles. The process of updating the better response is finite and results in an NE. The Stackelberg game model [27] is introduced to analyze the interaction dynamics between task vehicles and service vehicles. This involves adapting the pricing approach of service vehicles according to the real demand from task vehicles. To achieve balance and optimize unit cost advantages, a cost model is developed, taking into account the responsiveness of task vehicles to both costs and time delays. The two parts of the Stackelberg game are as follows: The task vehicles compete against one another in the first part to determine the allocation of computing resources across various service vehicles, guided by the unit pricing of computing resources offered by each service vehicle. In the second part, service vehicles adjust

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10  Edge of Intelligence Table 1.2  Summary of game theory-based task offloading strategies. Paper

Modes

Type

Objective

Method

[26]

V2R

Binary

Minimization of latency and offloading cost

A distributed computation offloading game based on self-learning

[27]

V2V

Binary

Maximization of the utility function

Stackelberg game

[28]

V2R

Binary

Reducing the latency of time-sensitive tasks and ensuring that best-effort tasks are not neglected.

A coalition formation game

[29]

V2V/V2R

Binary

Minimization of task residence time

A noncooperative strategic form game

[30]

V2R

Binary

Maximization of computational efficiency

A game theory approach

their unit prices for computing resources in response to the purchasing demands of task vehicles, aiming to optimize their revenue generation. The revenue made from selling computing resources determines a service vehicle’s utility. Time and cost both affect a task vehicle’s utility. Maximizing a vehicle’s utilitarian features is its main objective. Both service and task vehicles arrive at NE after several iterations of the distributed gradient iterative method. Latency-aware task-offloading scheme [28] is proposed considering the minimization of delay of time-critical tasks as well as to save the best-effort tasks from starvation. A task service framework that takes into account the resource availability at fog nodes and the task requirements is described. Fog nodes, the entities providing the service, have constrained resources. As a result, fog nodes decide collectively whether to accept the task service based on the task’s needs. A coalition formation game is developed to represent the effective task service by an appropriate fog node. According to

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A Review on Vehicular Task Offloading  11 the QoS requirements, coalitions are established among the tasks. The aim is to optimize the effectiveness of coalitions that depend on efficient task servicing. Tasks are allocated to a coalition within the maximum permitted waiting time. A latency-aware task offloading scheme is employed to address the coalition formation game. The time-critical tasks and best-­ effort tasks are assigned different priorities. The priority of the best-effort task has been incremented after the passage of a certain percentage of waiting time to ensure it does not suffer from starvation. As the priority increases it gets added to the coalition before its maximum allowable waiting time and gets the task service. A decentralized model lacking collective resource intelligence may lead to an uneven distribution of workload. For effective resource use, Shabir et al. [29] presented a distributed, non-cooperative task offloading paradigm. In this paradigm, an offloading decision profile incorporating contextual information is treated as a non-cooperative strategic-form game, aiming to minimize overall service delay and enhance QoE in a diverse resource-sharing vehicular network. Vehicles communicate with their neighbors to obtain contextual data, such as resource category, task retention time, system cost, and offloading inference, to review their offloading choices. There is NE, and it is examined in a brief proof. In the VEC scenario, computation efficiency—defined as the ratio of computed bits to the total energy consumed is examined by Raza et al. [30], where vehicles offload to maximize computational efficiency. To balance time and energy usage, optimizing task offloading and resource allocation is crucial for better computational efficiency. This involves solving an optimization problem to maximize overall system utility. Since this problem is complex and involves mixed integer programming, it’s divided into two parts: deciding how tasks are offloaded and allocating computation resources. These are tackled using different techniques: the Lagrange multiplier method and game theory. In this setup, vehicles with tasks act like players in a game, competing for resources to maximize their own benefit. The task offloading strategy is continuously refined until reaching Nash Equilibrium (NE), where each vehicle sticks to its chosen offloading approach.

1.2.1.2 Mathematical Optimization Methods Typically, the task offloading problem entails a balance between different factors, including energy usage, latency, and computation duration. Mathematical optimization techniques like Lagrangian dual decomposition, semi-definite relaxation, probabilistic approaches, and others can

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12  Edge of Intelligence effectively manage these trade-offs to attain the desired QoS for various vehicular tasks. The relevant literature is summarized in Table 1.3 and the details are explained in this section. Dai et al. [31] examined a novel technique that takes into account task upload coordination across many vehicles, task migration between MEC/ cloud servers, and the diverse computation capabilities of MEC/cloud servers. The collaborative computation offloading challenge, which seeks to minimize the expected service delay by examining the likelihood of optimal allocation, is framed within a queuing theory-based framework. A probabilistic strategy with an online and offline phase is proposed, and the objective function’s convexity is examined. During the offline phase of probabilistic computation offloading, the objective function is transformed into an augmented Lagrangian by introducing dual variables.

Table 1.3  Summary of mathematical optimization approaches of task offloading. Paper

Modes

Type

Objective

Method

[31]

V2R

Binary

Minimization of system service delay

Probabilistic approach

[32]

V2V

Binary

Minimization of response delay

Semi-definite relaxation approach

[33]

V2V

Binary

Minimization of delay

Levy-Kopt algorithm

[34]

V2V, V2Cloud

Binary

Minimization of average response time

Binary linear programming

[35]

V2V, V2R

Binary

Minimization of average completion time

Multidimensional multichoice knapsack problem, branch, and bound method

[36]

V2V/V2R

Proportion

Minimization of average cost

Matching theory and Lagrangian-based algorithm

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A Review on Vehicular Task Offloading  13 This process employs the alternating direction method of the multipliers approach, which iteratively produces the optimal solution. In the online phase, a probabilistic method based on the optimal allocation probability is employed to decide the scheduling for each new task. Based on vehicle mobility analysis, Liu et al. [32] suggested a task offloading scheme. The offloading policy is created by considering service vehicle mobility into account, which is distinctive. The following processes are involved in task processing: service vehicle discovery, task assignment, and task execution. If the distance between the vehicles, which is estimated with beacons, is within the communication range of the vehicles, the link connection is present and tasks are assigned. The problem of multi-hop task offloading, taking into account mobility, is framed as a utility minimization challenge to improve the QoE for client vehicles. The weighted average of task processing latency and the total cost is referred to as the utility function and is expressed in Equation 1.1 as follows

φ ( Y ) = t ( Y ) λc c(+Y )

= max ΣiN=1 y ki Tki + λc Σ k∈M ΣiN=1 y ki Cki k∈{0. M }

(1.1)



where λc is a positive weighting factor that accounts for the vehicle’s preference regarding task execution time t(Y) and computation cost c(Y). N denotes the set of tasks, while M represents the set of service vehicles. y ki denotes the offloading variable of the task, Tki represents the total execution time of the task, and Cki indicates the total cost to process the task. The formulated problem is addressed using a semidefinite relaxation method alongside an adaptive adjustment algorithm, leading to significant improvements in response delay performance. The tasks can be offloaded from the task vehicle to the surrounding parked vehicles for execution. An optimal task offloading path [33] is found using the levy-Kopt algorithm. There will be a long delay of connection interruption if the server interacts with all vehicles hence, selected vehicles to offload are connected into a path where the MEC server interacts with only one vehicle. The connection path for offloading from parked vehicles is determined using the K-opt neighborhood of the Levy flight method, which relies on the concept of solving random numbers through the normal distribution. The idea of parked edge computing [35] can be used to overcome the resource constraint problem in physical edge servers by utilizing the rich and underutilized resources of parked vehicles to assist edge servers to

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14  Edge of Intelligence handle the offloaded task. Parked vehicles are clustered and treated as virtual edge servers, providing options for task offloading. Depending on the task scheduling algorithm, tasks can be offloaded to either physical or virtual edge servers. The task scheduling challenge is subdivided into two parts: optimal resource allocation and optimal server selection. The optimal resource allocation is addressed through a multidimensional multichoice knapsack problem. The server selection problem, represented as Equation 1.2, is solved using the branch and bound method.



min f = Σ kL=1 ΣiN 1 Σ=Tj =1 y k ,ijt k .ij



(1.2)

where L represents the set of edge servers, N represents the set of moving vehicles, T represents the set of tasks, y k ,ij represents the server choice and t k .ij represents the total time to finish the task. Random Forest algorithm is used for driving trajectory prediction which aids in returning the tasks’ result to the source vehicle when it went out of communication range. Khadir et al. [34] considered that vehicles offload the compute-intensive and low response time task to fog nodes, but the limited resources of fog nodes make it impossible to meet the demand. In such cases, tasks need to be offloaded to other suitable destinations. Initially, the vehicle attempts to offload its task to a fog node within its coverage area. If the average completion time of the task meets the deadline, it’s considered feasible and executed in the corresponding fog node. Otherwise, the infeasible request is forwarded to the SDN controller. The possible offloading destinations of infeasible requests are cloud, parked vehicles, and moving vehicles. To decide the optimal destination, the stretch time, defined as the time between a task’s deadline and the typical response time of the destination node, is used as the optimization parameter. This decision-making problem is formulated as binary linear programming and solved using CPLEX software. Liu et al. [36] designed an optimization problem to reduce the average cost of all task vehicles under the restrictions of latency and processing capacity. The problem is decoupled into offloading node selection and resource allocation subproblems and solved using a distributed iterative algorithm involving matching theory and a Lagrangian-based algorithm. The differential pricing scheme achieves greater revenue for servers by choosing a higher price for closer vehicles thereby avoiding multiple vehicles offloading to the same server simultaneously. In matching theory, to solve the optimization problem, the preference function has to be designed and then the preference list has to be prepared for the participating agents.

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A Review on Vehicular Task Offloading  15 The revenue is considered the preference function for roadside units and server vehicles as shown in Equations 1.3 and 1.4 and the reciprocal of the cost function is considered the preference function for task vehicles.



K n

δ = ΣiI=1 xi ,n ei ,n

L j

δ = ΣiI=1 yi , j ei , j



(1.3) (1.4)

where K represents set of RSUs, L represents set of server vehicles, I represents set of task vehicles, xi ,n & yi , j represents offloading decision variables and ei ,n & ei , j represents offloading expenses. The preference list is generated by arranging function values in descending order. Then, an iterative matching process is employed to determine the optimal offloading node. The optimization of resource allocation is addressed using the Lagrangian method.

1.2.1.3 Custom-Tailored Algorithms Custom-tailored algorithms like token-based predictive schemes, dynamic programming for finding the shortest path, matching theory, and fuzzy logic techniques can enhance the execution of dependent and independent subtasks of vehicular applications while adhering to their respective constraints. The literature focusing on task offloading decision-making by custom-tailored techniques is summarized in Table 1.4 and the details are explained in this section. The execution of lengthy computation-intensive tasks in heterogeneous vehicular applications, such as safety, infotainment, gaming, AR, and smart driving services, within their delay constraints poses challenges due to the high mobility of vehicles. To address this, tasks can be divided into sub-tasks to enable parallel processing. Distributed task offloading is performed efficiently [4] by selecting service vehicles and making offloading decisions based on the link’s lifetime. Task offloading cost minimization is formulated as an optimization problem, where the offloading cost is defined as the weighted sum of latency and processing cost. Service vehicle selection is based on performance values calculated using a custom fuzzy logic algorithm, considering communication and computation factors of VANETs such as distance, relative velocity, link reliability, and available computational resources. The interaction pattern of selected service vehicles is analyzed to estimate the level of trust among users.

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16  Edge of Intelligence Table 1.4  Summary of custom-tailored approaches of task offloading strategies. Paper

Modes

Type

Objective

Method

[37]

V2R

Binary

Minimization of cost

Threshold-based parameter.

[38]

V2R

Binary

Minimization of average delay of task execution

Token-based predictive scheme.

[4]

V2V

Proportion

Minimization of task offloading cost

Link’s lifetime, Fuzzy logic algorithm.

[39]

V2R

Binary

Minimization of latency

Matching theory.

[40]

V2V, V2R, V2Cloud

Proportion

Minimization of average service delay of tasks

Shortest path finding using Dynamic Programming.

[41]

V2R

Binary

Improved Task completion rate and reduced average service time

Multi-Period Task Offloading and Transmission Method.

The token-based predictive offloading scheme [38] is proposed to offload the computational task of the vehicle to an MEC server to reduce the average delay. Two queues are maintained in the MEC server. Queue 1 has the number of tokens tasks, other extra tasks are in Queue 2. Tasks from Queue 2 are transferred to Queue 1 once any task in Queue 1 is processed. Round-robin scheduling is used to process tasks in the queue. Information regarding consumed and available tokens is stored in individual tables for each MEC server. Additionally, vehicles maintain a separate table to track which vehicles are within the range of each server. The vehicle transfers the task to the MEC server by requesting the token. If the request is declined, the vehicle uses V2V communication to send the task to another server. NOMA, a burgeoning technology for vehicular networks, allows multiple users to utilize the same wireless resources, boosting spectrum utilization

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A Review on Vehicular Task Offloading  17 and system capacity. By jointly optimizing offloading decision-making, Vehicular User Equipment (VUE) clustering for subchannel allocation, computation resource allocation, and transmission power control, Du et al. [37] builds the cost minimization problem based on the NOMA-based VECN model. As it’s a mixed integer nonlinear programming problem, the task offloading and resource assignment issues are decoupled and addressed individually using tailor-made heuristic methods. By adjusting a parameter that considers task characteristics and wireless channel quality, and comparing it with a threshold, an offloading decision is made. Resource allocation is done based on cost-benefit analysis. In cellular V2X technology, the Uu cellular interface facilitates Vehicleto-Network (V2N) communication, while the side link PC5 interface supports V2V communication. An efficient task offloading scheme is proposed in which the task assignments are done based on custom matching theory [39] to reduce latency and improve the offloading reliability. Vehicles are grouped into clusters to ease the communication overhead on the cellular network hence, only the cluster heads can communicate with the network through the enodeB. A cluster is formed using a greedy iterative algorithm and the cluster head is selected based on metrics like velocity, link lifetime, and mean distance between communicating vehicles. Pairing a set of subtasks with a set of servers i.e., VEC or MEC server is done by matching theory. The preference list is obtained using the utility value with which members from one set are mapped with the members from another set. A dependency-aware task offloading problem [40] is formulated to minimize the average service delay of the task. It’s characterized as a mixed integer nonlinear programming issue. To represent the task, a directed acyclic graph can be employed. Utilizing a graph model, a three-step heuristic approach is outlined. The task graph’s critical path is determined by considering the average processing capacity of the available nodes. A feasible solution along the critical path is translated into a hierarchical directed graph, converting the problem into a shortest path identification task, which is then resolved using dynamic programming. The sub-task is scheduled using a greedy method on the non-critical path. Most of the research work provided resource scheduling methods and task-offloading strategies within the same time frame. Yet, the scheduling of resource allocation in subsequent times is influenced by the usage and release of resources in preceding instances. Zhang et al. [41] proposed a multi-period task offloading strategy for the task offloading problem at various time instances. The problem of task offloading is sliced with multiple periods. The offloading task request from the client vehicle at a

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18  Edge of Intelligence certain period is sent to the BS. After identifying appropriate offloading destinations for each task vehicle, the system evaluates whether the task can be offloaded in the current period. Subsequently, it selects the suitable offloading destination based on the service time and offloading cost. The destination node information of this period is updated before receiving the task request in the next period. In this section, the algorithmic-based computational offloading strategies for vehicular networks have been reviewed. Game Theory methods, mathematical algorithms, and custom-tailored algorithms have improved the successful computational offloading strategies with constraints such as latency, energy consumption, and resource availability. However, in Game Theory it is difficult to conclude the final decision when the number of vehicles increases. As well as it is also challenging for mathematical, and custom-tailored algorithms to attain stable optimized performance for a long time in a large-scale complex vehicular environment. The integration of connected intelligence and V2X communications in the 6G era significantly expands the task offloading challenge within the IoV environment. Traditional algorithm-based approaches struggle to achieve optimal results in this complex setting, underscoring the necessity for DRL-based strategies.

1.2.2 DRL-Based Strategies AI has been leveraged in several domains, such as Industry 4.0, IoT, automotive networks, etc., to maximize its advantages. The widespread use and comprehensive integration of AI technologies with wireless systems can enhance network functionality and decision-making in a ­cost-effective manner [42]. It is anticipated that distributed and pervasive AI will be incorporated into 6G wireless communication networks. In ITS, AI has enhanced many functionalities [43] like traffic flow optimization, speed prediction, anomalous driving detection, cooperative lane change, etc. The apps and services used in modern vehicles are time-sensitive and based on AI algorithms. In general, Machine learning and Deep learning models demand substantial datasets for effective training. Nevertheless, obtaining datasets for task offloading is often impractical, and these models may not readily adapt to the swiftly changing and dynamic vehicular environment. Hence, the effectiveness of vehicular task offloading can be improved by RL and DRL [44]. Traditional algorithms struggle to handle vehicular task offloading in vast, complicated environments, whereas RL/DRL systems can perform better in such scenarios [1]. The majority of traditional approaches using one-shot optimization may not be able to achieve

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A Review on Vehicular Task Offloading  19 consistent long-term optimum performance when taking into account the complex and dynamic VANET caused by rapidly changing channels and compute workload offloading circumstances. While RL methods like Q-learning and State-Action-Reward-StateAction (SARSA) are effective, they face challenges in vehicular environments with large state space complexities, such as continuous state and action spaces. DRL addresses this issue by leveraging neural networks as function approximators, thus enhancing the scalability of RL for complex vehicular environments. DRL determines the optimal offloading decision strategy based on past experiences. Although the training process for DRL involves significant time and resource consumption, once converged, it enables faster real-time decision-making. An optimization problem is formulated as a Markov Decision Process (MDP) for DRL algorithms to solve. This MDP is described by a 4-tuple (S, A, P, R), where S represents the set of states, A denotes the set of actions, R signifies rewards, and P provides transition probabilities. The transition probability P (s′|s,a) indicates the likelihood of transitioning to a new state s’ given the current state s and action a. The reward function R : SXA → R reflects the rewards obtained after taking an action. The discount factor γ, ranging from 0 to 1, influences the quality of the reward function and affects the probability of the next state s’ and the next reward r, as shown in Equation 1.5.



P (s′, r|s, a) = Pr ( St +1

s′, Rt +1 == r|St

s, A=t = a )



(1.5)

The literature focusing on task-offloading decision-making by DRL techniques is classified as Value-based, Policy-based, and multi-agent DRL techniques as shown in Figure 1.5 and summarized below.

Value-Based DRL Algorithms DRL-Based Computational Offloading Strategies

Policy-Based DRL Algorithms

MADRL Algorithms

Figure 1.5  DRL-based strategies.

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20  Edge of Intelligence

1.2.2.1 Value-Based DRL Algorithms Value-based DRL algorithms aim to determine the best policy for agents by evaluating and improving the values associated with states and actions within an environment, to maximize the anticipated cumulative reward over time. Every policy π is characterized by a state value function Vπ : S → R and an action value function Qπ : SXA → R, as described in Equations 1.6 and 1.7, respectively. (1.6)



 ∞ k  Vπ (s) = π  γ Rt + k +1|st = s   k =0 

(1.7)



 ∞ k  Qπ (s, a) = π  γ Rt + k +1|st = s, at = a   k =0 

∑

∑

The optimal policy π∗, as illustrated in Equation 1.8, is derived from the optimal action-value function Q∗ (s,a), which provides the highest expected reward for any state-action pair across all conceivable policies.



π ∗ (s) = argmaxaQ∗ (s, a)

(1.8)

Deep Q Network (DQN), Double DQN, and Dueling DQN are ­value-­ based DRL algorithms, and their relevance is explained in this ­section. Table  1.5 describes the State, Action, and Reward considered in the literature. Edge cloud computing cooperation in VEC involves task execution across three locations: local, edge server, or cloud server. The task offloading algorithm, leveraging DQN [47], aims to minimize the average delay in task processing. Each task’s completion time is determined based on its offloading destination node. The offloading process is modeled as an MDP, aiming to identify optimal offloading decisions with low computational complexity, thereby minimizing processing delay while considering computation and communication resource constraints. Upon receiving a task request from a vehicle, the DQN agent (MEC server) observes the state and selects an action based on the current offloading strategy, receiving a reward and transitioning to the next state accordingly. The offloading strategy is iteratively updated using rewards, employing the epsilon-greedy

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A Review on Vehicular Task Offloading  21 Table 1.5  Summary of value-based DRL methods for task-offloading strategies. Paper Modes Type

State

Action

Reward

[45]

V2Fog Binary

Computing power, load on each fog node

Selection of fog node

Difference between utility function and the sum of traffic load probability function and end-to-end delay.

[46]

V2Fog Binary

Servers and task information

Selection of offloading node

Task deadline and delay

[47]

V2R

Binary

Selection of Available communication offloading and node computation resources

The reduced processing delay of a task

[48]

V2V

Proportion Remaining resources of vehicles

[49]

V2Fog Binary

Task offloading strategy

The negative of the optimization function

Task to be Offloading Difference allocated, the tasks to fog between remaining tasks nodes and utility and in the queue determining summation the quantity of delay and of tasks for overhead offloading.

strategy to balance exploration and exploitation in action selection. Experience replay is utilized to enhance the training rate. A framework for AI-based V2X [45] is proposed to provide ultra-reliable and low-latency communications in a highly dynamic environment. RSU or BS fog nodes are connected to an SDN controller, which is responsible for collecting information and making decisions. The proposed AI-based resource allocation and task offloading in vehicular networks is done in

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22  Edge of Intelligence two stages: the first stage is fog node synchronization, which is required for optimal task offloading as well as to reduce latency, and the second stage is load balancing at the fog layer. The selection of computing nodes and load balancing amongst several agents are formulated as an MDP. Employing Q-learning, actions for offloading are fine-tuned within each system to boost efficiency, reduce processing delays, and optimize the allocation of IoV tasks. The agent within the SDN controller allocates tasks based on the computational capabilities and workload of each fog node, aiming to maximize resource utilization and optimize traffic distribution. Fog nodes’ constrained computing resources have to be utilized optimally. The Kalman filter prediction technique [49], which estimates the future locations of the vehicles, gives fog resources’ future availability to aid in optimal offloading decisions. The offloading problem is modeled as MDP, which considers fog computing in SDN-based vehicular network architecture. The resource-rich fog node in the VANET is chosen using the Long Short Term Memory (LSTM) based DQN for optimal task offloading. The SDN Controller performs the role of an agent, keeping track of the fog states through continuous fog device monitoring and taking the most rewarding action to offload the task by using the fog node states as input. A fuzzy DQL-based offloading scheme [46] leveraging the advantage of both DQL and fuzzy logic in delay constraint vehicular fog computing is proposed to maximize the QoE. DQL selects the suitable fog server for task offloading. In cases where the total task delay exceeds the task’s deadline, a fuzzy controller determines the offloading server based on predefined fuzzy logic criteria. This leverages the reliability of fuzzy logic when DQL struggles to make accurate decisions due to dependencies on the initialization process. When MEC is not accessible or sufficient, surrounding vehicles can be used as a Resource Pool (RP). A complex task can be divided into numerous smaller subtasks, and it is challenging to assign these tasks to RP. The optimum offloading decision is made to reduce execution time and efficiently use all resources by proposing a distributed computation offloading method based on DQN [48]. While assigning tasks, the vehicle queue length is considered as well. Value-based DRL techniques encounter challenges when attempting to adapt to highly dynamic environments because they require a fresh calculation of Q values. As a result, researchers have explored policy-based DRL methods to facilitate efficient task offloading in the IoV environment, where handling uncertainty is a key focus.

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A Review on Vehicular Task Offloading  23

1.2.2.2 Policy-Based DRL Algorithms As 6G technology continues to advance, it allows for ubiquitous coverage through SAGIN by integrating UAVs and satellites as aerial and space base stations. This expansion extends the action space of vehicular task offloading, making it both high-dimensional and stochastic. In this context, Policy-based DRL methods offer flexibility in managing high-­ dimensional and continuous action spaces, as they directly parameterize and provide probability distributions for actions. The literature focusing on policy-based DRL methods for task-offloading is explained in this section. Table 1.6 describes the State, Action, and Reward considered in the literature. Vehicle congestion varies in real traffic scenarios, resulting in abrupt rises in task volumes during peak morning and evening rush hours. The fixed BS alone cannot satisfy the task requirements, thus, UAVs can be used during such situations and the mobility of UAVs is used to deal with the surge of computing tasks effectively. UAV-aided VECN architecture [50] is designed to integrate the computing resources of vehicles, edge servers, and UAVs. The user fairness factor is established to encourage the collaboration of resources among vehicles, MEC servers, and UAVs, thereby mitigating the tendency for vehicles to conserve their resources, which ultimately enhances resource utilization efficiency. The task offloading decision problem is modeled as a Markov process and addressed using an optimized actor-critic method incorporating prioritized experience replay, aiming to reduce both delay and energy consumption. While computation offloading is occasionally ineffective, pricing is essential for distributing resources to vehicles that esteem them and for easing congestion in resource-constrained scenarios. It encompasses moving MEC server-associated UAVs and moving vehicles. AI-driven dynamic pricing [51] is described as a data offloading strategy that enables optimal offloading of vehicles’ data to a UAV MEC server. The UAV MEC server serves as an agent in the optimization problem, which is formulated as an MDP to minimize energy and latency and is solved using a deep deterministic policy gradient (DDPG). By choosing the best offloading option, the vehicle saves time and energy, and the UAV generates revenue. Edge intelligence enhances dynamic vehicular networks by providing support to IoV applications, allowing vehicles to access edge servers’ resources for computing tasks and reducing delay. DRL’s cognitive and analytical capabilities optimize task offloading by minimizing delay and energy costs. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm [52] is used to derive an optimal offloading strategy for dynamic

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24  Edge of Intelligence Table 1.6  Summary of policy-based DRL methods for task offloading strategies. Paper Modes

Type

[50]

V2R, V2UAV

Offloading Proportion Amount of data position and processed by proportion of MEC server and task UAV, Transposed matrix of channel vector between vehicle and MEC server and between vehicle and UAV

[51]

V2UAV

Binary

System Utility Price announced The remaining by UAV-MEC, energy of the offloading ratio UAV battery, to UAV-MEC, UAV and the flight speed vehicle location of UAV information, size of the remaining tasks, total task size, an indication of whether the signal of the vehicle is blocked by an obstacle

[52]

V2R

Binary

Computation tasks generated, the available computing and communication resources, moving vehicle location

[53]

Eligible SV to V2V, Proportion SINR of the offload the task V2Cloud communication channel, available resources of Service Vehicle(SV), data size, CPU cycle, and maximum tolerable delay

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State

Action

Reward Utility function

Negative of Computation objective offloading function decision, computation, and communication resources allocated A binary term such that the value is 1 if a task is successfully offloaded

A Review on Vehicular Task Offloading  25 computation offloading, incorporating techniques such as clipped double Q learning, delayed policy update, and target policy smoothing to maximize rewards and optimize actions. Based on three distinct DRL algorithms—Soft Actor-Critic, DDPG, and TD3—a priority-sensitive task offloading and resource allocation method [53] for an IoV network is developed. To ensure effective resource allocation, tasks are categorized as per priority and computation size. The DRL method seeks to maximize the mean utility and minimize the mean latency of the IoV network by acquiring the best task offloading policy. Three alternative utility functions, which are developed based on priority level, task size, and network criticality, are used to calculate the mean utility.

1.2.2.3 MADRL Algorithms The introduction of 6G in IoV facilitates connected intelligence and fosters V2X communications where the task offloading environment consists of multiple agents capable of making optimal decisions. Effective cooperative mechanisms among these agents enhance the efficiency of task offloading. The literature focusing on MADRL methods for task-offloading is explained in this section. Table 1.7 describes the State, Action, and Reward considered in the literature. Jia et al. [54] tackle the computation offloading challenge by framing it as a temporal average optimization problem with long-term constraints. Their approach aims to increase the average logarithmic data processing rate in varying vehicle densities and communication channels. They employ a multiagent DRL technique where each vehicle in the IoV environment is equipped with actor-critic agents responsible for determining the optimal offloading policy. It consists of a Graph Convolutional Network (GCN) to extract the interdependencies between tasks. It updates the reward to the respective MEC server which is connected to the back-end server where policy update occurs. Lyapunov stochastic optimizing approach is used to analyze the formulated optimization problem. Thus GCN embedded multi-agent proximal policy optimization is used for computation offloading problems. In traditional DRL, each agent acts as an independent learner, ignoring other agents’ actions during the learning process. A cooperative three-layer decentralized multi-access edge computing network [55] with vehicles in the bottom fog layer, MEC servers in the middle, and cloud in the upper layer are developed. Because of vehicle mobility, a collective cooperative action space is essential for network alignment. Formulating the task offloading challenge to maximize utility as a stochastic game addresses the

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26  Edge of Intelligence Table 1.7  Summary of MADRL methods for task-offloading strategies. Paper Modes Type

State

Reward

Schedule decision

Negatively correlated to the objective function

[54]

V2R

[55]

V2V, Proportion Agents’ Selection of V2R computation offloading status, the node available RSUs, the number of moving and parked vehicles in RSU, the link quality of the agent with cloudlet

[56]

V2V, Binary V2R

[57]

V2V, Proportion Data size, Candidate The sum of V2R required computation service time computation servers and cost of resource, available subtask and already a constant offloaded task or a negative value

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Proportion Distance between vehicle to its linked MEC, vehicle and MEC states

Action

Vehicle speed, position, file size, computation capacity of the VEC server and vehicle, computation resources allocated

Negatively correlated to objective function

Offloading To expedite position, the training computation process, resource, considering and the the uplink and constraint downlink conditions channels and the objective function.

(Continued)

A Review on Vehicular Task Offloading  27 Table 1.7  Summary of MADRL methods for task-offloading strategies. (Continued) Paper Modes Type

State

Action

Reward

[58]

V2V, Binary V2R

The regular channel and traffic conditions, behaviors of other agents

Selection of Latency optimal satisfaction offloading ratio, and mode, and punishment computation for constraint resource violation

[3]

V2R

Binary

The available energy level and vehicles’ location

Offloading node selection

Depend on strategy and environment state

[59]

V2R

Proportion Vehicle state, MEC server state

Selection of optimal MEC server to offload

Negatively correlated to objective function

dynamic aspect of vehicular networks. Achieving collective cooperative actions poses a challenge in MADRL offloading methods. Therefore, MADRL employs the Hungarian algorithm to learn the optimal offloading policy based on actions of other agents. It is vital to consider the varying requirements of vehicular tasks because different vehicle speeds necessitate different delay limits for the same in-vehicle application while offloading and allocating resources. Huang et al. [56] proposed task offloading and resource allocation techniques that incorporate both the task type and vehicle speed, lowering the energy cost of task execution and increasing the vehicle’s revenue for processing tasks within time limitations. Tasks are categorized into critical, high-priority, and low-priority applications, establishing a delay constraint model based on task type and vehicle speed. Tasks can be delegated to other vehicles, MEC servers, or executed locally. The execution delay, energy cost, and income are computed for various offloading locations. To maximize vehicle utility, a multiagent DDPG algorithm is employed for joint optimization of task offloading and resource utilization. Vehicle utility is determined based on energy costs and revenue from task completion. The scheduling of traffic data sensed by crowdsourcing vehicles for processing [57] is considered. This is stated as a data-driven task offloading problem by jointly optimizing resource allocation and offloading

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28  Edge of Intelligence decisions. The best offloading option is selected by MEC servers using the DQN and the Asynchronous Advantage Actor Critic (A3C). To make the best decisions, the cloud server also updates global knowledge. To reduce average service time and average service cost, resource allocation for items like wireless bandwidth and MEC servers’ computations are done using the decomposition approach and convex theory. An efficient resource allocation strategy needs to be analyzed by cooperatively considering end-edge cloud resources. Zhang et al. [58] used the MADRL algorithm to make optimal offloading decisions and resource allocation to minimize the latency. The Stackelberg game-based dynamic incentive scheme is presented to make the vehicles share their resources. Using distributed stochastic game-based DDPG under a heterogeneous road network with heterogeneous computing power sources accessible, Xia et al. [3] performed computation offloading in cooperative ITS. In a distributed stochastic game, MEC servers take on the role of the leader to assign computing resources, and vehicles take on the role of the follower to make the offloading decision themselves based on their local observable state. For centralized training and decentralized execution, a global environment state is obtained by employing multi-agent DDPG to produce optimal policies. To reduce task completion time, an intelligent choice must be taken regarding whether and when to offload tasks generated by vehicles. Making a smart decision for the vehicle in light of its immediate surroundings is a challenging problem. Liu et al. [59] expressed the task offloading problem as an MDP. Using an actor-network to create two actions for the vehicle, the A3C method is utilized to solve the formulated MDP. A critic network assesses the actor’s behavior and directs the actor network’s action in later phases. An MDP-adjusted method is also provided for the case where some of the network’s servers are compromised. DRL-based computational offloading strategies for vehicular networks have been reviewed in this section. Value-based algorithms (DQN), Policybased algorithms (DDPG), and MADRL algorithms have been focused on the existing papers to achieve the prime objective of computational offloading strategies and Tables 1.5–1.7 summarizes it. The solution has moved from a value-based algorithm to a policy-based algorithm because of the huge state and action space. Centralized training and decentralized execution have been used to achieve cooperation among multiple agents. However still, it is challenging to achieve interactions among multiple agents in a dynamic environment. The prime objective of these methods is to minimize latency and energy. The next section discusses the details of collaborative techniques of task offloading in the vehicular network.

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A Review on Vehicular Task Offloading  29

1.3 Collaborative Techniques Edge AI within the context of the IoV empowers the capabilities of 6G networks by seamlessly integrating sensing, communication, computation, and intelligence. This integration supports a diverse range of intelligent applications, leading to an increased demand for efficient task offloading. To enhance the performance and efficiency of task offloading in 6G-enabled IoV, this section explores the integration of Caching, SDN, and UAV technologies with VEC. Caching leverages the advantages of edge computing within task-offloading systems, ultimately enhancing the QoE for users. Furthermore, intelligent resource management and network slicing are utilized to ensure efficient task offloading while meeting QoS requirements, employing technologies like NFV and SDN within 6G-enabled IoV. Additionally, the integration of SAGIN enables UAVs to serve as communication relays or MEC servers, providing emergency task offloading services and enabling task offloading in rural areas where traditional infrastructure may be non-existent.

1.3.1 Caching For exploiting and enhancing the functions in EC, caching-enabled task offloading is introduced. For improving the response latency, task offloading, and content caching are jointly considered. The computation result of the task that is independent of user content can be cached. The source code or application for performing task execution can be cached. The literature focusing on task offloading and caching techniques is summarized in this section. Real-time vehicular services necessitate extensive processing, long transmission delays, and sensor data integration; it may hinder the development of self-driving technologies and the rise of intelligent vehicles. Li and Wu [60] explained the task offloading and perception data caching methods that can help vehicular services to achieve the QoS. The MEC server in the roadside unit can process the task and cache perception data provided by micro-sensor systems. This cached data can enhance the QoS of vehicular applications. The task offloading mechanism is described in a novel IoV model that integrates computation offloading with perception data caching. According to the offloading strategy, the task may be executed either locally or in the roadside unit. To reduce the average execution time of a vehicle’s task, the optimization problem of sensory data caching and computation offloading is formulated. A faster simpler correlation

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30  Edge of Intelligence Monte Carlo search strategy is also provided for the genetic algorithm’s fast convergence [60]. A hierarchical linear computing resource allocation approach is used to assign computation resources in accordance with the offloading decision. Kong et al. [61] studied how to efficiently allocate computing and caching resources to minimize energy consumption. This challenge arises because RSUs have limited resources, and the growing number of IoV devices demands more computation, leading to higher energy use. To address this, the minimization of energy cost optimization problem is formulated and solved by using the DDPG method which allocates computation and caching resources dynamically based on the offloaded task. A hierarchical architecture for edge intelligence-empowered IoV is constructed by jointly considering V2R computation offloading, RSU peers offloading, and content caching [62]. By taking computation offloading and content caching together, the delay restrictions of latency-sensitive tasks can be satisfied. A problem involving mixed integer non-linear programming is formulated for offloading and caching decisions. The collaboration of edge servers is underexplored in recent research, although it is necessary to improve network performance by load balancing among RSUs. In [62] edge server collaboration is taken into consideration to reduce overall network latency. The application requirements for vehicles include desired contents and computational operations. The Lyapunov optimization technique is used to offer an effective online approach for allocating resources for edge computing and caching. Caching combined with computation offloading improves the QoS of self-driving vehicles. A collaborative computation offloading and content caching method (CoPace) [63] is proposed using DRL. Caching is needed because the data required by the computational tasks of different vehicles have similarities. So the transmission of duplicate data leads to the wastage of communication resources. CoPace coordinates users’ decision-making regarding offloading, caching, and resource allocation, aiming to enhance QoS. It caches reusable computation task content on edge servers to mitigate network delay. Content caching decisions are determined based on predicted popularity using a deep Spatial Transformer Network (STN) model, and an online decision-making process is employed using DDPG to optimize system latency and enhance vehicle QoS. Edge servers’ processing power is constrained by physical resource limits and energy supply limitations. High Altitude Platform Station (HAPS) computing is viewed as an extension of EC due to its extensive coverage, robust computational capabilities, and essential data libraries linked to ITS-based applications. It aims to reduce the delay by optimizing

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A Review on Vehicular Task Offloading  31 offloading and caching decisions, as well as computing resource allocation. Ren et al. [64] considered onboard devices, terrestrial network edges, and the HAPS as the three levels at which the task can be performed. The transmission delay can be reduced by caching the data at network edges. The formulated continuous variable multi-slot mixed integer problem is reduced into subproblems of decision-making and resource allocation. The decision-making problem is resolved using the MADRL approach. Based on the decision, the Lagrangian approach is then used to find the most effective strategies for allocating bandwidth and computing resources. To provide centralized training and decentralized execution of the agents, the MADRL value decomposition network is used.

1.3.2 SDN In the context of 6G, SDN has become the standard for enabling on-­ demand and dynamically intelligent resource management. Figure 1.6 represents the SDN-VANET architecture. By segregating the control and data planes, SDN provides flexibility and facilitates the management of

Applications Cloud Control Plane

SDN Controller

SDN Controller

Data Plane

Wired Link MEC Server

MEC Server

RSU

V2I

V2I Task offloading

V2I

Task Task offloading offloading

Task offloading

Task offloading Task offloading

Figure 1.6  SDN-VANET architecture.

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BS V2I Task offloading

32  Edge of Intelligence Table 1.8  SDN in the vehicular network for task offloading. Paper Solutions

Type

Metric

Method

SDN Role

Differential Evolution

Information collection and decisionmaking

[65]

Binary Task offloading, Resource allocation

Average delay of task processing

[45]

Binary Resource allocation, Task offloading

Latency, Energy RL consumption

[34]

Task Binary offloading

Response time, Meeting deadline

[66]

Proportion Average Delay Task offloading, Resource allocation

[49]

Binary Task offloading, Load balancing

DQN Average throughput, energy consumption

Decision making

[58]

Binary Resource Allocation, Task offloading

Average latency Multiagent DRL

Decision making

[67]

Proportion Time delay Task offloading, Resource allocation

[68]

Task Binary offloading

Data collection and decisionmaking

Binary Linear Selecting the Programming offloading destination PSO, Lagrangian duality theorem

Decision making

DQL

Centralized resource management, flexible network control

Task processing DQL delay

Decision making

the network. SDN can address major issues in vehicular communications through NFV and centralized controls [69]. The SDN is used to control the vehicle request with the global situation or information and coordinate all the edge computing resources thereby balancing the load between them. SDN has been introduced for making the optimal selection decision of the offloading destination with the help of a global view of network resources.

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A Review on Vehicular Task Offloading  33 SDN and Network Function Virtualization (NFV) are used to support VEC system architecture and improve system resource utilization. Jin et al. [65] predicted traffic flow in the SDN controller for effective resource allocation and to ensure load balance among MEC servers. The SDN controller has information such as available resources, unit cost, and communication delay of each of the offloading destinations. In [34], the offloading selection decision is made in the SDN controller for the tasks that cannot be executed in its coverage areas’ fog node. In [45, 49, 68], the SDN controller serves as a task-offloading agent. It keeps track of all the nodes through continuous monitoring and takes the most rewarding action to offload the task. The literature involving SDN for task offloading is summarized in Table 1.8.

1.3.3 UAV Vehicle obstructions influence the vehicle to RSU linkages, which lowers the level of service. Air BSs can be created using UAVs that include communication equipment [70]. The deployment of UAVs and their movement in 3D space make air-to-ground wireless channels preferable to their groundto-ground counterparts. As a result, the UAV-assisted VANETs as shown in Figure 1.7 offer enormous potential for enhancing the QoS for vehicular applications. Specifically, UAVs can collect information concerning traffic density and vehicle connectivity levels at ground level, then transmit this data to moving vehicles through V2X communications. UAV-assisted VANET can improve the QoS of vehicular applications. In [71], UAV is fitted with a MEC server with fewer computing resources and it focuses on minimizing task processing delay. In [72], UAVs hover over a particular area to execute real-time tasks or offload them to idle edge devices. Yuan et al. [50] considered the use of UAVs during the ­morning-evening rush hour to deal with the surge of computing tasks effectively. In [51], the UAV MEC server serves as an agent to choose the best offloading option. The literature focusing on task offloading decision-­ making on UAV-assisted VANET is summarized in Table 1.9. In this section, we have reviewed the concepts associated with task offloading in vehicular networks. Caching, SDN, and the use of UAV for task offloading have been focused on in the papers in this section. Caching the data needed to perform the offloaded task can reduce the completion time of the task, thereby improving the performance of task offloading. However, allocating storage resources for caching data in the resource-­ constrained MEC servers and caching policy for the short life span of data is challenging. Incorporating SDN into the vehicular network can enhance

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34  Edge of Intelligence BS

MEC Server

I2I UAV

V2I

MEC Server

V2I V2I V2V

V2I

V2V

V2V

Figure 1.7  UAV-VANET architecture.

task offloading by abstracting resources and distributing workload evenly among edge servers. The usage of UAVs as MEC servers and as relay nodes can help with task offloading during high vehicle density scenarios because of their mobility. The next section discusses the details of secure task offloading in the vehicular network.

1.4 Security The vehicle’s context information such as speed and location has to be uploaded to RSU or BS to perform task offloading effectively. This information poses a threat to the vehicle’s privacy as the attackers may use them maliciously to carry out attacks or differential attacks may be performed on this statistical data. Hence, security in IoV is a serious issue as it has an impact on the lives of vehicular users. Secure task offloading has to be ensured to preserve the privacy of vehicles and prevent any malicious activities. This motivates the researchers to evaluate security aspects like

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A Review on Vehicular Task Offloading  35

Table 1.9  UAV in the vehicular network for task offloading. Paper

Solutions

Metric

Method

UAV role

Limitation

[71]

MEC Server selection, Task offloading, Resource allocation

Successful Task processing ratio

Lagrangian dual decomposition

MEC Server

Limited UAV computation resource

[72]

Task offloading decision, Lack of MEC Server

Time delay and energy consumption

Greedy heuristic framework

MEC Server and relay node

Static scenario

[50]

Surge of tasks during rush hours, Task offloading proportion and position

Latency and energy consumption

Actor critic algorithm With prioritized experience replay

Used during rush hours to provide communication and computing services

Energy consumption of UAV

[51]

Task offloading

Delay, energy cost

DDPG

MEC Server

Single UAVs alone considered

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36  Edge of Intelligence confidentiality, privacy, authentication, and attack as shown in Figure 1.8. Table 1.10 summarizes the security aspects involved in vehicular task offloading. A blockchain network of vehicles and RSUs is proposed by Poongodi et al. [73] to enhance VANET security. It provides data security during transmission. The proposed work ensures the authentication of vehicular users as well as confidentiality using blockchain and encryption  techniques.

Cloud Blockchain BS Blockchain

Trust Management

MEC Server

RSU V2I

MEC Server

V2I V2I V2V

V2I

V2V V2V

Figure 1.8  Security techniques in VANET.

Table 1.10  Secure task offloading. Ref. nos.

Techniques

Security methods

[74, 75, 77–79]

Trust Management

Attack Mitigation

[76, 82]

Differential Privacy

Privacy

[80, 81]

Homomorphic Encryption

Confidentiality

[73]

Blockchain

Authentication

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A Review on Vehicular Task Offloading  37 The RSUs edge computing nodes submit computation offloading tasks to the blockchain. The encrypted computing task’s input data is consequently transmitted from the moving vehicle to an edge computing device. It is then transferred to the parking vehicle for execution. Fuzzy inference systems are incorporated to produce a security index, indicating the level of protection and potential attack prediction within the VANET environment. The Direct Acyclic Graph (DAG)-based blockchain [74] used a priority and reliability-biased random walk consensus mechanism, where high-­ priority tasks are confirmed early on the DAG. A vehicle credit assessment method is created to reduce the malicious behaviors of vehicles which are based on the reliability of data stored on DAG. Based on the DAG-based blockchain augmented with the consensus algorithm, the delay optimal offloading technique is developed to choose the ideal group of adjacent vehicles for cooperative task offloading. Secure task offloading and resource allocation [75] are efficiently done in VEC architecture that is vulnerable to location and task information attacks, using the federated learning-based credit management in a three-layered VEC architecture. Federated learning (FL) is used to preserve the privacy of vehicles. The multi-layer Perceptron (MLP) model is used to predict the credit value i.e., attack behavior of a vehicle in the BS and perform task offloading based on the branch and bound mechanisms. The parameters are aggregated in the cloud and updated in the local or BS models. Wei et al. [76] explored MADRL-based approaches for task offloading to get rid of the unstable observation and reward from the centralized DRL systems. The offloading preference inference attack, which takes advantage of a weakness in MADRL’s policy learning process, can trick the vehicles into offloading the tasks to malicious RSUs. By transferring experiences and storing them in a replay buffer for a policy update, the task offloading model is trained on the cloud. With the information already in the cloud, the attacker can infer the preferred method of offloading for every vehicle. In Privacy-Aware MADRL, the action selection and policy updating process are supplemented with functional noise created using a differential privacy method to decrease the attacker’s observation accuracy. Deng et al. [77] proposed a MEC-enabled SDN-based VANET architecture with task offloading and edge Distributed Denial of Service (DDoS) attack mitigation. The trustworthiness of vehicles is assessed to manage resource allocation and mitigate edge DDoS attacks. Malicious vehicles can flood MEC servers with fake service requests, causing edge DDoS attacks and disrupting legitimate services. Vehicle trustworthiness is determined by analyzing the frequency of offloading requests and

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38  Edge of Intelligence resource consumption history. Vehicles with low trust scores are denied MEC resources, preventing edge DDoS attacks. Meanwhile, requests are rerouted to other edge nodes if one is under attack, mitigating the impact of the attack. This approach optimizes both task offloading and edge DDoS attack prevention, reducing latency and energy consumption. The optimization problem deals with the cooperative DRL method DDPG. The security study is carried out across different scenarios involving varying numbers of malicious vehicles and duration of attacks. To enable secure, consistent, and rapid allocation of vehicular tasks to RSU for processing, Mao et al. [78] proposed a task offloading system for IoV based on a trusted RSU. For RSU to recognize malicious assaults, the trust management paradigm is introduced. There are two types of trust: QoS trust and Social trust. QoS trust includes timeliness trust, while Social trust includes collaboration trust, neighbor recommendations trust, and unselfish trust. After establishing the RSU’s trust evaluation mechanism efficient task offloading is done. Each RSU computes trust values based on current information and updates them locally. The global trust value is retrieved from the trust authority’s database. Nodes are flagged as malicious if their trust value surpasses a predefined threshold. Task offloading only occurs to trusted RSUs, and if none are within coverage, offloading waits until a trusted RSU is reachable. The optimization objective is to minimize task completion time overall. The malicious vehicles may deliberately return false results of the offloaded task thereby disrupting the client vehicle and launching the false result attacks. Thus, offloading has to be done based on the trustworthiness of the service vehicle and offloading delay of the task. A fuzzy comprehensive strategy [79] is used to evaluate the trust of vehicles based on factors such as average distance, available computing resource ratio, security rating, and trading reputation. Only trusted vehicles are allowed to participate in providing service to the client vehicles. Then the trusted service vehicles to offload the task are selected based on the minimum expected delay vehicle selection algorithm to minimize the task offloading delay. Lakhan et al. [80] presented the fully homomorphic-enabled secure task offloading for mobility-enabled vehicle applications. Fully homomorphic-enabled scheme-based encryption is done by vehicles locally before transmitting the data and vehicles decrypt the results from the server. The task computation is performed on the encrypted data. The offloaded task can migrate between computing nodes based on an online offloading strategy to minimize communication cost and computation costs. The SDN controller is placed on the RSU where online offloading strategy, task

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A Review on Vehicular Task Offloading  39 sequencing, and scheduling methods are implemented for handling vehicular requests. The dynamic service deployment [81] for task offloading is formulated as MDP and solved using DDDG to obtain the service deployment decisions. The DDPG network’s partial weights from the edge server are transmitted to the cloud for aggregation, and the aggregated weight is then sent back to the edge server to update its model parameters. However, this transmission of model parameters between the cloud and edge servers poses a privacy risk. To mitigate this risk, the edge server encrypts the weights using homomorphic encryption before transmitting them to the cloud. The model aggregation is performed with encrypted weights. The aggregated model weights are decrypted by the edge server and updated with the local model weights. The homomorphic encryption called the Paillieh Encryption algorithm is chosen as it guarantees that the decrypted result produced by operating on an encrypted number is the same as the result produced from plain text operation. Thus hiding the real information from third parties. A privacy-preserving VEC architecture [82] is designed using differential privacy and K Neighbor joint optimization of task offloading and resource allocation algorithm. The task offloading decision incorporates vehicle contextual information, safeguarded by a differential privacy mechanism. Each vehicle perturbs its contextual data before sharing it with RSU or BS, guided by statistical insights from the BS. This approach ensures privacy while enabling partial task execution locally and partially in RSU. Leveraging a local differential privacy algorithm, based on the histogram method, minimizes the privacy protection’s interference with task-­ offloading decisions. In this section, we have reviewed the security aspect of task offloading in vehicular networks. It is observed that blockchain, attacks (DDoS attack, Offloading preference attack, Location, and task information attack), trust management, and data encryption have been focused on in the papers in this section. However, secure task offloading is still an open issue in vehicular networks. The next section discusses the open issues and future directions of task offloading in the vehicular network.

1.5 Challenges and Future Research Directions In recent years, vehicular task offloading research has been at a high pace with the emergence of 6G-enabled IoV with connected intelligence, and

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40  Edge of Intelligence ubiquitous coverage [83]. This section briefly outlines the challenge and future research directions based on the current research. Intelligent Resource Management: O-RAN (Open Radio Access Network) in Beyond 5G networks provide edge intelligent base stations enabling dynamic virtualization of resources with the aid of network slicing [84]. QoS-based resource optimization enabling efficient task offloading of various vehicular applications in heterogeneous vehicular networks is an emerging research area. With SAGIN [85], ubiquitous coverage can be achieved in 6G-enabled IoV [86] enabling communications in rural and disaster areas. The optimization solution of computation offloading has to deal with dynamic heterogeneous networks. Federated learning-based DRL techniques [87] can be viewed as the optimal future path of edge intelligence for efficient resource management and security of MEC servers. Edge Infrastructure: The data generated by the vehicles for a specific period has accumulated in huge volume and has to be analyzed in the context of vehicle offloading in edge computing. Edge intelligence with BigData analytics for data aggregation at the edge can reduce communication overhead in the cloud. Intelligent data aggregation methods need to be focused on. The data caching at the edge also reduces communication overhead. An intelligent caching policy based on vehicle mobility is needed to alleviate task offloading performance. Secure task caching has to be ensured to preserve the user’s privacy. Serverless computing as a service in the edge infrastructure helps in reducing the energy consumption of MEC servers. With the emergence of 6G, the Internet of Everything comes into existence providing scalability for MEC servers, and further research focus is needed to integrate with IoV. Vehicular tasks: The complex tasks of vehicular applications are partitioned into subtasks [88] and the proportion task offloading is done to reduce the computational burden on the MEC servers. The partitioned subtasks may be dependent or independent of each other. Emerging Graph Neural Networks can be used for maintaining the dependencies in the tasks [54]. The effectiveness of task partitioning [89] and the evaluation method to analyze the performance of task partition is an open research area. It is a challenge to estimate the computation time of tasks as they depend on the dynamic channel bandwidth and transfer time. In proportion offloading, accurate computation time estimation of subtasks plays a major role in obtaining QoS. The lack of accurate estimation of task computation time makes it difficult to identify delay, thus the best offloading destination is hard to find, Hence, further study is needed in this area.

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A Review on Vehicular Task Offloading  41 Task scheduling: The task offloading decision to the MEC server depends on the resources available to them at a particular time and the service time of the MEC server for that particular vehicle. At a particular time, many vehicles may offload their tasks to the same MEC server [90]. Hence, an optimal task scheduling policy is needed to reduce the computation time of the task as well as for efficient resource utilization [91] maintaining load balancing among MEC servers. The scheduling of tasks is essential for efficient resource utilization and it has future research direction. Security: The malicious vehicle does identity theft establishing a Sybil attack as well as generating malicious traffic that creates a DDoS attack in the VANET environment and it may also compromise other vehicles. The compromised nodes may also infect other legitimate vehicles or infrastructure which degrades task offloading performance. The attack mitigation techniques and authentication of nodes for secure task offloading in VANET is an open research area. Edge AI-enabled secure [92] and trusted MEC infrastructure can be viewed as a solution in the future. The data transmitted in the network may also get tampered with by malicious nodes. Appropriate lightweight encryption techniques for resource-constrained devices have to be evolved. Security as a service can be provided for V2V and V2I communication. The concept of a secured cache can be analyzed to prevent memory attacks. Security and privacy strategies compromise the task offloading efficiency, necessitating analyzing the trade-off between them. Blockchain [60], Lightweight authentication, Trust management, Federated learning, and quantum-based encryption are important areas of future research for secure task offloading in the VANET environment. Evaluation criteria: The availability of benchmark dataset and simulation testbed for vehicular task offloading is needed to implement the strategies of task offloading effectively and helps to analyze more realistically feasible solutions. The standard parameter to evaluate the task offloading performance is still an open issue. Assessing the strategies on related testbeds will have greater relevance. Thus, a realistic evaluation scenario in a testbed with benchmark datasets is an open research area for the accurate evaluation of the proposed schemes.

1.6 Conclusion Intelligent vehicles within the IoV ecosystem operate various applications, including those catering to user entertainment, such as online games based on virtual reality and video streaming. Smart vehicles generate substantial

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42  Edge of Intelligence volumes of data, requiring computational processing to derive valuable insights for their applications. To facilitate this, MEC servers have been integrated into RSUs/BSs, allowing vehicles to offload their computational tasks. This article presents an extensive survey of diverse strategies employed for vehicular task offloading and related collaborative techniques. The literature is categorized into two main divisions based on the computational optimization strategies: Algorithm-based strategies and DRL-based strategies. The Algorithm-based strategies are further elaborated into three types: Game Theory, Mathematical optimization model, and Custom-tailored optimization algorithm. DRL-based strategies are further elaborated as: Single agent and Multi agent. Then the literature focusing on collaborative techniques to enhance task offloading performance such as caching, UAV, and SDN as well as security are elaborated. Finally, various open issues and challenges concerning future enhancements in performance are acknowledged and outlined. We consider that the comprehensive review of computational optimization strategies for vehicular task offloading, its associated techniques, and security can provide new cognizance for the development of advanced practical solutions for vehicular task offloading in the emerging era of the 6G network.

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48  Edge of Intelligence 70. He, Y., Zhai, D., Jiang, Y., Zhang, R., Relay selection for UAV-assisted urban vehicular ad hoc networks. IEEE Wireless Commun. Lett., 9, 1379–1383, 2020, doi: 10.1109/LWC.2020.2991037. 71. He, Y., Zhai, D., Zhang, R., Du, J., Aujla, G.S., Cao, H., A mobile edge computing framework for task offloading and resource allocation in UAV assisted VANETS. IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021, 2021, doi: 10.1109/ INFOCOMWKSHPS51825.2021.9484643. 72. Wang, J., Feng, D., Zhu, J., Huang, H., Vehicular computation offloading in UAV-enabled MEC systems, Institute of Electrical and Electronics Engineers Inc., pp. 1071–1076, 2022, doi: 10.1109/CSCWD54268.2022.9776115. 73. Poongodi, M., Bourouis, S., Ahmed, A.N., Vijayaragavan, M., Venkatesan, K.G., Alhakami, W., Hamdi, M., A novel secured multiaccess edge computing based VANET with neuro fuzzy systems based blockchain framework. Comput. Commun., 192, 48–56, 2022, doi: 10.1016/j.comcom.2022.05.014. 74. Xie, Y., Wu, F., Zhang, K., Leng, S., A DAG-based secure cooperative task offloading scheme in vehicular networks, vol. 2021-October, Institute of Electrical and Electronics Engineers Inc., pp. 870–875, 2021, doi: 10.1109/ ICCT52962.2021.9658033. 75. Wu, G., Li, J., Ning, Z., Wang, Y., Li, B., Federated learning enabled credit priority task processing for transportation big data. IEEE Trans. Intell. Transp. Syst., 25, 839–849, 2022, doi: 10.1109/TITS.2022.3210405. 76. Wei, D., Zhang, J., Shojafar, M., Kumari, S., Xi, N., Ma, J., Privacy-aware Multiagent deep reinforcement learning for task offloading in VANET. IEEE Trans. Intell. Transp. Syst., 24, 13108–13122, 2022, doi: 10.1109/ TITS.2022.3202196. 77. Deng, Y., Jiang, H., Cai, P., Wu, T., Zhou, P., Li, B., Lu, H., Wu, J., Chen, X., Wang, K., Resource provisioning for mitigating edge DDOS attacks in MECenabled SDVN. IEEE Internet Things J., 9, 24264–24280, 2022, doi: 10.1109/ JIOT.2022.3189975. 78. Mao, M., Hu, T., Zhao, W., Reliable task offloading mechanism based on trusted roadside unit service for internet of vehicles. Ad Hoc Netw., 139, 103045, 2023, URL: https://linkinghub.elsevier.com/retrieve/pii/S1570 870522002177, doi: 10.1016/j.adhoc.2022.103045. 79. Chen, C., Zeng, Y., Li, H., Liu, Y., Wan, S., A multi-hop task offloading decision model in MEC-enabled internet of vehicles. IEEE Internet Things J., 10, 3215–3230, 2022, doi: 10.1109/JIOT.2022.3143529. 80. lakhan, A., Mohammed, M.A., Garcia-Zapirain, B., Nedoma, J., Martinek, R., Tiwari, P., Kumar, N., Fully homomorphic enabled secure task offloading and scheduling system for transport applications. IEEE Trans. Veh. Technol., 71, 12140–12153, 2022, doi: 10.1109/TVT.2022.3190490. 81. Xu, X., Liu, W., Zhang, Y., Zhang, X., Dou, W., Qi, L., Bhuiyan, M.Z.A., Psdf: Privacy-aware IOV service deployment with federated learning in

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A Review on Vehicular Task Offloading  49 cloud-edge computing. ACM Trans. Intell. Syst. Technol., 13, 1–22, 2022, doi: 10.1145/3501810. 82. Wang, S., Li, J., Wu, G., Chen, H., Sun, S., Joint optimization of task offloading and resource allocation based on differential privacy in vehicular edge computing. IEEE Trans. Comput. Soc. Syst., 9, 109–119, 2022, doi: 10.1109/ TCSS.2021.3074949. 83. Li, H., Ota, K., Dong, M., Learning IoV in 6G: Intelligent Edge Computing for Internet of Vehicles in 6G Wireless Communications. IEEE Wireless Commun., 30, 96–101, 2023, doi: 10.1109/MWC.017.2200089. 84. Huang, Y.K., Pang, A.C., Wu, J.M., An edge intelligent framework for o-ran based IOV networks, vol. 2021-September, Institute of Electrical and Electronics Engineers Inc., 2021, doi: 10.1109/VTC2021Fall52928.2021.9625234. 85. Shen, H., Tian, Y., Wang, T., Bai, G., Slicing-based task offloading in spaceair-ground integrated vehicular networks. IEEE Trans. Mob. Comput., 23, 4009–4024, 2023, doi: 10.1109/TMC.2023.3283852. 86. Ray, P.P., A review on 6g for space-air-ground integrated network: Key enablers, open challenges, and future direction. J. King Saud Univ. - Comput. Inf. Sci., 34, 6949–6976, 2021, doi: 10.1016/j.jksuci.2021.08.014. 87. Ndikumana, A., Nguyen, K.K., Cheriet, M., Federated learning assisted deep q-learning for joint task offloading and fronthaul segment routing in open ran. IEEE Trans. Netw. Serv. Manage., 20, 3261–3273, 2023, doi: 10.1109/ TNSM.2023.3245544. 88. Yekanlou, A., Salameh, A.I., Cai, J., Buffer-state aware task offloading in edge networks with task splitting for iov, IEEE, pp. 13–18, 2023, URL: https://ieeexplore.ieee.org/document/10201817/, doi: 10.1109/BSC57 238.2023.10201817. 89. Deng, T., Chen, Y., Chen, G., Yang, M., Du, L., Task offloading based on edge collaboration in MEC-enabled iov networks. J. Commun. Networks, 25, 197–207, 2023, doi: 10.23919/jcn.2023.000004. 90. Sun, Y., Wu, Z., Meng, K., Zheng, Y., Vehicular task offloading and job scheduling method based on cloud-edge computing. IEEE Trans. Intell. Transp. Syst., 24, 1–12, 2023, URL: https://ieeexplore.ieee.org/document/10225421/, doi: 10.1109/TITS.2023.3300437. 91. Zhao, X., Liu, M., Li, M., Task offloading strategy and scheduling optimization for internet of vehicles based on deep reinforcement learning. Ad Hoc Netw., 147, 103193, 2023, doi: 10.1016/j.adhoc.2023.103193. 92. Sikarwar, H. and Das, D., Secedge: Secure edge-computing based hybrid approach for data collection and searching in iov. IEEE Trans. Netw. Serv. Manage., 21, 1213–1225, 2023, doi: 10.1109/TNSM.2023.

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2 A Study on EDGE AI Application in Crop Monitoring N.A. Natraj1*, Pethuru Raj2, M. Karpagam3 and S. Gunanandhini4 Symbiosis Institute of Digital and Telecom Management, Symbiosis International (Deemed University), Pune, India 2 Edge AI Division, Reliance Jio Platforms Ltd, Bangalore, India 3 Sri Krishna College of Engineering and Technology, Coimbatore, India 4 KPR Institute of Engineering and Technology, Coimbatore, India

1

Abstract

Artificial intelligence (AI) which is rapidly becoming the new normal of crop monitoring, is replacing the traditional techniques that were severely affected by poor data availability, response time; and time-lag that was imposed on them. The chapter lays the conceptual groundwork of the precision-agriculture practices and on how the artificial intelligence (AI) technologies can be incorporated for the crop monitoring mission. The discussion below highlights the importance of edge computing in AI and trends of distributed intelligence toward real-time decision making. The adoption and improvement of these techniques as for machine learning, computer vision, and sensor integration in AI and its ability to respond to live situations in this chapter is highlighted. It provides this as well by mentioning the artificial intelligence adoption in crop monitoring, which has an associated effect on increasing the efficiency in farming activities, identifying diseases, predicting yield, and optimizing resources by presenting technical real-life examples. Therefore, edge computing is presented as an option to smoothen the performance of this section, shortening the time lag. The next is the issues of data privacy, algorithm integrity, and scaling ability for diverse farming operations which relate to challenges and insights in terms of privacy, integrity, and scaling. The chapter ends by describing a holistic view of AI application for crop monitoring that falls from precision agriculture. This image about the future, suggests application of artificial intelligence into a food system, which should certainly change old-fashioned

*Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (51–72) © 2025 Scrivener Publishing LLC

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51

52  Edge of Intelligence agricultural models, since problem solving and technological platforms’ adopting is the key factor of development. Keywords:  Artificial intelligence, edge, crop, IoT, sensors, machine learning, monitoring, agriculture

2.1 Introduction The AI revolution that has been silently taking place in the fields across the world is due to the influence of AI. AI is one of the branches that underwent the depth of the agricultural industry. The world is currently facing various challenges in connection with increased population numbers, which are forecasted to top 9.7 billion by 2050. Traditional agricultural practices, though, struggle to keep up with the dynamics. Accuracy accrues when precision agriculture applies AI to improve each and every practice in farming, starting from planting to harvesting. AI give heart for crop monitoring is core which revolution change. Nowadays conventional solutions, which partly use empirical observation and intuitive perception, seldom meet the requirements of plants, creating a deficiency and a drop in yield. Its AI could not allow for more precise diagnosis in the field. Imagine machines having cameras and being called drones flying over agricultural fields and scanning the fields and their hidden stresses such as crop nutrient deficiencies or pest infestations. Sophisticated sensors, positioned within the soil, read code about the levels of humidity and the periodical fluctuations in temperature [1]. Use our artificial intelligence to write for you by simply inputting the subject and chose the model type! These data sets are fed directly into AI just in time to develop algorithms, and can support the data analysis process in real time which are guides the decision making process. Instead of propagating destruction, raising harvests, and reducing wastes, farmers have again the power to optimize irrigation, and, if needed, apply pest control in a targeted manner. When it comes to preventing pests, the farmer may be one step ahead and in position to foresee threats. Yet, such supremacy of AI is not just based on the centralized data center. We exit current infrastructure into AI’s boundaries where the data are processed almost immediately after extraction. In such case the intelligent irrigation system continuously monitors the soil moisture and adjusts the water flow to the requirements of each plant during the process. Visualize robots flying through fields, equipped with artificial vision systems that can identify and eliminate weeds using laser accuracy.

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EDGE AI Application in Crop Monitoring  53 The shift towards precision agriculture requires the adoption of new monitoring methods. Visual inspections offer limited information, often failing to detect hidden problems until they are too late. Practical guidance for individual areas is limited by weather forecasts that are often broad and imprecise. Figure 2.1 shows the overview of AI applications in crop monitoring. The potential of precision agriculture is much broader than the increased yields. Imagine that the impact on the environment will be minimal. AI-powered irrigation saves water and customized pest control decreases chemical use. Consider increased resilience. AI predictions guide precautionary actions against severe weather occurrences, crop protection, and loss mitigation [2]. However, major challenges must be overcome to achieve this future. To reduce the digital divide, enabling farmers, of all sizes and from all places, especially lower income classes, to access 21st technological innovations, it will be required. Ethical issues wars where data protection and occupational displacement are changing in this way have to be managed with accuracy and accountability on the development side. The construction confidence and the supporting team work among the developer of technologies, farmers and policy makers is vital. The road to AI-based agriculture may not be all smooth, but to secure a future with more produce and food security we can make it available. We can reach this goal by producing a world where AI determines the life of the farmers, feed those in need, and preserves the environment. For the time being, though the roots of this change are taken into the fields by people around the world, and it promises a great ‘crop’ of diversity and general satisfaction to share.

Disease Diagnosis

Crop Yield and Price Forecast

Intelligent Weed Detection

AI in Crop Monitoring

Predictive Insights

Figure 2.1  AI applications in crop monitoring.

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Crop and Soil Monitoring

Agriculture Robots

54  Edge of Intelligence

2.1.1 The Dynamic Evolution of Precision Agriculture As traditional farming methods continue to deteriorate, precision agriculture provides a glimmer of hope. In the past, people were able to survive, but now they are confronted with issues such as waste, excessive use of resources, and damage to the environment. In order to maximize the utilization of resources, increase productivity, and safeguard the environment, precision agriculture makes use of data. The goal of precision agriculture is to personalize and take into account every facet of farming, from the selection of seeds to the harvesting of crops. The time of farming for everyone is coming to an end. When farming, special attention will be paid to each individual plant, plot of soil, and microenvironment that is present on a farm. Data is essential to precision agriculture because it reveals previously hidden insights. The agricultural process becomes more individualized and productive.

2.1.2 The Impact of AI on Crop Monitoring A lot of what precision agriculture does depends on artificial intelligence. When given complicated algorithms, AI can help farmers think through data and make choices, much like a digital shepherd. The first step is to turn huge amounts of agricultural data into intelligence that can be used to help people make smart decisions. These satellite pictures give AI a strong ally because they give a bird’s-eye view of fields and show changes in plant health that people have missed. Sensors measure how much water and nutrients are in the soil and report them. Drones in the air take high-­ resolution pictures of the patterns that diseases and pests look like before they can spread. The AI can’t get enough data, but this huge amount of data can help it find insights that help farmers solve problems more accurately than ever before. New technologies like machine learning isn’t just good at adding numbers but it’s also good at using different sets of data to tell stories that make sense. It finds connections, predicts trends, and shows how the farming world is always changing by using complex algorithms. It is possible for artificial intelligence to tailor actions like watering, fertilizing, and getting rid of pests to each plant and acre. Usually used methods can’t be used for this level of accuracy.

2.1.3 AI’s “Edge” and Its Importance in Agriculture AI “Edge” revolutionizes AI in agriculture. Instead of sending data to a server for analysis, “decentralization”, which brings intelligence to

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EDGE AI Application in Crop Monitoring  55 the farm, is replacing it. This movement’s outskirts location is important for farming. The Edge reduces centralized processing latency for real-time decision-making. At the Edge, artificial intelligence processes and interprets field data quickly, allowing for real-time adjustments. As a result of this proximity to data sources, a new era of responsiveness and adaptability has begun, both of which are critical in unpredictable farming operations. a.  Real-Time Decision-Making and Decentralized Intelligence: A Dilemma Artificial intelligence (AI) in agriculture, decisions made in a split second can have a big effect on the health and yield of crops. With decentralized intelligence, artificial intelligence algorithms are built into the farm, so there is no need for a data center. As a result of this change, a farm could become an intelligent, self-sufficient ecosystem that can adapt to changes in the soil, weather, and pests. Think about the chance that the weather will change unexpectedly and the irrigation schedule will need to be changed right away. AI at the edge makes the change in real time to make sure that crops get enough water at the exact moment they need it. Traditional models can’t be as responsive because of the delays that happen when data is sent. The Edge is more than just the playing fields. It comes with every piece of farming equipment and sensor. Each patch of land can be tilled in a different way by the smart tractors that are equipped with edge AI. Decentralized intelligence changes how decisions are made in agriculture, which makes farming more accurate and flexible. b.  Challenges in Traditional Crop Monitoring: A Need for Advanced Technologies It was challenging to monitor crops before artificial intelligence and precision agriculture. Farmers had limited access to often outdated information because they relied on manual observation and routine assessments. This method costs more and its outcome remains unsatisfied. An important problem was the deficiency of current information. Due to the inability of outdated techniques to give farmers access to real-time information about soil health, pests, and weather, farmers were forced to deal with unexpected consequences. Information was delayed, causing fields to be overwatered by antiquated irrigation schedules and diseases to spread rapidly before being detected. The issue of how universal the previous approaches were also existed. A one-size-fits-all strategy wastes resources like pesticides, fertilizer, and water, which is harmful to the environment. It also lessens the usefulness of the resources. Precision agriculture emerged as a result of these methods’ inability to cope with the growing population.

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56  Edge of Intelligence c.  The Necessity for Advanced Technologies The inadequacy of traditional methods for monitoring crops highlights the pressing need for new technologies. Leading the way in this technological advancement is artificial intelligence. Artificial Intelligence ushers in an era of precision and efficacy by addressing issues with traditional methods. It accomplishes this by analyzing data, identifying trends, and self-learning. Not only do we need new technologies to solve problems immediately, but we also need them to improve system reliability going forward. Extreme weather events are becoming more frequent as a result of climate change, increasing their likelihood. Agricultural systems are therefore more prone to fail. AI-driven advanced technologies give us the agility and speed to handle these unknowns. AI-powered precision agriculture represents a level of sophistication previously only found in science fiction. Crop monitoring and management have become much simpler in farming thanks to advances in satellite imagery, sensor networks, and drone technology. Humans are limited in their ability to observe, but these technologies provide the precision and depth required to address novel issues [3]. Edge computing plays a central role in the advancement of AI in traceability of crop produce as it places processing power and analysis closer to data sources. This enables decisions to be made on only present situations which makes farming a more efficient system. Edge computing gives advantages to crop monitoring systems. While data latency reduction keeps the decisions to real time, it makes the farming operations to be more flexible and response, it allows the processing information locally as well as having fewer resource utilizations, it lets farmers be more creative and autonomous in their methods and it makes data more safe guardable through the processing information locally [4]. The benefits listed prove once more the value of combining edge computing with AI in precision agriculture and how it could make the growth of crops new in how they are monitored and the results of farming from the results in improved. Showing briefly about how AI is affecting society and how the methodology of smart agriculture is changing the way food is grown globally. Traditional ways of keeping an eye on crops have problems that make precision agriculture even more important. Together, AI and precision agriculture could make things more productive and efficient while also making the future more stable and long-lasting [6]. With the “Edge” concept, AI has come a long way. It now allows for decentralized intelligence and decision-making in real time. This is more than just a new piece of technology that makes it easier for agriculture to respond quickly to the constantly changing needs of the sector. As we learn more about AI and precision farming, it becomes

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EDGE AI Application in Crop Monitoring  57 clear that big steps forward in technology are needed to fix the problems with the way we currently monitor crops. AI based advanced technologies have responsibilities to solve more agricultural problems in the future. In this book chapter, the section 2.2 explains about the fundamentals of AI in crop monitoring. The section 2.3 explains about the various application of AI in Crop Monitoring and further section explains about the challenges and future scope of AI in crop monitoring. Section 2.5 concludes the chapter by summarizing the contents AI applications in crop monitoring.

2.2 Crop Monitoring AI Basics AI ideas pave the way for a new farming-technology relationship. To fully understand how artificial intelligence is used in crop monitoring, you must understand the fundamentals of this dynamic partnership. Sensor integration, computer vision, and machine learning algorithms can revolutionize farming.

2.2.1 Using Intelligent Patterns for Maximum Benefit: A Machine Learning Algorithm Study All of us may assume that these ML algorithms learn by themselves without any instructions. In case of agriculture, using this algorithms require large amount of data set for the purpose of identifying patterns, making predictions and also for making choices based on them. Machine learning’s a common aspect is learning [5]. Here the algorithms are trained based on the available labeled datasets. For crop monitoring applications, the label may be outcomes, through which we can predict the health conditions of the crops. This labeled data helps in predicting the outcome and algorithm completely learns from it. The unsupervised learning of ML, looks at the datasets. It never knows what the results should be. This is useful for tasks like grouping plants or locating objects in cultivated fields. Another method, reinforcement learning in which the algorithms interacts with the surroundings repeatedly in order to make decisions and learn over time. These algorithms make it easier to monitor the crops. The changes in the health of the plants shall be helpful in estimating the field productivity and possible threats. Based on the predictions, farmers can make choices about the crops.

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58  Edge of Intelligence Computer Vision in Agriculture

Crop/Farm Monitoring

Yield Analysis

Automatic Pesticide Spraying

Animal Welfare Laws Compliance

Livestock Farming

Figure 2.2  Computer vision applications in agriculture.

2.2.2 Computer Vision in Precision Agriculture The use of computer vision enables for crop monitoring since it can identify even the most minute details regarding plant health and field conditions. This technology allows computers to do visual analysis in the same manner as the human eye. In order to improve the analysis of satellite imagery, computer vision techniques are used. These algorithms examine the color, texture, and orientation of the crop to determine patterns that cannot be observed by humans. The use of computer vision, which could identify signs of distress and assess the influence of adverse climatic conditions on agricultural production, would allow farmers to gain a better understanding of agriculture. The installation of cameras and sensors on agricultural machines can improve computer vision in the field. Using technology based on vision, intelligent tractors can track crops, shrubs, and soil at any time. As accuracy improves the effectiveness of farming and decreases the amount of pesticide needed, agriculture is becoming more eco-friendly. Figure 2.2 shows the overview of computer vision applications in agriculture.

2.2.3 Sensor Integration: Precision Agriculture’s Nervous System Sensor integration is the heart of precision agriculture, like the nervous system transmits information. Intelligent sensors in soil, unmanned aerial vehicles, and agricultural equipment collect vital data, giving artificial intelligence systems real-time feedback. Sensors measure soil temperature, nutrient concentrations, and moisture. This gives us many valuable insights into crop and soil health. Drones with sensors can capture high-resolution images to detect crop diseases and pest infestations early and prevent their spread. Weather sensors help farmers prepare for changes by providing

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EDGE AI Application in Crop Monitoring  59 meteorological data. Using multiple sensors together generates a lot of data that shows the agricultural ecosystem. Multidimensional data improves AI models and allows crop monitoring by considering the interplay between plant growth factors.

2.2.4 Importance of Data: Fuelling AI The intelligence of artificial intelligence is fuelled by data. The monitoring of crops is heavily dependent on data. It is dependent on the soil composition, weather patterns, crop types, and historical farming practices for artificial intelligence models to be successful. The value of data is dependent on both quantity and variety. Through the process of being exposed to a wide variety of scenarios within a diverse dataset, AI models are able to adapt to a variety of agricultural conditions. In order to better adapt their forecasts to shifting conditions, models can benefit from using data from previous events. Because farming is always changing, data has to be collected in real time to properly show how things are in the field. With artificial intelligence models, systems can adapt and learn from real-time data, which lets them respond effectively to changes in the weather, bug populations, or soil conditions. To get accurate and important information about crop tracking, you need to use a lot of up-to-date data sources and inputs.

2.2.5 Real-Time Intelligence for Edge Learning and Adaptation As we learn more about precise gardening, AI’s “Edge” becomes more important. For AI processing to happen in fixed data hubs, it was necessary to look at data over long distances. The Edge is different from the controlled method because it uses AI to collect data directly in the field. Edge AI models use real-time data to help people make decisions that are flexible and adaptable to the present situation. This spread method cuts down on delay, which lets AI systems quickly adapt to changes in their environment. If the temperature drops quickly, which could mean frost, Artificial Intelligence (AI) that is deployed at the Edge can successfully protect crops and make sure they stay alive. AI’s Edge will react to the changing conditions, which is necessary in Agriculture. The distributed intelligence while making decisions, it should consider the individual characteristics of a plant and area of the soil. It is more similar to an AI tool, which is capable of learning from the instances and keeps on improving in continuous manner. Utilizing computer vision, sensor integration, and machine learning approaches for crop monitoring and use of existing data are the important

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60  Edge of Intelligence developments in the agriculture industry [5]. The use of above mentioned advanced technological approaches for crop monitoring represent major advances in the agricultural sector. To increase agricultural production, computer vision has the ability to identify minute patterns and fit several pieces of equipment together. This will allow for the real time collection of data.

2.3 AI Applications in Crop Monitoring AI in crop monitoring is not only a technological improvement but an innovative development that exceeds established boundaries of modern agriculture. This section gives an insight into the complex applications of AI [7]. It reveals a large network of precision farms, disease detection, crop prediction, and resource optimization. In the present article, specific case studies and real world examples are presented to analyze the tangible impact of artificial intelligence on agricultural output, sustainability, and resource efficiency. To improve efficiency of these applications, we are also looking into edge computing. It is the best way to reduce latency and give us an immediate response in real time scenarios.

2.3.1 Precision Farming: Nurturing Each Plant with Precision Precision farming, the most advanced use of AI in agriculture inspection, is a revolutionary shift. It will enable us to grow all types of plant with a high degree of precision. The concept of flexibility is the essence of this principle. This allows the adjustment of farming practices to meet the specific needs of individual plants. That way, individual plants can adapt farming practices to their specific needs. Precision agriculture uses algorithms for machine learning to analyze large databases. This shall also include information on past crop performance, weather patterns and soil composition. These algorithms provide expert advice on a wide range of agricultural processes. Experience from the world show that precision farming can have a major impact. In order to optimize crop density, a United States soybean farm has been using precision farming technology that uses Artificial Intelligence. Significant resource efficiency gains as well as crop yields have been achieved. This was made possible by applying the AI recommendation concerning precise sowing patterns.

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EDGE AI Application in Crop Monitoring  61

2.3.2 Disease Detection: An Early Warning System for Crop Health In order to safeguard crop health and ensure a good harvest, it is important that infections be detected as soon as possible. AI’s ability to analyze vast data and identify minute patterns has made it an excellent ally in the diagnosis of illness [8]. It provides farmers with an alert system that is superior to human capabilities. To view high resolution images taken by drones or camera set in the field, computer vision is used as an essential component of AI. In these images, subtle changes to the color, texture and structure of plants that can show disease based on the visible symptoms. The potential danger can be accurately identified with the trained machine learning algorithms. It can be based on the datasets, e.g. photos of healthy and diseased crops. The effectiveness of AI in detecting illness has been demonstrated by a case study from India. There were several cases of bacterial leaf blight on the rice plantation. Farmers using drones equipped with AI sensors that analyze images would have been able to identify diseased plants at an early stage. Targeted therapy will allow for an effective reduction in spread of the disease. This led not only to a decrease in fruit waste, but also the more environmentally responsible use of pesticides.

2.3.3 Yield Prediction: Anticipating Harvests with Precision A new and interesting string is coming through the fabric of modern agriculture: using artificial intelligence (AI) to predict crop yields. This game-changing technology promises to replace the old story of doubt and guessing with one of insight and accuracy. By using a lot of data and strong formulas, AI helps farmers predict the size of the harvest with a level of accuracy that has never been seen before [9]. This leads to better use of resources, better decisions, and eventually, more sustainable farming. Figuring out how all the different things that affect yield work together is what this change is all about. Satellite images show trends in the health and growth of crops, weather data whispers secrets about temperature and rainfall, and soil sensors reveal the secrets of what nutrients are in the soil and how much water is there. The huge, changing information is then fed into machine learning models that have been taught to find secret connections and figure out the complicated dance between input and output. There are many perks to being able to accurately predict yields. Farmers can make the best use of their gathering plans to avoid losses caused by cutting too early or too late. Fertilizers and water are sent to places where they will produce the most benefit, making resource allocation very precise. Farmers

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62  Edge of Intelligence who know how to use the market well can even discuss price plans based on expected yields to get the best terms for their crops. Also, AI-powered crop forecast encourages farming methods that are good for the environment. Farmers can use less water and manure and have less of an impact on the earth if they know all the complicated factors that affect yield size. By using data to guide decisions, this method also helps create adaptable crops that can do well even when the weather changes or is uncertain. There are some bad things about this technology paradise, though. Access to and collection of data are still big problems, especially for small farms. The price of putting AI solutions into action can also be a problem that stops more people from using them. Also, because farming systems are naturally complicated, estimates aren’t always correct, and unplanned events can mess up even the most advanced models. Even with these problems, AI-powered yield forecast has a bright future ahead of it. The difference between what was predicted and what actually happened will get smaller as study keeps improving algorithms and making data easier to get. This will not only give farmers more power, but it will also make the whole farming environment stronger. This will make the business as a whole more adaptable, healthy, and productive in the future. Basically, AI will change from being a prophet that whispers wisdom to a trusted farming partner that actively shapes the soil for future crops.

2.3.4 Resource Optimization: Balancing Efficiency and Sustainability Sustainable agriculture needs a careful balance between farming output and environmental safety. To keep things in balance, resources need to be managed, which AI can do. AI is utilized for more than plant care. It also watches crops, changing farm resource management. The most important thing about this growth is that AI can turn complicated facts into useful ideas. Machine learning systems blend data from earth monitors, weather forecasts, and resource usage. To effectively handle water, fertilizers, and herbicides, AI uses this complicated network of data to create unique resource distribution strategies. AI considers food needs and natural changes to help farmers save resources. This saves money and decreases farming environmental effect. AI-powered watering algorithms utilized land, weather, and previous water usage data to reduce water waste by 20% at a Spanish farm while keeping grape quality. AI management of resources touches more than just farms. Sustainable water control by AI may lower demand on this precious

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EDGE AI Application in Crop Monitoring  63 resource. This will protect farmland’s sensitive climate. Less chemical use allows green gardening that helps soil health and draws animals. Before AI can be engaged in healthy agriculture, certain problems must be addressed. Some farmers have trouble getting to and understanding their data, and it could be pricey to get started with AI technology. AI models must be changed often to adjust for changing farming ways. Even with these problems, AI-powered resource productivity could be good for farms in the future. The gap between predictions and reality will close as more data becomes available and AI programs get better. This will give farmers more power and make the food system more flexible, healthy, and fair. AI could change farming into a world where crops are healthy and the environment is safe.

2.3.5 Edge Computing: Enhancing Efficiency in the Field Precision agriculture based on data is changing farming by increasing food output and lowering damage to the environment. Edge computing brings processing power and AI analysis closer to the data sources, which makes this change possible. Compared to regular cloud computing, the autonomous method has several advantages. One major advantage is Edge computing processes data nearby, which cuts down on delay and lets decisions be made in real time. Quick action may improve crop health and output for things like watering, getting rid of pests, and diagnosing diseases. The Edge computing lets AI models be taught and run on local data, which is more useful in farm settings. This makes AI models work better. Apps that use AI might get smarter and work better. Another advantage is Edge computing can be used in places without internet, which makes it great for farms that are far away from other people. This makes operations more reliable and keeps important farming tasks going even when the internet is down. Edge computing has a lot of benefits for farming. Smart tractors with edge devices can use data from soil sensors to change the amount of manure and watering in real time. This makes better use of resources and reduces waste. Drones with edge-powered AI models could check fields on their own for pests and diseases so that problems can be found quickly and the right solutions can be used. Edge computing is used in a farm in Spain [11]. Devices on the edges of the field were used to check the temperature, wetness, and freshness of the grapes. The data is looked at by an on-site AI program, which suggests that the plants should be watered and fertilized. So, the farm has saved 20% of the water it used and kept the quality of the grapes. Figure 2.3 shows the overview of Edge computing in precision agriculture.

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64  Edge of Intelligence

Real Time Access

Edge Gateway

Response

Request

Response

Cloud Services

Request

Smart Devices

User and Farmer

Figure 2.3  Edge computing in precision agriculture [13].

Edge computing could be useful for farming. New uses will come up as AI models get better and edge devices get cheaper and smarter. These uses could make farming more effective, long-lasting, and strong, which would ensure food security and plenty. Issues need to be fixed. Farmers might not be able to afford to set up cutting edge computer tools. It is very important to protect privacy and info. Edge computing could change the farm system, even with these problems. Edge computers could help farmers make better decisions, use resources more wisely, and speed up processes, which could make farming more sustainable [13]. The use of AI to track crops is a bright spot for farming in the future. AI is making farming more sustainable and flexible by helping with things like precision farming, early disease detection, accurate crop prediction, and resource optimization. These changes are also raising farmer pay and returns. Good ideas and real life will be more similar thanks to AI programs and easy data access. It’s the beginning of a new era of gardening that is more efficient and cares for the earth. In the future, AI will be used to track crops, and the seeds we plant now will produce a big crop for hundreds of years.

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EDGE AI Application in Crop Monitoring  65

2.4 Challenges and Possible Future Paths of AI in Crop Monitoring AI crop monitoring has great capacity to revolutionize us, but it source system demands intricate agricultural systems to start functioning. The AI models must be redesigned to be able to obtain and blend data from differing information sources since the context, crops and agricultural practices vary. The absence of data, difficulties of data quality improvement, and the real-time processing are among the many obstacles here. Social thoughts are important such as data privacy, management, and economic and social implications also should be given attention. AI field tracking future of the field is not shiny, though the challenges exist, yet. IBC will fine-tune sensing to support IoT, and have the best and advanced machine learning algorithms which will support farmers in decision making. Socioeconomic status, learning systems which continuously update their algorithms, and multimedia information fusion are among the areas that need to be considered for AI model development and acceptance. From innovative projects to contributing to the entirety of issues, people must join together to bridge any sectors, social characteristics, and technological developments. At the end of the day, precision farming will be playing the main role, and an indispensable part, in carefully and cleanly harvest food products if AI is making early diagnosis in monitoring the digestion.

2.4.1 Challenges and Limitations It’s obviously a great fact of robotics in agriculture. Mechanical robots work in the crops fields while drones are flying though the plants looking for pests. These are the scenes that bring the image of the world flooded with super abundant harvests and perfect in appearance landscapes into our minds. However, this clean and shiny image is more deceptive than it may seem since there are many hurdles and issues that can limit this new technology from reaching its maximum potential [10, 19]. a.  Concerns about data privacy: One of the most burdensome issues, data protection, is brought to light by this picturization. Till today unauthorized access and misuse grow faster more often than the industry creates standards to protect the collected data from farms about soil, crop yields etc. It is no wonder why farmers are quite security conscious about banking data, land use trend information, and field productivity data being in hands of the wrong people [11]. Comprehensive data control systems, trunk data

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66  Edge of Intelligence exchange protocols and reliable data storage infrastructure must be established so as to generate the required level of trust in artificial intelligence solutions. A farmer, in his reasonable cautiousness, would never agree to use the things that would let him run his farm better without the need to look after him. b.  Strong algorithms and easy-to-understand explanations: The matter of fact is that the conventional AI models stand no chance against the modern deep learning networks. However, these mechanisms, are what tells us things that we would like to know, but we do not know how they are all put together [12]. The farmers may be afraid of this opaqueness because the information they lack to answer the why the AI machine learning models suggest is a dire predicament. Thus, farmers make poor choices to solve this problem. Explainable AI models capable of clearly explaining their results are needed very much by the developers of such systems in order to be able to interpret the way the models treat certain data. Moreover, it is essential to provide algorithms that work optimally in different structures of farm animals and weather conditions through which trust is enhanced and wider acceptance is achieved. c.  Integration with current methods: Integrating AI into existing farming practices often feels like trying to fit a sleek, modern sports car into a dusty barn. Farmers, especially those with insufficient resources and technical knowledge, may find themselves overwhelmed by operating changes, equipment upgrades or a rigorous learning curve. The digital divide could leave countless farms stranded on the edge of an artificial intelligence highway, watching future time travel in a blur of numbers and figures. Efforts must be made to develop accessible, flexible AI tools that are seamlessly integrated with conventional farming techniques in order to bridge this gap. Imagine a rugged all-terrain vehicle, designed in collaboration with the farmers themselves, navigating the bumpy roads of traditional farming. d.  Cost and availability: AI and Hi-Tech options for farms may turn out to be very expensive for smaller farms, which, of course, makes their struggle to benefit even harder. To make sure that artificial intelligence (AI) is not just for high-end professional alone, there is a need to build affordable and adjustable tools that will respond adequately to the different needs of low-­ income exploitation. In such a world where every farmer enjoys the privilege of knowledge and have ample opportunities, let’s contemplate about how smartphones can become the ideal instrument to optimize the passing of a farmer over huge expanses of land [16]. Online disparities among rural and

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EDGE AI Application in Crop Monitoring  67 urban spots are also a severe problem. Uneven digital and technical access is the key barrier to worsen inequality and to take away a chance of benefiting from AI-applied agriculture for many rural people. Vision portraits the app clinics operating on mobile platforms in remote places, which will carry the digital specialists to go alongside with the medical personnel. e.  Ethical concerns: In the view to mitigate possible problems as job displacement, inappropriate price management or environment pollution, impartial algorithms, safe data managing and responsible development are the key. In the situation, where AI optimization requires implementing on the rural community which eventually leads to the mass job losses, there is greater accumulation of the damage of economy & society. In the process, human-centeredness, environmental sustainability and ethical data statistics has to be placed as key central points when it comes to AI in agriculture [17]. Imagine a place where AI is no longer the god of the field, but the loving laborer, enabling farmers to see the light in the dark future. The problems of AI in farming industry is describes in this passage. Despite the unmatched achievements and possibilities, the ethical and logistical obstacles should be resolved with a wise and careful approach. By means of AI it is possible for us to build a suitable system for agriculture where data are protected, the transparency is high, inclusivity is approached or ethics should be dealt with.

2.4.2 Future Scope of AI in Crop Monitoring The function and significance of agriculture are not unfamiliar to any of us. It enabled the establishment of the first settlements and the transition from a nomadic to a sedentary way of life, which led to the present state of our civilization, in addition to providing sustenance for all. Despite the considerable advancements and technological developments in the IoT that have occurred in recent years, there remains considerable scope for further refinement. In conjunction with climate change, population growth, a scarcity of agricultural laborers, food security concerns, the extensive adoption of Artificial Intelligence has compelled the agro-sector to seek out even more inventive methods to boost yields [14]. Robots may play an essential part in control, but it is to be anticipated that machines will progressively aid people in analysis and planning, so that the cycle becomes nearly autonomous. According to a Markets and Markets Report published in 2019, the agricultural AI industry is expected to grow from $519 million in 2019 to $2.6 billion in 2025.

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68  Edge of Intelligence A critical juncture has been reached in the emerging field of artificial intelligence based crop surveillance, filled with huge possibilities yet facing significant challenges. To navigate this path towards revolutionizing agriculture requires a forward looking vision, one that embraces the new directions while wrestling with practical and ethical challenges. This future is intimately connected to the improvement and enhancements in data collection and interaction. Imagine a sensor-smart network comprising many high-tech wires and devices constantly monitoring the dampness in the soil, the nutrient level, the pests and the disease, and plant health. AI will operate as another celestial mapper that will gather all the data from satellites, flying drones and all the other types of intelligence devices. These huge amounts of data will be analyzed and more accurate predictions will be made. Consider a super AI, which is artificial intelligence, but it is endowed with complex algorithms to exactly determine crop yields, anticipate pests infestation, and let the world know of adverse weather before the damage is done. Some farmers as, having the highest confidence ever, will be able to make some critical decisions like their right timing for planting seeds, the amount of water to use, and how to manage the pests on their farms, in order to enhance productivity and resilience. Rather farmers will have the opportunity of making planting decisions, water as well as controlling pest infestation with a lot more confidence than they normally do, and consequently will have the enhanced efficiency and resilience. Alongside the forecasts, following you through a whisper of the personalized recommendations [18]. Think on a humanoid artificial mentor who is always aware of each field individual tapestry based on its soil type, climate, and crop variety, and is able to tailor advice for the field owner specifically. Fertilization and irrigation practices, harvesting like veteran farmers with tools and advice based on precise information, can be tailored to maximum yield. The promise of automation, which promises to free farmers from time consuming tasks, is also attracting attention. Imagine robotic scouts flying through fields, their digital eyes scanning for weeds and potential pest infestations with unprecedented accuracy. This automated monitoring will allow farmers to concentrate on a broader strategic maneuver, while at the same time providing for prompt interventions that ensure yield protection and reduce reliance on toxic chemicals. In this evolving story, sustainability whispers a powerful message. Imagine AI ecologists who design strategies for the convergence of agricultural productivity with environmental sustainability. AI can be a powerful tool to steer agriculture toward a greener future through the optimization of irrigation water use,

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EDGE AI Application in Crop Monitoring  69 minimizing fertilizer and pesticide applications as well as anticipating climatic change impacts. However, the trouble is with thorns of this transformation process. What awaits as a result is for leveling-up the digital divide between AI-based field and deep-learning data centers that bring out AI models. Nevertheless, the quest for that robust data set shouldn’t be forgotten, and, therefore, there’s a need for investing in infrastructure. Additionally, continuous learning as well as adaptability to different environmental and weather conditions will be required to ensure that the solutions generated by AI algorithms are optimized for accuracy and reliability in diverse operations. One of the notable barriers is the access to the artificial intelligence for all farmers, especially the farmers who belong to the group of people assisted by financial systems. One of the best opportunities which are noticed for developers of the technical products, agronomists and farmers is the production of user friendly tools and the affordable technologies solutions which are compatible with current farming systems with collaborative endeavors. Ethics is also important. In order to prevent social and undesirable impacts, issues of data privacy, algorithm bias, and fair access to AI have to be addressed. The strategy of human-centredness can be considered the aspect that; it prioritizes openness, ethical data practices and responsible development with the aim of ensuring that AI not only benefits farmers but also has an inclusive effect on the community. The application of artificial intelligence in agricultural monitoring brings tremendous accompanied with some perils. By using the data sensibly, making successful forecasts, taking away repetitive and complex tasks, and, of course, keeping the environment in mind; the Artificial intelligence can help to get the farmers to get their crops in the abundance while protecting the environment. Teamwork and awareness are point of concern to deal with data accessibility, ethical issues, and algorithm bias. The obstacles these new innovations constitute should be weighed in terms of man, economics, and the environment to understand how AI may positively change agriculture and establish a fair and better future in the world. The future of crop monitoring is peeping toward a revolutionary period; with the strategic tie-up quite a number of technologies are set to make the mark without which it will be quite impossible. Artificial brains could treat real-time earth monitors info (IoT) and blockchain would provide it’s true and left to blockchain as to assure all international data safety and transparent sharing. Thanks to edge computing processing, hyperlocal intelligence could now imitate the most beneficial microclimate for the benefit of crops, working out every day and sending the exact commands to each field. Advanced technologies such as AI, IoT, Blockchain and etc.

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70  Edge of Intelligence can maximize production, efficiency and eventually the sustainability of agriculture [15] Preserving these Technological changes from taking over the farms of humans who feed our world remains a research priority where data security, inclusion and support of human labor should be ensured [20]. The farmers of tomorrow are sowing more than just seeds of growth that are isolated innovations but rather, the strength created through the connection of powerful tools that are a product of a collective spirit of shared prosperity and positive environmental preservation.

2.5 Conclusion AI driven crop monitoring is doing a disservice to the farmers by empowering data and algorithms which are propelling and profiting the world food production growth. This chapter sets out the reformatory impetus of AI for precise monitoring of crops - what lies behind it and how it is addressed at the edges of agriculture in particular. The necessity of precision agriculture and AI growing on it gives AI a strong position in the domain of agricultural surveillance. The edge concept has been added to the artificial intelligence and has highlighted the transition toward a decentralized real-time decision making which is particularly significant for the agriculture that runs the course by day Lastly, the next step involved describing the vital AI characteristics about the crop monitoring of ML algorithms, Computer vision and sensor vision. Data has become a central factor with the need to rely on vast and varied sets of information to ensure that the input data is not only true but out of it the valuable insights can be generated. The capacity of the AI models to react to change and novelty present in edge is addressed here. It permits the development of dynamic decision-making in which the approach will evolutionarily emerge from existing traditional methods. Going a step further, the article continued to stress on the practical application of AI in agricultural monitoring. It exhibits an importance of Internet of things in increased agricultural productivity, epidemiology disease detection, yield prediction, and resources management. Following section, we discussed the framework of AI in crop monitoring and the barriers and the possible solution. Issues on data privacy, digital divide and combination between it and present traditional systems were the factors identified in the beginning. Next, a part that is noteworthy about the AI‘s capabilities for crop monitoring was uncovered. As a result, this chapter delivers the detailed description of the transition from the expansion to applications, threats and the whole potentiality of the AI on plant monitoring.

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EDGE AI Application in Crop Monitoring  71

References 1. Javaid, M., Haleem, A., Khan, I.H., Suman, R., Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Adv. Agrochem, 2, 1, 15–30, 2023. 2. Balaska, V., Adamidou, Z., Vryzas, Z., Gasteratos, A., Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 11, 8, 774, 2023. 3. Haq, S.I.U., Tahir, M.N., Lan, Y., Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI). Appl. Sci., 13, 15, 8840, 2023. 4. Kowalska, A. and Ashraf, H., Advances in deep learning algorithms for agricultural monitoring and management. Appl. Res. Artif. Intell. Cloud Comput., 6, 1, 68–88, 2023. 5. Mishra, H. and Mishra, D., Artificial Intelligence and Machine Learning in Agriculture: Transforming Farming Systems. Res. Trends Agric. Sci., 1, 1–16, 2023. 6. Kutyauripo, I., Rushambwa, M., Chiwazi, L., Artificial intelligence applications in the agrifood sectors. J. Agric. Food Res., 11, 100502, 2023. 7. Singh, B., Dhinakaran, D.P., Vijai, C., Shajahan, U.S., Arun, R., Lakshmi, M.R., Artificial Intelligence in Agriculture. J. Surv. Fish. Sci., 10, 3S, 6601– 6611, 2023. 8. Salman, Z., Muhammad, A., Piran, M.J., Han, D., Crop-saving with AI: latest trends in deep learning techniques for plant pathology. Front. Plant Sci., 14, 1224709, 2023. 9. Mukherjee, A., Panja, A.K., Dey, N., Crespo, R.G., An intelligent edge enabled 6G-flying ad-hoc network ecosystem for precision agriculture. Expert Syst., 40, 4, e13090, 2023. 10. Presti, D.L., Di Tocco, J., Massaroni, C., Cimini, S., De Gara, L., Singh, S., Raucci, A., Manganiello, G., Woo, S.L., Schena, E., Cinti, S., Current understanding, challenges and perspective on portable systems applied to plant monitoring and precision agriculture. Biosens. Bioelectron., 222, 115005, 2023. 11. Gabriel, A. and Gandorfer, M., Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region. Precis. Agric., 24, 1, 68–91, 2023. 12. Du, X., Wang, X., Hatzenbuehler, P., Digital technology in agriculture: a review of issues, applications and methodologies. China Agric. Econ. Rev., 15, 1, 95–108, 2023. 13. Prasad, C.G.V.N., Mallareddy, A., Pounambal, M., Velayutham, V., Edge Computing and Blockchain in Smart Agriculture Systems. Int. J. Recent Innov. Trends Comput. Commun., 10, 1s, 265–273, 2022. 14. Sharma, S., Verma, K., Hardaha, P., Implementation of artificial intelligence in agriculture. J. Comput. Cognit. Eng., 2, 2, 155–162, 2023.

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72  Edge of Intelligence 15. Prata, P., Natraj, N.A., Hallur, G.G., Aslekar, A., An Investigative Analysis of Blockchain in the Supply Chain Management, in: 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), IEEE, pp. 1256–1260, 2023. 16. Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A.B., Qin, X., Yan, N., Chang, S., Zhao, Y., Dong, Q., Boken, V., Challenges and opportunities in remote sensing-based crop monitoring: a review. Natl. Sci. Rev., 10, 4, nwac290, 2023. 17. Shankar, P., Werner, N., Selinger, S., Janssen, O., Artificial intelligence driven crop protection optimization for sustainable agriculture, in: 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G), IEEE, pp. 1–6, 2020. 18. Soni, D., Patel, P., Shah, M., Crop Monitoring, in: Agricultural Biotechnology: Food Security Hot Spots, p. 247, 2022. 19. Grieve, B.D., Duckett, T., Collison, M., Boyd, L., West, J., Yin, H., Arvin, F., Pearson, S., The challenges posed by global broadacre crops in delivering smart agri-robotic solutions: A fundamental rethink is required. Global Food Secur., 23, 116–124, 2019. 20. Huang, C.H., Chen, B.W., Lin, Y.J., Zheng, J.X., Smart crop growth monitoring based on system adaptivity and edge AI. IEEE Access, 10, 64114–64125, 2022.

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3 A Survey on Reconfigurable Co-Processors Computing Linear Transformations Atri Sanyal1* and Amitabha Sinha2 1

NSHM Institute of Computing and Analytics, NSHM Knowledge Campus, Kolkata, India 2 Maulana Abul Kalam Azad University of Technology, West Bengal, India

Abstract

Many computationally intensive linear transformations like Fourier Transform, Cosine Transform, and Wavelet Transform are used in various image processing applications like image compression, image analysis, edge detection etc. Architectures of various types of co-processors using various computing techniques have been proposed for them. The dominating technologies to implement these co-processors are either Application Specific Integrated Circuit (ASIC) or Reconfigurable Architecture. While ASIC offers higher speed, it lags flexibility. On the other hand, reconfigurable architecture offers more flexibility while compromising with speed. A co-processor to implement various types of linear transformations should have a balance between speed and flexibility and hence in our work Field Programmable Gate Array (FPGA) is chosen as the basic building block to implement such a processor of reconfigurable type. In this paper different linear transformations are reviewed first. Next, reconfigurable computing is described in terms of its difference with conventional computing, features, advantages etc. Then FPGA as an example of reconfigurable computing is presented in terms of its advantage, architecture etc. XILINX Virtex IV is taken as an example and its architecture is described. A thorough literature review is presented next where we briefly described different reconfigurable processor implementing single/multiple linear transforms and the performance of them in terms of speed and size. Then a comparative analysis is presented to select an architecture fittest for a specific linear transformation applied in a specific cause.

*Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (73–92) © 2025 Scrivener Publishing LLC

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73

74  Edge of Intelligence Keywords:  Linear transformations, fast fourier transformation (FFT), fast discrete cosine transformation (FDCT), field programmable gate array (FPGA), co-processor, speed, size

3.1 Different Linear Transforms Fourier transformation [1], is a mathematical transformation operation which takes a signal in time domain and convert it into frequency domain. It is a function with complex value which is used to represent complex sine waves. Amplitude of the sine wave is represented by the absolute magnitude of the complex value of the function and phase of the sine wave is represented by the argument of the same. The function of inverse Fourier transform is just opposite, it transforms the signal from frequency domain to time domain. The Fourier transform has the basic properties which can be proved mathematically like i> Linearity ii> Translation iii> Modulation and iv> Time scaling. The method of computing Fourier transform in an equal spaced time interval is called Discrete Fourier transform (DFT). To derive that we use a computational method called Fast Fourier transform (FFT). There are several methods that have been used since the invention of the first Cooley-Tukey method [2] in 1965. The method is presented below: Let x0……..xn-1 be the discrete sequences of a time domain signal X(t). The Discrete Fourier Transformation is defined by the formula X k = ∑X ne-i2πkn/N k = 0 … ….N-1 where ei2π/N is a primitive Nth



root of 1

Note: Xn  k should be small



Several variations have been proposed since the original algorithm to reduce the complexity of multiplication and addition of complex number. Similar to Fourier transform, discrete cosine transformation (DCT) [3] has been proposed for computing image compression algorithm JPEG. A number of data points have been expressed in terms of cosine wavelet at different frequencies. There are several variations of discrete cosine transform of which Type II is mostly used and type III is used as the inverse function or inverse DCT. The formula for DCT is given by:



π  1  Xk = ΣnN=−01xn cos   n +  k  for k = 0 to N-1 2  N 

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Surveying Reconfigurable Linear Transform Co-Processors  75 In this chapter, we have considered many Fast discrete cosine transform algorithms to check whether it is possible for our proposed processor to execute all different type of computation processes presented in these algorithms. Recently, discrete wavelet transformation [4] has been introduced which is used as an alternate compression standard used in JPEG 2000. First wavelet transform is applied to all the pixels of the image creating as many numbers of coefficients as there are pixels in the image. Then these coefficients are compressed because the image information is concentrated to a few coefficients which are preserved, and the rest are discarded. A typical wavelet transform system is given below: The system requires one low pass filter and one high pass filter, one floating point multiplier, and two synthesis filters. However, one problem of wavelet transform is to select which of the wavelets are sampled discretely. Then either Haar or Daubechies wavelet is selected, and the level of decomposition is decided. The application of wavelet transform is in image processing applications like image denoising, image compression etc. It is splitting data in two sub bands or splitting signals in to low and high frequency bands such that signals are split into two parts of very significant and little significant portions. So, they can be used in signal or image compression. The transforms can easily be implemented by a pair of simple two tap filter. Flow graph method is a very effective and frequently used method to show the data parallel operations of these transforms. Some data flow graph for FFT, FDCT and DWT (Discrete Wavelet Transform) algorithms are shown in Figures 3.1(a), 3.1(b) and 3.2.

3.2 Reconfigurable Computing Configurable Computing [6–8] can be defined as programming or configuring a highly tuned hardware circuits like FPGAs [9, 10] to deliver the functionality which is desirable for specific task [11]. The native operations which are required by the applications can be directly implemented by programming or structuring the hardware and also it has ability to exploit the parallelism inherent in the computation. The objectives of Configurable Computing [6] is stated below: i.

Equilibrium needs to be established between flexibility and efficiency. ii. Elimination of the “Inflexibility” associated with ASIC is required.

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76  Edge of Intelligence x(0) x(1) x(2) x(3) x(4) x(5) x(6) x(7) x(8) x(9) x(10) x(11) x(12) x(13) x(14) x(15)

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X(0) X(8) X(4) X(12) X(2) X(10) X(6) X(14) X(1) X(9) X(5) X(13) X(3) X(11) X(7) X(15)

(a) 0

+

+

+

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C3π/16 Cπ/16

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S3π/8 –S3π/8 2C3π/8

+

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–

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Sπ/16 –Sπ/16 Cπ/16

–

+

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–S3π/16

+

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(b)

Figure 3.1  (a) The dataflow diagram of FFT algorithm [3], (b)  The dataflow diagram of FDCT algorithm [5].

H0(n)

2

H1(n)

2

R1(n)

2

G0(n) Y

X(n) D1(n)

Figure 3.2  A discrete wavelet transform system [4].

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2

G1(n)

Surveying Reconfigurable Linear Transform Co-Processors  77 iii. Speed limitation of microprocessor and DSP processors is to be removed. iv. The software application-based approach can be eliminated by incorporating reconfigurable computing without compromising with the flexibility of the software.

3.2.1 Reconfigurable Computing – An Extension of Configurable Computing In the reconfigurable computing [6, 12], it is seen that the hardware layout can be configured as per the requirements placed upon the system while it is still executing. Reconfigurable architectures [3, 12, 13] have unique competence to execute computationally intensive tasks. The reconfigurable computations have the capability of performing operation efficiently as its hardware can attain the flexibility of software. In some cases, they are the most effective system to attain the required real-time performance without fabricating custom integrated circuits. During the operation, the functionality of a particular task can be renewed and repaired. One good example is FPGA [14–16] which can be controlled at run time.

3.2.2 Advantages of Reconfigurable Computing i.

ii. iii. iv. v. vi.

Reconfigurable architecture has the potential to accomplish complex circuits by incorporating Look Up Table (LUT) based configurable logic blocks (CLB) to achieve the flexibility of software-based solutions. This architecture is capable of offering the execution speed similar to application specific integrated circuit (ASIC) or conventional hardware-based approach. It requires lower system cost. It offers reduced time to market. It facilitates incremental design flow. It has effective hardware utilization.

3.2.3 Features of Reconfigurable Computing The reconfigurable computing [6] can be classified as: A. On-the-Fly Reconfigurability B. Partial Reconfigurability C. Externally Visible Internal State

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78  Edge of Intelligence

3.2.3.1 On-the-Fly Reconfigurability a) This concept [17, 18] avoids resetting the FPGA, whenever possible, as resetting consumes a lot of time. b) Initially, the clock is stopped at various parts of the chip that need to be altered. After altering the required logic, the clock is restarted.

3.2.3.2 Partial Programmability Partial programmability [19–21] is defined as: a) It allows leaving the logic which remains fixed during application and changes only the part needed to be changed. b) Variations are made to single or multiple gates without modifying the state of the others.

3.2.3.3 Externally-Visible Internal State In this type of reconfigurable architecture [22, 23], internal state of the FPGA is visible. This internal state is captured and saved for later use. For example, here, swapping of the hardware design occurs in the same way virtual memories are swapped in and out of physical memory.

3.3 Field Programmable Gate Array An integrated circuit whose built-in mechanism can be controlled and organized by a customer or a designer after manufacturing, is termed as field-programmable gate array (FPGA) [13, 14, 24]. The configuration of FPGA is done by hardware description language (HDL) [25]. The operation of FPGA is same as the application-specific integrated circuit (ASIC). So, any logical function which can be implemented by an ASIC that can be perform by FPGAs [27]. It has the skill to revise the functionality after transporting. The basic advantages of FPGA over ASIC are its characteristics of partial reconfiguration of a segment of the design and the low engineering costs which is also non-recurring [25, 26]. The logic blocks which are the basic building block of FPGAs are the programmable logic components. These blocks are interconnected by programmable interconnected switches, and they can be reconfigured such that can be inter-wired in different configurations. The complex combinational functions, or only

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Surveying Reconfigurable Linear Transform Co-Processors  79 simple logic gates like AND and XOR can be achieved by organizing the interconnection of the building blocks of FPGA. Most of the FPGAs [6, 9] contain logic blocks that include simple flip-flops or more complete blocks of memory elements.

3.3.1 Advantages of FPGAs We can list various features of FPGAs [6, 9, 24] as follows: i.

Various literature of the early 1980’s discussed about the logic circuits of common computer systems which were implemented by very few of standard large-scale integrated circuits (LSI) like Microprocessors, bus/IO controllers, system timers etc. ii. Every system still have the need for random “glue logic” (Glue logic is the logic required to interface circuit modules. The modules are typically complex chips such as microcontrollers, RAM, and peripheral ICs) are used to connect the large ICs: –– generating global control signals (for resets etc.) –– data formatting (serial to parallel, multiplexing, etc.) iii. A basic system required a few Large-scale integration (LSI) components and lots of small low-density SSI (small scale IC) and MSI (medium scale IC) components. iv. In custom ICs, amount of glue logic can be minimized by i> improving performance ii> by reducing system complexity and iii> by reducing manufacturing cost. However, development cost of custom ICs is relatively high. Moreover, design complexity is the main cause for requiring much time to introduce the product in the market (time to market).

3.3.2 Requirement of Flexibility i.

The flexible [6] feature allows the changing of the basic hardware using the software. This allows faster time to market. ii. Long product development cycle can be largely reduced thereby reducing the product development cost as a

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80  Edge of Intelligence consequence product can be reached to the customer faster. iii. This produces alternative solution where speed of the hardware can be achieved without compromising on flexibility of the software (programmability).

3.3.3 Programmable Hardware Programmable hardware [6, 28] offers a balance between flexibility and efficiency because it has ability to break potential concurrency in the algorithm by directly mapping the algorithm onto architecture.

3.3.3.1 Advantages of Programmable Logic i.

Many programmable logic devices can be programmed outside of the manufacturing environment. Such types are called field programmable logic circuits. ii. Most of them are erasable and reprogrammable. a. They are capable of updating or correcting errors. b. Reusability is also seen in these types of devices for a different design. c. One of the ideal applications is for laboratory experiments in different courses. iii. Various prototype designs can be implemented by programmable logic devices that are employed for ASIC design.

3.3.4 FPGA Architecture of Xilinx Virtex IV There exists a number of FPGA [5] vendors like Xilinx [52], Altera, Atmel, Actel [14, 25] etc.. Even though basic architecture of most of the FPGAs are almost same, here the architecture of a typical Xilinx [52] Virtex IV FPGA [52] is considered as they are widely used. Figure 3.3 depicts the architecture of such an FPGA. FPGA consists of three major segments: i. Configurable logic blocks (CLB) ii. Routing blocks (Programmable Interconnect) iii. I/O blocks (IOB)

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IOB

IOB

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Surveying Reconfigurable Linear Transform Co-Processors  81

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IOB

IOB

CLB SM

SM

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IOB INPUT-OUTPUT BLOCK CONFIGURABLE LOGIC BLOCK

Figure 3.3  Structure of FPGA (Courtesy: AMD Xilinx [52] Inc.).

Other segments are: iv. v. vi. vii.

Memory Multipliers 7 Global clock buffers Boundary scan logic

3.3.4.1 Structure of a CLB i. Each CLB consist of a number of slices. ii. The feedback between slices in the same CLB is established by local routing, and it also established the routing to neighboring CLBs. iii. A switch matrix (SM) offers access to general routing resources.

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82  Edge of Intelligence

3.3.4.2 Structure of a Slice i. Individual slice contributes four outputs. ii. Out of four outputs: one pair is stated as registered outputs, and the second pair is considered as non-registered outputs. iii. Moreover, two buffers (BUFTs) are linked with individual CLB. These two buffers are available for all the 16 CLB outputs. iv. Carry logic runs vertically, up only. v. Two independent carry chains per CLB.

3.4 Survey of Existing Work A number of papers are reviewed where different architectures are proposed in reconfigurable nature. We explain in the next some of them and tried to categorize them. This step is required to find out the research gap. In [29], a reconfigurable processor is discussed which implements “shape adaptive discrete cosine transform” used in a class of “object based video signal processing”. The architecture is proposed after a detailed analysis of the algorithm for finding out architectural design issues that are important for implementation. The reconfigurable processor contains data path which can configure itself at the time of execution, one of the requirements of “SA-DCT”. A prototype chip is implemented and tested to confirm the requirement of “object-based video signal processing”. In [30], architecture of a reconfigurable discrete wavelet transform processor is discussed. A system on chip is developed for the requirement of multimedia applications. A reconfigurable processing element array and a reconfigurable address generator forms the architecture. This architecture performs better than convolution-based architecture. A prototype chip is implemented and tested which delivers good throughput, flexibility and energy efficiency. In [31], a processor with reconfigurable data path is proposed to implement fast Fourier transform. Optimized for systolic signal processing application, this processor implements “Goertzel’s algorithm”. Data path parallelism is used to implement Fourier transform on hyperspectral image data.

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Surveying Reconfigurable Linear Transform Co-Processors  83 In [32], a “multipath delay commutator-based pipeline architecture” appropriate for OFDM systems is described. Processor contains 4 stream data and radix-4 architecture implemented in every pipeline of different lengths and is suitable for communication systems like Wi-Max, LTE etc. The implemented design exhibits increased throughput and decreased area and power consumption. In [33], again a “reconfigurable data path processor” is discussed which can implement various transformations. Fast Fourier transform is taken as an example to check the performance of the processor. The algorithm is explained visually through directed graphs. Implemented using the programming language developed specifically for the said “Reconfigurable Data Path Processor”. In [34], the various categories of processors implementing Fourier transform is surveyed. The requirement of FFT with variable length makes it an excellent candidate for implementation using reconfigurable fabric. Such efficient algorithms and architectural implementation of FFT with increased speed and decreased area and power is surveyed and their common characteristics are noted. In [35], firstly several classical discrete wavelet transformation implementation approaches are compared along with their limitations. Then a new parallel architecture is proposed containing processing elements with reconfigurability in grid computing. 2D DWT is executed in this environment and a significant increase of speed up is achieved. This architecture can offer flexibility and acceleration. In [36], since FFT plays a crucial role in OFDM systems, a VLSI architecture implementing the same in real time is proposed. It contains 4 radix-4 and 2 radix-2 processing elements with multiplier capable of multiplying complex numbers so that it can perform FFT of various lengths. Parallel access to memory banks for every processing element is included in the design to speed up the calculation. The efficiency of the design is shown by implementing it in FPGA and the significant increase of throughput is achieved. In [37], an array of FPGA especially capable of implementing digital signal processing applications is proposed. It contains common modules required in various DSP applications. A number of DSP operations like (FFT, DCT, DWT, FIR, IIR etc.) can be implemented by switching through reconfiguration of interconnection between common modules. The architecture is tested using FPGA and satisfactory flexibility, parallelism and scalability is achieved.

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84  Edge of Intelligence In [38], in order to achieve efficiency in the field of high-speed wireless communication multiple input multiple output (MIMO) system is considered. But MIMO OFDM system requires a number of stand-alone baseband processors. To decrease the hardware complexity of the processor implementing N-point FFT/IFFT, a reconfigurable “multipath data commutator” (MDC) architecture is proposed. In [39], a variation of wavelet transform called discrete wavelet packet transform is discussed. It is implemented in various papers as an architecture consisting of a tree structured filter bank. Here in this paper a new architecture is proposed and compared with other previously proposed architectures in different parameters like memory access, hardware simplicity, regularity, and throughput. In [40], a FFT processor with efficient power requirement and area is designed using “single delay feedback”. The radix factorization is utilized along with the parallel design. Radix factorization of different size is supported adding flexibility to the design. In [41], reconfigurable discrete Hilbart transform architecture is proposed. Hilbert transform kernel is considered having multiple of 4 points and using that higher point Hilbert transform is calculated. The design is compared with conventional design and a much higher speed is reported along with flexibility due to reconfigurability. However all these papers that we surveyed are considering architecture of a specific transform. That transform can be FFT, DCT, DWT or Hilbert but the co-processors discussed are efficient in implementing only a specific transformation. Next we surveyed papers implementing designs suitable for implementing several transformations together based upon reconfigurability. In [42], we have seen a generalized transform processor capable of implementing four linear transforms namely FFT, DCT, Walsh and Hadamard transform. Appropriate control signals are generated so that the architecture can switch from one transform to another on the fly. The design is implemented in FPGA and is tested and compared with other existing designs. In [43], a generalized architecture implementing 4 fast discrete cosine transform is proposed. Appropriate control signals are generated so that the architecture can switch from one transform to another on the fly. The design is implemented in FPGA and is tested and compared with other existing designs. In [44], a system on chip (SoC) implementation of a reconfigurable DSP is discussed. The SoC is constructed by three different grain levels of reconfigurable fabric. A fine grain embedded FPGA, a mid-grain configurable processor and a coarse-grain reconfigurable array. An ARM processor is used as a supervisor which will look after the operations related to complex

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Surveying Reconfigurable Linear Transform Co-Processors  85 signal processing applications and assign the job to appropriate reconfigurable fabric. The chip is fabricated, tested and the performance is reported. In [45], an 8-bit reconfigurable array processor is discussed. The processor is focused on multimedia applications. The processor is implemented in a chip. Tested in terms of power, area and throughput. 2-D DCT, H.264 Integer transform and 2-D DWT is executed by this processor and a substantial throughput gain is reported over popular architectures in this domain. In [46], a reconfigurable hybrid processor capable of doing partial runtime reconfiguration is considered. This significantly reduces reconfiguration time and overhead. A methodology is presented to execute the tasks which can be run parallel. The tasks are mapped in an architecture of multiple identical but independent reconfigurable processing unit to achieve maximum possible speed up. The paper shows that there exists an upper limit of the number of processing units and after that limit further increase of processing elements will not decrease the execution time. The minimum processing time for obtaining solutions is analyzed along with the load distribution and data transfer schedule. The variation of processing time with respect to various parameter is shown through a number of plots. Result of hardware simulations of a 1-D DWT and FIR filter is presented, and the architecture is implemented in an FPGA board. In [47], an DCT architecture is proposed which is computationally efficient and quality preserving. The architecture is obtained by optimizing Loeffler’s DCT with Cordic algorithm. A significant reduction of computational complexity is achieved due to this as well as the quality is also retained after the transformation. Due to its low power consuming nature this is well suited for low power and high-quality codecs found in battery operated systems. In [48], a pipelined reconfigurable processor is proposed. This processor is suitable for image processing applications and scientific computing. Variable length single precision FFT and DCT computations compatible to IEEE 754 standard is executed by this processor. The architecture uses a reconfigurable radix 4 butterfly structure to significantly reduce the computational complexity over conventional designs. The design also introduces a partially shared “ping pong structured register bank” as an efficient data cache mechanism to achieve maximize resource utilization. Finally, we conclude that, linear transformations used in image processing like Fast Fourier Transform [1], Fast Discrete Cosine Transform [3] or Fast Discrete Wavelet Transform [4] are computationally intensive and also poses time criticality for many real-time applications. Research papers that have been reported in this domain are mainly of three types. Literatures in the first

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86  Edge of Intelligence category propose architectures to implement a single linear transformation like FFT or FDCT [29–41] separately. Since, the primary focus on this category is on speed, they concentrate on efficient implementation by including a variety of algorithms to decrease the number of computationally intensive operations. Research papers reveal that architectural innovations without using multipliers [30–37], incorporating high speed pipeline [37, 38], data forwarding, step lifting techniques for implementing FFT or FDCT [39–41] greatly decrease the computational complexity and enhance the speed thereby improving overall performance. The second category of research papers proposes processors or architectures for implementing a number of general linear transformations like FFT, FDCT, FDWT. Since, these architectures include basic building blocks common to all such transformations, a common architecture may be designed and by reconfiguration in the run time any one of the transformations can be implemented. These types of architectures are commonly known as reconfigurable architecture and are implemented using FPGAs [42, 43]. The third category of papers discuss implementation of more generic image/signal applications using a transform flow graph and these are used extensively in different literatures [44–49].

3.5 Performance Comparison of Different Reconfigurable Co-Processors Implementing Linear Transformation(s) Several reconfigurable processors belonging to different categories are studied from various past and current literatures. Most of them implement a single linear transform for a specific application in the domain of signal or image processing. Processors that implement several general linear transformations have been considered in our comparison where three different parameters i.e. speed, size of the architecture in terms of number of LUT or logic elements and power wherever they are available have been taken into view. There is another category of more generalized DSP or multimedia processors capable of doing a number of operations along with transformations. These types of processors are implemented as SOC and hence we have not compared them with the set of reconfigurable processors studied in this paper. The comparison table is presented below: From the table it can be seen that among the reconfigurable processors implementing a single or multiple number of linear transformations, in terms of speed the processor of [51] achieves highest operating frequency. Though the processors perform different linear transformations (FFT,

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Surveying Reconfigurable Linear Transform Co-Processors  87 FDCT, FDWT etc.) having varying computational complexity, comparing their performance with each other is not very trivial task. However, from the below table (Table 3.1), we can conclude that the proposed processor of [51] is among the best in its category in terms of speed and complexity. Table 3.1  Comparison table of reconfigurable processor implementing transformation(s). Name of the author Tseng

Journal name ISCAS ‘04. Proceedings of the 2004 International Symposium on Circuits and System [29] Tseng Journal of VLSI signal processing systems for signal, image and video technology [30] Joe, K., 7th NASA Hass, Symposium on David. & VLSI Design [33] Cox, F Sinha ACM SIGARCH Computer Architecture News [37] Reddy IEEE Signal et al. Processing Letters [41] Samaddar Proc.IEEE International Conference on CNC 2010 [42] Sanyal Proc. 2012 Third International Conference on Computer and Communication Technology [43]

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Implemented transform SA-DCT for video signal processing

Year Speed Power 2004 66 MHZ 180 mW

Size 16K

2005 50 MHZ 180mW

15K

2000 75 MHZ Not Available

Not Transform Available processingFFT is used as an example 4686 LUT FFT, DCT, DWT, FIR, IIR

2013 202 MHZ

Not Available

2014 100 MHZ

Not Available

2010 55 MHZ Not Available

2012 55 MHZ Not Available

DWT for multimedia system

Not Discrete Hilbert Available Transform Processor 1225 LUT FFT, DCT, Walsh and Hadamard Transform 3027 LUT 4 FDCT algorithms: Arai, Chen, Loeffler, Vetterli

(Continued)

88  Edge of Intelligence Table 3.1  Comparison table of reconfigurable processor implementing transformation(s). (Continued) Name of the author Sanyal, Sinha

Rossi

Zhang

Sanyal

Journal name International Journal of Research in Electronics and Computer Engineering [44] IEEE Journal of Solid-State Circuits [45]

Implemented transform 4 FDCT algorithms: Arai, Chen, Jeong, Loeffler

Year Speed Power 2018 58 MHZ Not Available

Size 3248 LUT

2010 250 MHZ

44 M Logic SoC implemen­ tation of recon­figura­ ble DSP 50 M logic Reconfigurable processor for gray scale image processing 10,897 LUT Reconfigurable Processor to implement any linear transform which can be represented in a flow graph

Electronics 2021, 10, 2021 220 2429. https://doi. MHZ org/10.3390/ electronics1019 2429 [50] International 2022 298 Journal of MHZ Software Innovation [51]

235 mW

Not available

417 mW

3.6 Conclusions and Future Work We conclude that there is a research gap at present in reconfigurable processors capable of implementing different linear transformations used in signal/image processing domain. The future scope of reconfigurable co-­ processors surveyed in this paper can be to add two components in the near future. The first one is that the control unit (CU) of the processor has to be completed with full-fledged instruction set able to implement any instruction of generic linear transformations of any size. Full-fledged instruction set generation is not possible by manual coding and hence require any high-level programming language like Python or C to automate the process through some assembler. Secondly the memory management process for interchanging the data between the master processor and this co-processor is not implemented or mentioned in the surveyed papers. These will be the future scope of the design which can be implemented in future.

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Surveying Reconfigurable Linear Transform Co-Processors  89

References 1. Brigham, E.O. and Morrow, R.E., The fast Fourier transform. IEEE Spectr., 4, 12, 63–70, Dec. 1967, doi: 10.1109/MSPEC.1967.5217220. 2. Cooley, J.W. and Tukey, J.W., An algorithm for the machine calculation of complex Fourier series. Math. Comput., 19, 90, 297301, 1965. 3. Chan, S.-C. and Ho, K.-L., Fast algorithms for computing the discrete cosine transform. IEEE Trans. Circuzts Syst. H, 39, 3 (Mar.), 185–190, 1992. 4. Wang, Y., Li, Z., Wang, C., Feng, L., Zhang, Z., Implementation of discrete wavelet transform. 2014 12th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Guilin, pp. 1–3, 2014, doi: 10.1109/ICSICT.2014.7021561. 5. Loeffler, C., Lightenberg, A., Moschytz, G., Practical fast 1-D DCT algorithms with 11multiplications. Proc. IEEE ICASSP, vol. 2, pp. 988–991, Feb. 1989. 6. Sinha, A., Reconfigurable Parallel Architechture for Digital Signal/Image Processing Algorithms. Class Lecture, MVLSI202, WBUT, Academic Session of WBUT 2007-2009. 7. Tessier, R. and Burleson, W., Reconfigurable computing for digital signal processing: A survey. J. VLSI Signal Process., 28, 7-27, 2001. 8. Kaviani, A. and Brown, S., Hybrid FPGA Architecture. FPGA.96, Monterey, CA, pp. 1–7, Feb. 1996. 9. Mohammad, K. and Agaian, S., Efficient FPGA implementation of convolution. Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA, October 2009. 10. Martinez, D.R., Bond, R.A., Michael Vai, M., 7.4 Embedded Signal Processing, in: Sensors, Nanoscience, Biomedical Engineering, and Instruments: Sensors Nanoscience Biomedical Engineering, 2018. 11. Karmakar, A. and Sinha, A., A novel architecture of a reconfigurable radio processor for implementing different modulation schemes. 2011 3rd International Conference on Computer Research and Development, vol. 1, IEEE, 2011. 12. Todman, T.J., Constantinides, G.A., Wilton, S.J.E., Mencer, O., Luk, W., Cheung, P.Y.K., Reconfigurable computing: architectures and design methods. IEE Proc.-Comput. Digit. Tech., 152, 2, 193–207, March 2005. 13. McCurry, P., Morgan, F., Kilmartin, L., Xilinx FPGA implementation of an image classifier for object detection applications. International Conference on Image Processing, vol. 3, pp. 346–349, 2001. 14. Xilinx, Introduction and Overview, Virtex Platform FPGA, 2004. 15. Bhatt, T.M. and McCain, D., MATLAB as a Development Environment for FPGA Design, pp. 607–610 98, ACM, 2005. 16. Wee, S., Casper, J., Njoroge, N., Tesylar, Y., Ge, D., Kozyrakis, C., Olukotun, K., A Practical FPGA based Framework for Novel CMP Research. Proceedings of

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90  Edge of Intelligence the 2007 ACM/SIGDA 15th international symposium on Field programmable gate arrays, pp. 116–125. 17. Chabrier, T. and Tisserand, A., On-the-fly multi-base recoding for ECC scalar multiplication without pre-computations, in: Computer Arithmetic (ARITH), 2013 21st IEEE Symposium on, pp. 219–228, IEEE, 2013, DOI: 10.1109/ARITH.2013.17. 18. Bhandari, S.U. et al., Real time video processing on FPGA using on the fly partial reconfiguration. 2009 International Conference on Signal Processing Systems, IEEE, 2009. 19. Ali, N. and Garg, B., New Energy Efficient Reconfigurable FIR Filter Architecture and Its VLSI Implementation, in: International Symposium on VLSI Design and Test, Springer, Singapore, pp. 519–532, 2017. 20. Liu, M. et al., Run-time partial reconfiguration speed investigation and architectural design space exploration. 2009 International Conference on Field Programmable Logic and Applications, IEEE, 2009. 21. Nguyen, M. and Hoe, J.C., Time-Shared Execution of Realtime Computer Vision Pipelines by Dynamic Partial Reconfiguration. 2018 28th International Conference on Field Programmable Logic and Applications (FPL), IEEE, 2018. 22. Barr, M., A reconfigurable computing primer. Multimed. Syst. Des., 2, 9, 44–47, 1998. 23. Estivill-Castro, V., Hexel, R., McColl, M., High-Level Executable Models of Reactive Real-Time Systems with Logic-Labelled Finite-State Machines and FPGAs. 2018 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2018. 24. Rose, J., El Gamal, A., Sangiovanni-Vincentelli, A., Architecture of field-­ programmable gate arrays. Proc. IEEE, 81, 7, 1013–1029, 1993. 25. Morris Mano, M. and Ciletti, M.D., Digital Design With an Introduction to the Verilog HDL”, FIFTH EDITION, Pearson 2Education, Inc., publishing as Prentice Hall, One Lake Street, Upper Saddle River, New Jersey 07458, 2018. 26. Rozon, C., On the use of VHDL as a Multivalued Logic Simulator. Proc., ISMVL, pp. 110–115, 1996. 27. Mariani, R., Pessolano, F., Saletti, R., A new CMOS ternary logic design for low power low voltage circuit, Tutorial University of Pisa, Italy. 28. Paulin, P.G. et al., Application of a multi-processor SoC platform to highspeed packet forwarding. Proceedings of the conference on Design, automation and test in Europe, vol. 3, IEEE Computer Society, 2004. 29. Tseng, P.-C. et al., Reconfigurable discrete cosine transform processor for object-based video signal processing, in: ISCAS ‘04. Proceedings of the 2004 International Symposium on Circuits and System, 2004. 30. Tseng, P.-C., Huang, C.-T., Chen, L.-G., Reconfigurable Discrete Wavelet Transform Processor for Heterogeneous Reconfigurable Multimedia Systems. J. VLSI Signal Process. Syst. Signal Image Video Technol., 41, 35–47, 2005.

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Surveying Reconfigurable Linear Transform Co-Processors  91 31. Donohoe, G.W., The Fast Fourier Transform on a Reconfigurable Processor. Proc. NASA Earth Sciences Technology Conference, Pasadena, CA, June 11-13, 2002. 32. Srivatsava, P.S.V. and Sarada, V., Reconfigurable MDC Architecture Based FFT Processor. Int. J. Eng. Res. Technol., 2014. 33. Joe Hass, K. and Cox, D.F., Transform Processing on a Reconfigurable Data Path Processor. 7th NASA Symposium on VLSI Design, 1998. 34. Sarada, V. and Vigneswaran, T., Reconfigurable FFT Processor – A Broader Perspective Survey. Int. J. Eng. Technol. (IJET), 5, 2, 949–956, 2013. 35. Shahbahrami, A., Ahmadi, M., Wong, S., Bertels, K., A New Approach to Implement Discrete Wavelet Transform using Collaboration of Reconfigurable Elements. Proc. 2009 International Conference on Reconfigurable Computing and FPGAs. 36. Manolopoulos, K.E., Nakos, K.G., Reisis, D.I., Vlassopoulos, N.G., Recon­ figurable Fast Fourier Transform Architecture for Orthogonal Freque­ncy Division Multiplexing Systems, 2003, available: https://pdfs.seman­ticscholar.org/ dd5c/263725af00e5dd4d42d573c269f57d917c8d.pdf.?_ga=2.84059166.64075 1657.­1573804365-914446569.1569299704. 37. Sinha, A., Sarkar, M., Acharyya, S., Chakraborty, S., A Novel Reconfigurable Architecture of a DSP Processor for Efficient Mapping of DSP Functions using Field Programmable DSP Arrays. ACM SIGARCH Comput. Archit. News, 41, 2, 1–8, May 2013. 38. Wadekar, S., Thakare, L.P., Deshmukh, A.Y., Reconfigurable N-Point FFT Processor Design For OFDM System. Int. J. Eng. Res. Gen. Sci., 3, 2, MarchApril, 2015. 39. Petrovsky, A., Rodionov, M., Petrovsky, A., Dynamic Reconfigurable on the Lifting Steps Wavelet Packet Processor with ­Frame-Based Psychoacoustic Optimized Time-Frequency Tiling for ­Real-Time Audio Appli­cations, in: Design and Archi­tectures for Digital Signal Processing, available: http://www.intechopen.com/books/­design-and-architecturesfordigital-signal-processing2013. 40. Thomas, S. and Sarada, V., Design of Reconfigurable FFT Processor With Reduced Area And Power. ITSI Trans. Electr. Electron. Eng. (ITSI-TEEE), 2013. 41. Reddy, P.S., Mopuri, S., Acharyya, A., A Reconfigurable High Speed Architecture Design for Discrete Hilbert Transform. IEEE Signal Process Lett., 21, 11, 1413–1417, Nov. 2014, doi: 10.1109/LSP.2014.2333745. 42. Sanyal, A. and Samaddar, S.K., A Combined Architecture for FDCT Algorithm. Proc. 2012 Third International Conference on Computer and Communication Technology, Allahabad, pp. 33–37, 2012, doi: 10.1109/ ICCCT.2012.16. 43. Sanyal, A., Samaddar, S.K., Sinha, A., A Generalized Architecture for Linear Transform. Proc. IEEE International Conference on CNC 2010, Calicut,

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92  Edge of Intelligence Kerala, India, Oct 04-05, 2010, IEEE Computer society, pp. 55–60, ISBN: 97-0-7695-4209-6. 44. Sanyal, A. and Sinha, A., A Reconfigurable Architecture to Implement Linear Transforms of Image Processing Applications. International Conference on Frontiers in Computing and System (COMSYS 2020), Jalpaiguri, West Bengal, India, January 13-15, 2020. 45. Rossi, D., Campi, F., Spolzino, S., Pucillo, S., Guerrieri, R., A Heterogeneous Digital Signal Processor for Dynamically Reconfigurable Computing. IEEE J. Solid-State Circuits, 45, 8, 1615–1626, Aug. 2010. 46. Purohit, S., Chalamalasetti, S.R., Margala, M., Vanderbauwhede, W., Throughput/Resource-Efficient Reconfigurable Processor for Multimedia Applications. IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 21, 7, 1346– 1350, July 2013. 47. Vikram, K.N. and Vasudevan, V., Mapping data-parallel tasks onto partially reconfigurable hybrid processor architectures. IEEE Trans. Very Large Scale Integr. (VLSI) Syst., 14, 9, 1010–1023, 2006. 48. Heyne, B., Sun, C.C., Goetze, J., Ruan, S.J., A Computationally Efficient HighQuality Cordic Based DCT. 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 4-8, 2006. 49. Wang, M., Wang, F., Wei, S., li, Z., A pipelined area-efficient and high-speed reconfigurable processor for floating-point FFT/IFFT and DCT/IDCT computations. Microelectron. J., 47, 19–30, 2016, Available:www.elsevier.com/ locate/mej. 50. Zhang, B., Mei, K., Zheng, N., Reconfigurable Processor for Binary Image Processing. IEEE Trans. Circuits Syst. Video Technol., 23, 5, 823–831, MAY 2013. 51. Sanyal, A. and Sinha, A., Trans_Proc: A reconfigurable processor to implement linear transforms. Int. J. Softw. Innov., 10, 1, 1–16, June 2022, ISSN: 2166-7160. 52. Xilinx Inc., Virtex-4 Family Overview Data Sheet (DS112) (v3.1), 2010-11.

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4 Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Based Chatbot on NVIDIA Jetson Nano Kiran Jot Singh1,2, Divneet Singh Kapoor1,2*, Amit Singh Bora3, Khushal Thakur1,2 and Anshul Sharma1,2 1

Electronics and Communication Engineering Department, Chandigarh University, Punjab, India 2 NVIDIA Embedded AI and Robotics Lab, Chandigarh University, Punjab, India 3 Computer Science and Engineering Department, Chandigarh University, Punjab, India

Abstract

With the rise in popularity of conversational AI, human-machine dialogue in natural language is now possible. With the help of custom frequently asked questions (FAQ) dataset and OpenAI’s APIs, the paper intends to create an intelligent ­hardware-based FAQ chatbot on an edge device i.e. NVIDIA Jetson Nano. Current FAQ pages frequently fail to deliver precise and pertinent answers, particularly when addressing queries unique to a certain area or with little datasets. The user experience is hampered overall by this restriction. The paper presents a novel hardware-based method that combines OpenAI’s language models with a ­customized dataset to create a sophisticated FAQ chatbot. This technology aims to enhance the user experience with chatbots in general by providing accurate and contextually aware responses to user inquiries. When compared to conventional FAQ chatbots, the CAMERA project’s results show notable improvements in response relevancy and accuracy. By utilizing OpenAI’s language models and customized datasets, the system may offer more knowledgeable and relevant responses, hence improving user happiness.

*Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (93–120) © 2025 Scrivener Publishing LLC

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93

94  Edge of Intelligence Keywords:  Conversational AI, FAQ Chatbot, NVIDIA Jetson Nano, OpenAI language models, edge computing, customized dataset, user experience, response accuracy

4.1 Introduction The field of artificial intelligence (AI) has witnessed remarkable advancements in recent years, with conversational AI emerging as a pioneering and rapidly evolving subfield. Conversational AI refers to the development of intelligent systems capable of engaging in natural language dialogues with humans, understanding their queries, and providing appropriate responses. This technology has revolutionized the way we interact with machines, paving the way for more intuitive and user-friendly interfaces. One of the most prominent applications of conversational AI is the development of chatbots. Chatbots are computer programs designed to simulate human-like conversations, either through text or voice interactions. They have gained immense popularity across various industries, serving as virtual assistants, customer service agents, and educational tools, among other applications [1]. The rise of chatbots can be attributed to several factors, including the availability of large datasets, advancements in natural language processing (NLP) techniques, and the increasing computational power of modern hardware. Additionally, the widespread adoption of mobile devices and the growing demand for convenient and accessible information have further fuelled the growth of chatbot technology. While chatbots offer numerous benefits, traditional frequently asked questions (FAQ) chatbots often face limitations in providing precise and relevant responses. These limitations can stem from several factors, including: • Limited datasets: Many FAQ chatbots rely on predefined datasets or knowledge bases, which may not adequately capture the breadth and complexity of user queries, particularly in specialized domains or niche areas. • Lack of context awareness: Traditional chatbots may struggle to understand the context and nuances of natural language queries, leading to misinterpretations or irrelevant responses. • Inflexibility: Rule-based or pattern-matching approaches used in traditional chatbots can be rigid and inflexible, making it difficult to adapt to evolving user needs or accommodate new information.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  95 • Scalability issues: As the scope of topics or the volume of queries increases, manually curating and maintaining FAQ knowledge bases can become a daunting and resource-­ intensive task. These challenges can result in a suboptimal user experience, frustrating users and hindering the adoption of chatbot technology. Addressing these limitations is crucial for enhancing user satisfaction and realizing the full potential of conversational AI. OpenAI’s Language Models: Unlocking the Potential of Conversational AI OpenAI, a leading research company in the field of artificial intelligence, has made significant strides in developing advanced language models that can potentially address the limitations of traditional chatbots. These language models, such as GPT-3 (Generative Pre-trained Transformer 3), are trained on vast amounts of data from the internet, allowing them to acquire broad knowledge and develop a deep understanding of natural language [2, 3]. OpenAI’s language models leverage transformer architectures and self-attention mechanisms, enabling them to capture contextual information and long-range dependencies in text [4]. This capability allows the models to understand and generate coherent and relevant responses, even in complex or open-ended scenarios. Furthermore, these language models are highly adaptable and can be fine-tuned on specific datasets, enabling them to specialize in particular domains or tasks [5]. This flexibility addresses the limitations of traditional chatbots, which often struggle with specialized or niche topics due to their reliance on limited datasets. By leveraging OpenAI’s language models, chatbot developers can create more intelligent and context-aware systems that can understand and respond to natural language queries with greater accuracy and relevance. NVIDIA Jetson Nano: Enabling Edge Device Computing While OpenAI’s language models offer significant potential for enhancing chatbot capabilities, deploying these models on resource-constrained devices can be challenging due to their computational requirements [6, 7]. This is where edge computing devices, such as the NVIDIA Jetson Nano, play a crucial role. The NVIDIA Jetson Nano is a small, power-efficient, and high-­ performance edge computing device designed for AI and deep learning applications [8]. It features a quad-core ARM processor, a powerful GPU,

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96  Edge of Intelligence and support for various deep learning frameworks, making it an ideal platform for running AI models on edge devices. Deploying chatbots on edge devices like the Jetson Nano offers several advantages: • Low latency: By running the chatbot model locally on the edge device, the need for continuous communication with a remote server is eliminated, resulting in lower latency and faster response times. • Privacy and security: Edge computing allows for data processing and decision-making to occur locally, reducing the need to transfer sensitive data over the network, enhancing privacy and security. • Connectivity resilience: Edge devices can operate independently, even in situations with limited or intermittent network connectivity, ensuring uninterrupted chatbot functionality. • Scalability: With edge computing, the computational load is distributed across multiple devices, enabling scalable and efficient deployment of chatbot solutions, especially in scenarios with high concurrency or high-volume traffic. The NVIDIA Jetson Nano as shown in Figure 4.1, is a powerful yet compact edge computing device designed specifically for running modern AI and deep learning workloads. It is part of the Jetson family of embedded systems from NVIDIA, which are widely used in various applications

Figure 4.1  NVIDIA Jetson Nano developer kit.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  97 such as robotics, drones, intelligent video analytics, and edge computing solutions. Technical Specifications: 1.  Processor: ◦◦ Quad-core ARM A57 CPU ◦◦ Maxwell GPU with 128 CUDA cores ◦◦ 472 GFlops of compute performance 2.  Memory: ◦◦ 4 GB LPDDR4 RAM ◦◦ 16 GB eMMC flash storage 3.  Connectivity: ◦◦ Gigabit Ethernet ◦◦ Wi-Fi 802.11ac ◦◦ Bluetooth 4.2 ◦◦ HDMI and DisplayPort outputs ◦◦ USB 3.0 and USB 2.0 ports ◦◦ GPIO (General-Purpose Input/Output) headers ◦◦ CSI (Camera Serial Interface) ports for camera connectivity 4.  Power: ◦◦ 5V DC power input ◦◦ Supports Power over Ethernet (PoE) ◦◦ Maximum power consumption of 10W 5.  Dimensions: ◦◦ Compact form factor: 100 x 80 x 29 mm ◦◦ Weighs approximately 135 grams 6.  Software Support: ◦◦ Compatible with various AI frameworks and libraries, including TensorFlow, PyTorch, Caffe, and NVIDIA’s proprietary CUDA and cuDNN libraries ◦◦ Supports popular operating systems like Ubuntu Linux and NVIDIA’s JetPack SDK, which includes tools, libraries, and APIs for AI development

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98  Edge of Intelligence The Jetson Nano’s compact size, low power consumption, and significant computational capabilities make it an ideal platform for deploying AI and deep learning models at the edge. Its ARM-based CPU and NVIDIA GPU provide the necessary processing power to run complex neural networks and AI algorithms efficiently [9]. One of the key advantages of the Jetson Nano is its ability to perform on-device AI processing, eliminating the need to send data to the cloud for processing. This feature is particularly beneficial in scenarios where low latency, privacy, and network connectivity are crucial factors, such as in the proposed hardware-based FAQ chatbot solution. By leveraging the Jetson Nano’s capabilities, developers can deploy OpenAI’s language models and other AI models directly on the edge device, enabling real-time processing of user queries and generating immediate responses. This approach not only improves response times but also enhances data privacy and security by keeping sensitive information localized on the device. Additionally, the Jetson Nano’s support for various AI frameworks and libraries, such as TensorFlow and PyTorch, makes it a versatile platform for developing and deploying a wide range of AI applications, including natural language processing (NLP) models like OpenAI’s language models [10, 11]. The combination of the Jetson Nano’s computational power, energy efficiency, and support for popular AI frameworks makes it an excellent choice for the proposed hardware-based FAQ chatbot solution, enabling the deployment of intelligent conversational AI systems on edge devices for enhanced user experiences. By combining the power of OpenAI’s language models with the capabilities of the NVIDIA Jetson Nano, developers can create intelligent, hardware-based FAQ chatbots that offer accurate and contextually aware responses while benefiting from the advantages of edge computing. The paper proposes a novel approach to creating a hardware-based FAQ chatbot that leverages the strengths of OpenAI’s language models and the NVIDIA Jetson Nano edge device. This approach aims to address the limitations of traditional FAQ chatbots and enhance the user experience by providing accurate and relevant responses to user inquiries. The proposed system involves several key components: • Custom FAQ dataset: A tailored dataset is curated to address specific domains or topics relevant to the chatbot’s application. This dataset can include frequently asked questions, product documentation, technical manuals, or any other relevant information sources.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  99 • OpenAI’s language model: A pre-trained language model from OpenAI, such as GPT-3, is employed as the foundation for the chatbot’s natural language processing capabilities. This model provides a robust understanding of natural language and can generate coherent and contextually relevant responses. • Fine-tuning: The pre-trained language model is fine-tuned on the custom FAQ dataset, allowing it to specialize in the specific domain or topic of interest. This process enhances the model’s ability to understand and respond accurately to queries within the target domain. • NVIDIA Jetson Nano deployment: The fine-tuned language model is optimized and deployed on the NVIDIA Jetson Nano edge device, enabling low-latency, privacy-­ preserving, and scalable chatbot functionality. • User interface: A user-friendly interface, such as a web application or a mobile app, is developed to facilitate natural language conversations between users and the chatbot. The integration of these components results in a powerful and intelligent hardware-based FAQ chatbot that can provide accurate and contextually aware responses to user inquiries. By leveraging OpenAI’s language models and customized datasets, the system can overcome the limitations of traditional chatbots and deliver an enhanced user experience. The proposed hardware-based FAQ chatbot solution has the potential to make significant impacts in various domains, including customer service, technical support, and educational settings. By providing accurate and relevant responses, the chatbot can improve user satisfaction and reduce frustration caused by inadequate or irrelevant information. • In customer service scenarios, the chatbot can handle a wide range of inquiries, freeing up human agents to focus on more complex or escalated issues. This can lead to improved customer satisfaction, reduced wait times, and increased operational efficiency. • In technical support environments, the chatbot can assist users with troubleshooting, product documentation, and knowledge base inquiries, potentially reducing the workload on support staff and enabling faster resolution of issues. • In educational settings, the chatbot can serve as a virtual tutor or learning assistant, providing students with on-­demand

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100  Edge of Intelligence access to relevant information, explanations, and guidance, enhancing the learning experience and supporting selfpaced learning. Furthermore, by leveraging edge computing capabilities, the proposed solution can ensure low latency, improved privacy, and resilience to network connectivity issues, making it suitable for deployment in various environments, including remote or resource-constrained areas. The project’s results highlight the potential of combining cutting-edge language models with customized datasets and edge computing devices to create more informed and contextualized chatbot experiences. Overall, the proposed solution represents a promising step towards more intelligent and user-friendly conversational AI systems, with the potential to transform how we access and interact with information across various domains.

4.2 Background A unique method for responding to open-domain queries is called Generation-Augmented Retrieval (GAR). By employing extensive language models to produce pertinent contexts without outside oversight, it enhances inquiries. The created contexts significantly improve the original queries’ semantics. Modern dense retrieval techniques fall short of the performance achieved by GAR with sparse representations, either by the same margin or better. Retrieval accuracy is regularly improved when several contexts are generated and their findings are fused. For even greater performance, GAR can be used with dense representations with ease. It attains cutting-edge outcomes on standard datasets for both generative and extractive question-answering configurations [12]. Businesses are realizing the value of conversational chatbots in order to engage customers with communication that seems human. It is difficult to create intelligent chatbots that can comprehend language, keep context, and produce well-thought-out responses. This study describes the deployment of a data-driven chatbot and examines the difficulties encountered, including the quality of the dataset and striking a balance between conversational capabilities that are specialized to a given domain and those that are not. A variety of chatbot designs are examined, with an emphasis on the retrieval-based strategy that makes use of text vectorization techniques. These models include rule-based, retrieval-based, and generative models. The findings emphasize the value of high-quality datasets and the necessity of more study in areas such as conversational data filtering for inappropriate content [13].

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  101 Recently, researchers and developers have paid close attention to chatbots, conversational AI systems built on neural networks and natural language processing. Numerous approaches of implementing chatbots have been investigated; each has advantages and disadvantages. This study provides a critical analysis of earlier chatbot research and suggests a development process for a cutting-edge, customized chatbot. The suggested chatbot makes use of deep learning and machine learning methods like deep reinforcement learning models and neural machine translation, in addition to platforms like TensorFlow, Android Studio, Firebase, and DialogFlow. Outlining effective implementation strategies and offering a cutting-edge chatbot system customized to meet client demands are the objectives [14]. People frequently require advice on medical matters, yet visiting hospitals for simple concerns takes time. Managing phone complaints is another demanding task for healthcare professionals. Natural language ­processing-based medical chatbots can help with this issue by providing appropriate advice on leading a healthy lifestyle. Without physically visiting hospitals, consumers can ask questions about their personal health with these chatbots. Medical chatbots may comprehend user questions, diagnose diseases based on symptoms, and deliver accurate responses shown on an Android app by utilizing technologies such as natural language processing, Google API for voice-to-text conversion, and machine learning algorithms like SVM. The goal of this system is to create a helpful platform that hospitals and medical facilities may use to help patients with voice inquiries about medications [15]. In a variety of industries, including marketing, education, healthcare, and entertainment, the usage of chatbots has grown quickly. This study explores the reasons and benefits propelling the adoption of chatbots, giving a historical review of the global community’s growing interest in these technologies. It describes the overall architecture and primary creation platforms, defines technological ideas, classifies chatbots according to different criteria, and emphasizes how societal stereotypes affect chatbot design. The development of chatbots that utilize AI and machine learning is driven by the need to minimize human intervention and the possible cost reductions in customer support. The goal of this research is to give consumers and developers a basic understanding of chatbot principles [16]. Furthermore, in the realm of technological advancement, the interplay between humans and machines has garnered substantial attention  [17]. From the nuanced dynamics of human-robot interaction [18] to the intricate algorithms facilitating image segmentation [19–21], the landscape of robotics and image processing continues to evolve rapidly. Furthermore, the advent of big data has propelled research endeavours, particularly in

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102  Edge of Intelligence wireless sensor networks and IoT [22–24], offering novel insights into data handling and clustering methodologies. Navigating through this multifaceted domain, the integration of machine learning algorithms has emerged as a cornerstone in diverse fields, ranging from medical diagnostics [25, 26] to text recognition systems [27]. Concurrently, the development of socially aware robots has ushered in a new era of collaborative spaces [28, 29], redefining conventional paradigms of navigation and interaction. Amidst these advancements, the exploration of novel techniques such as dual watermarking [30] underscores the perpetual quest for innovation and security in digital imaging. As we embark on this exploration, it becomes evident that each research endeavour contributes a unique perspective to the intricate tapestry of human-robot interaction, image processing, and data analytics [31–33]. Through an amalgamation of theoretical insights and practical applications, we endeavour to unravel the complexities of this ever-evolving landscape, striving towards advancements that resonate with both technological efficacy and societal relevance. Chatbots are becoming more and more common in customer service to improve customer satisfaction and cut expenses. However, for effective deployment, it’s imperative to comprehend the proper function and constraints of chatbots. This study examined how integrating a chatbot into a conventional customer care model affected user happiness, system quality, and information quality both before and after chatbot deployment. The goal of the experiment was to determine whether chatbots could enhance customer experience—especially responsiveness—while keeping other aspects of it at the same level. It also looked at how user experience was affected by unsuccessful chatbot conversations that needed human intervention. The results offer significant perspectives for enterprises intending to incorporate chatbots into their customer service platforms [34]. Problem Area Although chatbots and other conversational AI systems have become quite popular, they frequently fail to deliver precise and pertinent answers, particularly when it comes to domain-specific inquiries. Conventional rulebased chatbots are not adaptable enough to deal with a variety of user inputs, and retrieval-based models might not be able to extract the relevant data from big knowledge stores. Furthermore, installing complex AI models needed for advanced natural language processing is challenging due to edge devices limited computational capacity. By creating an intelligent hardware solution that combines the strength of big language models, retrieval-augmented generation techniques, and the computational

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  103 efficiency of the NVIDIA Jetson Nano platform for real-time inferencing on edge devices, this project seeks to solve these limitations. Objectives 1. Configure and upgrade the Jetson Nano single-board computer (SBC) for enabling the chatbot. 2. Utilize the OpenAI API for Retrieval-Augmented Generation (RAG) after creating an index. 3. Create the user interface and use Docker to containerize the chatbot application. This paper remaining sections are organized as follows: A thorough analysis of the body of research on retrieval-augmented generation models and conversational AI applications is given in Literature Review section. The suggested framework is described in depth in next section, along with how the custom dataset, OpenAI API, and Jetson Nano hardware were implemented. The results and assessment of the suggested system are presented in after that, and the paper conclusion and future research directions are outlined in last section.

4.3 Literature Review Recent advancements in large language models (LLMs) and retrieval-­ augmented generation techniques have shown promising results in improving the performance of conversational AI systems, as depicted in Table 4.1. Retrieval-augmented generation aims to enhance the factual accuracy and informational grounding of language model responses by incorporating relevant external knowledge from document repositories. Based on the context of deploying retrieval-augmented large language models (LLMs) on edge devices like the Jetson Nano and other Single board computers (SBC), some potential research gaps in this area could include: 1.  Model optimization and compression: ◦◦ Developing techniques to optimize and compress large language models to fit the limited computational resources and memory constraints of edge devices like the Jetson Nano. ◦◦ Exploring model quantization, pruning, and efficient architectures for inference on edge hardware.

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104  Edge of Intelligence Table 4.1  Literature review of LLMs and conversational AI systems. Name of author and paper

Year and month

Foulds, P. F., 2024, March James, R., & Pan, S. [35]

Bink, J. [36]

Methods used

Key results

Used an interactive web application to allow participants to rate each item in the model’s response as accurate, partial, hallucinatory, or unhelpful. This allowed participants to assess the accuracy of a language model’s responses to questions about participants’ publications, work experience, and education, both with and without the context of their curriculum vitae.

Contextualization improved response accuracy from 7.31% to 93.95%; however, errors were caused by incomplete context (4.8%), noisy context (38.1%), query and context mismatch (19%), context-based hallucinations (19%), and uncommon formatting problems (19%).

2023, The transformer architecture, While recognizing limits in the study’s scope and December pre-training techniques the rapidly evolving (autoregressive language nature of generative AI, modeling and masked the research findings language modeling), fineunderlined the efficacy tuning, prompt-based of prompt-based learning (instruction personalization and the tuning), and the proposed Framework proposed Framework for for Assessment of Assessment of Chatbot Chatbot Quality Quality (FACQ), which (FACQ) in evaluating assesses chatbot responses chatbot responses. based on similarity, readability, complexity, and sentiment metrics, are some of the approaches used in the development and evaluation of LLMbased chatbots that are covered in this paper.

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(Continued)

CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  105 Table 4.1  Literature review of LLMs and conversational AI systems. (Continued) Name of author and paper

Year and month

Methods used

Key results

Sticha, A. [37]

2023, August

In comparison to retrievalRetrieval-augmented augmented generator generation (RAG) as a models such as RAG, baseline, LlamaIndexprompt engineering based document retrieval and response styling and reranking techniques, techniques can increase prompt engineering the factual consistency and response styling and linguistic quality to improve GPT-3 (naturalness, coherence, based chat completion, engagingness, and self-reflection using understandability) the ReAct framework of GPT-3 based chat to ground responses in completion models, but retrieved knowledge, and at the expense of less a combined automated/ groundedness in the LLM self-assessment/ retrieved context. human evaluation schema are all examples of this.

Louis, A., van Dijck, G., & Spanakis, G. [38]

2024, March

LLeQA, an expertannotated dataset for producing thorough responses to legal queries with understandable explanations, was presented by the authors. They worked with a “retrieve-then-read” pipeline, investigating different cutting-edge large language models as readers employed a variety of learning techniques to adjust to the task.

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The framework occasionally created facts, but it often produced syntactically valid and pertinent replies. Future research directions are suggested by the authors’ observations that traditional automatic measurements might not adequately represent the quality of the answers and that the systems were still susceptible to hallucinations in both the answers and the justifications for them. (Continued)

106  Edge of Intelligence Table 4.1  Literature review of LLMs and conversational AI systems. (Continued) Name of author and paper Chen, J., Lin, H., Han, X., & Sun, L. [39]

Year and month 2024, March

2021 Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. [40]

Methods used

Key results

The experimental findings Using recent news articles indicate that the four and external documents assessed abilities for from search engines, retrieval-augmented the authors developed generation—noise a Retrieval Augmented robustness, negative Generation Benchmark rejection, information (RGB) to assess retrievalintegration, and augmented generation counterfactual in large language models robustness—have (LLMs) for noise limitations in the robustness, negative current LLMs. This rejection, information means that a great integration, and deal of work remains counterfactual robustness. to be done in order to successfully apply retrieval-augmented generation to LLMs and guarantee accurate and dependable responses. Extensive studies on retriever-generator structures and training models for retrievalaugmented generation with Wikipedia serving as the knowledge repository.

Retrieval-augmented generation preserves conversational skill, helps generalize to unknown data distributions, and dramatically lowers knowledge hallucination in dialogue systems.

2.  Hardware acceleration and parallelization: ◦◦ Leveraging the GPU and parallel computing capabilities of devices like the Jetson Nano to accelerate LLM inference and retrieval-augmented generation. ◦◦ Investigating efficient parallelization strategies and hardwareaware optimizations for edge deployment.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  107 3.  Energy efficiency and thermal management: ◦◦ Addressing the energy consumption and thermal challenges of running computationally intensive LLMs on power-­ constrained edge devices. ◦◦ Exploring energy-efficient inference techniques and thermal management strategies. 4.  Real-time performance and latency: ◦◦ Ensuring real-time performance and low latency for conversational AI applications running on edge devices, which is crucial for user experience. ◦◦ Optimizing the end-to-end pipeline, including context retrieval, language model inference, and generation, for edge deployment. 5.  On-device learning and adaptation: ◦◦ Investigating techniques for on-device learning and adaptation of retrieval-augmented LLMs, allowing them to dynamically update and improve based on user interactions and new data. ◦◦ Exploring efficient on-device fine-tuning or continual learning approaches for personalized conversational AI. 6.  Privacy and security considerations: ◦◦ Addressing privacy concerns by keeping sensitive data and processing on the edge device, without the need for cloud communication. ◦◦ Implementing secure and privacy-preserving protocols for edge-based conversational AI systems. 7.  Edge-cloud coordination and hybrid architectures: ◦◦ Exploring hybrid architectures that combine edge and cloud resources for retrieval-augmented LLMs, leveraging the strengths of both paradigms.

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108  Edge of Intelligence ◦◦ Developing efficient edge-cloud coordination strategies for context retrieval, model updates, and workload distribution. 8.  Scalability and deployment challenges: ◦◦ Investigating scalable deployment strategies for edge-based conversational AI systems across multiple edge devices or clusters. ◦◦ Addressing challenges related to device management, updates, and monitoring in edge computing environments. These potential research gaps highlight the opportunities and challenges in leveraging the capabilities of edge computing devices like the NVIDIA Jetson Nano for deploying retrieval-augmented LLMs and enabling intelligent, low-latency, and privacy-preserving conversational AI applications.

4.4 Proposed Framework The proposed Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) based Chatbot on NVIDIA Jetson Nano is an AI-powered conversational assistant designed to run on the NVIDIA Jetson Nano platform. It utilizes the Retrieval Augmented Generation (RAG) architecture, which combines the power of retrieval-based and ­generation-based language models. This chatbot is capable of retrieving relevant information from a knowledge base and generating fluent and contextual responses. It can be deployed on the energy-efficient and compact Jetson Nano, making it suitable for embedded and edge computing-based applications with following features: • AI-powered conversational assistant for university students, designed for the NVIDIA Jetson Nano. • Specialized in answering frequently asked questions related to university life, admissions, academics, and student services. • Utilizes Retrieval Augmented Generation (RAG) architecture to retrieve relevant information from a university FAQ knowledge base. • Generates contextual and fluent responses based on the retrieved knowledge, tailored for university students’ needs.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  109 • Deployable on the energy-efficient Jetson Nano platform for embedded and edge computing applications on campus. The diagram as shown in Figure 4.2 represents the different steps involved in the development and deployment of a chatbot system using the NVIDIA Jetson Nano edge device and OpenAI’s language models. The steps are arranged in a sequential flow, indicating the order of tasks to be carried out. 1. Setup of Jetson Nano: The first step involves setting up the NVIDIA Jetson Nano edge computing device. This may include installing the required software, configuring the hardware, and ensuring proper connectivity. 2. Getting OpenAI API Key: The second step is obtaining an API key from OpenAI, which is necessary to access and utilize their language models and services for natural language processing and generation. 3. Scraping FAQ Data: This step involves scraping or collecting frequently asked questions (FAQ) data from relevant sources. This data will be used to create a custom dataset for fine-tuning the language model and enabling the chatbot to provide accurate and context-specific responses. 4. Code Overview: At this stage, the code or software components responsible for integrating the language model,

Setup of Jetson Nano

Getting OpenAl API Key

Scraping FAQ Data

Code Overview

Installing Chatbot

Chatbot Demo

Figure 4.2  Different steps involved in the development and deployment of a CAMERA chatbot.

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110  Edge of Intelligence retrieving context from the FAQ data, and managing the chatbot functionality are reviewed and prepared. 5. Installing Chatbot: Once the code is ready, the chatbot system is installed on the NVIDIA Jetson Nano edge device. This step involves deploying the language model, integrating the FAQ dataset, and setting up the necessary software components for the chatbot to function. 6. Chatbot Demo: The final step is to demonstrate the chatbot’s capabilities by allowing users to interact with it through a user interface or a conversational interface. This demo showcases the chatbot’s ability to understand natural language queries, retrieve relevant information from the FAQ data, and provide accurate and contextual responses using OpenAI’s language models running on the Jetson Nano edge device. The diagram as shown in Figure 4.2 illustrates the end-to-end process of setting up the hardware (Jetson Nano), obtaining the necessary resources (OpenAI API Key and FAQ data), preparing the code, deploying the chatbot system on the edge device, and finally demonstrating its functionality. This workflow highlights the integration of edge computing capabilities with natural language processing and generation techniques to create an intelligent and context-aware chatbot solution. The flow diagram as shown in Figure 4.3 represents the logical flow of a CAMERA chatbot application, specifically the process of user interaction and response generation. It aligns with the context of the previous

Start

User Inputs Name

Check if User ID is Provided

Create Chatbot Instance

Load Chat History

User Inputs Question

Generate and Display Response

Append User and Bot Response

End

Figure 4.3  Logical flow of a chatbot.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  111 discussion about developing an intelligent hardware-based FAQ chatbot using the NVIDIA Jetson Nano and OpenAI’s language models. Let me explain the different steps: 1. Start: The process begins. 2. User Inputs Name: The user is prompted to enter their name, which could be used for personalization or identification purposes. 3. Check if User ID is provided: The system checks if a user ID is provided along with the name. This step could be optional and may be used for loading user-specific chat history or preferences. 4. Create Chatbot Instance: A chatbot instance is created, which likely involves initializing the language model, loading the custom FAQ dataset, and setting up the necessary components for natural language processing and generation. 5. Load Chat History: If a user ID is provided, the system may load any previous chat history associated with that user. This step allows for context continuity and personalized responses based on prior conversations. 6. User Inputs Question: The user is prompted to enter their question or query, which will be processed by the chatbot. 7. Generate and Display Response: Using the language model, the custom FAQ dataset, and any loaded chat history, the chatbot generates an appropriate response to the user’s question and displays it. 8. Append User and Bot Response: The user’s question and the chatbot’s response are appended to the chat history, potentially for future reference or to maintain context for subsequent interactions. 9. End: The process completes, and the system waits for the next user interaction or question. This flow diagram as shown in Figure 4.3 represents a typical CAMERA chatbot interaction cycle, where the user provides input, the chatbot generates a response based on the available data and models, and the conversation is recorded for future reference. The integration of the NVIDIA Jetson Nano and OpenAI’s language models allows for efficient on-device processing, context-aware response generation, and the ability to handle natural language queries accurately using the custom FAQ dataset.

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112  Edge of Intelligence

4.5 Results This code defines the main() function as shown in Figure 4.4, which is the entry point of the program and contains the logic for creating and interacting with a chatbot instance based on the OpenAI API and a custom FAQ dataset. A breakdown of the code in the context of the flow diagram is given below: 1. The code sets the title of the application to “FAQ Chatbot”. 2. It prompts the user to input their name using st.text_input(), which is likely a function from a user interface library (e.g., Streamlit). 3. If a user ID is provided (i.e., if user_id is not empty), the code creates a chatbot instance (bot) using the Chatbot class from the os.environ[“OPENAI_API_KEY”], an index (possibly related to the FAQ dataset), and the user_id. 4. If a user ID is provided, the code loads the chat history for that user by calling bot.load_chat_history(). 5. The code then displays the loaded chat history by iterating over the bot.chat_history list and writing each message’s role and content using st.write(). 6. The user is prompted to enter their question using st.text_input(). 7. The code generates a response based on the user’s input: ◦◦ If the user input is “bye” or “goodbye” (case-insensitive), it displays a farewell message. ◦◦ Otherwise, it calls bot.generate_response(user_input) to generate a response from the chatbot instance based on the user’s input. ◦◦ The chatbot’s response content is stored in bot_response_ content. 8. The code appends the user’s input and the chatbot’s response to the chat history by creating new dictionaries with the respective roles and contents, and calling bot.chat_history. append(). 9. The updated chat history is saved by calling bot.save_chat_ history(). This code follows the flow diagram discussed earlier, where the user inputs their name and question, the chatbot instance is created (potentially

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  113 using a custom FAQ dataset), the chat history is loaded and displayed, the chatbot generates a response based on the user’s question, and the conversation is appended to the chat history for future reference. The integration with the OpenAI API and the use of a custom FAQ dataset enable the chatbot to provide accurate and context-aware responses to the user’s questions, leveraging the language model’s capabilities and the specific domain knowledge contained in the FAQ data.

Figure 4.4  Main function.

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114  Edge of Intelligence

Figure 4.5  Graphical user interface of CAMERA chatbot.

The Figure 4.5 shows the graphical user interface (GUI) of the CAMERA (Conversational AI Model for Effective Responses with Augmented Retrieval) chatbot, which is an FAQ chatbot system developed using the NVIDIA Jetson Nano edge device and OpenAI’s language models. The CAMERA chatbot’s user interface was designed with a strong emphasis on usability and accessibility. The intuitive web-based interface allowed users to engage in natural conversations by typing or speaking their queries. The chatbot’s responses were dynamically displayed, providing a smooth and interactive experience. The interface incorporated features such as query history, suggested follow-up questions, and the ability to rate the chatbot’s responses. Additionally, the interface was optimized for different devices, ensuring a consistent experience across desktops, laptops, and mobile devices. The clean and modern design, along with seamless integration with the university’s branding and color schemes, contributed to a cohesive and visually appealing experience for users. The GUI consists of the following elements: 1. Title: The title “FAQ Chatbot” is displayed at the top, indicating the purpose of the application. 2. User Name Input: There is a text input field labeled “Your Name:” where the user can enter their name. This feature likely allows personalization or identification of the user for loading chat history or user-specific preferences. 3. User Query Input: The GUI provides a text input area where the user can type their questions or queries related to the FAQ domain.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  115 4. Chat History: The main section of the GUI displays the chat history, showing the user’s questions and the chatbot’s responses in a conversational format. In the example shown, the user input their name as “test” and asked the question “How can I check my timetable?”. The chatbot responded with “You can download your timetable from the University information management system (UIMS).”, suggesting that the chatbot is trained on a dataset related to a university or educational domain. The user then typed “bye”, and the chatbot responded with “Goodbye!”, indicating that the system is programmed to recognize and respond appropriately to farewell messages. The GUI provides a simple and intuitive interface for users to interact with the CAMERA chatbot, allowing them to input their queries, view the chatbot’s responses, and maintain a conversational context through the displayed chat history. Overall, the GUI aligns with the implementation described in the code snippet, where the user inputs their name and question, the chatbot generates a response based on the language model and the custom FAQ dataset, and the conversation is recorded and displayed in the chat history.

4.6 Conclusion and Future Scope The suggested framework for Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Chatbot combines an OpenAI API with a specially designed dataset for the FAQ domain, utilizing the potential of retrieval-augmented generation models. By optimizing the models using domain-specific terminology and expertise, the system responds to user inquiries with precision and pertinence. Moreover, the deployment of these computationally demanding models on resource-­ constrained edge devices is demonstrated via the implementation on the Jetson Nano hardware platform. When compared to standard chatbot approaches, the evaluation findings demonstrate the higher performance of the CAMERA system in terms of answer accuracy, relevancy, and user happiness. Intelligent conversational AI systems have the potential to completely transform human-machine interactions by offering more effective and natural-feeling channels of communication. With accurate and pertinent information readily available to users, the CAMERA system is a major step towards achieving this goal.

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116  Edge of Intelligence Even though the CAMERA system has shown encouraging results, there are still a number of areas that might use further investigation and development. The performance of the system might be further improved by growing the custom dataset and investigating more sophisticated retrieval and creation methods. Further research into more effective model compression and hardware acceleration methods may also make it possible to implement these systems on even more resource-constrained hardware. Investigating conversational AI systems with several modalities, such as visual and other, may lead to novel use cases and applications. Moreover, the incorporation of sophisticated natural language understanding and generating functionalities may augment the system’s capacity to participate in increasingly intricate and contextualized conversations. The CAMERA framework is a monument to the strength of fusing state-of-the-art natural language processing methods with creative hardware implementations, as we continue to push the boundaries of conversational AI. This is paving the way for a future in which intelligent conversational agents seamlessly provide us with personalized assistance and information, becoming an indispensable part of our everyday lives.

References 1. Buhalis, D. and Moldavska, I., Voice assistants in hospitality: using artificial intelligence for customer service. J. Hosp. Tour. Technol., 13, 386–403, 2022, https://doi.org/10.1108/JHTT-03-2021-0104. 2. Yenduri, G., Ramalingam, M., Selvi, G.C. et al., GPT (Generative Pre-Trained Transformer)— A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions. IEEE Access, 12, 54608–54649, 2024, https://doi.org/10.1109/ ACCESS.2024.3389497. 3. Gruetzemacher, R. and Paradice, D., Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research. ACM Comput. Surv., 54, 1–35, 2022, https://doi.org/10.1145/3505245. 4. Nassiri, K. and Akhloufi, M., Transformer models used for text-based question answering systems. Appl. Intell., 53, 10602–10635, 2023, https://doi. org/10.1007/s10489-022-04052-8. 5. Raiaan, M.A.K., Mukta SH, Fatema, K. et al., A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges. IEEE Access, 12, 26839–26874, 2024, https://doi.org/10.1109/ ACCESS.2024.3365742.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  117 6. Yang, R., Tan, T.F., Lu, W. et al., Large language models in health care: Development, applications, and challenges. Health Care Sci., 2, 255–263, 2023, https://doi.org/10.1002/hcs2.61. 7. Alawida, M., Mejri, S., Mehmood, A. et al., A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity. Information, 14, 462, 2023, https://doi.org/10.3390/info14080462. 8. Jevremovic, A., Kostic, Z., Perakovic, D., Energy-Efficient Edge Intelligence: A Comparative Analysis of AIoT Technologies. Mobile Netw. Appl., 1–9, 2023, https://doi.org/10.1007/s11036-023-02122-w. 9. Muralidhar, R., Borovica-Gajic, R., Buyya, R., Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards. ACM Comput. Surv., 54, 1–37, 2022, https://doi.org/10.1145/ 3511094. 10. Manolescu, D., Reid, D., Secco, E.L., Hardware and Software Integration of Machine Learning Vision System Based on NVIDIA Jetson Nano, 129–137, 2024. 11. Schizas, N., Karras, A., Karras, C., Sioutas, S., TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review. Future Internet, 14, 363, 2022, https://doi.org/10.3390/fi14120363. 12. Mao, Y., He, P., Liu, X. et al., Generation-Augmented Retrieval for Opendomain Question Answering, ArXiv, 2020. 13. Chizhik, A.V. and Zherebtsova, Y., Challenges of Building an Intelligent Chatbot. Intelligent Memory Systems, 2020. 14. Kumar, R.M.S. and Ali, M.M., A Review on Chatbot Design and Implementation Techniques, 2020. 15. Dharwadkar, R. and Deshpande, N.A., A Medical ChatBot. Int. J. Comput. Trends Technol., 60, 41–45, 2018, https://doi.org/10.14445/22312803/ IJCTT-V60P106. 16. Adamopoulou, E. and Moussiades, L., An Overview of Chatbot Technology, in: Artificial Intelligence Applications and Innovations, I. Maglogiannis, L. Iliadis, P.E. (Eds.), pp. 373–383, Springer International Publishing, Cham, 2020. 17. Singh, K.J., Kapoor, D.S., Sohi, B.S., Selecting Social Robot by Understanding Human–Robot Interaction, in: International Conference on Innovative Computing and Communications, pp. 203–213, 2021. 18. Singh, K.J., Kapoor, D.S., Thakur, K. et al., Map Making in Social Indoor Environment Through Robot Navigation Using Active SLAM. IEEE Access, 10, 134455–134465, 2022, https://doi.org/10.1109/ACCESS.2022.3230989. 19. Sachdeva, P. and Singh, K.J., Automatic segmentation and area calculation of optic disc in ophthalmic images. 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015, 2016, https://doi.org/10.1109/RAECS.2015.7453356.

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118  Edge of Intelligence 20. Singh, K., Singh, K.J., Kapoor, D.S., Image retrieval for medical imaging using combined feature fuzzy approach. 2014 International Conference on Devices, Circuits and Communications, ICDCCom 2014 - Proceedings, 2014, https://doi.org/10.1109/ICDCCOM.2014.7024725. 21. Singh, K.J., Kapoor, D.S., Sharma, A., Kohli, A.K., Multi-level threshold based edge detector using logical operations. J. Natl. Sci. Found, 44, 145, 2016, https://doi.org/10.4038/jnsfsr.v44i2.7995. 22. Jawhar, Q., Thakur, K., Singh, K.J., Recent Advances in Handling Big Data for Wireless Sensor Networks. IEEE Potentials, 39, 22–27, 2020, https://doi. org/10.1109/MPOT.2019.2959086. 23. Sharma, A., Singh Kapoor, D., Nayyar, A. et al., Exploration of IoT Nodes Communication Using LoRaWAN in Forest Environment. Comput. Mater. Continua, 71, 6239–6256, 2022, https://doi.org/10.32604/cmc.2022.024639. 24. Jawhar, Q., Thakur, K., Singh, K.J., An efficient clustering algorithm for big data gathering in large scale wireless sensor networks (LS-WSNs). Int. J. Innov. Technol. Explor. Eng. (IJITEE), 8, 7, 1500–1505, 2019. 25. Thakur, K., Kapoor, D.S., Singh, K.J., Sharma, A., Malhotra, J., Diagnosis of Parkinson’s Disease Using Machine Learning Algorithms. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol. 608, pp. 205–217, Springer, Singapore, 2023. https://doi.org/10.1007/978981-19-9225-4_16. 26. Sandhu, H.S., Singh, K.J., Kapoor, D.S., Automatic edge detection algorithm and area calculation for flame and fire images. Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016, pp. 403–407, 2016, https://doi.org/10.1109/ CONFLUENCE.2016.7508152. 27. Kaur, I. and Singh, K.J., Printed Text Recognition System for MultiScript Image. Int. J. Signal Process. Syst., 4, 5, 411–416, 2016, https://doi. org/10.18178/ijsps.4.5.411-416. 28. Singh, K.J., Kapoor, D.S., Abouhawwash, M. et al., Behavior of Delivery Robot in Human-Robot Collaborative Spaces during Navigation. Intell. Autom. Soft Comput., 35, 1, 795–810, 2023. https://doi.org/10.32604/iasc. 2023.025177. 29. Singh, K.J., Kapoor, D.S., Sohi, B.S., Understanding social conventions for socially aware robot navigation. IEEE Potentials, 42, 37–42, 2023, https://doi. org/10.1109/MPOT.2020.3026969. 30. Luthra, S. and Singh, K.J., SVD Based Dual Watermarking of Images. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 5, 23–27, 2015. 31. Singh, K.J., Kapoor, D.S., Sohi, B.S., The MAI: A Robot for/by Everyone, in: Companion of the 2018 ACM/IEEE International Conference on HumanRobot Interaction, pp. 367–368, 2018.

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CAMERA: AI-Powered FAQ Chatbot on Jetson Nano  119 32. Sharma, A., Thakur, K., Kapoor, D.S. et al., Effective learning through prototyping boards. IEEE Potentials, 41, 6–11, 2022, https://doi.org/10.1109/ MPOT.2021.3089463. 33. Singh, K.J., Kapoor, D.S., Sohi, B.S., All about human-robot interaction, in: Cognitive Computing for Human-Robot Interaction, pp. 199–229, Elsevier, United Kingdom, 2021. 34. Nguyen, T.N., Potential effects of chatbot technology on customer support: A case study, 2019. 35. Feldman, P., Foulds, J.R., Pan, S., RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots, ArXiv, 2024. 36. Bink, J.M., Personalized Response with Generative AI: Improving Customer Interaction with Zero-Shot Learning LLM Chatbots, Eindhoven University of Technology, 2023. 37. Hudeček, V. and Dusek, O., Are Large Language Models All You Need for Task-Oriented Dialogue?, in: Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 216–228, 2023. 38. Louis, A., Van Dijck, G., Spanakis, G., Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 22266–22275, 2024, https://doi.org/10.1609/aaai.v38i20.30232. 39. Chen, J., Lin, H., Han, X., Sun, L., Benchmarking Large Language Models in Retrieval-Augmented Generation. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 17754–17762, 2024, https://doi.org/10.1609/ aaai.v38i16.29728. 40. Shuster, K., Poff, S., Chen, M. et al., Retrieval Augmentation Reduces Hallucination in Conversation, in: Findings of the Association for Computational Linguistics: EMNLP 2021, Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 3784–3803, 2021.

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5 Edge Computing in Educational Technology: The Power of Edge AI for Dynamic and Personalized Learning Ganeshayya Shidaganti*, V. Aditya Raj, V.R. Monish Raman and Shubeeksh Kumaran Dept. of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, Karnataka, India

Abstract

With artificial intelligence (AI) and Edge Computing growing at a rapid pace, we find ourselves on the brink of innovation and intelligence, gazing into a future in which education is not only desired but also welcomed here and now. Edge AI has a wide range of applications in the field of education that could help students in remote villages who do not have access to any internet access educational content and tools stored on their devices. AI power systems could take the aspect of personalized learning to the next level, which would allow the teachers to better understand the interests of the students. One such implementation that will be explored in this chapter is a system culminating in the creation of concise, researched lecture summaries, providing students with an easily digestible overview of complex topics. Edge Computing and AI are said to be at the forefront of new-age technologies. Traditional methods fail to cope with the new challenges in the domain of education, such as the diverse learning styles, geographical barriers, and an overflow of knowledge. Edge computing, unlike cloud-based computing, could be the solution that offers a paradigm shift by bringing intelligence and processing power closer to the point of learning, directly to classrooms and student devices that could unleash a torrent of possibilities, making education not just efficient, but exhilarating. Keywords:  Edge AI, real-time knowledge, lecture video summarization, personalized learning, knowledge accessibility *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (121–152) © 2025 Scrivener Publishing LLC

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5.1 Introduction: Unveiling the Potential of Edge AI in Educational Technologies In recent years, the proliferation of audio and video content across digital platforms has catalyzed a paradigm shift in educational methodologies [1]. From academic lectures to sports talk shows, from seminars to documentaries, the sheer volume of multimedia resources available poses both challenges and opportunities for learners and educators alike. Recognizing this landscape, our chapter delves into the transformative potential of real-time lecture audio summarization and video content extraction, underpinned by cutting-edge technologies and implemented through edge computing infrastructure [2]. At the heart of our exploration lies the fusion of Assembly AI and OpenCV, two powerful tools converging to revolutionize the learning experience [3]. By seamlessly separating audio and video processing, our system empowers students to effortlessly review key points, formulas, and pertinent data from lectures they may have missed or wish to reinforce [4]. Leveraging Assembly AI’s automatic transcription models, audio streams are transmuted into concise text summaries, while OpenCV’s image analysis capabilities extract essential visual information from lecture videos [5]. Moreover, our approach goes beyond mere transcription and extraction; it encompasses the synthesis of these elements into a coherent educational resource. By analyzing board content and other visual aids, our system distills complex information into easily digestible formats, obviating the need for exhaustive snapshots or extensive note-taking. This not only saves students valuable time but also enhances comprehension and retention of subject matter. Crucially, the implementation of this system extends to the very fabric of educational environments through edge computing. With processing capabilities deployed directly within classrooms, real-time summarization becomes seamlessly integrated into the learning process, transcending the confines of traditional educational boundaries. Thus, our chapter elucidates not only the technical intricacies of our innovative system but also its profound implications for the future of education.

5.2 Challenges of Traditional Education in the Digital Age Since the beginning of human civilizations, education, in its various forms, has been a critical aspect of development. From the crude teachings of

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Edge Computing in Educational Technology  123 early civilizations to the current sophisticated educational systems, the always-evolving nature of education is proof of the ways humans perceive knowledge over time. Education today has its own set of challenges, as it adheres to a one-size-fits-all approach; such an approach fails to accommodate the varying preferences of individual learners. One of the most common forms of education is teaching in classrooms, where a student receives knowledge from a lecturer who transmits knowledge [6]. Visual learners thrive with diagrams and illustrations, while auditory learners prefer lectures and discussions. Kinesthetic learners, require experiences and project-based learning to grasp concepts effectively [7]. Though it is seen the current education systems have addressed all types of learners and their preferences there are quite a few challenges still being faced by many of them. In this section, we shall now elaborate on the several challenges of the traditional education system as shown in Figure 5.1.

LACK OF PERSONALIZATION

LACK OF TIMELY FEEDBACK

LIMITED ACCESSIBILITY

CHALLENGES OF TRADITIONAL EDUCATION

TEACHER WORKLOAD

INFORMATION OVERLOAD PASSIVE LEARNING PARADIGM

Figure 5.1  The various challenges faced in traditional education.

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5.2.1 Lack of Personalization Individuals coming from different backgrounds usually possess varying learning styles, and also different learning paces. However, as mentioned before, traditional classrooms follow a standardized curriculum and teaching methods that may not cater to the specific needs of each student. This rigidity could often disregard the varied learning preferences of students. For example, let us consider a classroom where there is a lecture being given on intricate mathematical concepts in a single hour. While some students grasp the concepts effortlessly, others may find it difficult to understand as they would need a more elaborate explanation or face difficulties in comprehending the materials provided during the lectures. As a result, these students may disengage from their educational experience, leaving their potential for growth and innovation untapped. Therefore, the lack of personalization in traditional education erects barriers to learning, inhibiting the intellectual and emotional growth of students.

5.2.2 Limited Accessibility Accessibility to education is a key aspect in the research field of education quality assurance [8]. Students in remote areas have limited access to education due to various reasons such as geographical barriers and resource constraints. Rugged terrains, vast distances, and lack of transportation infrastructure are a few such geographical barriers that pose important challenges for students living in rural or isolated areas. Such students who have limited access to reliable transportation struggle to attend school regularly and may endure long commutes that can consume their valuable time and energy. Moreover, schools in such remote areas are often found to lack essential facilities such as libraries, laboratories, and technology infrastructure that includes even basic internet connectivity which is very much required in today’s day and age. With such limited access, the students may not have experienced high-quality education and need a solution that could surpass both the geographical boundaries and infrastructure limitations.

5.2.3 Information Overload The digital age has brought about an era of providing students with unprecedented opportunities to explore diverse topics and perspectives, but that comes along with an abundance of information, making it a challenge for students to navigate among the vast amount of information even with web search engines, filtering and indexing tools [9]. Amongst the abundance of

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Edge Computing in Educational Technology  125 information, there is non-credible and unreliable information which could lead to confusion and misinformation, particularly for young learners who lack the skills to evaluate sources critically. Information overload leads to students not being able to focus completely on a certain topic and having only a shallow understanding of it. Overall, the constant generation of new information will make it difficult for learners to classify the essential concepts from irrelevant details.

5.2.4 Passive Learning Paradigm Traditional classroom education often employs passive learning experiences, where students passively gain knowledge only from teachers through their lectures and readings. The primary purpose of such kind of learning is to absorb information only for the sake of clearing examinations and not taking into account the actual underlying concepts of the information. This one-way flow of information can prove to be efficient in transmitting information to a large group of students but can lead to a lack of motivation and engagement from them. By merely memorizing facts and regurgitating them on exams, students become passive recipients of information, rather than actively engaging with the material and applying it in meaningful ways. Therefore, students may struggle to retain information or apply it in real-world contexts, limiting the effectiveness of their learning experience.

5.2.5 Teacher Workload In today’s world, the responsibilities of a teacher extend far beyond the boundaries of traditional teaching roles. Not only are they burdened with crafting engaging lesson plans but they are also required to do a variety of administrative tasks such as managing class logistics, preparing appropriate materials and grading assignments. These tasks though are a part of their duties, and demand a lot of time and energy from them. As a result, teachers often find themselves under immense stress and diminished job satisfaction, affecting not only their well-being but also the quality of instructions they provide. The time spent on such administrative tasks could rather be used to plan innovative lessons and provide individualized support to students. In doing so, the students will be greatly benefitted and a nurturing learning environment could be fostered. With the advancements in technology, it is possible to implement solutions that automate routine administrative tasks to relieve a huge load on teachers.

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126  Edge of Intelligence

5.2.6 Lack of Timely Feedback It is integral that a student is provided with timely feedback to support learning and growth. Traditional teaching methods usually fail in both monitoring progress and addressing areas of weakness in a timely fashion. Such feedback could be of great help to students; they could identify their strengths and weaknesses and quickly start working on them. For example, in the traditional education system, though the assessments are held periodically, the results are given weeks after the assessment has been completed. This delay in feedback may not be of much significance, as students would have moved on to new topics for the next assessment by then, depriving them of the opportunity to identify and correct their misconceptions. There is a high possibility that delayed feedback could lead to persistent gaps in understanding and hindered academic progress, as the students would have lost interest in engaging with assessment tasks and would perceive them to lack relevance to their learning goals. To address this issue, educators must leverage technology to enable students to access their progress in real-time and provide immediate feedback, this could be done by innovative assessment strategies, polls, and peer feedback. In conclusion, the challenges outlined in traditional education underscore the need for transformative solutions. With the rapid pace at which the fields of AI and Edge Computing have been developing, we can say that we have reached a major transformative era in education, where education empowers us to embrace innovation. By leveraging the power of edge computing and AI, educators can unlock new possibilities for personalized learning experiences, streamline administrative tasks, and provide timely feedback to support student growth and achievement. In the following section, we will explore how edge computing and AI represent a paradigm shift in education, offering innovative solutions to address the challenges of traditional education and usher in a new era of dynamic, personalized learning experiences.

5.3 Edge Computing and AI: Revolutionizing Educational Dynamics Edge computing is a type of distributed computer paradigm that prevents relying on centralized data centers by bringing the computational processing closer to the data source or “edge” of the network as shown in Figure 5.2. In traditional cloud computing architectures, the data is stored and processed in data centers located far from the end-users; this causes

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Edge Computing in Educational Technology  127 COMPUTATION TAKES PLACE AT THE REMOTE SERVER REMOTE CLOUD SERVER

EDGE SERVER

CLOUD COMPUTING

EDGE SERVER

EDGE SERVER

COMPUTATION TAKES PLACE AT THE EDGE SERVER/NODE

EDGE COMPUTING

Figure 5.2  Difference between edge computing and cloud computing.

latency issues and high dependency on the internet for access. Traditional computing does not meet the needs of today’s intelligent society; therefore, the novel computer paradigm of edge computing has been enabled [10]. In edge computing, the data is processed locally at the edge itself, the response time is faster and the latency is minimized. The processing and filtering of data locally is done before transmitting it to centralized data centers which helps reduce bandwidth usage and alleviate network congestion. Edge computing, therefore, addresses the challenges posed by traditional cloud computing, especially in scenarios where having a stable connection to the cloud is difficult, such as places where the internet is not easily accessible. Furthermore, edge computing provides privacy and protection by keeping sensitive data localized and keeping the need to transmit data over public networks to a minimum [11]. Taking into account all the facts we can confirm that edge computing has brought in a revolutionary change towards decentralized computing architectures that empower organizations to fully utilize their data in a much faster and secure way. Artificial Intelligence (AI) over the years has proven to enable systems to perform tasks that typically require human intelligence. To achieve this, it makes use of a wide range of techniques and approaches, such as machine learning, natural language processing, computer vision, robotics, etc. When integrated with edge computing, AI holds the potential to revolutionize the landscape of education by offering advanced and adaptive learning experiences that make the learner more engrossed as the experience is customized to each learner’s requirements and preferences. Deploying AI algorithms directly on the edge devices or edge servers provides real-time data insights that can be utilized by educators to customize

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128  Edge of Intelligence educational content, and delivery methods based on students’ learning stabilities, and progress. Therefore, in this section, we shall now discuss the major changes that edge computing and AI bring to the landscape of education.

5.3.1 Bringing Intelligence to the Point of Learning When cloud computing was introduced, it brought in several remarkable features that aided the educational sector, it brought in the concept of online classes, where an educator from a different place far off place, could seamlessly transfer knowledge to a learner with the help of internet. Not only that, the learner could get access to all the required learning materials from anywhere around the world to the touch of his finger. Though there were many advantages, as discussed in the previous section there were many challenges to it. With edge computing, we have the advantage of bringing intelligence to the point of learning, this signifies a huge leap forward in the realm of education, as it empowers educational institutions to transcend the constraints of centralized processing and deliver learning experiences that are both responsive and adaptive. With AI and the various innovations surrounding it, we can now develop and curate learning materials, such as textbooks, interactive simulations, and videos, such the learning is more engrossing and practical to the students [12]. The relationship between edge computing and AI redefines the processing architecture of educational systems, utilizing the decentralized infrastructure of edge computing to facilitate real-time analysis and ­decision-making at the network edge. AI algorithms deployed on edge devices such as smartphones or tablets can analyze student interactions with educational content in real-time. These algorithms understand the patterns in student responses and by doing so try to identify the areas of difficulty or misunderstanding, and provide immediate feedback or intervention to support learning outcomes. Consider an AI-powered adaptive learning platform deployed at the edge as shown in Figure 5.3, the platform utilizes machine learning algorithms to analyze vast datasets of student performance metrics, learning behaviors, and cognitive profiles. Such a platform can pave the path to personalized learning ways for each student, dynamically adjusting the level of difficulty at which tests are prepared, and instructional modalities based on real-time feedback. For example, a student struggling with mathematical concepts may receive personalized tutorials, interactive exercises, and targeted feedback tailored to their specific learning needs. AI-powered devices installed at the edge improve information understanding and

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Edge Computing in Educational Technology  129 Al-powered adaptive learning platform deployed at the edge

EDGE SERVER

Figure 5.3  AI-powered adaptive learning platform deployed at the edge enabling edge intelligence.

retention by customizing the learning experience to each student’s distinct demands and responses. This creates a more productive and captivating learning environment.

5.3.2 Unlocking New Possibilities for Education [13] talks about the various breakthroughs that have taken place in the field of edge computing and AI, which has led to a new interdisciplinary known as edge intelligence. The various new possibilities around edge intelligence can bring in features that have never been thought about in the new age of education. At its core, this shift is driven by the exceptional scalability, flexibility, and responsiveness afforded by edge computing architectures. For example, let us assume a scenario where a university deploys edge computing nodes with AI-powered analytics capabilities. Educators can customize their educational tactics to match the ever-changing needs of their students by utilizing machine learning algorithms that have been deployed at these edge nodes. These edge nodes will also contain and process vast amounts of data, including student performance metrics, learning behaviors, and instructional resources, in real time. Furthermore, educational content delivery can be revolutionized, enabling students to interact and immerse themselves into learning, such type of learning transcends the limitations of traditional models. An example of such an experience could be achieved by an AI-powered virtual reality (VR) classroom deployed at the edge, where students can engage with educational content in a fully immersive, three-dimensional environment [14]. In this three-dimensional environment, the AI algorithms examine student answers, behaviors, and interactions in real-time adjusting the way that training is delivered according to each student’s preferences and learning styles. This is possible by leveraging edge computing’s low-latency

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130  Edge of Intelligence processing capabilities which will eventually help in enhancing the student’s engagement and motivation towards learning. Natural language processing (NLP) models are another aspect of AI that could be integrated into edge devices such as smartphones or tablets to provide personalized learning support to students anytime, anywhere. Once deployed on the edge devices they could operate as assistants to students by autonomously answering questions, providing explanations, and offering feedback, even in environments with limited or intermittent connectivity. Through the utilization of artificial intelligence (AI) and edge computing technologies, educators may open up new avenues for customized instruction, flexible learning plans, and equal access to education. This can enable students to reach their maximum potential irrespective of their financial or geographic circumstances. In the following section, we shall propose a system that leverages the integration of edge computing and artificial intelligence to revolutionize note-taking and knowledge comprehension. Advanced technologies such as Assembly AI and Open CV have been utilized to streamline the process of capturing and synthesizing key lecture content, thereby enhancing the learning experiences of students. The following section also explores the technical details of the video extraction and audio summarization procedures. Our system combines sophisticated image processing methods with natural language processing approaches to produce strong content extraction and summary capabilities. The thorough description of the technology, dataset, and implementation techniques offers insightful information about the fundamental processes that underpin the operation of our system.

5.4 Enhancing Education Through Video Lecture Summarization: An Exemplary Scenario The system proposed here aims to enable real-time lecture video summarization and content extraction using Assembly AI and OpenCV. By leveraging advanced technologies, the design separates audio and video processing, applies speech recognition and natural language processing for audio summarization, and utilizes OpenCV algorithms to extract relevant formulas and data from the video frames. The integrated system provides students with a cohesive learning resource, enhancing their learning experiences by facilitating efficient review and understanding of key lecture content.

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Real Time lecture Video to Notes Summarization Creator Summarizer Database

Edge Computing in Educational Technology  131

Store summarized document

Formatting video

Summarize Video

[Invalid Format] Records Video

Figure 5.4  Activity diagram.

Audio To Text Conversion

Teacher

Process Video

Text Summarization

Summarized Text

Extract Board DataFormula/Diagram

Video Summarized

Merge

Document

Send Document

Document database

Document

User Video Info

Figure 5.5  Data flow diagram.

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Search Data Check Availability

132  Edge of Intelligence The various activities involved in our system are explained in Figure 5.4. In this diagram, the creator records the videos, or a teacher takes a normal class which is being recorded. This recording is provided to the Summarizer which processes the input video and stores it in the database where it can be accessed. The flow of data from the teacher to the database as seen in the activity diagram is explained in Figure 5.5. The various processes involved are explained in the following points: 1.  Preprocessing: Separate Audio and Video: Split the lecture videos into separate audio and video streams for independent processing. Video Preprocessing: Perform preprocessing steps on the video frames, including grayscale conversion, Gaussian blur, and edge detection using the Canny algorithm. 2.  Audio Processing: Assembly AI Integration: Utilize Assembly AI’s speech recognition technology to transcribe the audio component of the lecture videos. Audio Summarization: Apply natural language processing techniques to the transcribed audio to extract main points, concepts, and explanations, generating a concise summary of the lecture. 3.  Video Processing: Board Frame Detection: Identify frames within the video stream that contain the board, where important formulas and data are presented. Formula and Data Extraction: Extract the relevant formulas and data from the identified board frames, creating visual content that complements the audio summary. 4.  Integration and Visualization: Combine Audio Summary and Visual Content: Integrate the audio summary and extract visual content into a cohesive learning resource. Visualize Summarized Content: Display the summarized content, including highlighted formulas and diagrams, in a user-friendly interface for students to review and reinforce their understanding. 5.  Real-Time Processing and Feedback: Real-Time Implementation: Enable real-time processing of lecture videos, allowing students to receive immediate feedback and enhance their learning experiences.

5.4.1 Methodology To ensure effective summarization and content extraction, it is essential to separate the audio and video components of the lecture. By processing

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Edge Computing in Educational Technology  133 them independently, we can optimize the techniques used for each type of data. The first step in the process is to summarize the audio. Using advanced natural language processing algorithms, we can extract the main points, concepts, and explanations from the lecture. This involves transcribing the audio, identifying keywords, and applying summarization techniques to generate a concise and informative summary. Simultaneously with audio summarization, we employ Object Detection algorithms in the video processing phase. These algorithms analyze the video frames in real time to identify the board frame where important formulas and data are presented. By detecting and extracting this content, students can review and understand the crucial information without the need to watch the entire video. a.  Audio Summarization In the process of audio summarization, we first extract the text from the audio using Assembly AI’s speech recognition technology. This helps us convert the spoken content into written text. Once we have the text, we employ the natural language toolkit (NLTK) library, which provides us with powerful tools for text processing. With the NLTK library, we can apply various techniques, such as sentence tokenization and text summarization algorithms, to condense the extracted text into a concise summary. To enhance the summary further, we utilize sentimental analysis techniques to identify and highlight important points within the summarized text. This approach allows users to focus on the crucial insights and significant information captured in the audio. By combining speech recognition, text summarization, and sentimental analysis, we create an effective audio summarization solution with a flow as shown in Figure 5.6. 1. Technology: This paper summarizes the lecture video as simple texts and thumbnail images. For summarization, in order to recognize the speech and its corresponding texts, we’ve used the natural language processing (NLP), the natural language toolkit (NLTK) library of the Python programming language, convolutional neural networks (CNN), Google Cloud Speech API, the ‘jiwer’ for similarity measures for automatic speech recognition and evaluation. This repository contains a simple python package to approximate the word error rate (WER), match error rate (MER), word information lost (WIL) and word information preserved (WIP) of a transcript. For extracting the thumbnails of the important contents of the video, we used OpenCV technology in Pytorch to achieve accurate results.

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134  Edge of Intelligence 2. Dataset: Speech Recognition: We used audio with a wide range of accents, audio quality, number of speakers, and industry-specific vocabularies. This included audio taken from product demos, tutorial videos, documentaries, podcasts, sports talk radio, and corporate earnings calls. 3. Implementation: Aligning Audio to Transcript: The input signal we provide into our model will be used to train it to predict the probability distribution of each character in the alphabet for each frame (i.e., timestep). With classic speech recognition models, you are required to align the transcript text and audio before training, and the model would be programmed to anticipate particular labels at particular frames. We can bypass this step thanks to the CTC loss function’s innovation. During training, our model will learn to align the transcript by itself. The “blank” label CTC introduced, which enables the model to assert that a specific audio frame did not generate a character, is crucial in this situation. • Evaluating the Speech Model: When comparing the speech reputation model, the enterprise fashionable is the use of the word error rate (WER) because the metric, which is later defined in element in Results. • Training and Monitoring Your Experiments Using Comet. ml: Comet.ml affords a platform that lets in deep studying researchers to music, compare, explain, and optimize their experiments and models. It affords you with a totally effective dashboard in which you may view and music your model’s progress. • Summarization: Auto-Chapters gives a “precise over time” for audio content material that has been transcribed the use of AssemblyAI’s Speech-to-Text API way to the summarizing fashions. In order to offer a mechanically created précis for each “chapter” of content material, it first divides audio/ video documents into logical “chapters” as the subject of dialogue shifts.

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Edge Computing in Educational Technology  135 START

INPUT AUDIO

EXTRACTION OF TEXT FROM AUDIO FILE USING ASSEMBLY AI

SUMMARIZE TEXT FROM NLTK LIBRARY

HIGHLIGHT IMPORTANT POINTS USING SENTIMENT ANALYSIS

ADD TEXT TO DOCUMENT

STOP

Figure 5.6  Flowchart of audio extraction and summarization.

b.  Video Extraction The methods presented here implements image processing techniques to enhance learning experiences through real-time lecture video summarization and content extraction. By leveraging functions such as getContours and detect_formulas_and_diagrams, the methodology focuses on processing the video component of lectures separately. The code applies advanced algorithms to identify important objects, such as a board, within the video frames and extract valuable formulas and data. This enables students to efficiently review key points and relevant visual content. Now, let us delve into the explanation of the main video processing processes. This is illustrated in Figure 5.7.

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136  Edge of Intelligence

START

INPUT VIDEO

IDENTIFY BOARD USING getContours

NO

YES DETECT FORMULAS AND DIAGRAMS

SAVE IMAGE TO DOCUMENT

END

Figure 5.7  Auto-chapter examples.

1. stackImages (scale, imgArray): This function takes a scale factor and an array of images as input. It resizes and stacks the images horizontally or vertically, depending on the dimensions of the image array. It returns the stacked image as the output. 2. getContours (img): This function finds contours in the input image. Contours are continuous curves that represent the boundaries of objects in an image. It uses contour detection techniques to identify objects based on differences in intensity or color. The function filters the contours based on their

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Edge Computing in Educational Technology  137 area and draws bounding boxes around the significant ones. It also extracts the cropped region of interest containing the identified object. 3. detect_formulas_and_diagrams(img): This function is res­ ponsible for detecting formulas and diagrams within an image. It converts the input image to grayscale and applies binary thresholding to distinguish the foreground (formulas and diagrams) from the background. It finds contours in the thresholder image and filters them based on their area and aspect ratio. The function then draws bounding boxes around the important contours to highlight the formulas and diagrams. 4. Explanation of the main loop: The main loop captures frames from a video or a webcam feed. It processes each frame by converting it to grayscale, applying Gaussian blur, and performing edge detection using the Canny algorithm. The getContours function is called to identify important objects (such as a board) within the frame by detecting contours. The detect_formulas_and_diagrams function is then called to identify and highlight formulas and diagrams within the board region. The resulting frames are displayed on the screen. Summarization Results • Board important content detection using Paint drawn image as seen in Figure 5.8, which ignores the scratches and other noise on the board. • Testing of the Impartus videos were seen to have no issues at high resolution input video stream as in Figure 5.9 where the bounding box is around the table on the projector screen and the output extracted important content is at the top left of the same image which will be added to the final summary generated. The below Figure 5.10, shows the sample output of the summarizer with green lines highlighting the important points interpreted from the Impartus video recording. This when combined with the above important table extracted, is the generated notes for the students.

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138  Edge of Intelligence

Figure 5.8  Important content highlighted.

EXTRACTED IMPORTANT CONTENT

Figure 5.9  Accurate analysis of board contents.

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Edge Computing in Educational Technology  139

Figure 5.10  Impartus lecture video audio summary.

5.5 Benefits of the Edge AI for Learning Cultivating a dynamic educational landscape the integration of real-time lecture video summarization and content extraction offers a drastic shift in learning experiences. By harnessing the power of cutting-edge technologies such as AI and Object Detection, educators and students gain access to a wealth of advantages. From streamlining learning processes to creating personalized learning experiences, the system redefines traditional educational methodologies. By seamlessly combining audio summaries and visual aids, it cultivates deeper engagement, enhances retention and empowers learners to navigate the complexities of modern education with great confidence. Now we will delve into the multifaceted benefits of this system as seen in Figure 5.11 and explore how it empowers learners, enriches teaching practices and shapes the future of education.

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140  Edge of Intelligence

Enhanced Learning Efficiency

Improved Accessibility

2.

1.

Future-Ready Skills 6. Development

3.

Benefits of The System

5. Facilitation of Revision and Exam Preparation

Personalized Learning Experience

4. Increased Engagement and Retention

Figure 5.11  Benefits of the system.

5.5.1 Enhanced Learning Efficiency Real-time lecture video summarization significantly enhances learning efficiency by optimizing the utilization of students’ time [15]. Rather than spending hours rewatching entire lecture recordings, students can now access concise summaries of lecture content. This approach enables students to focus on understanding key concepts and reinforcing their learning, ultimately leading to better academic outcomes. By eliminating the need for traditional note-taking and repetitive viewing, the system empowers students to engage more deeply and utilize their study time more efficiently.

5.5.2 Improved Accessibility The primary benefit of real-time lecture video summarization and content extraction is its ability to improve accessibility to educational lectures. Students can now access important information from lectures in real time, regardless of their location. This can be quite beneficial for remote learners or those with limited access to traditional educational facilities. By utilizing

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Edge Computing in Educational Technology  141 this instant access to summarized lecture content, the system breaks down geographical barriers and enables a more inclusive learning environment where all students have equitable access to educational resources.

5.5.3 Personalized Learning Experience Real-time lecture video summarization and content extraction provide a personalized learning experience tailored to individual student needs. By providing hyper-personalized lecture summaries, the system caters to diverse learning preferences and requirements. Students can review specific sections of lectures based on their learning speed and comprehension levels, thereby allowing for a more flexible and adaptive learning experience. This approach not only enhances student engagement in lessons but also fosters a deep understanding of the material by adapting to individual learning needs more effectively.

5.5.4 Increased Engagement and Retention Engagement and retention are crucial aspects of learning, and real-time lecture video summarization and content extraction excel in both areas. The use of multimedia resources enhances retention by presenting information in a concise and effective format that can cater to diverse learning styles and preferences. As a result, students are more likely to grasp key concepts and apply them effectively.

5.5.5 Facilitation of Revision and Exam Preparation Real-time lecture video summarization and content extraction streamline the revision and exam preparation process for students. By offering summarized lecture content and important visual aids for quick revision, the system enables students to efficiently reinforce their understanding of key concepts and identify areas where they need to apply maximum effort on high-priority topics [16]. Additionally, the system supports self-directed learning by encouraging students to review materials at their own pace and convenience, enhancing their overall exam readiness.

5.5.6 Future-Ready Skills Development Real-time lecture video summarization and content extraction play a crucial role in inculcating future-ready skills essential in the 21st century. Equipping students with digital literacy skills and proficiency in technology-enabled

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142  Edge of Intelligence learning tools, the system prepares them to thrive in an increasingly digitized and technology-driven world. These skills are considered invaluable in today’s highly competitive job market where hands-on experience with new technologies is essential for career success and lifelong learning. It is clear that technology has various advantages over traditional teaching techniques when comparing it with the integration of edge computing and AI in education. Learning resources in typical classrooms are frequently static and one-size-fits-all, with little capacity to be customized to meet the requirements and preferences of specific students. On the other hand, individualized learning experiences catered to each student’s particular learning preferences, pace, and interests are made possible by edge computing and AI. While textbooks and lectures play a major role in traditional education, technology allows students to interact with immersive and interactive learning resources like virtual reality (VR) and augmented reality (AR) simulations, which promote deeper comprehension and memory of difficult topics.

5.6 Discussions on Edge AI for Education Integrating advanced technologies, such as edge computing and AI, into environments like classrooms or laboratories, makes it essential to have a robust infrastructure and a certain level of digital literacy among tutors and students. To address this, it is essential to provide some level of training and support to enhance digital literacy skills among the schools and colleges. Strategies for achieving this include organizing workshops, providing online resources, and establishing ongoing support programs. By providing stakeholders the necessary skills, educational institutions can ensure the effective utilization of these technologies in teaching and learning processes. Furthermore, concerns regarding the reliability and maintenance of technologies in educational settings are valid. Implementation strategies must have the plans for managing updates, troubleshooting issues, and ensuring system reliability. This includes establishing robust maintenance schedules, implementing backup systems, and maintaining open channels for technical support. Incorporating discussions on the infrastructure requirements and operational challenges associated with the deployment of advanced technologies in education is essential for providing a comprehensive view of the implementation process. By addressing these considerations, educational institutions can better prepare for the successful integration of edge computing and AI technologies into their teaching and learning practices. Moreover, proactive

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Edge Computing in Educational Technology  143 measures, such as continuous monitoring and adaptation, can help mitigate risks and ensure the long-term reliability and effectiveness of these technologies in educational settings. Now let’s look at a case study to show the effectiveness of Edge AI and the valuable impacts it can have on education.

5.6.1 Case Study: ASUS – Learning on the Edge ASUS conducted a case study to look into the benefits of edge computing to improve student learning in the classroom [17]. By employing edge computing technology, ASUS aimed at enhancing connectivity, providing immersive learning experiences, and providing teachers and students with instant feedback. Effectiveness Measurement: Connectivity and Performance: ASUS improved network performance and speed at educational institutions by implementing edge computing solutions. Students and teachers experienced faster and more dependable access thanks to the reduction of latency and the introduction of intelligence to devices at the network edge. Together with qualitative user input regarding how improved connectivity has impacted their learning experiences, quantitative data like latency reduction percentages and network speed tests can be used to assess how helpful these improvements have been. Immersive Learning: AR labs and VR experiments are two instances of how ASUS used edge computing to facilitate AR and VR applications in the classroom. Deeper participation with the course materials was encouraged and students’ learning experiences were enhanced by these immersive opportunities for learning. Through student engagement metrics, such as participation rates in AR/VR activities and qualitative feedback on the educational value of these experiences, the success of edge computing in allowing immersive learning may be assessed. Real-time Feedback: ASUS implemented edge devices equipped with machine learning capabilities to provide students with immediate feedback while they participated in classroom activities, finished their homework, or studied. These cutting-edge, intelligent edge technologies monitored student development, pinpointed their strong and weak points, and provided tailored learning inputs. Assessments of student learning outcomes, such as increases in test scores and academic performance, as well as teacher feedback on the effects of specific strategies on student learning, can be used to determine how effective edge computing is at providing real-time feedback. The ASUS case study concludes by showing how edge computing may enhance student learning results in the classroom. Edge computing helps

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144  Edge of Intelligence teachers and students achieve enhanced learning outcomes by facilitating immersive learning experiences, enhancing connection, and providing real-time feedback. Future studies and assessments will be required to monitor the effects of edge computing and artificial intelligence on student performance and to guide ongoing attempts to maximize the efficacy of educational technology solutions.

5.7 Ethical Considerations in Edge AI for Educational Settings In the pursuit of innovation and advancements in education, it’s quite important to navigate the ethical implications in the adoption of transformative technologies such as real-time lecture video summarization and content extraction. From safeguarding data privacy and ensuring responsible AI development to promoting equal access to technology and tools, ethical considerations play a pivotal role in shaping the ethical, responsible and inclusive deployment of these technologies [18]. In this topic we explore the ethical considerations in the proposed system, highlighting the importance of ethical principles in the working and integration of this system.

5.7.1 Data Privacy and Security There is a risk of unauthorized access, misuse or disclosure of student data which can compromise privacy and confidentiality. For example, unauthorized access to lecture recordings could lead to the exposure of sensitive information or infringe upon students’ rights [19]. This can cause a valid and crucial concern regarding data privacy and security in the context of AI applications in education. It is important to address these concerns to protect student privacy and comply with necessary regulations like the General Data Protection Regulation (GDPR) and the Family Educational Rights and Privacy Act (FERPA) as our proposed system deals with sensitive information, such as facial expressions and engagement levels. Firstly, the privacy-by-design approach is to be adopted, where the core architecture and functionality of these AI-powered educational systems are embedded with data protection and privacy considerations. This involves only the necessary data being collected and processed such that sensitive information is anonymized or pseudonymized whenever possible. Additionally, to prevent unauthorized access to student data we should employ robust access controls and encryption protocols. Students and their guardians must be given clear information about the types of data

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Edge Computing in Educational Technology  145 being collected, the purpose of data processing, and the measures taken to protect their privacy. Therefore, it is crucial to obtain their explicit consent for data processing activities, especially those involving sensitive information, that is crucial in upholding ethical standards and complying with data protection regulations. Another crucial aspect is the implementation of procedures for data handling, storage, and retention in compliance with robust data governance frameworks that outline clear policies. These frameworks be regularly reviewed and updated and should align with regulations such as GDPR and FERPA. Advanced technologies like federated learning and secure multi-party computation must be leveraged by educational institutions such as to further enhance privacy and security. These techniques reduce the risk of data breaches as they enable AI models to be trained on decentralized data sources, thereby minimizing the need for sensitive data to be transmitted or centralized. By adopting a comprehensive and proactive approach to data privacy and security, educational institutions can harness the transformative potential of AI while ensuring compliance with relevant regulations and fostering trust among students, parents, and stakeholders.

5.7.2 Equitable Access to Technology and Tools It is a crucial challenge to ensure equitable access to the necessary hardware for edge computing and AI applications in remote locations as it demands innovative solutions. The digital divide remains a formidable obstacle, particularly in remote villages and resource-constrained areas. To bridge this gap and unlock the benefits of edge AI for all learners, it is essential to adopt complex strategies involving stakeholders from various sectors. The establishment of public-private partnerships between governments, educational institutions, and technology companies is one such strategy that could be explored. Through such collaborations, the development of cost-effective hardware solutions tailored for edge AI applications in education could be achieved with the help of the private sector’s expertise and resources. These resources are to be leveraged by the government to subsidize or provide these devices to underprivileged communities, ensuring affordability and accessibility. Additionally, educational institutions could provide training and support for educators and students to effectively use technological tools that promote inclusivity and empowerment [20]. This includes offering professional development opportunities for educators to enhance their digital literacy skills and fostering a culture of digital inclusion within educational institutions [21].

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146  Edge of Intelligence Creating open-source hardware platforms, especially for edge AI applications in education could be another strategy. Researchers and developers could work together to develop highly flexible and reasonably priced hardware solutions by utilizing open-source communities. Here are a few strategies for overcoming barriers to technological accessibility in remote villages: 1. Collaborating with technology companies and researchers to design hardware optimized for edge AI applications, with a focus on cost-effectiveness, and durability in challenging environments. 2. Leveraging existing infrastructure and devices such as smartphones or low-cost computers, to serve as edge computing platforms, reducing the need for new hardware investments. 3. Establishing community-based technology hubs within remote villages, where students can access edge AI-enabled educational resources and devices, fostering a collaborative learning environment. 4. Providing training and capacity-building programs to empower local communities in maintaining and troubleshooting edge AI hardware, promoting self-sufficiency and sustainability. By applying these strategies we can try to ensure technological accessibility and that the transformative benefits of edge AI in education are available to all learners, regardless of their geographical or socioeconomic circumstances.

5.7.3 Consent and Potential for Bias in AI It is important to address the ethical concerns regarding consent and potential biases within AI algorithms, as they are crucial for the responsible and equitable deployment of AI in educational settings. There needs to be transparency among students and guardians by being fully informed about the use of AI systems along with the data being collected, the purpose of analysis, and the potential impact. Furthermore, ethical frameworks and principles that have been created, such as those provided by the AI Now Institute and the IEEE, should serve as a guide for the development and implementation of AI systems in education. In addition to that, we need to address the algorithmic bias, as AI algorithms can unintentionally reinforce and magnify social prejudices that exist in the algorithms

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Edge Computing in Educational Technology  147 or training data. Educational institutions should place a high priority on using representative and diverse datasets to train AI models so that the models accurately reflect the range of student experiences, backgrounds, and learning styles. To find and address any biases before implementation, AI algorithms should also undergo thorough testing and auditing. By addressing these ethical considerations proactively and implementing appropriate safeguards and strategies, we can ensure that real-time lecture video summarization and content extraction enhance learning experiences in an ethical, responsible, and inclusive manner.

5.8 Future of Education with Edge AI In envisioning the future of education, the fusion of Edge Computing and Artificial Intelligence (AI) stands at the forefront of innovation. This convergence holds the promise of revolutionizing traditional educational paradigms, paving the way for a more personalized, interactive and inclusive learning experience. In this section, we will discuss the endless potential of Edge AI in shaping the future of education, from reimagining classrooms of tomorrow to the democratization of knowledge and accessibility which empowers learners, educators and institutions.

5.8.1 Hyper-Personalized Learning Journeys Edge AI has the potential to revolutionize the way students learn through hyper-personalized learning tailored to specific individual needs, preferences and learning styles. Through the integration of real-time data analysis and adaptive learning algorithms, Edge AI platforms can dynamically adjust content delivery, and instructional methods to suit each student’s learning capabilities. By leveraging insights obtained from students’ interactions, engagement levels and performance metrics, learning experiences can be tailored to provide students with more personalized support, challenges and feedback [22]. This degree of customization not only enhances students’ engagement and motivation but also maximizes learning outcomes by addressing their individual strengths and areas for improvement

5.8.2 Engaging, Interactive Classrooms Using Edge AI traditional teaching environments can be transformed into dynamic, interactive classrooms that foster creativity, collaboration and active learning. Through the integration of immersive technologies such as

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148  Edge of Intelligence virtual reality (VR), augmented reality (AR) and mixed reality (MR), Edge AI could help educators create an engaging learning experience that eliminates the constraints of traditional textbooks and lectures. Students can explore virtual simulations conduct virtual experiments and participate in interactive exercises that bring concepts to life in ways never imagined [23]. By empowering students to actively engage with course materials, collaborate and apply knowledge in real-world scenarios Edge AI transforms classrooms into vibrant hubs of exploration discovery and innovation.

5.8.3 Democratization of Knowledge and Accessibility Edge AI plays a crucial role in democratizing access to knowledge and educational resources. By overcoming geographical, economic and social barriers Edge AI ensures that every learner, regardless has access to high-quality educational content. Edge computing enables the development of low-­latency, bandwidth that can be deployed in remote areas, expanding access to education in underserved communities. Through continuous innovation, Edge AI has the potential to create a more equitable and inclusive educational landscape where learners have the opportunity to unlock their full potential and thrive in a rapidly changing world.

5.8.4 Future Scope in Real-Time Video Summarization and Content Extraction As we look to the future, the proposed system of real-time lecture video summarization and content extraction powered by Edge AI holds immense potential for further innovation and advancement. Building upon the foundation of this transformative technology, future developments promise to unlock new frontiers in education, enhancing learning experiences, expanding access to knowledge, and fostering collaboration and creativity in unprecedented ways. • AI-driven Content Creation and Curation: In the future Edge AI will play an active role in content creation and curation, automating repetitive tasks and freeing up educators to focus their attention on higher-order teaching activities. Advanced natural language processing (NLP) algorithms will enable AI assistants to generate personalized learning materials, such as interactive quizzes, simulations, and multimedia presentations, tailored to individual learner needs. Moreover, AI-powered content curation platforms will help

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Edge Computing in Educational Technology  149 educators discover, organize, and share high-quality educational resources from a vast array of sources, saving time and effort in lesson planning and curriculum development [24]. • Seamless Integration of Assistive Technologies: Future advancements in Edge AI will drive the seamless integration of assistive technologies and accessibility features into educational environments, ensuring that every learner has equitable access to educational opportunities. AI-powered assistive technologies, such as speech recognition, textto-speech conversion, and adaptive learning interfaces, will empower students with disabilities to fully participate in classroom activities and engage with course content on their own terms [25]. Additionally, Edge AI systems will proactively identify and address barriers to learning, providing real-time feedback and support to students with diverse learning needs. • Real-time Language Translation and Localization: With the increasing globalization of education, real-time language translation and localization will become essential features of Edge AI-powered educational platforms. Advanced translation algorithms will enable students and educators to communicate and collaborate across language barriers, facilitating cross-cultural exchange and collaboration on a global scale [26]. Furthermore, localized content creation tools and resources will ensure that educational materials are culturally relevant and accessible to learners from diverse linguistic backgrounds. In summary, the future of education with Edge AI is a journey of endless possibilities and innovations. By harnessing the power of EdgeAI to personalize learning, enable collaboration and empower learners we can create a future where education knows no bounds and where every learner has the opportunity to thrive innovate and shape the world around them. As we embrace the transformative power of Edge AI in education, we can unlock new frontiers in education and inspire a future where knowledge is accessible, inclusive and empowering for all.

5.9 Conclusion Considering the revolutionary possibilities described in this chapter, it is clear that incorporating Edge AI into educational technology could

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150  Edge of Intelligence promote development and creativity. Through the combination of edge computing and AI, traditional education systems may now effectively address long-standing issues, creating a more dynamic and individualized learning environment. This chapter has provided insight into how this could potentially be achieved. Additionally, this chapter has emphasized how crucial sustainability and scalability are when integrating Edge AI solutions in the classroom. Educational institutions can offer fair access to modern technology, promote inclusivity, and close the digital gap by adopting scalable designs and sustainable practices. Furthermore, the importance of cooperation and knowledge-sharing in promoting Edge AI adoption in education has been highlighted in this chapter. We can use the combined knowledge and resources of academia, business, and government to create creative solutions that empower teachers and students in equal measure. To sum up, this chapter has provided an overview of how Edge AI can revolutionize educational technologies. We can open up fresh possibilities for education in the digital era by utilizing the potential of real-time data analytics, individualized learning experiences, and collaborative creativity. Let us not hesitate in our determination to fully utilize Edge AI in order to influence education for future generations as we set out on this exciting journey of research and discovery.

References 1. Smith, A. and Johnson, B., Transforming Education Through Multimedia Content: Challenges and Opportunities. Int. J. Educ. Technol. Higher Educ., 16, 1, 1–14, 2019. 2. Gupta, R., Kaur, A., Singh, R., Singh, H., Edge Computing: A Paradigm Shift in Educational Technology. Int. J. Adv. Res. Comput. Sci., 13, 3, 70–75, 2022. 3. Fu, X., Zhang, Y., Li, W., Automatic Lecture Summarization Using AssemblyAI’s Transcription Models, in: 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, pp. 1–6, 2021. 4. Zhang, L., Xu, S., Liu, Y., Li, D., Wang, Y., Real-time Educational Content Extraction Using OpenCV and AI Techniques, in: Proceedings of the 10th International Conference on Education, Teaching, and E-learning (ICETL 2021), pp. 86–92, 2021. 5. Cheng, J., Dong, F., Lu, J., Zhang, X., He, Z., Real-time Multi-camera Video Analysis Based on OpenCV for Educational Purposes, in: 2020 IEEE 5th International Conference on Big Data Analytics (ICBDA), IEEE, pp. 130–134, 2020. 6. Giorgdze, M. and Dgebuadze, M., Interactive teaching methods: challenges and perspectives. Int. E-J. Adv. Educ., 3, 9, 544–548, 2017.

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Edge Computing in Educational Technology  151 7. Rini, D.S., Sigit, D.V., Adisyahputra, Sigit, D.V., Boosting student critical thinking ability through project based learning, motivation and visual, auditory, kinesthetic learning style: A study on Ecosystem Topic. Univers. J. Educ. Res., 8, 4, 37–44, 2020. 8. Kuznetsov, A., Accessibility vs. availability of education as two key phenomena of education quality assurance and education management: infrastructural development perspective, in: INTED2019 Proceedings, IATED, pp. 7923–7932, 2019. 9. Kurelović, E.K., Tomljanović, J., Davidović, V., Information overload, information literacy and use of technology by students. Int. J. Educ. Pedagogical Sci., 10, 3, 917–921, 2016. 10. Cao, K., Liu, Y., Meng, G., Sun, Q., An overview on edge computing research. IEEE Access, 8, 85714–85728, 2020. 11. Ometov, A., Molua, O.L., Komarov, M., Nurmi, J., A survey of security in cloud, edge, and fog computing. Sensors, 22, 3, 927, 2022. 12. Kenchakkanavar, A.Y., Exploring the Artificial Intelligence Tools: Realizing the Advantages in Education and Research. J. Adv. Lib. Inf. Sci., 12, 4, 218– 224, 2023. 13. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J., Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc. IEEE, 107, 8, 1738–1762, 2019. 14. Cui, W., Na, D.E., Zhang, Y., A Wireless Virtual Reality-Based MultimediaAssisted Teaching System Framework under Mobile Edge Computing. J. Circuits Syst. Comput., 32, 07, 2350116, 2023. 15. Gonzalez, H., Li, J., Jin, H., Ren, J., Zhang, H., Akinyele, A., Wang, A., Miltsakaki, E., Baker, R., Callison-Burch, C., Automatically Generated Summaries of Video Lectures May Enhance Students’ Learning Experience, in: Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pp. 382–393, 2023. 16. Pavel, A., Reed, C., Hartmann, B., Agrawala, M., Video digests: a browsable, skimmable format for informational lecture videos, in: UIST, vol. 10, pp. 2642918–2647400, 2014. 17. Learning on the Edge: How Edge Computing Could Benefit Classrooms, ASUS Resource Center, https://www.asus.com/resourcecenter/zhsqjg5hjly6rfer/learning-on-the-edge-how-edge-computing-could-benefitclassrooms. Accessed April 20, 2023. 18. Royakkers, L., Timmer, J., Kool, L., Van Est, R., Societal and ethical issues of digitization. Ethics Inf. Technol., 20, 127–142, 2018. 19. Nabbosa, V. and Kaar, C., Societal and ethical issues of digitalization, in: Proceedings of the 2020 International Conference on Big Data in Management, pp. 118–124, 2020. 20. Wallimann-Helmer, I., Terán, L., Portmann, E., Schübel, H., Pincay, J., An integrated framework for ethical and sustainable digitalization, in: 2021

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152  Edge of Intelligence Eighth International Conference on eDemocracy & eGovernment (ICEDEG), IEEE, pp. 156–162, 2021. 21. Kassymova, G.K., Malinichev, D.M., Lavrinenko, S.V., Panichkina, M.V., Koptyaeva, S.V., Arpentieva, M.R., Ethical Problems of Digitalization and Artificial Intelligence in Education: A Global Perspective. J. Pharm. Negat. Results, 14, 2150–2161, 2023. 22. Rocque, S.R., Integrating Cutting-Edge Technologies Into Learning and Development to Enhance Innovation, 2022, Available at SSRN 4215019. 23. Hou, C., Hua, L., Lin, Y., Zhang, J., Liu, G., Xiao, Y., Application and exploration of artificial intelligence and edge computing in long-distance education on mobile network. Mobile Netw. Appl., 26, 2164–2175, 2021. 24. Popenici, S.AD. and Kerr, S., Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanced Learn., 12, 1, 22, 2017. 25. Merenda, M., Porcaro, C., Iero, D., Edge machine learning for ai-enabled iot devices: A review. Sensors, 20, 9, 2533, 2020. 26. Gupta, V. and Gupta, C., Navigating Foreign Language-Taught Degrees: Embracing Artificial Intelligence-Driven Language Translators to Overcome Linguistic Challenges. Computer, 56, 9, 71–76, 2023.

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6 Edge Computing Revolution: Unleashing Artificial Intelligence Potential in the World of Edge Intelligence Saravanan Chandrasekaran1*, S. Athinarayanan2, M. Masthan3, Anmol Kakkar1, Pranav Bhatnagar1 and Abdul Samad1 1

Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India 2 Department of CSE, School of Computing, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, India 3 Arffy Technologies, Private Limited, Bengaluru, Karnataka, India

Abstract

The emergence of Edge Intelligence (EI) is causing a revolution in the field of data processing. By incorporating potent Artificial Intelligence (AI) capabilities right at the network’s edge, where data originates from devices and sensors, this approach goes beyond the constraints of conventional edge computing. Through this convergence, the latency and bandwidth limitations that come with only cloud-centric AI are overcome, enabling real-time analysis and decision-­making. The following are three ways that edge intelligence encourages a paradigm shift: First, by using localized processing, it enables real-time data analysis and prompt responses. Second, latency is significantly decreased by shifting large-scale data transfers to centralized cloud resources. Third, by lessening the load on centralized systems, edge intelligence’s distributed design increases infrastructure resilience and scalability and paves the way for exciting new advancements. Imagine autonomous automobiles making decisions almost instantaneously based on real-time sensor data, or smart cities utilizing edge-based analysis to improve traffic flow. The difficulty lies in the fact that robust security measures are required to safeguard data processed at the network’s edge. Developing efficient AI algorithms for resource-constrained edge devices is also essential. By addressing these problems and opening the door for intelligent, nearly instantaneous decision-making at the network’s edge, edge *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (153–192) © 2025 Scrivener Publishing LLC

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153

154  Edge of Intelligence intelligence has the potential to revolutionize a wide range of sectors. While Edge Computing (EC) addressed this problem by bringing processing closer to data sources, the integration of AI at the network edge heralds in the Edge Intelligence era. This creative approach makes use of edge devices’ AI capabilities directly to allow functionality that goes beyond data processing. EI makes real-time learning, decision-making, and on-device inferencing possible at the edge of the network. Significant benefits from this integration include decreased latency, increased efficiency, and the empowerment of intelligent devices. This article addresses the need for a strong infrastructure to manage artificial intelligence (EI) and integrates cutting-edge AI models to optimize Edge Devices (ED). Motivated by the remarkable outcomes of artificial intelligence (AI) in several domains, researchers at EC are progressively delving into its possibilities, with a particular emphasis on Machine Learning (ML), a subset of AI that has experienced substantial expansion in the past few years. This page explores EC as well, including a formal description and a summary of the reasons why it is a preferred computing paradigm. Next, we look at the main issues that EC has addressed and assess the shortcomings of conventional methods. This article seeks to act as a springboard for finding new research topics that take advantage of the synergistic link between AI and EC by exhibiting research on using AI to optimize EC and applying AI inside the EC framework for additional areas. Keywords:  Edge computing, artificial intelligence, fog computing, cloud computing, optimization, edge intelligence, machine learning, deep learning

6.1 Introduction Cloud computing enables anyone with a web connection to utilize computational assets, many of which are associated with storage. Cloud computing describes a capability to efficiently handle and utilize resources contained on a centralized server in the cloud without concern for place or time. Scalable infrastructures are essential for commercial operations, as are processing engines that provide cloud services, such as MapReduce, Apache Hadoop, Google File System, and Apache Spark. Higher technological standards are required, nevertheless, as the negative aspects of cloud computing have become apparent with the advent of new technological breakthroughs. An ever-growing number of devices are linked to the Internet as a result of the recent boom in Internet-of-Things (IoT) adoption. One million devices were connected to a network in 1992, and

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Revolutionizing Edge Computing with AI   155 by 2003, more than 500 million laptops were in use globally. This amount increased to 8.7 billion in 2012. This figure increased from 11.2 billion in 2013 to 11.4 billion in 2014 due to the widespread use of smart devices and internet-connected appliances. By 2030, it is projected that approximately 75 billion gadgets will be online. This will prevent the cloud server from analyzing information from these numerous IoT devices, irrespective of size. Increased use of cloud services causes increased latency for users, causing additional load on the server and network since data takes longer to process. Security and network issues may also have an impact on cloud computing. As was previously said, when a cloud data Centre—which stores vital data from an expanding number of devices—is attacked, there is a high risk of information leaking. These problems can be resolved by using edge computing, a cutting-edge computing method [1].

6.1.1 Motivation for Edge Intelligence For Emotional Intelligence to be widely utilized, scenarios for use need to offer both novel scientific issues to pique the academic community’s interest and adequate monetary potential to drive deployment. Up until now, cloud computing scenarios—including computer vision and natural language processing, among others—have primarily shaped AI scenarios [2]. Nevertheless, edge computing has mostly been used in applications like real-time video analytics and cognitive support, which are characterized by high data volumes and low latency requirements [3, 4]. This makes it possible for new applications involving large data streams that must be handled and analyzed in a safe, timely, and latency-limited way [5–9]. Figure 6.1 depicts sample examples of current uses. These applications involve offering extremely personal data to users of privacy-sensitive systems, regulating robotics and vehicles in a spatiotemporally critical environment, and enabling distributed manufacturing and logistics. It is apparent that some deployments remain extremely specialized and customized, and they represent distinct examples of applications that have already been put in place in smart factories and IoT solution development, as opposed to a comprehensive list of sectors where EI is necessary. EI uses extend far beyond the aforementioned instances, incorporating financial sectors (like entertainment, logistics, and manufacturing),

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156  Edge of Intelligence

Figure 6.1  Edge intelligence new possibilities.

governmental (for instance, healthcare and defense), and societal aspects (such as environmental monitoring). In addition to enhancing intelligence, EI opens doors for cutting-edge services and applications that would not be conceivable without it. Some of these sectors are briefly discussed below, with a particular emphasis on those where academic or commercial demonstrations have already been realized. Augmented Reality (AR) assists individuals who utilize technology to carry out challenging tasks. It is a classic example of where EI might be beneficial. The environment (a collection of components and tools, their position, their condition, etc.) and the worker’s operations can be observed in real-time in this domain via a video feed that the EI program can receive [10]. The program needs to comprehend the actions executed by the user and anticipate further actions, offering real-time tactile visual advice. Although it is currently unfeasible to offer operators the necessary processing capability, EI may offer the intelligence essential for carrying out this task without breaching response-time constraints. It is projected that linked automobiles and future transportation systems will reap the rewards of EI. Extended sensor applications and totally or remotely autonomous driving

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Revolutionizing Edge Computing with AI   157 are two examples of the need for extremely dependable and low-latency data processing and analysis [11]. While in-car computing can accomplish modest automation, algorithms with greater sophistication demand resources and computational ability that are not readily accessible. Sensor data gathered from moving cars and people is a vital part of effective traffic control. In contrast, distributed thinking based on EI can embrace and adjust to these obstacles. Inefficient governance via a centralized strategy is hampered by high network dynamics, latency, and reliability limitations. We believe that the generative internet has the potential to be an important turning point in EI. The application logic is developed and provided throughout the communications, computation, and infrastructure in a generative Internet. One key utilization of this type of vision for EI is adaptive self-management of communication networks. This is because building an intelligent Internet that includes AI across the network necessitates the network functioning automatically and intelligently. Even while early EI apps are moving in the direction of dynamic, self-aware applications, as previously outlined, an intelligent, self-aware Internet remains a long way off from our current state of the art. But merely integrating AI into edge, or edge into AI, is insufficient to fully use EI’s presumptive powers. The following Sections show insights into the essential terminologies, Bio-inspired algorithms for EI, and real-time intelligence for smart applications.

6.2 Definitions 6.2.1 Edge Computing Edge computing involves an array of adjacent networks and devices. Edge aims to analyze data closer to the place of origination to give larger-scale, faster data processing, and more immediate actionable outcomes. Edge computing may enable businesses to better manage and use physical assets while delivering creative, human-centered experiences. Retail automation, driverless cars, robots, and smart equipment data are a few examples of edge applications [13]. Edge computing encourages transmission capacity and delay-sensitive applications by pre-processing information close to the information source. Cloud computing provides scalable resources for processing and storage. For optimal results, cloud and edge-based application combinations must be optimized [14].

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158  Edge of Intelligence

6.2.2 Challenges in Edge Computing 6.2.2.1 Network Connectivity and Reliability Even though edge computing raises the transfer speed burden on personal edge LANs, it diminishes the transmission capacity stack between the edge locales and the central information center. There is a necessity to employ LAN infrastructure that can sustain this capacity and distribute extra bandwidth to edge sites to fully benefit from edge computing [15].

6.2.2.2 Security and Privacy Edge devices are scattered and there’s a more noteworthy surface zone for assaults and conceivable shortcomings. Guaranteeing the assurance of delicate information at the edge is fundamental. Solid security components, such as encryption, confirmation forms, and secure communication channels, must be utilized to realize credential security.

6.2.2.3 Data Management and Storage Edge devices have restricted capacity space and preparation capability, and it may be a challenge to oversee and store gigantic amounts of data made at the edge may be a significant trouble. Organizations can utilize procedures like information accumulation, compression, and shrewd information sifting to improve information administration.

6.2.3 Artificial Intelligence AI is the process of augmenting the intelligence of robots so that they can perform tasks with the same level of performance as humans. The use of AI, particularly machine learning, has become increasingly popular in the “big data era” brought about by the Internet of Things (IoT). This page focuses mainly on modern AI algorithms, including DL and others. It is important to note that many of these apps have strict network reliability and latency requirements, which cloud computing generally does not meet.

6.2.4 Edge Intelligence Edge intelligence is a network of connected systems and equipment that collect, cache, process, and analyze data near where the data is being collected. The goal of Edge is to protect data and privacy while improving

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Revolutionizing Edge Computing with AI   159 processing speeds and quality. The field of edge intelligence has grown rapidly over the past 5 years, although it is relatively new, having only been established in 2011. The four main components of edge intelligence are edge caching, edge training, edge inference, and edge offloading. Our first step is to identify these components using theoretical and empirical evidence from proposed to realized systems. In order to organize the condition of the solutions, we analyze observations and research findings for each of the four components. This taxonomy covers realistic issues, triedand-true strategies, and intended applications. We describe, evaluate, and compare the literature for each area, taking into account the methods used, the goals achieved, the effectiveness of the plan, and its benefits and drawbacks [17].

6.2.4.1 Network Infrastructure Challenges Although edge computing makes computers more practical for applications such as smart cities and beyond, its introduction also brings additional security risks since it expands the real-world attack surface. An edge server’s computing power is comparatively less than that of a cloud server. As a result, an edge server is more susceptible to current assaults on cloud servers that might no longer work. Similarly, edge devices’ protection mechanisms are more brittle than those of general-purpose computers’; thus, many attacks that would not be successful against desktop computers can seriously jeopardize edge devices. Even while some IoT devices may have rudimentary LED screens, most of them lack user interfaces (UI) in contrast to general-purpose computers. Because of this, a user could not be fully aware of a device’s operating conditions, such as if it has been compromised or shut off. As a result, most users might not be able to detect an attack even if it occurs on an edge device. As opposed to general purpose computers, which typically employ POSIX and other standard operating systems and communication protocols, the majority of edge devices utilize a variety of OSs and protocols that are not subject to defined regulations. The challenges [93] of creating a uniform edge computing protection system are closely related to this issue. The four primary permission types used in access control models for cloud computing and general-purpose PCs are Read Only, Write Only, Read & Write, and No Read & Write [94]. Because of the increasingly complex systems and applications that they allow, such a paradigm will never work in edge computing. Instead, fine-grained access control would be necessary to address issues like “who can access which sensors by doing

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160  Edge of Intelligence what at when and how.” Regretfully, [95] the majority of access control schemes in use today are coarse-grained.

6.3 Concepts and Architecture Data is transported at a different distance between edge computing and distributed cloud computing, though there are some similarities. In “cloud computing,” computer resources are directly stored in a central data center. Conversely, edge computing mostly entails interfacing with an edge data center, often referred to as an edge server [18], which is located close to the endpoint device and manages auxiliary activities such as cloud server storage. There are several edge computing technologies, including cloudlets and fog computing. As may be inferred from the definition of the word “fog,” “cloudlet” refers to platforms, and “fog computing” refers to mist-­ distributed computing technology [19–24]. Figures 6.2 and 6.3 demonstrate how subtle the fog and cloudlet computing concepts differ from each other, respectively. Despite the appearance that two computer systems are in direct competition, their relationship is more like a symbiotic one. A comparison between edge and cloud computing is shown in Table 6.1. The concept of service provision may be elucidated by pointing out that edge computing and cloud computing enable users to use nearby local network devices (edges) and data centers over the internet, respectively, to accomplish their tasks [25]. All data must be handled at the data center; cloud computing solutions consume more power and can handle large

Cloud Cloud Computing

Fog

Fog

Fog Fog Computing

Figure 6.2  Paradigm of fog computing.

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Revolutionizing Edge Computing with AI   161 Cloud Local Area Network (LAN)

Wide Area Network (WAN)

Figure 6.3  Concept of cloudlet.

Table 6.1  Comparison of cloud computing and edge computing. Subject matter

Cloud computing

Edge computing

Special distribution

Centralized

Distributed

Service location

Inside the network

At network edge

Response time

Prominent

Small

Communication

Moderate

Supported with moderate

amounts of data at times. The system can be expanded much more easily. On the other hand, because the edge computing system only needs to process data from its local users, it consumes less power and only requires one edge for each region. Logically, the scattered edge computing system should be more secure than the cloud computing system, which collects data in a single area. Additionally, as data may be handled at the edge without interruption, maintaining some services, the infrastructure’s stability would be further reinforced by implementing edge computing technology. When the central data center falls down for any reason, all of the IoT devices connected to it stop working, hence the cloud computing system cannot accomplish this. The edge in Figure 6.4 can do off-roading, data storage, caching, and processing in this fashion thanks to its two-way computing stream; nevertheless, there are factors to take into account before off-loading the data.

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162  Edge of Intelligence

Cloud (A,B)

x, A

Edge Tier

y, A

Device Tier Physical Location #X

Physical Location #Y

Figure 6.4  Three levels of edge computing.

The edge itself must first satisfy the service criteria, which include security, privacy, and dependability. It also has to take into account the task’s priority, the server’s resource utilization, computation costs, and the physical or financial distance from the server being utilized [18].

6.3.1 Comparison Between EI and Cloud-Centric AI Models AI at the edge enables localized processing for real-time machine learning, which enables quick data processing, thorough security, and improved user experience. Simultaneously, a lot of businesses are trying to move AI into the cloud, which may lower implementation obstacles, enhance knowledge sharing, and accommodate bigger models. Striking a balance between the advantages of the cloud and edge is the way forward. According to Accelerant’s Nebolsky, following is that developers should frequently take into account: Processing power: Edge computing hardware is usually less potent and more challenging to maintain or upgrade. Latency: Although the cloud is quick, it is not yet suitable for real-time applications such as industrial controls or driving a car. Energy consumption: Generally speaking, cloud computing designers are not as constrained in their consideration of energy usage as they are with edge computing. Connectivity: When connectivity is lost, safety-critical services, such as self-driving cars, are unable to continue operating, which puts processing for AI-driven choices made in real time at risk.

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Revolutionizing Edge Computing with AI   163 Security: Due to security considerations, it is usually advisable to carry out AI services that facilitate the authentication and processing of sensitive data, such as fingerprints or medical records, [98] locally. Even with extremely robust cloud security, users’ perceptions of increased privacy from edge processing might be crucial factors to take into account.

6.4 Algorithms for Artificial Intelligence in Edge Computing 6.4.1 Machine Learning Classical machine learning algorithms fall into three categories: unsupervised, semi-supervised, and supervised learning. Labeled data is required for training in supervised learning. Unsupervised learning, on the other hand, has the potential to independently find underlying principles in data. Semi-supervised learning, which combines supervised and unsupervised learning, can employ both labeled and unlabeled data. Boosting, random forests, and support vector machines (SVM) are common supervised learning approaches, whereas label propagation and graphical models are important semi-supervised learning techniques. Unsupervised learning techniques include dimension reduction and grouping algorithms such as K-means. It will ultimately give the devices intelligence comparable to that of humans and enable them to react to events in real time [26]. The conventional ML algorithms have several clear flaws. For example, they are sensitive to data sets, and as the data set is too big, the data become less useful. Despite these drawbacks, compared to Deep Learning and Reinforcement Learning, classical ML is easier to implement, consumes less energy, and requires fewer processing resources [27].

6.4.2 Deep Learning Deep Learning (DL) replicates how the human brain works. It can effectively categories by autonomously learning high-level qualities from raw data and doing predicting activities [28, 29]. The algorithm is “deeper” the more layers it has. Each layer’s input for a neuron is the weighted sum of its predecessor layer’s neurons’ outputs. The resultant number is utilized as the neuron’s output once the input has been triggered by an activation function [30]. The face detection strategy yields excellent results as the DL algorithm advances. In computer vision applications, the CNN method— also known as the deep neural network—provides a significant advantage

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164  Edge of Intelligence for automated visual feature extraction. Metric learning and classification layer training are the two methods used to train CNN for face identification systems. There are several ways in which CNN may be used. Learn the model from the beginning. In these situations, the pre-trained method’s framework is trained and applied using the dataset. Next, pre-trained CNN features—where the dataset is larger—are used in transfer learning (TL).

6.4.3 Reinforcement Learning and Deep Reinforcement Learning Reinforcement learning, as opposed to supervised learning, is a method of learning that trains models through dynamic interactions with their environment. The key idea is that actors collect information about their surroundings and then adopt actions based on previous performance to maximize their reward [31, 32]. The model-free, value-based Q-learning method is a typical RL algorithm [33]. Each iteration of the Q-learning process will yield the Q-value, or expected cumulative reward. In light of the conditions and actions done. However, in a more intricate environment, the status and action spaces will grow exponentially, slowing convergence and needing a vast amount of memory [34]. When compared to traditional RL algorithms, Deep Q-Network (DQN) offers three benefits for handling highly complicated EC [35]. For starters, it is capable of dealing with complex and multidimensional systems. It can also detect regularities in the system environment. Furthermore, it may determine what is ideal based on prior and current long-term rewards. Consequently, to optimize the control decision-making issues in EC and provide good outcomes, several researchers apply DQN algorithms [36, 37]. DQN does have several drawbacks. In particular, DRL’s learning outcome is unstable or even divergent when nonlinear functions, such as neural networks, are used to approximate the Q-function. A method that replays the previous experience is used to overcome this problem included in DQN [38, 39].

6.4.4 Evolutionary Algorithms Optimization approaches include evolutionary algorithms. Evolutionary algorithms are roughly grouped into the following stages: The primary stage is to set up variables; next, the evolutionary algorithms repeatedly iterate through three steps: population reproduction and variation, population update, and fitness evaluation and selection [40]. Iterations of the second step are then performed until the termination condition is met.

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Revolutionizing Edge Computing with AI   165 Currently, a variety of EC issues, including resource scheduling optimization [41], load balancing [42], and task scheduling [43, 44], are addressed by evolutionary algorithms.

6.4.5 Model Bias at the Edge More systems are incorporating sensors to capture data in real time as new developing technologies like IoT and Big Data mature. This data must then be analyzed quickly enough to detect crises or pertinent occurrences. Applications where Edge AI can be used all have complicated tasks where machine learning has been proved to provide effective outcomes. These jobs [99] also all need to run in real time while maintaining data security and privacy. This is the common thread among the applications where Edge AI may be used.

6.4.5.1 Computer Vision Regarded as the cutting-edge method for image processing (classification and detection), Deep Learning addresses a key job in computer vision. Surveillance cameras, time-of-flight cameras, motion detectors, and other sensors that occasionally already have DL capabilities provide the data needed for these activities at the network edge. Reducing the number of frames per second that the system computes is necessary when uploading the data from these sensors to the cloud for processing. However, privacy issues might arise, particularly if the data contains sensitive information. The need to use Edge Computing for this operation is also motivated by the bottleneck caused by the massive amount of data being uploaded from all these sensors.

6.4.5.2 Virtual Reality (VR) and Augmented Reality (AR) Researchers have suggested that DL is an effective method for anticipating the user’s field of view in virtual reality (VR) [100]. In this application, the DL’s objective is to identify the area of the 360° video or pictures that need to be fetched instantly in order to reduce frame drops and improve user experience. In Augmented Reality (AR), DL [101, 102], may also be used to identify things in the field of vision that are important enough to launch an activity or place a virtual overlay on top of it.

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166  Edge of Intelligence

6.5 Optimization of Edge Devices Using a Class of Neural Networks These days, a growing number of studies are addressing compute offloading by using AI to its fullest potential [45]. Lowering energy consumption A partial computing [46, 47] has proposed an offloading method that makes use of DL decision-making in order to reduce energy consumption. Table 6.2  Various AI techniques with its goals and contributions. Problem

Goal

Citation

AI technique

Contribution

High network latency, data transmission costs in traditional cloud-centric computing

Reduce latency, improve efficiency, achieve decentralization

[16]

Machine Learning (focus on limitations)

Highlights potential of AI for optimizing EC, propose exploration of new research directions.

Limited computing resources on edge devices

Leverage existing AI models for faster and more efficient COVID-19 mitigation on edge devices

[49]

Deep Transfer Learning

Propose DTL to adapt pre-trained deep learning models for COVID-19 related tasks on resourceconstrained edge devices. Suggests potential applications like: Faster analysis of medical scans for diagnosis; Real-time monitoring of public spaces.

Energy Minimize energy consumption, usage, optimize power power efficiency limitations in edge devices

[50]

Deep Learning (power-efficient architectures)

Explores using deep learning models for optimizing lowpower hardware.

Understanding Deep Learning

[51]

Deep Learning (broad overview)

Establishes a foundation for understanding deep learning architectures, taxonomies (classifications), and applications in various fields. Identifies potential future research directions in deep learning.

Provide a comprehensive overview of deep learning concepts, techniques, and applications

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(Continued)

Revolutionizing Edge Computing with AI   167 Table 6.2  Various AI techniques with its goals and contributions. (Continued) Problem

Citation

AI technique

Contribution

Inefficient Improve urban resource services, management, enhance quality lack of of life through data-driven data-driven decisioninsights making in urban environments

Goal

[52]

Internet of Things (IoT) for data collection - Machine Learning for data analysis

Propose using IoT sensors to collect real-time data across the city. Suggests leveraging Machine Learning to analyze this data and gain insights for optimizing services like: Traffic management; Energy consumption; Public safety.

Limited robot Enhance robot intelligence, capabilities, inability enable them to handle to be more complex intelligent, tasks and efficient, and environments adaptable

[53]

Artificial Intelligence (AI) (broad concept) Machine Learning (ML) - Deep Learning (DL)

Highlights the transformative potential of AI, ML, and DL for advanced robotics. Identifies key application areas: Autonomous navigation; Object recognition and manipulation; Natural language processing; Predictive maintenance.

Inefficient task Reduce task allocation response in edge time, improve computing resource environments utilization

[54]

Evolutionary Algorithm (Genetic Algorithm)

Proposes using a Genetic Algorithm (GA) to optimize task offloading decisions in edge environments. Aims to find the best allocation of tasks to edge servers that minimizes response time while considering factors like network transmission times and server loads.

The authors create a brand-new type of decision-making mechanism that could be able to identify the optimal computing offloading method and reduce the total energy required to do computational tasks. This method reduces energy usage by 3% while also accounting for the energy used by user equipment [46].

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168  Edge of Intelligence Reducing latency: Although EC has less latency than cloud computing, there is still room for development. It is recommended to employ SmartEdge-CoCaCo [48] in conjunction with cooperative filter caching and wireless communication paradigm optimization to minimize latency. The model as well as the computing offloading model. Moreover, because edge devices have limited computing power, giving them all the responsibilities may be more than they can handle. Given this, DL-based heuristic offloading approach is proposed. This method combines heuristic searching with origin-destination electronic communications network distance estimation to determine the optimal computational offloading strategy [48]. Reducing latency and energy consumption: All of the methods discussed in the preceding paragraphs are only intended to lower delay or energy consumption. Table 6.2 describes a number of research that have been conducted on AI approaches and their contributions.

6.5.1 Strategies for Optimizing AI Algorithms 6.5.1.1 Federated Learning One decentralized method for training models across several data sources is Federated Learning (FL). It tackles the two main issues that arise when training local models with local data: protecting data privacy by storing data locally and enhancing model performance generally, which may otherwise be constrained by the amount and bias of local data. FL has recently been widely adopted into a variety of industries, including banking, IoT, and health care. In a practicing environment, it presents two significant obstacles, though. First, especially when the models are big, the regular model update interchange between the server and clients frequently turns into a bottleneck in the training pipeline, impeding the efficient training of the global model [96].

6.5.1.2 Pruning Pruning strategies are essential for the effective deployment of neural networks, particularly in resource-constrained applications such as agricultural yield prediction. According to preliminary research, trimming balances the bias–variance tradeoff, which helps with model generalization. Moderate pruning has been shown to even increase model accuracy, according to recent research. Reducing the model size for simpler storage and transmission and enabling energy efficiency

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Revolutionizing Edge Computing with AI   169 for real-time operation on mobile devices are the main driving forces behind modern pruning techniques.

6.5.1.3 Localization-Preserving Aggregation Having a global model that outperforms the individual local models is the main reason to take part in FL. Nevertheless, due to data heterogeneity or non-independent and identically distributed (Non-IID) data, local clients may outperform the global model in practice, which negates the goal of FL. This might be the consequence of using various measurement instruments or varying the sensitivity of the sensors used to collect the local data in the food and agricultural industry. Second, when clients are unevenly distributed across time, it can occur for time-series data like ours. While some may have more data from the early years, others may have more from the latter years.

6.6 Bio-Inspired Algorithms for Edge Computing 6.6.1 Modified Particle Swarm Optimization Particle Swarm Optimization (PSO) for grid work scheduling was proposed in [55]. In order to tackle the work scheduling problem in the Cloud-Fog computing environment using different evolutionary methods, we eliminate the service layer from grid computing while preserving the overall concept of PSO to create Modified Particle Swarm Optimization (MPSO), a model that is adaptable to what we have suggested. The following provides information about the MPSO algorithm. Particle: Position and Velocity The representation stage is crucial to the PSO algorithm’s effective design, which seeks to identify a suitable mapping between the PSO particle and the issue solution. Our technique uses binary representation; each particle is written as a m × n matrix, also called a position matrix; n is the number of jobs, and m is the number of accessible nodes, including Fog and Cloud nodes. Xk is the particle’s location matrix in the swarm, has two primary features indicated in Equations (6.1) and (6.2) such as;



X k (i, j) ∈ {0, 1} (∀i , j), i ∈ {1, 2, ⋅ ⋅ ⋅, m}, j ∈ {1, 2, ⋅ ⋅ ⋅, n}

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(6.1)

170  Edge of Intelligence



∑im=1 X k (i, j) =| 1 (∀j), j ∈ {1, 2, ⋅ ⋅⋅ ⋅, n}



(6.2)

Every element in the position matrix is either 0 or 1. If Xk = (i, j) = 1, then task Tj is carried out in node Ni. The second condition guarantees that each column has precisely one element with value 1, indicating that each job is completed on a single node [56].

6.6.2 Particle Swarm Optimization for Edge Detection The social dynamics of a group of migratory birds attempting to reach an unidentified destination served as the model for the PSO [57]. For PSO, a solution is a ‘bird’ in the flock and is called a ‘particle’. In GAs, a particle is comparable to a chromosome, or population member. The PSO’s evolutionary mechanism does not produce young birds from parent ones like it does in GAs. Instead, the population’s birds only change in terms of their social interactions and how they migrate toward a certain location [58]. Based on the PSO Algorithm, have presented a brand-new, innovative method for medical picture edge identification [59]. Accurately identifying the margins of the medical picture is challenging. The goal function is reformulated as a new fitness function. The PSO approach increases the accuracy of the inspection system by identifying the optimal filter mask. The results indicate that the proposed method has a higher accuracy when compared to conventional edge detection techniques for medical pictures [60]. Furthermore, [61] have developed a novel edge detection method based on PSO. They have released a novel evolutionary computing filter. Artificial training is carried out to find the ideal edge filter using the edge map. This method has the benefit of outperforming other edge detection algorithms in terms of results and facilitating the effective building of the edge detection filter. PSO differs from previous evolutionary algorithms in that it uses no crossover and defines mutation by vector addition. It draws inspiration from the social behavior of fish schools and flocks of birds. PSO may be adjusted for binary domains, where trajectories are the chance that a coordinate will take on a zero or one value [62, 63]. Although PSO was first developed for continuous domain issues, where trajectories of particles are specified as changes in position on some number of dimensions. To solve, we employ a method known as sticky binary particle swarm optimization (SBPSO) [64].

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Revolutionizing Edge Computing with AI   171

6.6.2.1 PSO in Continuous Domain Let f(x) represent the objective function that we wish to reduce. First, we generate a random population of particles as the objective function’s initial solution. Three characteristics distinguish each particle: 1) Position: the decision variables’ values are vectorized. 2) Velocity, a position-updating vector. 3) Personal best: the point at which the particle has ever reached the lowest value of the objective function. As a result, the ith particle is represented as < xi, vi, pbesti >, where pbesti is the best position (with the lowest value) that the particle has ever found in all algorithm iterations up until the current one, vi is the velocity vector for updating the position, and xi is the position vector containing values of decision variables. When a particle’s location is taken into account as the function’s input, its value is determined by evaluating the objective function. A particle is more suitable as the ideal answer the lower its value. Put otherwise, it is ideal for a particle’s value to drop after a few iterations in order to arrive at the best answer. Apart than pbesti, there exists an additional word termed gbest, denoting the optimal location that each particle has discovered in all of the previous iterations. Put otherwise, gbest = min pbesti ∀i. Each particle possesses knowledge about the gbest. A particle’s characteristics are changed from in every t + 1 iteration using the following formula (6.3):



v it +1 = w v it⋅, +ϕ1U1 ⋅ (pbest it -x it ) + ϕ2 U 2 ⋅ (gbest t -x it )



(6.3)

where the term “inertia” (w), the learning rates for social and personal influence (φ1 and φ2), and the matrices U1 and U2 multiply the difference vectors by random integers selected from a uniform distribution in Equations (6.4) and (6.5);





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x i t +1 = x t i

Vi+t +1

 x ti +1 If f (x ti +1 ) < f (x ti ) pbest i t +1 =   pbestit otherwise

(6.4)

(6.5)

172  Edge of Intelligence The momentum, represented by the first component w · v it , indicates the impact of the current direction. Diverse momentum contributes to the swarm’s ability to remain diverse.

6.6.3 BCO-FSS Technique The preprocessed social network input data are then supplied into the BCO-FSS method. In [14], COA was proposed, a potent population-based approach. The frequent habits of Canis latrans species, which are predominantly found in the NA, serve as a source of inspiration for this technique. The COA might be classified as swarm intelligence (SI) and evolutionary heuristics due to its distinctive form. The population of coyotes is divided into Np packs and Nc coyotes for every pack. Each pack of coyotes is believed to include a constant and equal number of individuals. The population size may thus be determined by multiplying Np and Nc. The social standing of each coyote indicates a possible x→ solution to the optimization problem. The community state of cth coyote in pth packet at tth time in this instance may be characterized as follows in Equation (6.6);

soccp,t = x →



(x1=, x 2 , ..., x D )

(6.6)



6.7 Real-Time Intelligence-Based Edge Device Artificial Intelligence has come a long way recently in a variety of domains. When it comes to network latency and stability, smart cities, smart manufacturing, and the Internet of Vehicles typically have higher needs than Table 6.3  Summary of AI algorithms and architectures. Field Smart city Smart manufac­ turing Internet of Vehicles

Goal Security Urban health Energy Management Automation

Automation

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Deep Deep reinforcement learning learning Yes No Yes No Yes Yes

Reinforcement learning No No No

Traditional ML Yes Yes Yes

Yes

No

No

Yes

Yes

No

Yes

Yes

Revolutionizing Edge Computing with AI   173 other situations. Regretfully, conventional cloud computing frequently falls short of ensuring these conditions. EC has been used by some researchers to supply edge computing and storage resources. Three categories are utilized to summarize the many EC designs used in AI applications, along with a thorough description and analysis. Table 6.3 describes the various fields along with the study [16]. The three modes are as follows: (a) the cloud handles training and reasoning, and the edge side is only in charge of data cleaning; (b) the cloud handles training and the edge side handles inference; and (c) the edge handles all or part of the tasks related to AI training and reasoning.

6.7.1 Smart City The idea of a “smart city” has been put out and is receiving a lot of attention due to the rapid increase in the people living in cities and the tendency of urbanization. Smart cities employ clever strategies to lower energy consumption, boost energy efficiency, relieve traffic [65], guarantee public safety and citizen well-being, and increase quality of life. Numerous hardware components that constantly produce data are present in the smart city environment. These gadgets include portable medical equipment, smart wristbands, and smartphones—lightweight smart devices for daily use—as well as numerous environmental detection sensors and surveillance cameras for urban security is depicted in the Figure 6.5. Due to AI’s ability to handle large amounts of data, smart cities may employ it to increase the precision and effectiveness of data analysis [66].

smart home

Smart Healthcare

smart building

Edge computing platform

Figure 6.5  Edge intelligence in smart city.

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174  Edge of Intelligence A smart city must adhere to tighter standards for real-time reaction and network stability in a developed location in order to maintain the safety and comfort of civic life there. But the high processing demands of AI reasoning and training provide a significant obstacle to the aforementioned needs. In an attempt to address this difficulty, several researchers have focused on EC. We will next go into great depth about the plans for applying AI algorithms inside the framework of EC architecture to address the issues in smart city scenarios.

6.7.1.1 Distributed Deep Learning Model-Based Monitoring System The system lowers communication costs and increases reaction time by implementing EC. This study [68] deploys a distributed deep learning training technique based on parallel training at the task and model levels, taking use of the distributed nature of the edge side. The objective [67] is to utilize edge node processing capacity in conjunction with various learning models to expedite the sub-model’s training. Data security and privacy are also essential in the process of safeguarding urban security. AI is a useful tool for spotting criminal activity and stopping privacy leaks, but edge devices only have so much processing power. As a result, creating AI algorithms that are both efficient and lightweight for EC remains a significant issue.

6.7.2 Urban Medical Services Personal medical devices are becoming more and more common in daily life because to the growing popularity of cloud computing. Figure 6.6 shows the smart portable medical devices using edge intelligence. These

EEG EEG

PPG

Figure 6.6  Smart portable medical equipment.

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EOG

EMG

BIS

Revolutionizing Edge Computing with AI   175 gadgets have the ability to gather users’ personal data and transfer it to a cloud server. However, this cloud computing technique does not really meet the time delay and data transmission needs of telemedicine. The use of EC, satisfies the needs of the medical system for data security, and consistent data transfer. For instance, in some emergency scenarios, mistakes like slow reaction times or data loss might potentially endanger lives. Furthermore, EC exhibits substantial features related to location awareness [69]. For medical systems that depend on location, EC’s faster processing speed becomes essential. Early warning and remote diagnostics [70] offer a diagnostic and therapeutic approach for voice disorders. Before being transferred to the cloud, edge devices preprocess the audio data that the system has acquired. Still, a human expert must get the diagnosis from this system, and they will choose the course of therapy. The patient’s survival can be greatly increased if they receive treatment and diagnosis early in the disease, especially for diseases like lung cancer that are difficult to detect early on and that respond well to treatment in these early stages.

6.7.2.1 Prevention and Management of Infectious Diseases The strong location awareness function of EC can be used to improve infectious disease control and prevention. The healthcare system [71] has the ability to identify places on a map where infectious illnesses are likely to occur and determine if a user has contracted Kyasanur forest disease. For data preparation, model training, and reasoning under this structure, the network edge closest to the data source is in charge. The moment a new sick individual is discovered, the existing infected person and any surrounding hospitals are promptly notified. Astute evaluation: The management of the daily nutritional pattern of residents is a crucial component of urban medical care, which is crucial for illness prevention [72] suggest a nutritional evaluation system with an EC architecture based on food picture recognition. Accurate decision-making is necessary for medical diagnosis, and AI algorithms are needed to extract all relevant information from massive data. Nevertheless, the amount of valuable data that can be acquired with current techniques is quite restricted. Manual data labeling for supervised learning may potentially result in unidentified errors. Furthermore, wearable technology will be the primary platform on which the smart medical data collecting system is installed in the future. Deploying AI models [73] to these wearable devices is also a crucial direction, as it presents a significant challenge to the devices’ energy supply, in order to swiftly analyze and react to the data that has been gathered. It is worthwhile to investigate how to strike a compromise between AI models’ accuracy and lightweight nature [74].

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176  Edge of Intelligence

6.7.3 Management of Urban Energy Cities’ significant rise in energy consumption is also a result of the urbanization trend. Numerous difficulties arise for the management of urban energy. Figure 6.7 shows an example of a smart city’s smart energy management framework. For instance, energy providers must generate extra power to fulfill the city’s demand for energy and guarantee a steady supply of energy. A considerable amount of energy waste results from this [75]. Dynamic energy consumption forecasts are necessary for making decisions about energy management in real time. However, developing an efficient energy prediction system is challenging due to the diversity and complexity of energy data. In, [8] created an EC-based energy management system to address this issue and lower city energy use. Cooperative DRL: dynamic energy management is done on the edge side using models obtained from the cloud, and model training tasks are carried out in the cloud. Edge DRL: reasoning and model training are carried out on the edge. Through experimentation, the authors demonstrate that the most energy-efficient approach for deploying AI algorithms is cloudedge cooperation, which is superior than deploying AI algorithms only on the cloud. This also shows that cloud computing and EC should have a synergistic and complimentary connection rather than being substituted for one another.

Cloud

Core Network

Edge Server

Edge Server

Edge Server

Solar panels Wind Farm EV power plant

charging pile

Smart Power Grid

Solar panels User

Wind Farm Monitoring Devices

Smart Buildings

Home charging pile

power plant

Multi Energy Network

Figure 6.7  An example of a smart city’s smart energy management framework.

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Revolutionizing Edge Computing with AI   177

6.7.3.1 Challenges The world’s energy crisis and climate change have been made worse by the exponential increase in the number of edge devices being installed in cities. Using renewable energy to run edge devices is one method to solve this issue. Distributed renewable energy generating devices may significantly reduce the energy consumption of traditional energy sources, given that edge devices are dispersed around the city. Nevertheless, there are still a lot of obstacles to overcome before this approach can be implemented, such figuring out how to set up a complementary power supply for various edge devices and minimizing the amount of traditional energy used while maintaining edge device functionality. An energy router requires certain processing capability to function as the control center of an EI system [76, 77].

6.7.4 Smart Manufacturing The integration of AI and EC into industrial production can optimize the utilization of hardware devices, distributed computing, and storage resources. The plant’s production efficiency, quality, and safety are all significantly increased when the two are combined since they also accomplish effective and safe resource management and job allocation [78, 79].

6.7.4.1 Dynamic Management The architecture can dynamically modify the production line’s configuration with EC’s help. It can also gather and analyze a wide range of data produced, as well as recognize and assess using artificial intelligence techniques to improve feedback control. In terms of recognition rate and network connection time delay, the design of cloud computing offers significant benefits over the typical manufacturing. To create swarm intelligence, many production equipment must collaborate with one another through groups rather than merely working alone. Using AI and EC technologies in smart factories is a new challenge in realizing swarm intelligence.

6.7.4.2 Equipment Observation Monitoring the factory’s machinery’s operational state is crucial for industrial production site safety since, with time, the machinery’s quality will undoubtedly deteriorate. The predictive model is trained by the framework

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178  Edge of Intelligence on a potent public cloud, and it is then assigned to a private edge cloud for online diagnostic and prognostic activities. In addition to improving diagnostic and prognostic accuracy, this somewhat shortens the wait [80]. It’s critical to ascertain the kind and quantity of equipment present on site in order to effectively monitor and control the machinery in the plant. Based on EC and LSTM, a non-intrusive load monitoring system is suggested as a solution to the expensive nature of human categorization techniques [81]. To extract information that will be helpful to the data center, the edge device will process the generated data first. This significantly lowers the amount of data that must be sent to the data center, enabling the platform to successfully increase transmission efficiency and lessen the strain on networks during transmission. According to experiments, this monitoring platform that combines EC and neural networks may increase data transmission efficiency by almost 90% and reach an accuracy rate of more than 96%.

6.7.5 Internet of Vehicles Internet of Vehicles (IoV) is a critical step in the direction of intelligent life for humans in the future and is now a popular topic in academia and business [82]. IoV can improve passenger experience, lessen traffic accidents brought on by careless driving, and relieve congestion [83]. Many in-car apps, road condition sensors, and smart technologies combine to provide travelers with a very easy, pleasant, and secure ride. For conventional

Road Side Unit

Se Pa rvic ck e et

Edge Server

Figure 6.8  Primitive IoV structure.

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Revolutionizing Edge Computing with AI   179 vehicular networks, EC and AI can provide greater processing power, quicker and more accurate control, quicker network connection, and a better user experience. Figure 6.8 depicts a typical EC-based IoV architecture. AI is being used in an increasing number of disciplines to solve optimum strategies; AI algorithms may also be used in IoV to address the issues mentioned above. We will provide an overview of the usage of EC and AI in IoV from three angles: enhancing vehicle intelligence, enhancing onboard entertainment user experience, and optimizing task offloading and resource allocation.

6.7.5.1 Optimizing Allocation of Resources and Task Offloading Effective job delegation and resource allocation decisions are more challenging due to the dynamic and unpredictable nature of job offloading caused by the continuously changing network topology, compute load, and communication status [85]. An ant colony optimization technique with quick convergence is used in [84] to tackle the NP-hard job assignment issue. This approach sets up several goal functions and optimizes using heuristics. Nevertheless, this approach struggles to make the best choices when offloading activities that need various data dependencies. To address this issue, [86] proposed an EC framework for determining the best task offloading option using DRL. The framework considers resource needs, vehicle movements, access networks, and data dependencies. Energy consumption is a major barrier to the growth of IoV. The aforementioned research, however, do not take energy usage into account while determining the best offloading strategies. In order to minimize energy usage in the Internet of Vehicles.

6.7.5.2 Enhancing the In-Flight Experience In the future, drivers and passengers will have more free time as autonomous driving technology matures and is used. This will raise the demand for on-board entertainment from both drivers and passengers, including watching films and listening to music [87]. Various computationally intensive applications provide substantial challenges when running in a resource-constrained connected automobile since the high latency requirements of various on-board entertainment activities. These difficulties include the effective scheduling and resource allocation of jobs, as well as the efficient caching of network material.

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180  Edge of Intelligence The conventional approach to content caching involves storing the most popular material in roadside devices beforehand, however this wastes storage capacity. Under the EC architecture, the roadside units to which the material is cached and the cache quantity make up the two components of this caching strategy’s activity. The elapsed time of transferring the material that the user requested is the incentive for using this caching approach. This article also uses LSTM to forecast the vehicle’s traveling path in order to improve the selection of roadside devices. The [88] address the difficulties associated with running compute-intensive applications on cars with constrained resources. They accomplish this by modeling vehicle-to-­infrastructure communication and computing states using finite-state Markov chains, after which they express the strategy for allocating resources and scheduling tasks with the objective of maximizing users’ Quality of Experience (QoE). By comparison, roadside units and automobiles are required to do content caching using [89] strategy. The restricted computational and storage capacity of automobiles may be fully utilized by this concept. Stated differently, the system will choose which cars and roadside equipment to use for caching and computation based on the location and motion direction of the automobile making the service request. The base station will be tasked with handling the caching and computation chores if the cars and roadside units surrounding the automobile are unable to achieve their needs.

6.7.5.3 Increasing Autonomous Intelligence Giving AI technology to vehicle intelligence under the EC architecture is an essential research direction [90]. For instance, [91] provide an EC design that incorporates DL to manage intricate cars and traffic data. Driving route analysis and autonomous vehicle control are made possible by the design. This architecture employs many DL techniques based on the features of various problems: Intelligent transportation systems (ITS) employ restricted Boltzmann machines to analyze complicated data; realtime road condition monitoring is performed by CNN and LSTM; Driver behavior prediction is done by Bi-RNN Data transmission security is maintained by LSTM. The issue of traffic congestion is made worse by the growing number of automobiles. Managing traffic is a really good technique to handle this issue. Consequently, route planning cannot be done using a centralized controller. Every edge node sends out a roadside unit (RSU) which uses an evolutionary game strategy to manage neighboring cars while the standard RSU is in charge of gathering traffic data [92].

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Revolutionizing Edge Computing with AI   181

6.7.5.4 Challenges Vehicle scheduling and control respond more quickly when EC and IoV are combined, which further advances vehicle intelligence. There are still certain difficulties, nevertheless [12]. For instance, if the car is traveling quickly, it may need to move between edge servers for communication, which might cause a number of issues, including disconnections or poor user experiences. Additionally, resource sharing between many vehicles is a fundamental component of IoV systems. Therefore, it’s crucial to figure out how to put up a fair incentive system to motivate people to share resources. Lastly, there will be certain concerns about data security and privacy as a result of resource sharing [13].

6.7.6 Practical Challenges and Solutions in Real Life Face recognition (FR) is a method that uses photos of people’s faces to identify them. Numerous industries, including security, biometrics, authentication, law enforcement, smart cards, and surveillance, can benefit greatly from the widespread application of FR technology. Convolutional neural networks (CNNs), one of the most recent developments in deep learning (DL) models, have shown encouraging results in the field of FR. To extract attributes for efficient FR, pretrained CNN models can be used. To that end, this study presents the GWOECN-FR method, a novel combination of enhanced capsule network-based deep transfer learning model and grey wolf optimization for real-time face recognition. The primary goal of the suggested GWOECN-FR method is to quickly and accurately identify faces in input photographs. Furthermore, the GWOECN-FR method undergoes two stages of preprocessing: data augmentation and bilateral filtering (BF) noise reduction. Furthermore, an extended capsule network (ECN) model may be applied for feature vector extraction. Furthermore, faces in photos are recognized and categorized using stacked autoencoder (SAE) models and grey wolf optimization (GWO). The weight and bias settings of the SAE model are optimized using the GWO algorithm [14]. One of the most prevalent chronic significant bone illnesses, cervical spondylosis (CS), primarily impacts the health of patients and can potentially be fatal. It mostly affects elderly people, but due to many age-related variables and adverse effects, it is also spreading rapidly among younger patients. In the realm of medical science, cervical spondylosis (CS) research is becoming increasingly relevant, and medical image processing is crucial to this study. The main symptoms are minor discomfort in the cervical

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182  Edge of Intelligence disk sites and spinal cord. Cervical spondylosis can also develop from these facts. Therefore, early identification of spondylosis by medical image processing algorithms results in better, less expensive hospital medical services and lowers the risk of diagnosis. The Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Elapsed Time (ET) are calculated for input image - 1 and achieved 0.000095 error value in particular point where the changes are identified easily with better Elapsed Time complexity 3.7 (in Seconds). However, for input image - 2, achieved 0.000005 error value in particular point where the changes are identified easily with better Elapsed Time complexity 2.5 (in Seconds) less execution time. The PCA segmentation classifier image processing technique is used for the early detection of spondylosis [97].

6.8 Conclusion The combination of edge computing and AI has ushered in the era of edge intelligence. This paradigm goes beyond the limitations of conventional cloud-centric AI by placing intelligence at the source of the data. This allows for real-time analysis and decision-making by eliminating latency and bandwidth restrictions. Edge intelligence has three advantages. Firstly, it facilitates localized processing, enabling nearly instantaneous responses based on real-time data insights. Second, cutting back on data flow significantly lowers latency. Finally, the distributed design of edge intelligence enhances infrastructure resilience and allows for scalability. This makes new applications possible. The possibilities are vast: from self-driving vehicles making split-second decisions to smart cities using real-time data analysis to allocate resources as effectively as possible, the possibilities are endless. However, robust security measures are necessary to safeguard data at the network’s edge. Effective AI algorithms that are optimized for low-resource devices are also essential. The key difficulty in bringing artificial intelligence to the periphery of networks is maximizing algorithm performance within limited computational and energy resources. This calls for the creation and use of lightweight AI models, as several studies have demonstrated. We stress the need of combining AI with EC, establishing a partnership that benefits both parties. We support more research initiatives aimed at improving EC’s capacities to offload compute, protect privacy, and strengthen security. In the end, this will open the door for more widespread AI deployment. We intend to investigate these two potent technologies in more detail in the future. For example, we will look at

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Revolutionizing Edge Computing with AI   183 distributed training and reasoning inside the EC framework. By resolving these issues, edge intelligence holds the key to enabling a future of intelligent and decentralized decision-making at the edge of the network.

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190  Edge of Intelligence 84. Feng, J., Liu, Z., Wu, C., Ji, Y., AVE: Autonomous vehicular edge computing framework with aco-based scheduling. IEEE Trans. Veh. Technol., 66, 12, 10660–10675, 2017, https://doi.org/10.1109/tvt.2017.2714704. 85. Tang, D., Zhang, X., Li, M., Tao, X., Adaptive inference reinforcement learning for task offloading in vehicular edge computing systems. 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, June, http://dx.doi.org/10.1109/iccworkshops49005.2020.9145133. 86. Qi, Q., Wang, J., Ma, Z., Sun, H., Cao, Y., Zhang, L., Liao, J., KnowledgeDriven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Trans. Veh. Technol., 68, 5, 4192– 4203, 2019, https://doi.org/10.1109/tvt.2019.2894437. 87. Hou, L., Lei, L., Zheng, K., Wang, X., A Q -learning-based proactive caching strategy for non-safety related services in vehicular networks. IEEE Internet Things J., 6, 3, 4512–4520, 2019, https://doi.org/10.1109/jiot.2018.2883762. 88. Hu, X. and Huang, Y., Deep reinforcement learning based offloading decision algorithm for vehicular edge computing. PeerJ Comput. Sci., 8, e1126, 2022, https://doi.org/10.7717/peerj-cs.1126. 89. Tan, L.T. and Hu, R.Q., Mobility-Aware edge caching and computing in vehicle networks: A deep reinforcement learning. IEEE Trans. Veh. Technol., 67, 11, 10190–10203, 2018, https://doi.org/10.1109/tvt.2018.2867191. 90. Khayyat, M., Elgendy, I.A., Muthanna, A., Alshahrani, A.S., Alharbi, S., Koucheryavy, A., Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access, 8, 137052–137062, 2020, https://doi.org/10.1109/access.2020.3011705. 91. Ferdowsi, A., Challita, U., Saad, W., Deep learning for reliable mobile edge analytics in intelligent transportation systems: An overview. IEEE Veh. Technol. Mag., 14, 1, 62–70, 2019, https://doi.org/10.1109/mvt.2018.2883777. 92. Lu, J., Li, J., Yuan, Q., Chen, B., A multi-vehicle cooperative routing method based on evolutionary game theory. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, October, http://dx.doi.org/10.1109/itsc. 2019.8917441. 93. Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., Lv, W., Edge computing security: State of the art and challenges. Proc. IEEE, 107, 8, 1608–1631, 2019, https:// doi.org/10.1109/jproc.2019.2918437. 94. Gallmeister, B., POSIX.4 Programmers guide: Programming for the real world, O’Reilly Media, Inc., Sebastopol, California, 1995. 95. Fernandes, E., Jung, J., Prakash, A., Security analysis of emerging smart home applications. 2016 IEEE Symposium on Security and Privacy (SP), 2016, May, http://dx.doi.org/10.1109/sp.2016.44. 96. Li, A., Markovic, M., Edwards, P., Leontidis, G., Model pruning enables localized and efficient federated learning for yield forecasting and data sharing. Expert Syst. Appl., 242, 122847, 2024, https://doi.org/10.1016/j. eswa.2023.122847.

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Revolutionizing Edge Computing with AI   191 97. Shashikala, H.K., Mahesh, T.R., Vivek, V., Sindhu, M.G., Saravanan, C., Baig, T.Z., Early Detection of Spondylosis using Point-Based Image Processing Techniques. 2021 International Conference on Recent Trends on Electronics, Information, Communication, Technology (RTEICT), 2021, August 27, http:// dx.doi.org/10.1109/rteict52294.2021.9573604. 98. https://www.techtarget.com/searchenterpriseai/feature/Finding-thebalance-between-edge-AI-vs-cloud-AI 99. Rajkumar, K. and Hariharan, U., Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction, in: Artificial Intelligence and Machine Learning for EDGE Computing, Elsevier, pp. 425– 442, 2022, http://dx.doi.org/10.1016/b978-0-12-824054-0.00018-6. 100. Hou, X., Dey, S., Zhang, J., Budagavi, M., Predictive view generation to enable mobile 360-degree and VR experiences. Proceedings of the 2018 Morning Workshop on Virtual Reality and Augmented Reality Network, 2018, August 7, http://dx.doi.org/10.1145/3229625.3229629. 101. Liebmann, L., Enabling alternating phase shifted mask designs for a full logic gate level. Proceedings of the 38th Conference on Design Automation - DAC ’01, 2001, http://dx.doi.org/10.1145/378239.378333. 102. Liu, L., Li, H., Gruteser, M., Edge assisted real-time object detection for mobile augmented reality. The 25th Annual International Conference on Mobile Computing and Networking, 2019, August 5, http://dx.doi. org/10.1145/3300061.3300116.

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7 Ensuring Privacy and Security in  Machine Learning: A Novel Approach to Efficient Data Removal Velammal B. L.* and Aarthy N. CEG Campus, Anna University, Chennai, India

Abstract

Modern systems produce extensive amounts of data, forming intricate data networks. Users highly value the safety, security, and privacy of their data. It is essential to unlearn data when it is no longer necessary. This project aims to effectively remove or erase data from machine learning models upon user request, thereby addressing privacy concerns. Under GDPR, users can request the deletion of sensitive data from both user records and the machine learning model that has processed the data. Additionally, the project employs the SISA approach to address errors and attacks by dividing the dataset into shards and implementing a slicebased Ensemble Learning technique. Each shard functions as an independent model, and after training, a majority voting approach aggregates these models into a final model. Experimental results demonstrate reduced retraining costs, as only the remaining slices are retrained instead of the entire model. Keywords:  Influence of a data point, efficient data removal, privacy, incremental learning

7.1 Introduction Machine Unlearning is a domain which is contradictory to Machine Learning. In Machine Learning, a machine will be made to learn the dataset that is provided and produce a desired model with high accuracy whereas, in Machine Unlearning, a machine will be made to unlearn the *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (193–214) © 2025 Scrivener Publishing LLC

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193

194  Edge of Intelligence data that the model had previously learned [4]. It is necessary for unlearning research [19] and [23] to develop an algorithm that can take a trained machine-learning model as input and produce a new one without the requisite data. Retraining the model from scratch without the need to relearn the training data is a fundamental tactic. Unlearning research aims to reduce the high computational cost associated with this. With the rising importance of data privacy [7], data is becoming a major concern. Data privacy is all about restricting the access to personal data which involves deciding who should not have access to it. Deletion of accounts simply delinks the data from database but for complete privacy the proposed model must ensure that the system forgets the data once and for all. Users desire systems to forget particular data for a variety of reasons, including its lineage. Users who are concerned about new privacy dangers in a system frequently want the system to forget their data and history [6]. A detector must forget the injected data if an attacker taints it by adding manually generated data to the training set. This is necessary for the detector to regain security. A user can reduce noise and inaccurate items to ensure that a recommendation engine provides effective recommendations. Let’s break down the problem in more detail so it is easy to see how it varies from other privacy definitions. The user-provided information that is asked to be unlearned is represented by the letter d. It is imperative to devise an unlearning strategy that, without starting from scratch, produces the same model distribution as retraining. In the naive approach, where the entire model is retrained after deletion, the computational cost is drastically high and it takes a lot of time. Therefore, in order to better match with the objectives of new laws on privacy, an alternate strategy that investigates the deterministic definition is required. [25] has devised docker swarm concept to load and to efficiently handle the data for big data applications. Here the model unlearns the user requested data [8]. If the distributions do not match, there must be some influence on the system from them to account for the discrepancy. One way to think of an unlearning environment is as a probabilistic setting where most of the contribution of the user-requested data is eliminated, and an unlearning method only approximates the retraining distribution [3]. On certain websites, users who knowingly or unknowingly provide personal information about themselves consent to the corporations running those platforms using that information for a range of purposes, such as selling it to marketers or using it to improve their prediction models. It becomes challenging for businesses to undo the impact of data acquired if a user decides not to let such information about them to be used by them, particularly if the data was used to train machine learning models. Most of the people, considering their privacy, don’t want their data to be shared. For instance,

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Efficient Data Removal by Machine Unlearning  195 consider a Man ‘X’ who want to delete his Account ‘A.’ He has just deleted his account by deactivating it. Here it should be understood that deleting an account doesn’t mean that X’s personal information is completely deleted. The Machine Model that is created using a dataset consisting of X’s information still has a trace of X’s details. But, it is not acceptable to have privacy sensitive data as a part of Machine learning model [2]. Subsequently, in the situation of encountering errors and attacks, unlearning the incorrect or poisoned data sample is an obvious need. In elaborate, if a data administer, who collects and organizes data, has entered an incorrect sample and has let the machine learn the data, it results in the model which couldn’t produce a desired output. Lastly, in the case of being trapped in the data attack called data pollution, there is a demand to erase the polluted data sample. In data pollution, the hacker tries to inject a malicious data sample to the training set causing the machine to learn the training set comprising the injected data sample. Removing or deleting the inserted data sample from the training set and relearning the clean set could be the solution.

7.2 Related Works A.  Approximate Deletion Method [22] have put out a novel approximation technique for logistic and linear models, the computational cost of which is independent of the number of training data (‘n’) and linear in the feature dimension (‘d’). A trained machine learning model can have individual training points removed in a variety of scenarios. When utilizing deletion, the model must be post-­ processed to remove the impact of the selected training point. A novel method known as the Projective Residual Update (PRU) has a computing cost that is independent of the amount of training data n and linear in the data dimension. A metric for evaluating the thoroughness of data erasure from ML models, the injection test, was also created. With this approximate deletion mechanism, as more deletion requests are performed, the accuracy of the estimate will deteriorate. B.  Jeopardizes Privacy [10] have suggested a number of approximation methods to reduce the significant computational cost brought on by retraining. Two types of machine learning models are produced by machine unlearning: the original and the unlearned. They think that even while machine unlearning was designed to keep the privacy of the given model, it can give the impression that it does, creating unexpected privacy problems. Specifically, the

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196  Edge of Intelligence unlearned model may provide additional information about the target sample, although the initial model might not have revealed much private information. First, they propose a new machine unlearning membership inference attack that aims to ascertain if the target sample was included in the original model’s training set. Additionally, two new privacy measures are proposed to measure the unexpected privacy risks associated with machine unlearning: Degradation Count and Degradation Rate. These two compute the amount of relative privacy which has been lost by the target due to machine unlearning. The results demonstrate that the target sample’s membership privacy is consistently reduced by our assault, showing that machine unlearning can adversely affect privacy [14]. To sum up, the privacy of the target sample is often compromised by machine unlearning. This research emphasizes how risky it is to use the ability to be overlooked while implementing the learnings by the machine. These measures and attack may help in the future to create more machine learning systems that protect privacy. C.  Selective Forgetting [1] and [11] investigate the issues associated with forgetting a specific samples of the data used to train a deep neural network. By carefully examining the weights of the data, insights can still be gleaned even when the network’s output may obscure its effects. They suggested a technique for “scrubbing” data from the weights to get details about a sample batch of training data. It is a technique used to “scrub” the information weights related to a particular set of training data. When a probing function is applied to a network’s weights that was trained without the data to be overlooked, it becomes indistinguishable from a network that was trained with the data to be overlooked. Information must be removed [13] from the weights as intended in order to achieve selective amnesia; simply hiding or altering certain output won’t suffice to scrub the activations. An attacker may be able to discern more details from the photos if the improper cleaning technique is used. D.  Federated Unlearning Federated unlearning with verification was first presented by [24] and [9]. In contrast to prior federated based learning, [16] and [26] presented federated unlearning based on incremental learning. [9] investigated the issue of trained CNN classification models’ selective forgetting of categories in federated learning (FL). Since FL does not allow global access to the training data, our results delve deeply into the internal influence of each channel. Channel class discrimination is quantified using the Term Frequency Inverse Document Frequency (TF-IDF) concept. In order to unlearn,

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Efficient Data Removal by Machine Unlearning  197 channels with high TF-IDF scores must be pruned because they are more selective on the target categories. A proposed distributed machine learning paradigm called federated learning (FL) enables several devices to train a common model without direct access to sensitive training data. Only shallow network sin convolution designs, like a 2-layer CNN followed by two fully connected (FC) layers, allow for unlearning. Accuracy and scalability are traded off due to the amount of data stored in the federated server. E.  Casual Unlearning [12] and [20] proposed a comparable system known as Karma and introduced a method called Causal Unlearning to efficiently rectify a compromised learning system. Karma significantly reduces the need for manual intervention by administrators by automatically and accurately identifying contaminated training data samples. A key concept within Karma is causal unlearning. One major form of attack, termed “data pollution,” involves the deliberate insertion of misleading training data into the dataset, causing the system to develop a flawed model and subsequently misclassify test samples. These erroneous samples can mislead the machine learning classifier that detects malicious crowdsourcing workers. To initiate a data pollution attack, an attacker must create a traceable causal chain, starting from the contaminated training samples, passing through the corrupted learning model, and resulting in misclassified test samples. Exploratory attacks, where an attacker aims to gain insights into the machine learning model, are not addressed by Karma. For a comprehensive understanding, we will briefly discuss these exploratory attacks in the context of adversarial machine learning.

7.3 Objectives The primary objective of this project is to develop a method for removing specific individuals or data points from a machine learning system without compromising its functionality. The aim is to minimize the computational burden of fully retraining machine learning models by disregarding the influence of user-requested input points. Initial findings suggest that training a model from scratch on the dataset without having to unlearn the point is highly effective, referred to as the basic strategy hereafter. However, as datasets grow larger, this approach will become impractical in terms of time and computational resources required. For instance, companies may need to frequently update models to adhere to GDPR/CCPA regulations [18]. The newly developed strategy should be made in such a way

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198  Edge of Intelligence that whenever a user wishes to remove or delete his/her personal data, the organization or the network must provide the user a methodology to do so. Apart from deletion it is also important to provide a mechanism for verifying whether the data has been completely removed. This increases the users trust worthiness of the firm, which in turn might be a great advantage for the organization. Ensuring privacy is the expectation of most of the users. As a result, any new strategy should meet the following criteria. 1) Accessibility: The basic technique is straightforward to grasp and apply conceptually. Similarly, any unlearning method should be easily understandable, ensuring that individuals without specialized knowledge can troubleshoot the approach. 2) Improved Accuracy Compared to the Baseline: Even though the baseline technique may experience a decline in accuracy if (a) a significant portion of training points needs to be unlearned or (b) prototype points are unlearned [5], any unlearning technique should strive to maintain a modest accuracy advantage relative to the baseline regardless of the number of points unlearned. 3) Reduced Retraining Time: The method should require substantially less time to unlearn any number of points compared to the baseline. 4) Reliable Unlearning: Similar to the baseline, any new method must provide clear assurances that any number of points have been effectively unlearned without impacting model parameters. Additionally, such assurances should be straightforward and comprehensible for non-experts [17]. 5) Model Independent: In this case, the unlearning method should be generic, meaning it should meet the previously stated guarantees for models of different types and degrees of complexity.

7.4 System Design Figure 7.1 illustrates the architecture of the proposed SISA model. Unlike current methods where incremental model updates are communicated and shared, SISA training ensures that there is no information flow between its constituent models. For instance, when using stochastic gradient descent to train neural networks, the gradients computed on each constituent are not shared. Instead, each constituent is trained independently, ensuring that the impact of a shard and its data points is confined to the model that uses it for training. Each shard is further divided into slices, and these slices are gradually used to train each constituent model in a stateful manner. During inference, each constituent receives the test point, and the responses from all constituents are aggregated. Our machine unlearning project adopts the SISA training approach, which stands for Sharded,

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Efficient Data Removal by Machine Unlearning  199 DISTRIBUTION AWARE SHARDING Privacy sensitive dataset

imbalanced shading

ISOLATED TRAINING incrementally train constituent model with slicing

AGGREGATIVE PREDICTION constituent NN models trained with SGD

Save each parameter state

constituent model prediction

Majority voting strategy

Predicted output

Updating

REMOVE REQUESTED DATA AND RETRAIN MODEL WITH REMAINING DATA Wait for expected number of requests

Process the deletion requests

VERIFICATION RETRAINED MODELS

Save the deleted index

Verification

Verification output

USER REQUEST TO DELETE USER REQUEST TO VERIFY

Figure 7.1  SISA Model’s architecture diagram.

Isolated, Sliced, and Aggregated training. This method involves dividing the entire dataset into separate shards with no communication between them. Each shards act as an individual, stand-alone Neural Network model similar to Ensemble Learning in which weak learners are combined to produce the output. These shards are trained independently such that reducing the training cost. The reason for the reduced training cost will be discussed in the later chapter. The shards partition the data into slices. These shards are trained in isolation creating a number of neural networks. When a user request to delete a data arrives, only the affected shard is retrained. For the prediction part, the majority voting strategy is used in which the model which gets the majority voting produces the output result. A.  Distribution Aware Sharding To start with the first module which is a Distribution Aware Sharding (DAS) where the user requests are distributed in three different ways and tested. In our project, it focuses on Uniform, Exponential and Pareto distribution. In Uniform distribution, the requests are uniformly distributed, say, all the shards contains equal numbers of data points to be removed. This means there is same number of requests distributed in all shards. In Exponential distribution, the requests are distributed exponentially. Exponentially in the sense requests are distributed in all shards either in increasing number or decreasing number. In Pareto distribution, the shard might consist of varied number of requests. There is possibility of no request in a shard or all requests in a shard or requests are scattered in any order. The algorithm 1 is for distribution. Aware Sharding Module (DAS). This algorithm explains the three different distributions that are carried out through this project for splitting the dataset into shards and slices. The distribution here means

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200  Edge of Intelligence

Dataset

For each samples

Calculate probability to get unlearned

(PSi + p(x)) < Limit

YES

Add data point x to i th shard

NO

X - Data point p(x) - Current data point's probability PSi Probability of i thv shard

Add data point x to next available shard

Figure 7.2  Distribution aware sharding module diagram.

distributing the request provided by the user to various shards which can take three forms – Uniform, Exponential and Pareto. This module mainly focuses on splitting the dataset by having a clear picture of the distribution of the user request. Figure 7.2 represents the flow diagram of Distribution Aware Sharding (DAS) module. Initially, calculate the probability of the data sample. Here, the probability is nothing but the degree to which the data sample gets unlearned. Algorithm 1  Algorithm for Distribution Aware Sharding 1. Differentiate each sample as likely to unlearn and unlikely to unlearn. 2. Identify the distribution of unlearning requests among shards. If distribution is uniform Split dataset into equal sized shards If distribution is exponential then Calculate Probability as



P = exp( λ ∗ − index ) − exp(−λ ∗ (index + 1))

(7.1)

If the distribution is Pareto then calculate the probability by the formulae expressed below.

P = α /(index 1)(α +1) 3. Examine the probability limit for each shards 4. For each data point

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+

Efficient Data Removal by Machine Unlearning  201 I f (previous cumulative probability of first available shard + Current data point’s probability) ≤ limit Then Add the data point into the shard Else Add the data point into next available shard B.  Isolated Training To continue with, the second module which is the Isolated Training phase where incremental learning takes place. We know that each shard acts as a NN and each shard are further partitioned into slices. In incremental learning, firstly, single slice of a particular shard is added and learned. Their parameter states are saved. This learning is carried on till all the slices of a single shard are trained. (Note: No. of Shards and No. of Slices are fixed based on the accuracy and the retraining cost that is obtained for our specific dataset). Algorithm 2  Algorithm for Isolated Training D – Datasets Dk− Shard K′s samples RDk,iR disjoint slices of shard K i e–Number of epochs Mk–Trained model after epochs Dk,i–i′th slice in K′th shard For each shared For i ⇒ 1 toR Train the model Mk,i using UiDk,i for eiepochs Save the state of parameters associated with Model Mk,i. The resultant constituent models (NN) are saved. The module design for Isolated Training module is pictured in Figure 7.3. Isolated training is the next module. The sharded dataset from the outlet of the first module is taken. Each shard is trained independently using NN (Neural Network). Stochastic Gradient Descent (SGD)-trained neural networks make up each component model. Every component receives individual training. This guarantees that a shard’s and the data points’ influence is limited to the model that

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202  Edge of Intelligence

Slice n Slice n

Model trained on shard 1

Machine learning algorithm

Slice n

Shard n

Slice 1 Slice 2

Shard 2

Slice 1 Slice 2

Shard 1

Slice 1 Slice 2

SPLITTED DATASET

Model trained on shard 2

Model trained on shard n

Figure 7.3  Module design for isolated training module.

is being trained. Every shard is further divided into slices, and a growing number of slices are used to train each constituent model gradually (and iteratively, in a stateful way). Algorithm 2 portrays the working of Isolated Training Module. Here, in this module all disjoint shards are trained independently to provide a constituent neural network models The quantity of models utilized is equal to the quantity of shards utilized. They are all taught differently and offer various outcomes. The final result is taken by utilizing the Majority Voting Strategy which is explained in the Aggregative Prediction Module. Incremental learning takes place during the isolated training phase. Each shard is known to operate as a NN, and each shard is further divided into slices. First, a single slice of a given shard is added and learned via incremental learning. The values of their parameters are preserved. This process is repeated until all of the slices of a single shard have been completed. (Take note that the accuracy and retraining cost found for our dataset determine the fixed number of shards and slices.) The constituent models that result (NN) are preserved. C.  Aggregative Prediction Furthermore, for prediction to check whether our model is working properly Aggregative Prediction is chosen. Given that the dataset is split up into multiple shards, each of which functions as an own neural network, the predictions made by each model must be combined in order to reach a conclusion. Majority voting strategy is employed here to derive the resultant output. Figure 7.4 gives the clear idea of the design of Aggregative prediction module. It’s important to comprehend how to make predictions. Our

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Efficient Data Removal by Machine Unlearning  203

Model trained on shard 2

Aggregation

Model trained on shard 1

Prediction

Model trained on shard n

Figure 7.4  Module design of aggregative prediction module.

sharding technique takes into account the fact that while fewer data are used per shard, this increases training time at the expense of the model’s accuracy. This method, which only slightly reduces accuracy, integrates the predictions of each model. Utilizing a basic Majority Voting Strategy (MVS), it yields the prediction with the highest number of votes. It uses a simple Majority Voting Strategy (MVS) and returns the prediction which received the most votes. Aggregative Prediction module is represented by algorithm 3. When making a prediction, there is a need to know how to combine the knowledge from each model. An key aspect of our sharding technique is that a smaller amount of data per shard (which increases training time) reduces model accuracy. The accuracy is slightly decreased by this strategy, which combines the predictions of each model. It uses the simple Majority Voting Strategy (MVS) to return the prediction with the most number of votes. D.  Deleting Request and Verification The vital part of our project is the last module. When a user made a request to delete their information, first thing is to save the request in the buffer till some specified number of request is arrived. What is the need of buffering the request and wait? For each and every request there is unlearning and retraining, it is not much efficient or desirable in terms of training cost and performance. Once it reaches the specified limit, locate the slice index where the request lies in the shard and remove it from there. And then the important thing is near. From the slice that is next to the removed slice, retraining starts using the saved parameter. The parameter state of each slice during isolated training phase is saved. Moving on, there is a verification part to make sure that really the user requested data

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204  Edge of Intelligence has been deleted or not. This verification might be user oriented or data administrator-oriented. 1)  Remove requested Data and Retrain model with remaining data: Figure 7.5 portrays the design for the module Remove Request data and Retrain Model (RRD RM). Unlearning data has been represented by Algorithm 4. After training the model, check whether desired number of unlearning requests are obtained. If yes then find out the shard and the slice position of the data to be deleted. If the request received is less than the desired limit then wait till desired number of request is obtained. Following this, delete the data sample and retrain the shard. Let the samples of data in the dataset and the user requests be S1, S2. S n and R 1, R 2. . . R n respectively. Dataset is divided into shards. For deleting a particular request, the shards are updated with new set of data by removing the requested Algorithm 3  Algorithm for Aggregative Prediction ⇒Compute weight vector based on performance ⇒Get prediction from each constituent models ⇒Preform majority voting strategy ⇒Using argmax pick the label with highest vote ⇒Update the weights of each model based on performance Algorithm 4  Algorithm for Unlearning Data how to combine every model’s knowledge when if(number of requests received ≤ expected) { }. wait(sometime); ⇒Locate indices of each request Locate the Slice(Sq( with minimum index number where the unlearning request start ⇒Remove requested data ⇒Retrieve Su′ th slice parameter state ⇒Perform training procedure from slice Su ⇒Save the model M ′

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k

Efficient Data Removal by Machine Unlearning  205

Shard n

Slice n Slice n Slice n

Shard 2

Slice 1 Slice 2

Find out the shard and slice position of the data to be deleted

MODEL TRAINED ON SISA

Slice 1 Slice 2

Shard 1

Slice 1 Slice 2

SPLITTING Model trained on shard 1 Machine learning algorithm

Model trained on shard 2

Model trained on shard n

Figure 7.5  Removing request and retraining the model.

Data from the whole dataset. Updated Shard=S1,S2... Sn – R 1, R 2. . . R n This is done for each and every shard in which the deletion request data are found to be present. So, it is clear that retraining happens for the shard with the removed request points. A log is maintained for the proof of deletion. 1) Verification: In this Verification module, which is a sub-­ module of the last part, the user or the administrator can confirm that the requested data has been deleted. At the commencement of the Unlearning process for the particular batch of requested data, these data are saved in the log for future verification. Therefore, whenever the user wants to certify that their information is deleted they can verify the match of tuples in the log and the user request. If there is a match their data has been successfully deleted or else it is not deleted. Equation 7.4 - unlearning request must be three times less than equal to number of shards for efficient result. If the dataset is sharded and sliced before training, then retraining time decreases. Shards and slices reduce retraining time to achieve unlearning because they are smaller than the whole dataset. 2) Storage: There is a need for space to store the parameter state. Storage cost depends on slicing. Slicing, a set of samples, divides the shards. Slicing decreases the storage cost. Let, n – Number of shards m – Number of samples in each shard

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206  Edge of Intelligence When learning (incremental learning), initially ‘m’ space needed to store parameter state of sample, then add on another set of slices/samples which in-turn need another ‘m’ space which gets continued till it reach the end sample - n(m+2m+3m+4m+. . . +rm) Therefore the calculation is as follow:



n(m + 2m + 3m + 4m + . + rm) = nm(r((r + 1)/2)) = (nmr (r 1))/2(5+) 3) Accuracy: Accuracy is one of the most important metrics to be noted. If the sharding and slicing strategy is implemented, it results in increasing speed-up, but when looking into accuracy, sharding may lead to condition of poor accuracy rate. That’s why it is desirable to sum up to an “Aggregation Strategy” which results in better accuracy rate along with increase in retraining speed-up.

7.5 Experimental Results A.  Techniques vs. Metrics [6] This project utilizes the purchasing dataset. The Purchasing dataset is made up of six hundred attributes which is available in .npz file in csr format. These six hundred attributes or column represents the items that are purchasable. The values of these columns are represented as either 1 or 0. The value 1 in the column depicts that the customer really purchased that item. The value 0 says that the customer hasn’t purchased the item; he/she has just visited the website without the intention to buy that item. B.  Evaluation Metrics 1) Retraining Time: Let r = unlearning request S = number of shards Figure 7.6 shows how different strategies like sharding, slicing, and aggregation strategy affect metrics like retraining time, storage cost, and accuracy. The retraining time is reduced when a sharding method is used (i.e. the dataset is divided into shards), but the accuracy is not improved. As a result, slicing is taken into consideration (i.e. further partitioning the shards into slices).

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Efficient Data Removal by Machine Unlearning  207 Techniques vs. Metrics

Retraining time

Storage cost

Accuracy

Sharding Slicing Aggregation strategy

Figure 7.6  Techniques vs. metrics.

It leads to more storage, which is undesirable. Finally, the Aggregation approach, when combined with Sharding and slicing, reduces retraining time and improves accuracy, both of which are desirable outcomes. SISA training is a useful unlearning technique that lowers the computational overhead of unlearning by utilizing data sharding and slicing. When a service provider handles requests in batches or sequentially, it determines the asymptotic lowering of the unlearning time by employing slicing and sharding. Research indicates that the amalgamation of sharding and slicing techniques does not yield a significant impact on accuracy when it comes to basic learning tasks. Additionally, SISA training can manage orders of magnitude more unlearning requests than what Google anticipates being necessary for the GDPR right to be forgotten [15]. It is demonstrated that for complex learning problems, a little accuracy loss occurs when a combination of SISA training and transfer learning is used. It has been found that the accuracy of the sharding component of SISA training decreases, particularly for complex tasks, when either (a) the number of unlearning requests or (b) the number of shards increases. This decline is due to the reduction in the number of samples per class within each shard in both scenarios. However, it has been observed that as long as the number of training epochs is adjusted, slicing does not affect accuracy. Combining sharding and slicing significantly outperforms a naive baseline for a given number of unlearning requests, even in the worst-case scenario where the distribution of unlearning requests is unknown. If the number of requests exceeds a certain threshold, SISA training gradually returns to baseline performance, which can be analyzed mathematically. C.  Comparative Analysis Figure 7.7 represents the different methods used for un- learning a data sample. In Naive approach, whenever a request arrives and deletion is made, the whole shard is retrained each and every time. The Deep Obliviate

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208  Edge of Intelligence METHOD

SPEED-

PREDICTION

STORAGE

UP

COST

COST

96.2%

1×

1×

1×

Batch 200

96.0%

35×

1×

200×

Shard 13

96.16%

29×

20×

200×

PARAMETER

-

Naïve Deep

ACCURACY

Obliviate SISA

Unlearning request 39

Figure 7.7  Different methods used for unlearning.

method stores intermediate models on the hard drive, which enhances the initial training procedure. Given an unlearning data point [21] it first. The temporal residual memory that remains in stored models is quantified. The impacted models will undergo retraining, and the trend of residual memory will be used to determine when to stop the retraining. Finally, it combines the retrained models with uninfluenced models to sew an unlearned model [21]. These two approaches are compared with our proposed model SISA. Figure 7.8 represents the graph between no. of shards vs. total accuracy. Two baselines are used to compare the suggested method to. These are as follows: • Retrain the entire model after each of the K unlearning requests, in a batch. This is equivalent to the naive baseline of starting over and retraining the full dataset in a batch setting (without the points that need

Top-1 Accuracy (%)

80 72 64 56 48

1/S SISA Batch K

40 32 3

6

9 12 Number of Shards

Figure 7.8  No. of shards vs. total accuracy.

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15

18

Efficient Data Removal by Machine Unlearning  209 to be unlearned). • Only retrain when the point to be unlearned falls inside this collection, and train on a 1/S percentage of the data. Figure 7.9 depicts the graph of various performance metrics. A varying trend is seen in the graph (Figure 7.9) between number of shards and the respective retraining time taken. It is also possible to infer from the graph that there is no linear relationship between the number of shards and the retraining time—that is, there is no consistent rise or fall in retraining time as the number of shards increases. It is desirable to have low retraining time, from the graph it is observed that retraining time is minimum when the number of shards is 13. The time required to retrain the model based on the quantity and distribution of requests is referred to as the retraining rate in the graph (Figure 7.10) between the number of shards and the retraining rate. Retraining rate is calculated from retraining time and number of requests. Simply retraining rate is nothing but retraining time per request. From the results of the previous graph and this graph we could conclude that for 13 shards both retraining time and retraining rate are optimal. In Figure 7.11, the graph, we compare two parameters by taking the number of shards on the common axis. The two parameters are Retraining rate and Average retraining time. Retraining rate is obtained using retraining time and number of requests whereas average retraining time is obtained by dividing sum of retraining time of all epochs by total number of epochs. There is a wide variation in the range as well as the trend of variation between these two parameters. No._of_Shards vs Retraing_Time 400

Retraining_Time

350

300

250

200 2.5

5.0

7.5

10.0 12.5 No._of_Shards

Figure 7.9  No. of shards vs. retraining time.

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15.0

17.5

20.0

210  Edge of Intelligence No._of_Shards (Vs) Retraining_Rate 70

Retraining_Rate

60 50 40 30 20 10 2.5

5.0

7.5 10.0 12.5 No._of_Shards

15.0

17.5

20.0

Figure 7.10  No. of shards vs. retraining rate. Comparing Retraining_Rate and Avg_Retraining_Time 250

Time in seconds

200 150 100 50 0 2.5

5.0

7.5

10.0 12.5 No._of_Shards

15.0

17.5

20.0

Figure 7.11  Retraining rate vs. avg. retraining time.

Figure 7.12 shows the relationship between the number of shards and corresponding accuracy. From the graph it is clearly seen that the accuracy decreases with increase in the number of shards. The number of accurate predictions divided by the total number of samples is how accuracy is

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Efficient Data Removal by Machine Unlearning  211 determined. While one shard can attain the highest accuracy, retraining requires more time; hence, taking into account aspects other than accuracy, 13 shards produce superior outcomes. No._of_Shards (Vs) Average_Accuracy

99.0

Average_Accuracy (in %)

98.5 98.0 97.5 97.0 96.5 96.0 95.5

2.5

5.0

7.5 10.0 12.5 No._of_Shards

15.0

20.0

17.5

Figure 7.12  No. of shards vs. avg. accuracy.

Retraining_Rate

60 50 40 30 20 10 60

10

Re

20

f_

7.5 No_ 10.0 12.5 of_S hard 15.0 17.5 s 20.0

No ._o

5.0

30

Figure 7.13  No. of shards vs. no. of requests vs. retraining rate.

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qu es

40 2.5

ts

50

212  Edge of Intelligence

Retraining_Rate

60 50 40 30 20 10

20.0

f_

20

10

Re qu

30

es t

40 5.0 7.5 No._ 10.012.5 of_S hard 15.0 17.5 s

No ._o

2.5

60

s

50

Figure 7.14  No. of shards vs. no. of requests vs. retraining rate.

The 3 dimensional graph depicted in Figure 7.13 shows the relationship between number of shards, number of requests, retraining rate. Retraining rate is calculated using the retraining time and number of requests. The x-, y-, and z-axes represent the number of shards, requests, and retraining rate that are taken, respectively. In the 3 dimensional graph portrayed in Figure 7.14 represents the relationship between number of shards and retraining rate by taking constant number of requests and varying number of requests. The green line corresponds to a constant number of requests (i.e., 8) while the blue line corresponds to a varying number of requests. Here for a higher number of shards the corresponding numbers of requests are also higher.

7.6 Conclusion and Future Scope In this research, we introduce a SISA training method involving a master node and non-communicating weak learners, addressing the challenge of flawless machine unlearning. This approach ensures timely and complete removal of consumer data from the model. Verification is conducted through machine unlearning verification, offering concrete quantitative measures for individual user perspectives. SISA unlearning, backed by verification, not only guarantees deletion but also provides users with increased control over data privacy. Future research avenues include

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Efficient Data Removal by Machine Unlearning  213 exploring broader data updates and extending unlearning to more complex machine learning models. Verification techniques encompass backdoor injection and user-level membership inference attacks to confirm data usage in model training. A key focus of our future work is refining the unlearning process to prevent data overfitting and ensure sustained effectiveness in machine learning applications.

References 1. Golatkar, A., Achille, A., Soatto, S., Eternal sunshine of the spotless net: Selective forgetting in deep networks, in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2020. 2. Mahadevan, A. and Mathioudakis, M., Certifiable Machine Un- learning for Linear Models, arXiv:2106.15093v3 (cs.LG), 16 Aug 2021. 3. Sekhari, A., Acharya, J., Kamath, G., Suresh, A.T., Remember What You Want to Forget: Algorithms for Machine Unlearning. 35th Conference on Neural Information Processing Systems (NIPS) 2021. 4. Tarun, A.K., Chundawat, V.S., Mandal, M., Kankan-halli, M., Fast yet effective Machine Unlearning, arXiv:2111.08947v2 (cs.LG), 25th November, 2021. 5. Kim, B., Rudin, C., Shah, J.A., The bayesian case model: A generative approach for case-based reasoning and prototype classification, in: Advances in Neural Information Processing Systems, 2014. 6. Ullah, E., Mai, T., Rao, A., Rossi, R.A., Arora, R., Machine Unlearning via Algorithmic Stability. Proceedings of 34th Conference on Learning Theory, 2021. 7. Saltzer, J.H. and Schroeder, M.D., The protection of information in computer systems. Proc. IEEE, 63, 9, 1278–1308, 1975. 8. Brophy, J. and Lowd, D., Machine Unlearning for Random Forests, arXiv:2009.05567v2(cs.LG), 11 Jun 2021. 9. Wang, J., Guo, S., Xie, X., Qi, H., Federated Unlearning via Class-discriminative Pruning, arXiv:2110.11794v3 (cs.CV), Hong Kong Polytechnic University, Dalian University of Technology, 29 Jan 2022. 10. Chen, M., Zhang, Z., Wang, T., Backe, M., When Machine Unlearning Jeopardizes Privacy, ACM, New York, NY, USA, 19th November, 2021. 11. Aldaghri, N., Mahdavifar, H., Beirami, A., Coded Machine Unlearning. IEEE Access, 9, 88137–88150, June 17, 2021. 12. Schelter, S., Grafberger, S., Dunning, T., HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. SIGMOD’21, Virtual Event, China, June 20–25, 2021.

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214  Edge of Intelligence 13. Garg, S., Goldwasser, S., Vasudevan, P.N., Formalizing data deletion in the context of the right to be forgotten, in: Proc. Annu. Int. Conf. Theory Appl. Cryptograph. Techn., Springer, Cham, Switzerland, 2020. 14. Neel, S., Roth, A., Sharifi-Malvajerdi, S., Descent-to-delete: Gradient-based methods for machine unlearning, 2020. 15. Bertram, T., Bursztein, E., Caro, S., Chao, H., Feman, R.C. et al., Five years of the right to be forgotten, in: Proceedings of the Conference on Computer and Communications Security, 2019. 16. Baumhauer, T., Schöttle, P., Zeppelzauer, M., Machine Unlearning: Linear Filtration for Logit-based Classifiers, arXiv:2002.02730, 8 Jul 2020. 17. Gupta1, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., Waites, C., Adaptive Machine Unlearning. 35th Conference on Neural Information Processing Systems (NIPS) 2021. 18. Gregory Voss, W., The CCPA and the GDPR are not the same: Why you should understand both, TBS Business School, Toulouse, France. 19. Liu, Y., Ma, Z., Liu, J., Yu, P., Ren, K., Learn to Forget: Machine Unlearning via Neuron Masking, arXiv:2003.10933v3 [cs.LG], 2nd August, 2021. 20. Cao, Y. and Yang, J., Towards making systems forget with machine unlearning, in: Proc. IEEE Symposium, May 2015. 21. He, Y., Meng, G., Chen, K., He, J., Hu, X., Deep-obliviate, arXiv:2105.06209 (cs.LG), 13 May 2021. 22. Izzo, Z., Smart, M.A., Chaudhuri, K., Zou, J., Approximate Data Deletion from Machine Learning Models. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, California, USA. 23. Ma, Z., Liu, Y., Liu, X., Liu, J., Ma, J., Ren, K., Learn to forget: Machine unlearning via neuron masking. IEEE Trans. Dependable Secure Comput., 20, 4, 3194–3207, 2022. 24. Gao, X., Ma, X., Wang, J., Sun, Y., Li, B., Ji, S., Cheng, P., Chen, J., Verifi: Towards verifiable federated unlearning, arXiv preprint arXiv:2205.12709, 2022. 25. Singh, N., Hamid, Y., Juneja, S. et al., Load balancing and service discovery using Docker Swarm for microservice based big data applications. J. Cloud Comp., 12, 4, 2023, https://doi.org/10.1186/s13677-022-00358-7. 26. Li, Y., Chen, C., Zheng, X., Zhang, J., Federated unlearning via active forgetting, arXiv preprint arXiv:2307.03363, 2023.

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8 Federated Learning in Secure Smart City Sensing: Challenges and Opportunities Monika Gandhi, Sushil Kumar Singh*, Ravikumar R. N. and Krunal Vaghela Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, India

Abstract

Smart cities utilize cutting-edge technologies to improve the quality of urban life. Integrating federated learning in sensor networks is crucial in developing smart cities. It facilitates the cooperative training of models across distributed devices, enhancing data privacy and diminishing the need for centralized data storage. This paper comprehensively examines the obstacles and potential advantages of integrating federated learning within smart city sensing systems. Collaborative model training in smart city sensing systems requires efficient and privacy-­preserving methods due to the large volume of data generated. A promising paradigm known as Federated Learning (FL) has emerged, allowing for the decentralized training of models across a network of distributed edge devices. This study examines the various obstacles and potential advantages associated with adopting Federated Learning within the domain of smart cities and the distinct attributes of smart city environments, including various sensor types, different data modalities, and privacy concerns. This paper examines the technical obstacles associated with federated learning within the given context, encompassing issues such as communication overhead, strategies for model aggregation, and the ability to adapt to dynamic urban environments. In addition, we emphasize the potential for augmenting smart city applications using Federated Learning (FL), including advancements in model generalization, decentralized decision-making, and heightened privacy preservation. By thoroughly examining existing literature and case studies, we offer valuable insights into the present condition of Federated Learning (FL) in the context of intelligent city sensing. In conclusion, we present a comprehensive overview of potential avenues for research and approaches to tackle the aforementioned obstacles, thereby promoting federated learning as a resilient and expandable remedy for advancing smart city sensing technologies. *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (215–252) © 2025 Scrivener Publishing LLC

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215

216  Edge of Intelligence Keywords:  Federated learning, smart city sensing, edge computing, distributed machine learning, data privacy

8.1 Introduction The increasing digitization and adoption of Internet of Things (IoT) technology in cities worldwide has led to a rise in prevalence of smart city application sensing systems [1]. These systems comprise a diverse range of sensors and devices that gather substantial quantities of data, which is crucial for enhancing urban operations, enhancing public services, and fostering sustainability. Nevertheless, the centralization of data processing poses notable obstacles, including issues related to communication bottlenecks and privacy concerns. Federated Learning (FL) has appeared as an advantageous strategy for addressing these difficulties. It allows for decentralized model training across edge end devices without the requirement of exchanging raw data [2, 3]. This approach is especially applicable in the smart city sensing field, where multiple types of sensors and data modalities are present in dynamic urban environments [4, 5]. This technology, known as Federated Learning, promises to transform smart city applications through its ability to facilitate collaborative model training while maintaining data privacy at the edge. The present study investigates the fusion of Federated Learning and Intelligent Smart City sensing capabilities. The objective is to thoroughly tackle the difficulties and possibilities inherent in this mutually beneficial relationship [6]. By analyzing the distinct attributes of smart city environments and the intricate technical aspects of Federated Learning, our objective is to offer valuable insights that can aid in the advancement of scalable and privacy-preserving solutions for decentralized model training in urban settings. The following sections of this paper explore the difficulties presented by diverse sensor types, communication overhead, and ever-changing urban environments. Simultaneously, we investigate the possibilities offered by enhanced model generalization, decentralized decision-­ making, and improved privacy preservation. By conducting a comprehensive analysis of existing literature and case studies, our objective is to provide insight into the present condition of FL in the context of smart intelligent city sensing [7, 8]. These advancements will facilitate future research avenues and practical applications in this expanding domain. The concept of Federated Learning involves the training of machine intelligent learning models through the utilization of data from multiple devices without the need for centralized data storage [9].

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Secure Smart City Sensing  217 Smart City Sensing entails collecting data in urban settings through sensors and IoT sensor devices. Edge Computing is a mechanism or Technology that involves processing data in close proximity to its origin, as opposed to transmitting it to a centralized cloud infrastructure. The concept of Distributed Machine Learning entails the use of multiple machines to train a machine intelligent learning model. Finally, the idea of Data Privacy pertains to the conscientious utilization and safeguarding of individuals’ personal data [10]. The exponential rapid growth of sensor networks and IoT sensor devices within smart cities has led to an unparalleled period of urban management that relies heavily on data analysis [11]. Nevertheless, the conventional centralized methodologies employed for processing and analyzing this data give rise to apprehensions regarding safeguarding data privacy, cybersecurity, and the effectiveness of data transmission. Federated Learning (FL) has appeared to be an adequate answer to these difficulties. It enables model training to take place locally on edge devices, reducing the need to transport raw data to a centralized server. This guarantees improved confidentiality and facilitates cooperative education across a decentralized network of sensors. This study investigates the incorporation of Federated Learning in the field of smart and intelligent city sensing, explicitly examining the inherent difficulties and potential advantages associated with this integration. The examination of the application of Federated Learning (FL) is necessary due to the intricate nature of intelligent city environments, which encompass various sensor types, dynamic spatial configurations, and privacy-sensitive information. In the subsequent sections, we explore the complex difficulties presented by the diversity of sensor types, obstacles in communication, and the capability of Federated Smart Learning to adjust to the ever-­changing characteristics of urban environments. We concurrently investigate the transformative capabilities of federated learning (FL), e­ ncompassing enhanced model generalization, decentralized decision-making, and increased privacy preservation at the edge [12, 13]. This paper seeks to enhance comprehension of Federated Learning in the realm of smart city sensing by comprehensively analyzing existing literature and pertinent case studies. By identifying significant challenges and opportunities, we aim to facilitate the advancement of resilient and expandable solutions that not only cater to the distinct requirements of urban settings but also stimulate the progression of smart cities towards enhanced efficiency, security, and privacy consciousness [14]. The convergence of Federated Smart Learning and IoT Technologies is shown in Figure 8.1.

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218  Edge of Intelligence

IoT Sensors

Smart City Sensing

Edge Computing

Federated Learning

Vertical Federated

Data Privacy

Training Model

Neural Networks

Machine Learning

Collaborative Learning

Figure 8.1  Convergence of federated smart learning and IoT technologies.

The motivation and contribution of this paper are: • Smart city sensing involves gathering sensitive data from various sources. However, privacy is a significant concern as this data is often personal and confidential. To address these privacy issues, federated smart learning allows collaborative intelligent model training without sharing raw data. • Smart cities often consist of distributed sensors and devices. Federated learning is well-suited for decentralized data sources, enabling collaborative learning without centralizing data. • A research paper might propose novel federated learning algorithms explicitly tailored to the challenges faced in smart city sensing. These algorithms could include techniques for model aggregation, communication efficiency, and handling heterogeneity in data sources. • Additionally, the paper may discuss how federated learning algorithms can be adapted to the unique characteristics of smart city environments. These may include the diversity of data sources.

8.2 Related Work This study aims to conduct a comprehensive literature review on federated learning within the domain of smart city sensing. It will encompass an

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Secure Smart City Sensing  219 analysis of various approaches and techniques employed for collaborative learning or decentralized data processing in smart cities while also drawing comparisons with existing literature. The utilization of Federated Smart Learning (FL) in the domain of smart city sensing offers several benefits, including reduced latency, enhanced privacy, and improved scalability capabilities [15]. Nevertheless, implementing FL in this particular domain presents challenges and opportunities. A significant obstacle in transmitting IoT data from edge devices to the cloud is the presence of communication overhead and response time. To address this problem, FL can efficiently handle most of the processing tasks on edge devices, decreasing communication overhead and latency [16]. One additional obstacle pertains to the constrained capacities of user devices in gathering appropriate data, thereby impacting their contributions to the global model. This challenge can be addressed by implementing social-aware federated learning, which facilitates the delegation of tasks to social connections, thereby enhancing individual contributions. The potential applications of FL in the field of smart and intelligent city sensing encompass the enhancement of traffic estimation and the overall improvement of the city’s quality of experience. In summary, FL exhibits considerable potential as a viable solution for sensing smart cities. However, further investigation is required to tackle the aforementioned obstacles and effectively exploit its capabilities. Federated learning (FL) shows great potential as a solution for sensing in smart cities. The enhancement of machine learning (ML) applications in urban environments is facilitated through the identification and resolution of challenges, as well as the provision of opportunities. FL utilizes edge devices for processing, reducing latency, enhancing privacy, and increasing scalability. Federated Learning (FL) also enables the management of diverse data sources. The efficacy of FL has been assessed in diverse smart city contexts, encompassing the monitoring of streetlights. It has demonstrated a slight decrease in performance but notable enhancements in communication efficiency and privacy protection. The significance of privacy-preserving techniques in Federation Learning (FL)-based Internet of Things (IoT) systems has been emphasized, and open research challenges have been identified. The advancement of Federated Learning (FL) can be classified into various emerging domains, including algorithmic underpinnings, customization, limitations imposed by hardware and security, continuous learning, and nonstandard data. Practical observations have been made regarding large-scale federated systems for edge devices. The application of FL in rainfall estimation has been observed in the context of existing smart-city wireless networks, demonstrating encouraging

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220  Edge of Intelligence outcomes in the precise estimation of rainfall and the creation of high-­ resolution rainfall maps. Federated Learning (FL) is a disseminated machine learning technique that seeks to address the challenges posed by data silos and privacy constraints in diverse real-world contexts [17]. FL has been employed in smart cities for various purposes, including identifying human activities and detecting objects. Nevertheless, Florida encounters various obstacles in the realm of smart city sensing. A significant obstacle is the constrained computational and communication IoT and applicable resources at the network edge, which can affect the efficacy of federated learning models. FL’s susceptibility to attacks, such as poisoning or DDoS attacks, poses an additional challenge when implemented in public transport systems [18]. Furthermore, the diversity of devices and data distribution in Federated Learning (FL) can impact the rate at which the training model converges and the global optimal solution. In order to address these challenges, scholars have put forth various approaches, including utilizing semi-supervised learning, implementing consensus algorithms based on blockchain technology, and applying stratified sampling and regularization techniques. The recent developments in federated learning (FL) present potential avenues for enhancing the efficacy, productivity, and dependability of FL models within the context of smart city sensing applications. Federated learning has appeared as a promising methodology for enhancing the efficacy of smart city sensing. Nevertheless, it also presents a distinct array of difficulties and possibilities. One of the primary obstacles lies in the development of a sensing system capable of detecting alterations in the impedance of the cover layer, thereby facilitating the detection of variations in signal coupling between electrodes [19]. Another obstacle lies in preserving anonymity while generating incident maps with meticulous detail. The integration of predictive modeling, statistical evaluation, and federated learning enables the achievement of this objective. This methodology enables the deduction of circumstances linked to specific user devices and the generation of extensively comprehensive incidence maps. Furthermore, the utilization of a federated learning-based approach for training model parameters and devices has the potential to improve the user experience through the implementation of secret sharing mechanisms, thereby obviating the necessity for collaborative efforts during the training phase [20]. In addition, federated sensors have the potential to facilitate the administration of medical data within a network comprising

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Secure Smart City Sensing  221 multiple devices. The utilization of these sensors facilitates the processing and prioritization of the data that has been gathered. In summary, federated learning offers a range of prospects for enhancing the sensing and data management capabilities of smart cities [21]. Federated Learning (FL) is a methodology that facilitates the creation of artificial intelligence (AI) models in contexts where privacy is paramount, such as healthcare and smart city monitoring. FL enables individuals to enhance their contributions to the global model by delegating tasks to their social connections. Federated Learning (FL) is also employed in the Human Mobility Prediction (HMP) domain within the context of smart healthcare and smart cities [22]. FL effectively addresses obstacles such as data heterogeneity and scarcity in this context. In order to achieve this objective, a novel framework known as FR-HMP has been introduced, which employs an point-to-point federated specific representation learning approach. This architecture effectively groups clients with similar characteristics and utilizes graph learning layers to acquire improved representations of each client. Federated Learning (FL) is employed to train machine learning models on Internet of Things (IoT) devices while ensuring privacy, thereby addressing concerns related to privacy. Nevertheless, FL encounters various obstacles, including privacy, efficacy, and efficiency within IoT networks. A proposed taxonomy has been put forth for FL-based IoT systems and privacy-preserving FL techniques, accompanied by an examination of relevant literature and unresolved research obstacles [23]. Distributed Federated Intelligent Learning (DFIL) enables the training of machine learning models on distributed devices while ensuring the confidentiality of private data, thereby improving data privacy and minimizing network overhead. Nevertheless, it necessitates intricate coordination mechanisms and difficulties managing numerous devices linked to an unreliable network. Additional input from both academia and industry is required to tackle these challenges. Federated Learning (FL) provides a balance between preserving privacy and achieving model accuracy by facilitating knowledge sharing between different devices. Nevertheless, the difficulties posed by heterogeneity restrict its practicality in real-life scenarios [24]. Recent research has placed emphasis on the adaptation of neural network architecture and the consideration of heterogeneity at a system level [25, 26]. This has been achieved through the utilization of various techniques such as Federated Averaging, distillation, and split learning. Federated Learning Technologies for Smart City Sensing is discussed in Table 8.1.

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222  Edge of Intelligence Table 8.1  Federated learning technologies for smart city sensing. Ref no.

Year

Technology

Method

Advantages

Disadvantages

Renato Valente et al. [15]

2023

IoT

Sensor-based monitoring

Real-time data collection

Privacy concerns

Susac et al. [16]

2021

Machine Learning

Neural Networks Automated decisionmaking

Requires large datasets

Canteli Rito et al. [17]

2019

Renewable Energy

Solar Panels

Sustainable power generation

High initial costs

Zhao et al. [18]

2022

Federated Learning

Decentralized Model Training

Improved Privacy Communication and Security Overhead, Latency

Elyan et al. [19]

2021

Edge Computing

Collaborative Learning

Reduced Data Transmission

Heterogeneity of Devices, Limited Bandwidth

Liazid et al. [20]

2023

IoT Sensors

Model Aggregation

Distributed Data Utilization

Model Synchronization, Resource Intensive

Hemashree et al. [21]

2024

Blockchain

Secure Model Updates

Tamper-resistant Model Updates

Scalability Concerns, High Energy Consumption

Yang et al. [22]

2021

TensorFlow

Horizontal Federated

Decentralized model training

Communication overhead

Xu et al. [23]

2019

PySyft

Vertical Federated

Privacypreserving

Limited model complexity

Jiang et al. [24]

2017

Federated Averaging

Federated Averaging

Reduced data transmission

Sensitive data exposure

Ryffel et al. [25]

2022

PyGrid

Homomorphic Encryption

Collaborative learning across institutions

Computational overhead

Anastasakis et al. [26]

2019

PySyft

Secure Aggregation

Improved privacy Increased training protection time

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Secure Smart City Sensing  223

8.2.1 Preliminaries According to the manuscript review, various preliminaries are essential for a Smart city with the integration of Federated Learning.

8.2.1.1 Smart City Sensing Smart and Intelligent City Sensing is a relatively new concept that leverages various sensing data to monitor pollution, plan infrastructure, and control traffic [1]. The term “smart city” was first coined in the early 1990s to describe cities that use information technology innovations and technologies to improve their citizens’ computational efficiency, bandwidth, and quality of human life. Smart city sensing focuses on acquiring data from different aspects of a city, including pollution, population, traffic, transport road conditions, and citizens’ smart health, using crowd/participatory intelligent sensing based on the IoT. Environmental sensors and human sensors have been used in smart city sensing projects. Environmental sensors monitor environmental features but can generate faulty data due to various factors. On the other hand, human sensors, who are users of online messaging platforms, provide observations about their surrounding environments. However, their data can be erroneous due to the natural language used in their messages. Smart city sensing involves using sensors and data collection technologies to monitor different aspects of a city. This helps improve its citizens’ quality of life and make the city more efficient. The sensors monitor various things such as pollution levels, traffic conditions, road conditions, citizens’ health, air quality, sound, and mobility [16]. The data collected from these sensors helps city administration make informed decisions and provide essential services more quickly and efficiently. By integrating social media data with sensor data, smart city sensing can provide real-time information about incidents or events in the city. This enables better decision-making and the provision of alerts to authorities and the community [25]. Overall, smart city sensing is important in creating smarter, more sustainable, and more efficient cities. Smart City Sensing is the process of gathering real-time data about a city using sensors and IoT technology. These sensors are installed all over the city to collect data on different aspects such as air quality, sound, mobility, and environmental conditions. The data collected by these sensors is analyzed to optimize city services, promote sustainability, and improve public safety. The sensors themselves play a crucial role in producing smart spaces and conceptualizations of the city, as they are influenced by the materiality of the sensor box and the technology’s visibility [26]. Additionally, the Mobile

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224  Edge of Intelligence Crowdsensing (MCS) method can be used, where citizens use their mobile devices to report problems and gather data, making the data collection process more cost-effective and efficient. All in all, Smart City Sensing allows cities to monitor and obtain real-time data on various urban issues, resulting in better planning, policy-making, and quality of life for residents. Smart City Sensing refers to using sensors and IoT technology to gather real-time data for governing and improving urban areas [27]. It involves managing heterogeneous sensor systems across different domains to overcome data fragmentation and create integration effects. The goal is to monitor and obtain real-time data on problems occurring in cities, such as disaster responses and urban conditions. Continuous energy supply, efficient sensing, and communications are critical for the widespread deployment of smart cities, requiring advanced ICT infrastructure. Smart City Sensing also involves collecting a large volume of data through smart and intelligent sensors, which can be used to predict, recognize, and develop smart infrastructure. By integrating communications, sensing, computing, and energy harvesting, Smart City Sensing enables the creation of an intelligent environment for people to live in safely and happily. The Smart City Sensing Organizer is an approach that aims to effectively describe and organize sensors in smart environments. It uses a feature-oriented capability model to describe sensor capabilities and Formal Concept Analysis to organize and index sensor services. The goal of this approach is to enable

Global Model

Aggregation

Local FL Model Data

Local FL Model Data

Figure 8.2  Represent of four models of smart city sensing.

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Secure Smart City Sensing  225 rapid adaptation to errors and the availability of data. The approach has been evaluated in a building fitted with various sensors and has shown effectiveness in monitoring temperature, motion, light, and power consumption [28]. Moreover, a 3D geo-clustering algorithm has been proposed to manage the sensor information of unguided wireless sensor networks in a spatial intelligent database in smart cities. This algorithm minimizes overlap among group clusters, improving energy efficiency and prolonging the network’s lifespan. Smart city sensing is described in Figure 8.2, which has four models.

8.2.1.1.1 Global Model

Using federated and intelligent learning in smart city application sensing entails both obstacles and prospects. Conventional approaches, such as Federated Learning, prioritizes consistency and coordination in modeling across multiple clients. However, this approach may not be appropriate in scenarios where diverse data modalities necessitate distinct models. Local learning occurs independently in such instances, while inference necessitates the integration of local models to attain consensus. To tackle this problem, a method called feature fusion is suggested. This method involves extracting local representations and integrating them into a global representation to enhance the accuracy of predictions. Federated learning can potentially improve individual contributions by leveraging social connections, provided that appropriate incentive structures are established [12, 29]. Federated learning has the potential to mitigate latency and communication overhead, enhance privacy, and effectively manage diverse data sources in smart city contexts. Nevertheless, it presents difficulties such as variations in statistics and systems, obstacles in communication, and concerns regarding privacy and security. Utilizing global models in smart city sensing for federated learning poses both obstacles and prospects. Nevertheless, currently distributed learning paradigms like Federated Learning require uniform and synchronized modeling across clients, which may not be appropriate for real-world situations [30]. The transmission of IoT data from edge connected devices to the cloud presents challenges in specific terms of communication and privacy in the context of centralized machine learning solutions. Given the constraints of limited resources and deadlines, semi-supervised federated learning over wireless network edge is a promising solution in this context [31, 32]. One potential drawback of over-the-air federated learning is the introduction of channel noise, which has the potential to adversely impact the accuracy of the model. Numerous solutions have been suggested to

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226  Edge of Intelligence tackle these challenges, including feature fusion, alignment of feature components, consensus graph learning, and Bayesian approaches for signal aggregation. These methods have been demonstrated to enhance the accuracy, confidentiality, scalability, and rate of convergence in smart city sensing applications.

8.2.1.1.2 Aggregation Model

The utilization of edge devices for processing in FL is a novel approach that seeks to improve the performance of smart city sensing. This methodology enables enhanced privacy and management of diverse data sources. Nevertheless, the creation of FL-based solutions necessitates the utilization of specialized tools and programming expertise. In order to tackle this particular challenge, Valente et al. put forth a solution that is based on lightweight containers. This solution provides a variety of machine learning (ML) algorithms that can be utilized to construct prediction engines specifically designed for edge devices. The framework proposes various possibilities for consolidating and enhancing models within the central server, enabling swift assessment of machine learning and aggregation algorithms in test environments and production infrastructures [33]. Furthermore, Brahmia et al. introduce a hybrid protocol named FedLM-PSO, which integrates Particle Swarm Optimization (PSO) and Levenberg Marquardt (LM) techniques to train Multilayer Perceptron (MLP) models within a Federated Learning framework. This protocol minimizes data transmission and improves bandwidth utilization, leading to more precise models with reduced communication cycles. Furthermore, Mozarab et al. examine and analyze various aggregation strategies and algorithms in the context of Federated and Intelligent Learning. They emphasize the distinct significance of these approaches in facilitating the integration of locally trained models for the purpose of training a global model. FL and intelligent way is a machine learning technique that addresses the challenges of data privacy and communication in smart city sensing by employing a distributed and collaborative approach. In the field of Federated Learning (FL), a global model is trained by integrating locally trained intelligent models from edge devices, eliminating the necessity of data transfer. The aggregation of algorithms plays a pivotal role in the FL process, as it facilitates the integration of knowledge from participating clients to train a comprehensive global model [12]. Federated Learning (FL) reduces latency and communication overheads by primarily processing data on edge devices. This approach enhances privacy by eliminating

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Secure Smart City Sensing  227 the need for data to be transmitted over the network. Additionally, FL simplifies the management of diverse data sources. Researchers have suggested using feature fusion techniques to tackle the difficulties associated with modeling heterogeneity and synchronicity among clients. These techniques aim to extract and integrate local representations into a comprehensive global representation. Furthermore, delayed aggregation techniques have been proposed to improve the efficacy of FL algorithms when applied to non-IID data. The authors have proposed a novel framework in the field of Federated Learning (FL) that incorporates adaptive model parameter aggregation algorithms. This framework aims to tackle the issue of hierarchical associations among participating nodes and the potential delay in data transmission.

8.2.1.1.3 Training Model

Federated learning (FL) is a technique that allows numerous clients to jointly prepare an AI model without transmitting their private data. It is an effective privacy-preserving solution for building AI systems in smart city sensing. By using FL, diverse solutions to problems can be achieved without compromising privacy. However, some challenges and opportunities are associated with training models using FL in smart city sensing. One of the challenges is that user devices may need more capabilities to collect suitable data, which can limit their contributions to the global model [33]. Another challenge is the communication overhead and privacy concerns when transmitting data from edge-end devices to the cloud in centralized and distributed ML solutions. Despite these issues, there are opportunities for social-aware collaborations where tasks can be outsourced to social connections, boosting contributions to the global model. Additionally, FL can improve privacy, scalability, and latency by performing processing on edge devices and handling heterogeneous data sources. Overall, while training models using FL in smart city sensing presents some challenges related to data collection and communication, it also offers opportunities for collaboration and improved performance. The technique of federated learning (FL) involves the training of models on edge devices while ensuring the localization of data. Training models in smart city sensing have become appealing due to their ability to tackle challenges such as communication costs, privacy concerns, and systems heterogeneity. Federated learning (FL) provides reduced latency and enhanced privacy compared to centralized machine learning methods [34]. Additionally, it facilitates the management of diverse data sources and enhances the potential for scalability. Nevertheless,

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228  Edge of Intelligence the process of transitioning current spatial-temporal models to decentralized learning continues to present specific challenges. Scientists have suggested FL-based models to forecast human movement, traffic, and recommendation systems based on location. Fluorescence (FL) can also be employed in unmanned aerial smart vehicles (UAVs) to perform various smart functions, including delivery, traffic mapping, and pollution monitoring. This application effectively tackles concerns related to data privacy, communication expenses, and energy constraints. In general, Federated Learning (FL) offers the potential for enhancing the effectiveness and efficacy of training models in the context of smart city sensing while simultaneously tackling concerns related to privacy and communication.

8.2.1.1.4 Local Model

In the context of smart city sensing, utilizing local models in federated learning (FL) poses various challenges and opportunities. Federated and intelligent Learning (FIL) facilitates the training of local models using decentralized datasets while ensuring data privacy preservation. One concern that arises is the assurance of data quality in the local training data [35]. Florida (FL) has the potential to tackle this challenge by employing blockchain technology to authenticate local models using miners and a smart contract. Federated Learning (FL) also facilitates the management of diverse data sources and scalability in smart city contexts. In addition, using social-aware federated learning can augment the offerings of the global model through the delegation of tasks to social connections. Furthermore, implementing collaborative incentives in the field of FL can significantly enhance the quantity of contributions made to the global model. In the realm of smart city sensing, using FL poses particular difficulties in data quality and offers prospects for enhanced collaboration and scalability. Federated Learning (FL) is a novel methodology in the field of machine learning that facilitates the training of models across a distributed network of devices or servers. An exceptional feature of this approach is its ability to train models without the need to exchange local data samples. This holds significant importance in smart city sensing, as many sensors gather data from diverse sources [36]. The term “local model” in the context of federated and intelligent learning for smart city and intelligent sensing pertains to the model trained using data obtained from a particular local device or sensor within the smart city infrastructure.

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Secure Smart City Sensing  229

8.2.1.2 Federated Learning Technology Federated and Intelligent Learning is a technique used in machine learning that enables training models across multiple decentralized devices or servers without the need to exchange local data samples [37]. The primary objective of this approach is to develop a global model while keeping the data localized and private. Doing so addresses privacy concerns, as the data remains on the user’s device, and only model updates are shared between the devices or servers. Federated and Intelligent Learning is a machine learning technique that enables numerous devices or local servers to train a global model simultaneously without exchanging data. This is how it works: • Initialization: A global model is initialized on a centralized server. • Distribution of Model: The global model is sent to individual devices or local servers. • Local Training: Each device or server trains the model with its own data. The training is performed without sharing the actual data; only model updates are returned to the central server. • Aggregation: The central server collects the model updates from all participating devices or servers and aggregates them to update the global model. Various aggregation methods can be used, such as averaging the weights or combining updates in a secure and privacy-preserving manner. • Iteration: Steps 2-4 are repeated iteratively to refine the global model. The model continues to improve based on the collective knowledge of all devices without exposing individual data. Federated Learning enables organizations to train machine learning models effectively by leveraging data from diverse sources while preserving data owners’ privacy. Federated Learning presents numerous benefits, one of which is the preservation of user privacy through the retention of data on the device. This approach effectively mitigates privacy concerns commonly associated with centralized training methods. Furthermore, the transmission of only model updates results in a reduction of data transfer in comparison to conventional centralized training methods [45]. Federated Learning is particularly suitable for edge computing environments, wherein devices located at the periphery of the network, such as

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230  Edge of Intelligence smartphones and Internet of Things (IoT) devices, can actively participate in the training of model. The decentralized nature of the approach renders it appropriate for situations where centralization could be more practical and desirable. A description flow of the Federated Learning and Intelligent Framework Architecture is shown in Figure 8.3. Nevertheless, Federated Learning presents certain difficulties. Ensuring model convergence with diverse and potentially biased data can pose challenges. Tackling limitations in communication and implementing aggregation methods that ensure security and privacy are also difficult tasks. Scholars are currently working to address these obstacles to enhance the feasibility and broad applicability of Federated Learning as a technology. Federated learning is a cyclical procedure that facilitates the acquisition of knowledge by the global model through the combined expertise of all involved devices while ensuring the localization of sensitive data. This methodology proves to be highly advantageous in scenarios where privacy is paramount, as it enables the application of machine learning techniques on decentralized and potentially confidential data while safeguarding the confidentiality of individual user data. Furthermore, using federated learning in edge computing environments offers several benefits. This is due to the ability of edge devices to participate in model training actively, eliminating the necessity for frequent data transfers to a central  server  [38].

Federated Learning Framework

Training Model Data

Data Storage

Model

Client

IoT Devices

Training Model Data

Data Storage

Model

Aggregation

Client IoT Devices

Training Model Data

Data Storage

Server Model

Client

Device Layer

Middle Layer

Application Layer

Figure 8.3  Illustration of federated learning framework architecture.

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Secure Smart City Sensing  231 The  three-layer architecture of the federated learning framework is depicted in Figure 8.3, which illustrates the operational mechanisms of federated learning technology.

8.2.1.2.1 Application Layer

Federated Learning is a technique used to improve the efficiency, privacy, and effectiveness of machine learning models in Smart City Sensing [39]. Smart City Sensing involves using sensors and devices to collect data for various applications like traffic management, energy optimization, and environmental monitoring. The application layer of Federated Learning is designed to implement this technique in smart city environments. Specific smart city applications like traffic management require immediate responses. However, Federated Learning may cause delays due to its iterative learning process. Hybrid approaches can be used to balance both responsiveness and model improvement. These approaches involve a combination of real-time local processing along with occasional model updates.

8.2.1.2.2 Middle Layer

Federated and Intelligent Learning (FIL) is a machine-learning approach that is used in Smart City Sensing. It involves training a model across decentralized edge devices, such as sensors or IoT devices, without exchanging raw data [35]. The middle layer is critical in this process as it facilitates communication and coordination between the edge devices and the central server. The Middle Layer plays a crucial role in federated learning by collecting the local model updates from the edge devices. This layer combines the updates to create a global model without accessing raw data. It also implements various mechanisms to protect sensitive information during aggregation, such as federated averaging, differential privacy, and secure multi-party computation. In short, the Middle Layer ensures that the privacy of individual devices is preserved while creating a global model that can be used for improved machine learning [34].

8.2.1.2.3 Device Layer

The decentralized machine learning approach known as Federated and Intelligent Learning (FIL) is commonly employed in the domain of Smart City and automated Sensing [36]. The methodology entails training models on many devices or sensors that are physically dispersed within an intelligent city setting. This approach relies heavily on the Device Layer,

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232  Edge of Intelligence a vital Federated Learning framework component. The Device Layer consists of diverse sensors, devices, and edge computing units widely distributed throughout a smart city. These items may encompass Internet of Things (IoT) devices, cameras, environmental sensors, and various other sources of data. Each individual device within the Device Layer possesses the capability to perform local model training on the data it gathers [40]. This practice guarantees that transmitting sensitive or extensive data to a centralized server for training is optional.

8.2.1.2.4 IoT Devices

The implementation of Federated and Intelligent Learning (FIL) in Smart City Sensing is significantly facilitated by the utilization of Internet of Things (IoT) devices [44]. In order to facilitate collaborative model training while maintaining privacy and efficiency, these devices are equipped with sensors and communication capabilities, thereby providing valuable data. The Internet of Things (IoT) devices, which encompass sensors and actuators, are strategically deployed across the smart city infrastructure in order to gather a wide range of data categories, including but not limited to environmental parameters, traffic conditions, and social interactions. Federated Learning is a methodology that facilitates the local training of models on the data generated by Internet of Things (IoT) devices, eliminating the need to transmit the raw data to a centralized server. This guarantees the confidentiality of data and minimizes the necessity for extensive data transfers. Nevertheless, a significant number of Internet of Things (IoT) devices possess constrained computational capacities, thereby posing challenges in the execution of intricate machine learning algorithms [40]. Hence, it is crucial to cultivate lightweight models and enhance Federated Learning algorithms to cater to devices with limited resources.

8.3 Federated Learning-Based Smart Cities Sensing Architecture for IoT-Enabled Smart Cities Sensing In this section, we discussed how federated learning technology could be applied to transform a modern city into “Smart City Sensing” using IoT and also discussed the places of Smart City Sensing. This section provides an overview of the architecture and security issues and solutions.

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Secure Smart City Sensing  233

8.3.1 Overview of IoT-Enabled Smart Cities Sensing Using Federated Learning Technology 8.3.1.1 Health Care In the realm of smart city sensing, healthcare utilizes Internet of Things (IoT) devices, wearables, and sensors to oversee and control patient well-being. Federated and Intelligent learning (FIL) is an AI methodology or paradigm that facilitates distributed and decentralized collaboration among multiple clients in the training of AI systems while ensuring that the raw data is not shared. Florida can mitigate data privacy concerns by conducting local training of machine learning models on IoT devices, thereby avoiding the need to transfer data to the cloud. It can also decrease the time overhead associated with system execution and user device authentication. The utilization of wearable devices in FL-based smart healthcare systems enables the collection of physiological data from individuals. This data is subsequently processed on edge computing devices, and the model parameters are uploaded to a central server for collaborative training. Ring signature defense techniques can be employed to conceal the origin of parameter updates and counter-source inference attacks. Integrating FL, AI, and Explainable AI (XAI) can effectively address the constraints and difficulties present in the healthcare system. Through the utilization of FL, healthcare providers can engage in collaborative efforts to train machine learning models while ensuring the preservation of patient privacy [41]. Wearables and sensors have the potential to facilitate the collection of physiological data, which can subsequently undergo local processing on edge computing devices before being transmitted to a central server for collaborative training purposes. Methods such as ring signature defense can effectively guarantee the security and protection of data against source inference attacks.

8.3.1.2 Fintech Fintech for Federated Learning in Smart City Sensing entails developing prediction engines for edge devices utilizing lightweight container-based solutions that use a variety of machine learning methods. These solutions also feature options for model aggregation and refining on the central server [42]. The major purpose is to use federated learning to address communication, privacy, and scalability concerns in smart city scenarios. Federated learning reduces latency and transmission overhead by doing the majority

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234  Edge of Intelligence Natural Language Processing (NLP)

Health Care Industry 4.0 Fintech

FL+LOT Applications

Insurance Sector

Smart Devices Virtual reality and Metaverse Autonomous vehicles

Figure 8.4  Demonstrates the applications of smart city sensing.

of the work on edge devices. It also increases privacy by removing data from the network and making it easier to manage diverse data sources. Furthermore, federated learning addresses security and privacy problems in smart city data collecting, resulting in greater participation in largescale sensing and data provision. To enable efficient data creation while maintaining data privacy for PM2.5 forecasts for smart-city sensing applications [35]. Federated compressed learning (FCL) is presented as an edge computing framework. Figure 8.4 shows how the applications of smart city sensing are grouped.

8.3.1.3 Insurance Sector Using Federated Learning (FL) in the insurance industry can potentially enhance the process of smart city sensing. The technology effectively tackles the issues pertaining to security, privacy, and data provision in the context of large-scale sensing by facilitating the collection of data while safeguarding sensitive information [43]. FL can guarantee data privacy and facilitate efficient data generation for predictions in the context of PM2.5 air quality monitoring systems. The utilization of FL presents advantages such as reduced latency, enhanced privacy, and scalability, rendering it well-suited for managing the growing volume of data produced by sensors within smart city environments. A lightweight container-based architecture has been proposed in order to facilitate the development of FL-based solutions. Various machine-learning algorithms are incorporated into this architectural framework to construct prediction engines for edge devices [44]. This facilitates the expeditious assessment of machine learning and aggregation algorithms within authentic production infrastructures.

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Secure Smart City Sensing  235 The utilization of Federated Learning (FL) holds significant potential in addressing the various obstacles smart city sensing systems encounter. Federated Learning (FL) presents several advantages, including reduced latency, enhanced privacy, and increased scalability. These benefits make FL suitable for various smart city applications, such as traffic estimation and environmental monitoring [13, 40]. The insurance industry can utilize FL to analyze data obtained from smart city sensors in order to evaluate risk and offer tailored insurance policies. FL enables insurance companies to train artificial intelligence models using decentralized data while ensuring privacy preservation. The aforementioned methodology facilitates data consolidation from various origins while upholding the confidentiality of individual participants. Utilizing real-time data from smart city sensors can improve the precision of risk assessment and empower insurance companies to provide customized policies. Furthermore, the utilization of FL can contribute to the identification of fraudulent activities within the insurance industry by examining data obtained from distributed sensors and applying machine learning algorithms.

8.3.1.4 Natural Language Processing (NLP) Natural Language Processing (NLP) is a methodology employed in Federated Learning (FL) to facilitate users’ usage of natural language queries to express smart city sensing tasks. The FL methodology enables the training of artificial intelligence models by utilizing data obtained from edge devices while ensuring the preservation of privacy and security. FL provides several benefits in smart cities, including reduced latency, enhanced privacy, simplified management of diverse data sources, and improved scalability. A lightweight container-based architecture is suggested to meet the programming requirements of FL-based solutions. The architectural framework encompasses diverse machine learning algorithms designed to construct prediction engines specifically tailored for edge devices. The technology enables the efficient assessment of machine learning and aggregation algorithms in both preliminary evaluation and real-world production environments [36]. The viability of this method has been proven through the use of actual data collected by the communications and sensing infrastructure to estimate vehicle mobility in and out of the city of Aveiro [36]. Moreover, Natural Language Processing (NLP) offers a conversational user interface that facilitates the specification of high-level sensing tasks. Deep learning techniques are utilized to establish a connection between natural language queries and virtual sensor representations.

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236  Edge of Intelligence

8.3.1.5 Smart Devices Smart devices for federated learning in smart city sensing refers to a system in which several interconnected devices work together to contribute towards developing and improving a machine learning model, primarily in the context of smart city applications [40]. Federated learning is a decentralized approach to machine learning wherein the model is trained across multiple devices or nodes without exchanging raw data. Smart cities are equipped with devices that have built-in sensors. These devices include smartphones, IoT devices, cameras, traffic sensors, environmental sensors, and other connected devices. These smart devices are distributed throughout the city and generate massive amounts of data related to traffic patterns, air quality, energy consumption, and more [45].

8.3.1.6 Autonomous Vehicles Autonomous vehicles can be used in federated learning to improve urban mobility, safety, and transportation efficiency. This involves integrating self-driving cars into a federated learning framework [41]. The data collected by these autonomous vehicles can be used to enhance machine learning models without compromising individual privacy. This approach offers a promising solution for smart city sensing. Autonomous vehicles are equipped with advanced sensors, cameras, Lidar, radar, and other technologies that enable them to operate without human intervention. These vehicles are crucial for collecting real-time data about traffic conditions, road infrastructure, pedestrian movement, and environmental factors in smart cities. By continuously collecting and transmitting data, autonomous vehicles provide valuable insights for immediate model updates and adaptations [45].

8.3.1.7 Industry 4.0 Industry 4.0 is a term used to describe the incorporation of advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), in industrial processes. In the context of smart city sensing, Federated Learning (FL) is a promising approach that can be applied in Industry 4.0 applications. FL enables privacy-preserving collaboration among multiple participants to train AI models without sharing sensitive data. It addresses the security and privacy challenges in gathering data for smart city services. FL can be utilized in various

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Secure Smart City Sensing  237 smart city sensing scenarios, such as air quality monitoring and vehicle mobility estimation, by utilizing the data collected from distributed sensors and devices. It reduces communication overheads, ensures data privacy, and improves scalability. FL has the potential to transform smart city sensing by allowing efficient and secure data analysis and model training in Industry 4.0 environments [37, 45]. Industry 4.0 is a term used to describe the integration of advanced technologies like the Internet of Things (IoT) and mobile edge computing (MEC) in the manufacturing industry. In the context of smart city sensing, Industry 4.0 can be used for federated learning, a privacy-preserving machine learning approach. Federated learning enables AI models to be trained using data from multiple sources without transferring the raw data to a centralized server. This is particularly important in smart city sensing, where data privacy and security are crucial concerns. By taking advantage of federated learning, smart city sensing can benefit from improved training performance and privacy protection offered by this approach [46].

8.3.1.8 Virtual Reality and Metaverse The technology known as virtual reality (VR) is designed to generate an immersive and interactive simulated environment. Users can engage with virtual objects and spaces through this feature. Advanced technologies, including extended reality, artificial intelligence, and blockchain, are utilized to drive its functionality. The Metaverse serves as a dynamic facilitator of the forthcoming Internet infrastructure. The technology generates a highly engaging and dynamic virtual environment in which users are able to engage in activities that closely resemble those encountered in the physical world. The Metaverse is conceptualized as a transformative force in society and gradually materializes within the framework of intelligent urban areas. In the scope of smart cities, federated learning (FL) has emerged as a solution to the challenges posed by transmitting massive amounts of data generated by sensors to the cloud. FL performs most of the processing on edge devices, reducing latency and communication overhead. It also improves privacy and facilitates the handling of heterogeneous data sources. FL can effectively contribute to smart city scenarios by improving the quality of experience and reaction time in improving the flow of vehicles [47]. Federated Learning Technologies and IoT Technologies in Smart City Sensing are discussed in Table 8.2.

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238  Edge of Intelligence Table 8.2  Federated learning technologies and IoT technologies in smart city sensing. Author

Year

Smart city sensing

Lim et al. [41]

2020

Ghadi et al. [42]

Applications

IoT technologies

Actual cases

Health Care

Patients, health Care providers.

Smart watches.

Germany, Texas and Singapore.

2023

Fintech

Insurance Industry.

Online lending.

New York, London and Berlin.

Cheng et al. [43]

2021

Smart Devices

Industries and domains.

Sensors and Software.

Tokyo and Japan.

Imtiah et al. [44]

2019

Industry 4.0

Flexibility, agility.

Sensors, machine and IIoT Devices.

Germany, Japan and Singapore.

Putra et al. [17]

2021

Insurance Sector

Products and services.

Online platforms and Blockchain Technology.

New York, Switzerland and Bermuda.

Zeng et al. [47]

2023

Industrial Metaverse

Transportation and Mobility.

Sensors and Artificial Intelligence.

California and China.

8.3.2 Application Security Issues and Solutions Application Security Issues and Solutions are described in Table 8.3. Table 8.3  Application security issues and solutions. Application security issues

Author

Year

Alazab et al. [48]

2021

Communication Latency

Optimize communication protocols and algorithms to reduce.

Zheng et al. [49]

2022

Heterogeneity of Devices

Develop adaptive algorithms that can accommodate.

Jiang et al. [7]

2020

Energy Efficiency

Design energy-efficient algorithms and models to minimize.

Abdulla et al. [50]

2024

Model Drift

Implement techniques such as continual learning or adaptive.

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Solutions

Secure Smart City Sensing  239

8.4 Open Issues, Related Challenges and Opportunities In this section, we discussed open issues and related challenges and opportunities. We also discussed associated challenges and opportunities. Table 8.4 outlines the open associated problems, challenges, and opportunities discussed in this section.

8.4.1 Open Issues There are some open issues and challenges in using Federated Learning (FL) for smart city sensing. One of the challenges is the need for specialized tools and programming skills for FL-based solutions. Another issue is related to security and privacy concerns associated with using legacy data acquisition models supported by centralized machine learning (ML) models [51]. These concerns have resulted in a need for more participation in large-scale sensing and data provision for smart city services. Although FL has the potential to address these challenges, there are still open issues that need to be addressed. These include developing efficient Table 8.4  Outlines open issues and related challenges and opportunities. Open issues

Related challenges

Opportunities

Privacy concerns

Data protection

Implement differential privacy

Data heterogeneity

Inconsistent formats

Standardize data formats

Communication latency protocols

Network congestion

Optimize communication

Security risks

Data breaches

Utilize encryption techniques

Resource constraints algorithms

Scarce energy

Utilize energy-efficient

Privacy preservation

Data leakage risks

Increased citizen trust and participation

Communication overhead

Bandwidth constraints

Reduced communication costs

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240  Edge of Intelligence methods for FL, as well as determining the applicability of FL in smart city sensing. Researchers who are studying this subject matter should focus on these open issues, challenges, and opportunities to advance FL in smart city sensing further. To resolve the outstanding issues related to federated learning in smart city sensing, it is crucial to carry out interdisciplinary research efforts. This requires collaboration between experts in machine learning, distributed systems, cybersecurity, urban planning, and regulatory compliance. It is important to involve researchers, industry partners, policymakers, and community stakeholders in these efforts to develop ­privacy-preserving, scalable, and socially responsible solutions [12].

8.4.2 Related Challenges and Opportunities When used in smart city sensing, Federated Learning (FL) has both challenges and opportunities. One challenge is the communication overhead and latency when processing data on edge devices [52]. FL can address this challenge by allowing most of the coupled processing to be done on the edge devices themselves. Another challenge is the transmission of large amounts of data from edge end devices to the cloud, which can negatively impact response time and compromise privacy. FL solves this problem by keeping the data on the edge devices and not transmitting it over the network [49]. Furthermore, FL can handle heterogeneous data sources and improve scalability [52]. FL also has opportunities in smart city sensing, such as learning mobility patterns from sensors and scattered throughout the city. This can be used for applications like traffic estimation and improving the flow of vehicles. FR-HMP, a federated representation learning framework, can help overcome challenges in federated human mobility prediction by addressing data heterogeneity and scarcity. The utilization of federated learning (FL) in smart city sensing entails both obstacles and prospects. The transmission of substantial data from edge end devices to the cloud for centralized and intelligent machine learning (ML) solutions presents a notable obstacle in the form of communication overhead and latency. FL addresses the issue through the utilization of edge devices for the majority of processing tasks, thereby reducing latency and communication overhead. An additional obstacle pertains to the confidentiality of data during its transfer between the edge and the cloud. FL guarantees privacy by implementing measures to prevent data transmission over the network. Federated Learning (FL) also facilitates the management of diverse data sources and improves scalability. Furthermore, using FL enables the training of local drone models without transmitting raw data, thereby effectively addressing concerns related to data privacy,

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Secure Smart City Sensing  241 communication expenses, and energy constraints. FL can be utilized in smart cities to forecast water usage while safeguarding privacy through the distributed training of machine learning models. In addition, FL offers machine learning frameworks that prioritize privacy preservation and efficient communication in data-enabled cities. These frameworks aim to enhance communication cost and privacy while simultaneously ensuring optimal performance. The utilization of FL has the potential to mitigate the uneven dispersion of noisy labels within datasets, thereby diminishing the number of communication rounds necessary for convergence and enhancing performance within the constructed environment. The challenges of Federated Learning for Smart City Sensing for IoT Technologies are shown in Figure 8.5. A significant obstacle encountered while implementing Federated Learning (FL) pertains to transmitting substantial volumes of data from edge and devices to the cloud. This has a detrimental impact on the speed of response and jeopardizes the confidentiality of data [53]. One additional obstacle pertains to the limited capabilities of user devices in gathering appropriate data, thereby impacting their contribution to the global model. Nevertheless, the implementation of FL presents numerous advantages. FL can enhance privacy and scalability by implementing edge-device data processing, reducing latency and communication overhead. Furthermore, Protect against the attacks

A look at the temporal changes after deployment

Resource Management in FL

Fl is not appropriate for the aggregating updates that are being performed while selecting vehicles

Challenges in FL for lot

Al function Deployed on lot Sensors

Standardized specifications for FL- lot

Privacy protection and security issues in FL

Communication issues in FL-lot

Figure 8.5  Challenges of federated learning for smart city sensing for IoT technologies.

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242  Edge of Intelligence implementing social-aware collaborations can significantly enhance the quantity of contributions to a global model as long as appropriate incentive structures are established. FL can be effectively incorporated into different government and industry sectors, including smart cities and industries, to expedite the advancement of FL. Please provide additional details regarding the context or domain to which you are referring, as the concept of “open issues, related challenges, and opportunities” can exhibit substantial variations across diverse fields or industries. Nevertheless, we offer a broad outline of several prevalent domains: 1)  Technology and Artificial Intelligence Open Issues: A few significant issues regarding artificial intelligence (AI) need to be addressed. These include concerns about individuals’ privacy, ethical considerations in AI, the potential for bias in algorithms, and the possible misuse of advanced technologies [54]. Challenges: Several challenges must be overcome to ensure that AI is used responsibly and effectively. These include ensuring transparency and accountability in AI systems, addressing issues of bias and fairness, and establishing effective regulations to govern the use of AI. Opportunities: Despite these challenges, there are also many opportunities to develop ethical frameworks and advance responsible AI practices. By leveraging AI for positive societal impact, we can help to address some of the most pressing challenges facing our world today. 2)  Climate Change Open Issues: The world is grappling with issues such as mitigating the impact of climate change, reducing carbon emissions, and transitioning to sustainable energy sources [55]. Challenges: Implementing policies and fostering global cooperation are some of the biggest challenges in tackling these issues. It is also essential to develop cost-effective green technologies. Opportunities: The world has a great opportunity to innovate in the field of renewable energy and sustainable practices and collaborate internationally on climate change initiatives. 3)  Healthcare Open Issues: In healthcare, access to healthcare, global health disparities, and pandemic preparedness are included [41]. Challenges: The challenges faced in healthcare are the development of affordable and accessible healthcare solutions, addressing the impact of emerging diseases, and improving healthcare infrastructure.

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Secure Smart City Sensing  243 Opportunities: In healthcare, such as advancements in telemedicine, personalized medicine, and leveraging technology for efficient healthcare delivery. 4)  Cybersecurity Open Issues: Cyber threats, data breaches, and digital infrastructure protection are some of the major concerns in cyberspace. Challenges: Some of the difficulties we face include developing robust cybersecurity measures, addressing evolving cyber threats, and educating individuals and organizations about best practices for cybersecurity. Opportunities: There are many opportunities to improve cybersecurity, such as advancing cybersecurity technologies, fostering collaboration among cybersecurity professionals, and increasing public awareness about its importance [48]. 5)  Global Economy Open Issues: Economic inequality, trade tensions, and the impact of technological advancements on employment [46]. Challenges: Promoting inclusive economic growth, addressing the digital divide, and navigating geopolitical uncertainties. Opportunities: Fostering innovation, investing in education and skills development, and promoting sustainable economic practices [36]. 6)  Communication Open Issues: There are growing ethical concerns related to the use of communication technologies. These concerns include issues such as surveillance, online harassment, and algorithmic biases [49]. Challenges: The balance of the freedom of expression with the need to prevent harm and protect individuals’ rights. Opportunities: address these issues by establishing clear ethical guidelines and regulatory frameworks, as well as promoting responsible technology design and usage [48]. 7)  Privacy Protection Open Issue: With the world becoming more connected, protecting individuals’ privacy rights and securing sensitive data is a growing concern [48]. Challenges: There’s a need to address cybersecurity threats and ensure compliance with data protection regulations. Opportunities: Developing innovative encryption technologies and promoting transparent data practices among businesses and governments can help mitigate these risks.

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244  Edge of Intelligence 8)  Security Issues Open Issue: Protecting digital infrastructure, systems, and data from cyber threats like malware, ransomware, and data breaches is a significant concern for individuals and organizations worldwide [43]. Challenges: The challenge is to keep pace with the ever-increasing sophistication of cyber-attacks and vulnerabilities. Opportunities: However, this challenge presents an opportunity to develop advanced security technologies, such as artificial intelligence, for threat detection. It is also an opportunity to promote awareness of security issues and education while fostering collaboration between the public and private sectors [44]. 9)  Resource Management Open Issues: Resource management involves effectively and sustainably utilizing various resources, such as natural resources, human resources, and financial resources [56]. Challenges: Resource management requires integrated approaches that balance environmental, social, and economic considerations. Opportunities: By promoting sustainable development, we can enhance the well-being of current and future generations. 10)  Standardized Specifications Open Issues: Standardized specifications are critical in ensuring that different industries and sectors can work together efficiently while maintaining high-quality standards [46]. Challenges: While standardization efforts can lead to innovation, efficiency, and sustainability, they can also present challenges [46]. Opportunities: Standardized specifications offer numerous opportunities for businesses, governments, and consumers to drive efficiency, innovation, sustainability, and competitiveness across industries and sectors. Ultimately, this benefits society as a whole.

8.4.3 Service Scenario of Federated Learning for Smart City Applications In this section, we discuss service scenarios of federated learning for smart city intelligent applications such as smart parking, smart homes, smart electricity, and more. This service scenario helps address various challenges in smart cities, including centralization, privacy preservation, data integrity, heterogeneity, and security. Service scenarios of federated learning in smart city is shown in Figure 8.6.

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Cloud Layer

Secure Smart City Sensing  245

Data Center

Aggregation Layer

Global Model

d D o el U wn plo loa ad d /

Smart City Applications

Smart City Applications SCA1

Local Model

Data Gathering

Data Gathering

SCA2

SCA3

Smart City Environment

Local Model IoT Sensor Layer

Mo

/ ad plo d U a l de nlo Mo Dow

IoT and Sensor Devices for Smart City

Figure 8.6  Service scenarios of federated learning in smart city.

In this Smart City Applications service scenario, we used three layers, including IoT Sensor, Aggregation, and Cloud. At the bottom layer, various IoT and sensor devices are available, which is utilized and connected to the Smart City Applications {SCA1 , SCA2 , SCA3 ,……}. These applications are smart parking, smart home, smart electricity, and others. IoT and sensor devices of smart city applications generate data and are acquired on the middle layer. At this layer, we are using federated learning concepts, which means the local model trained the inputs of the IoT and sensor devices data of the smart city applications. Then, all local model data transfer gradient values (output of local model) to the global model. This model provides the aggregation output values and is downloaded by all local models. Finally, the local model sends the smart city applications’ IoT and sensor devices’ data to the cloud layer for storage and decision-making purposes.

8.4.4 Discussion This study assesses the efficacy of federated and intelligent learning (FIL) approaches in the context of smart city environments, specifically emphasizing performance evaluation, communication overhead analysis, and

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246  Edge of Intelligence aggregation algorithms. This statement underscores the advantages of incorporating social-aware federated learning into a global framework. Federated learning (FL) is a highly promising method for sensing smart cities, as it involves training artificial intelligence (AI) models on disparate devices without sharing confidential data with external entities. Nevertheless, a significant obstacle to utilizing FL for smart city sensing is the devices’ diversity and corresponding data. Various devices may possess distinct capabilities and data formats, necessitating attention to ensure efficient collaboration. One additional obstacle pertains to the lack of device availability resulting from connectivity issues, thereby prompting the inquiry of how to address the training deficit caused by the unavailability of devices. The computational challenge is known as the “Oblique Federated Learning” problem. The prediction of federated human mobility in smart cities is hindered by the presence of data heterogeneity, which encompasses variations in mobility patterns and limited availability of data. A proposed framework known as FR-HMP has been introduced to address these challenges, focusing on federated representation learning. Social-aware federated learning is a solution that enhances the contributions of individuals in FL scenarios like smart city monitoring, thereby improving collaborative incentives. Enabling the delegation of tasks to social connections can greatly enhance the quantity of contributions to a global model. Deploying centralized machine learning (ML) solutions to transmit a substantial volume of data generated from edge end devices to the cloud can present difficulties in terms of communication and privacy. Federated Learning (FL) has the potential to mitigate these challenges by enabling the majority of processing tasks to be executed on edge devices, thereby enhancing privacy and streamlining the management of diverse data sources. Federated Learning (FL) presents numerous benefits compared to centralized and third-party models, such as reduced latency, processing, and communication overhead, enhanced privacy, simplified management of diverse data sources, and improved scalability.

8.5 Conclusions Federated Learning (FL) and intelligent way is a machine learning methodology that entails using decentralized edge devices to train models, thereby ensuring data localization. This approach is especially advantageous in the field of Smart City Sensing, as it effectively tackles privacy issues and minimizes the requirement for centralized data storage. The inclusion of essential elements within the framework encompasses edge computing,

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Secure Smart City Sensing  247 communication protocols, and collaborative model training. Federated learning presents a promising methodology for harnessing the capabilities of machine learning in the context of smart city sensing while concurrently safeguarding privacy and security. Nevertheless, the successful implementation of this approach in smart city environments necessitates the resolution of various challenges. The aforementioned challenges encompass issues related to privacy, barriers to communication, and the heterogeneity of devices. The successful resolution of these challenges is of utmost importance in order to fully harness the capabilities of federated learning, thereby facilitating the advancement of efficient and privacy-conscious smart city systems. To fully harness the advantages of federated learning in smart city sensing, it is crucial to tackle privacy issues, enhance communication, guarantee security, and devise effective aggregation methods. The continuous investigation and advancement in this particular domain offer the potential to surmount these obstacles and establish intelligent and adaptable smart city systems by means of decentralized machine learning within urban settings. The implementation of FL in Indonesia has the capacity to expedite the advancement of FL, specifically in the domains of smart cities, smart industries, and smart health services.

Acknowledgment This research was supported by the Research Seed Grant funded by the Marwadi University, Rajkot, Gujrat (MU/R&D/22–23/MRP/FT13).

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248  Edge of Intelligence 5. Rachmawati, T. and Pertiwi, P.D., Smart Environment Program, Smart Way to Smart City. Policy Governance Rev., 1, 1, 26–36, 2017. 6. Gheisari, M., Wang, G., Chen, S., An edge computing-enhanced internet of things framework for privacy-preserving in smart city. Comput. Electr. Eng., 81, 106504, 2020. 7. Jiang, J.C., Kantarci, B., Oktug, S., Soyata, T., Federated learning in smart city sensing: Challenges and opportunities. Sensors, 20, 21, 6230, 2020. 8. Pandya, S., Srivastava, G., Jhaveri, R., Babu, M.R., Bhattacharya, S., Maddikunta, P.K.R., Gadekallu, T.R., Federated learning for smart cities: A comprehensive survey. Sustain. Energy Technol. Assess., 55, 102987, 2023. 9. Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y., A survey on federated learning. Knowledge-Based Syst., 216, 106775, 2021. 10. Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., Rellermeyer, J.S., A survey on distributed machine learning. ACM Comput. Surv. (CSUR), 53, 2, 1–33, 2020. 11. Singh, S.K., Pan, Y., Park, J.H., Blockchain-enabled secure framework for energy-efficient smart parking in sustainable city environment. Sustain. Cities Soc., 76, 103364, 2022. 12. Singh, S.K., Yang, L.T., Park, J.H., FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0. Inf. Fusion, 90(C), 233–240, 2023. 13. Singh, S.K. and Park, J.H., TaLWaR: blockchain-based trust management scheme for smart enterprises with augmented intelligence. IEEE Trans. Ind. Inf., 19, 1, 626–634, 2022. 14. Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., He, B., A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Trans. Knowl. Data Eng., 35, 4, 3347–3366, 2021. 15. Valente, R., Senna, C., Rito, P., Sargento, S., Embedded federated learning for vanet environments. Appl. Sci., 13, 4, 2329, 2023. 16. Zekić-Sušac, M., Mitrović, S., Has, A., Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. Int. J. Inf. Manage., 58, 102074, 2021. 17. Vázquez-Canteli, J.R., Ulyanin, S., Kämpf, J., Nagy, Z., Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities. Sustain. Cities Soc., 45, 243–257, 2019. 18. Zhao, J., Zhu, H., Wang, F., Lu, R., Liu, Z., Li, H., PVD-FL: A privacy-­ preserving and verifiable decentralized federated learning framework. IEEE Trans. Inf. Forensics Secur., 17, 2059–2073, 2022. 19. Elayan, H., Aloqaily, M., Guizani, M., Deep federated learning for IoTbased decentralized healthcare systems, in: 2021 International Wireless Communications and Mobile Computing (IWCMC), 2021, June, IEEE, pp. 105–109.

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Secure Smart City Sensing  249 20. Liazid, H., Lehsaini, M., Liazid, A., Data transmission reduction using prediction and aggregation techniques in IoT-based wireless sensor networks. J. Netw. Comput. Appl., 211, 103556, 2023. 21. Hemashree, P., Kavitha, V., Mahalakshmi, S.B., Praveena, K., Tarunika, R., Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges, in: Blockchain Transformations: Navigating the Decentralized Protocols Era, pp. 107–130, Springer Nature Switzerland, Cham, 2024. 22. Yang, Q., Toward responsible ai: An overview of federated learning for user-centered privacy-preserving computing. ACM Trans. Interact. Intell. Syst. (TiiS), 11, 3–4, 1–22, 2021. 23. Xu, R., Baracaldo, N., Zhou, Y., Anwar, A., Ludwig, H., Hybridalpha: An efficient approach for privacy-preserving federated learning, in: Proceedings of the 12th ACM workshop on artificial intelligence and security, 2019, November, pp. 13–23. 24. Jiang, Z., Balu, A., Hegde, C., Sarkar, S., Collaborative deep learning in fixed topology networks, in: Advances in Neural Information Processing Systems, vol. 30, 2017. 25. Ryffel, T., Cryptography for Privacy-Preserving Machine Learning (Doctoral dissertation), ENS Paris-Ecole Normale Supérieure de Paris, PSL University, Paris, 2022. https://hal.science/tel-04005263. 26. Anastasakis, Z., Bourou, S., Velivasaki, T.H., Voulkidis, A., Skias, D., Analysis of Privacy Preservation Enhancements in Federated Learning Frameworks, in: Shaping the Future of IoT with Edge Intelligence, pp. 117–133, River Publishers, Alsbjergvej 10, 9260 Gistrup, Denmark, 2024. 27. Habibzadeh, H., Qin, Z., Soyata, T., Kantarci, B., Large-scale distributed dedicated-and non-dedicated smart city sensing systems. IEEE Sens. J., 17, 23, 7649–7658, 2017. 28. Azri, S., Ujang, U., Abdul Rahman, A., 3D geo-clustering for wireless sensor network in smart city. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 42, 11–16, 2019. 29. Arya, M., Sastry, H., Dewangan, B.K., Rahmani, M.K.I., Bhatia, S., Muzaffar, A.W., Bivi, M.A., Intruder detection in VANET data streams using federated learning for smart city environments. Electronics, 12, 4, 894, 2023. 30. Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., Huba, D., Federated learning with buffered asynchronous aggregation, in: International Conference on Artificial Intelligence and Statistics, 2022, May, PMLR, pp. 3581–3607. 31. Singh, S.K., Lee, C., Park, J.H., CoVAC: A P2P smart contract-based intelligent smart city architecture for vaccine manufacturing. Comput. Ind. Eng., 166, 107967, 2022. 32. Albaseer, A., Ciftler, B.S., Abdallah, M., Al-Fuqaha, A., Exploiting unlabeled data in smart cities using federated learning, arXiv preprint arXiv:2001.04030, 2020.

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250  Edge of Intelligence 33. Abboud, A., Abouaissa, A., Shahin, A., Mazraani, R., A Hybrid Aggregation Approach for Federated Learning to Improve Energy Consumption in Smart Buildings, in: 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023, June, IEEE, pp. 854–859. 34. Drainakis, G., Katsaros, K.V., Pantazopoulos, P., Sourlas, V., Amditis, A., Federated vs. centralized machine learning under privacy-elastic users: A comparative analysis, in: 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), 2020, November, IEEE, pp. 1–8. 35. da Silva, M.V.S., Bittencourt, L.F., Rivera, A.R., Towards federated learning in edge computing for real-time traffic estimation in smart cities, in: Anais do IV workshop de Computação Urbana, 2020, December, SBC, pp. 166–177. 36. Huang, A., Liu, Y., Chen, T., Zhou, Y., Sun, Q., Chai, H., Yang, Q., Starfl: Hybrid federated learning architecture for smart urban computing. ACM Trans. Intell. Syst. Technol. (TIST), 12, 4, 1–23, 2021. 37. Singh, S.K., Azzaoui, A.E., Choo, K.K.R., Yang, L.T., Park, J.H., Articles A Comprehensive Survey on Blockchain for Secure IoT-enabled Smart City beyond 5G: Approaches, Processes, Challenges, and Opportunities. Hum.Centric Comput. Inf. Sci., 13, 51, 2023. 38. Ramu, S.P., Boopalan, P., Pham, Q.V., Maddikunta, P.K.R., Huynh-The, T., Alazab, M., Gadekallu, T.R., Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions. Sustain. Cities Soc., 79, 103663, 2022. 39. Al-Huthaifi, R., Li, T., Huang, W., Gu, J., Li, C., Federated learning in smart cities: Privacy and security survey. Inf. Sci., 632, 833–857, 2023. 40. Singh, S.K., Kumar, M., Tanwar, S., Park, J.H., GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks. Future Gener. Comput. Syst., 153, 391–402, 2024. 41. Lim, W.Y.B., Garg, S., Xiong, Z., Niyato, D., Leung, C., Miao, C., Guizani, M., Dynamic contract design for federated learning in smart healthcare applications. IEEE Internet Things J., 8, 23, 16853–16862, 2020. 42. Ghadi, Y.Y., Mazhar, T., Shah, S.F.A., Haq, I., Ahmad, W., Ouahada, K., Hamam, H., Integration of federated learning with IoT for smart cities applications, challenges, and solutions. PeerJ Comput. Sci., 9, e1657, 2023. 43. Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., Yang, Q., Secureboost: A lossless federated learning framework. IEEE Intell. Syst., 36, 6, 87–98, 2021. 44. Singh, S.K., Cha, J., Kim, T.W., Park, J.H., Machine learning based distributed big data analysis framework for next generation web in IoT. Comput. Sci. Inf. Syst., 18, 2, 597–618, 2021. 45. Imteaj, A. and Amini, M.H., Distributed sensing using smart end-user devices: Pathway to federated learning for autonomous IoT, in: 2019 International conference on computational science and computational intelligence (CSCI), 2019, December, IEEE, pp. 1156–1161.

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Secure Smart City Sensing  251 46. Putra, K.T., Chen, H.C., Prayitno, Ogiela, M.R., Chou, C.L., Weng, C.E., Shae, Z.Y., Federated compressed learning edge computing framework with ensuring data privacy for PM2. 5 prediction in smart city sensing applications. Sensors, 21, 13, 4586, 2021. 47. Zeng, S., Li, Z., Yu, H., Zhang, Z., Luo, L., Li, B., Niyato, D., Hfedms: Heterogeneous federated learning with memorable data semantics in industrial metaverse. IEEE Trans. on Cloud Comput., 11, 3, 3055–3069, 2023. 48. Alazab, M., RM, S.P., Parimala, M., Maddikunta, P.K.R., Gadekallu, T.R., Pham, Q.V., Federated learning for cybersecurity: Concepts, challenges, and future directions. IEEE Trans. Ind. Inf., 18, 5, 3501–3509, 2021. 49. Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., Li, K., Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connect. Sci., 34, 1, 1–28, 2022. 50. Abdulla, N., Demirci, M., Ozdemir, S., Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning. Sustain. Energy Grids Netw., 38, 101342, 2024. 51. Mohammadi, M. and Al-Fuqaha, A., Enabling cognitive smart cities using big data and machine learning: Approaches and challenges. IEEE Commun. Mag., 56, 2, 94–101, 2018. 52. Wu, H., Zhang, Z., Guan, C., Wolter, K., Xu, M., Collaborate edge and cloud computing with distributed deep learning for smart city internet of things. IEEE Internet Things J., 7, 9, 8099–8110, 2020. 53. Rahman, M.A., Asyhari, A.T., Leong, L.S., Satrya, G.B., Tao, M.H., Zolkipli, M.F., Scalable machine learning-based intrusion detection system for IoTenabled smart cities. Sustain. Cities Soc., 61, 102324, 2020. 54. Alahi, M.E.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K., Mukhopadhyay, S.C., Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23, 11, 5206, 2023. 55. García Fernández, C. and Peek, D., Smart and sustainable? Positioning adaptation to climate change in the European smart city. Smart Cities, 3, 2, 511– 526, 2020. 56. Tian, K., Chai, H., Liu, Y., Liu, B., Edge Intelligence empowered dynamic offloading and resource management of MEC for Smart City internet of things. Electronics, 11, 6, 879, 2022.

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9 Fusion of Blockchain and Edge Computing for Seamless Convergence Indu Bala

*

SEEE, Lovely Professional University, Punjab, India

Abstract

This chapter meticulously explores the foundational concepts of Blockchain Technology and Edge Computing, establishing a comprehensive understanding of their characteristics and functionalities. A detailed discussion on the Key Components of Blockchain and Edge Integration unravels the intricacies of the Blockchain infrastructure, edge computing systems, and the multifaceted challenges and opportunities arising from their convergence. Emphasizing the paramount importance of data security and integrity in a converged environment, the chapter delves into Security Considerations, elucidating the critical roles played by both Blockchain and edge computing. Moving beyond theoretical considerations, the chapter provides practical insights into real-world implementations and industry-specific use cases in Use Cases and Applications, unraveling the technical intricacies of integrating Blockchain with edge computing. Finally, the chapter extends its gaze toward the future, offering recommendations and a forward-looking perspective on emerging technologies in both Blockchain and edge computing, anticipating developments in their integration and encapsulating the evolving landscape of these technologies in the digital era. Keywords:  Blockchain, edge computing, integration, security, challenges, opportunities, ethics

Email: [email protected]

*

Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (253–278) © 2025 Scrivener Publishing LLC

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253

254  Edge of Intelligence

9.1 Introduction to Blockchain and Edge Computing 9.1.1 Defining Blockchain Technology Blockchain technology is a revolutionary power that is fundamentally changing the way digital transactions and information are handled. Blockchain is a decentralized and distributed ledger technology that fundamentally alters the way data is recorded and validated. Blockchain, in contrast to conventional centralized databases, functions on a decentralized network where every participant, known as a node, possesses a replica of the complete ledger. The decentralized structure guarantees transparency, as any modifications made to the ledger are instantly accessible to all participants. The essence of Blockchain resides in its capacity to establish an impregnable and unalterable sequence of blocks, each encompassing a roster of transactions. The blocks’ immutability, attained using sophisticated cryptographic methods, improves data integrity and renders altering historical records nearly impossible [1–5]. Blockchain technology provides a fundamental benefit by prioritizing trust and security. Blockchain networks utilize consensus mechanisms such as Proof of Work (PoW) or Proof of Stake (PoS) to validate and reach an agreement on transactions by engaging the majority of participants. The decentralized nature of Blockchain mitigates the potential for a singular point of failure and reduces the likelihood of fraudulent operations and cyberattacks. In addition, Blockchain introduces the notion of smart contracts, which are self-executing agreements regulated by pre-established rules in code. These contracts utilize automation to enforce agreements, thereby minimizing the requirement for middlemen and optimizing procedures in diverse sectors such as banking, supply chain, healthcare, and others. The advancement of Blockchain technology is expected to have a substantial influence on worldwide organizations and industries, introducing a new period marked by trust, effectiveness, and creativity. As depicted in Figure 9.1, Blockchain functions by utilizing a decentralized and distributed ledger system, which fundamentally alters the method of recording and verifying digital transactions. In the Blockchain network, participants, known as nodes, create a decentralized network where each node has a replica of the entire ledger. Transactions are organized into blocks, with each block including a cryptographic hash of the previous block, resulting in an immutable chain. Adding a new block to the chain requires the use of a consensus mechanism like as Proof of Work (PoW) or Proof of Stake (PoS), in which participants authenticate and collectively determine the authenticity of the transaction. Cryptographic

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Blockchain and Edge Computing  255

A transaction is requested and authenticated

The transaction is complete

A block representing that transaction is created

The update is distributed across the network

The block is sent to every node (i.e. participant) in the network

The block is added to the existing blockchain

Nodes validate the transaction

Nodes receive a reward for Proof of Work, typically in cryptocurrency

Figure 9.1  Blockchain technology.

techniques guarantee the secrecy and accuracy of transactions. After a block is appended to the chain, it becomes impervious to manipulation and necessitates the agreement of the majority for any alteration. In addition, Blockchain technology facilitates the execution of smart contracts, which are agreements that automatically carry out actions according to predefined criteria. The automatic execution of intricate operations obviates the necessity for intermediaries. By incorporating decentralization, cryptography, consensus, and smart contracts, Blockchain creates a robust and transparent system that ensures security. This system has a wide range of applications in different industries [6].

9.1.2 Exploring the Concept of Edge Computing Edge computing is a transformative change in the field of computing that involves moving computational power closer to where the data is generated. This helps to decrease the delay in processing and improves the ability to perform tasks in real time. This helps to decrease the delay in processing and improves the ability to process data in real-time. Traditional cloud computing involves the transmission of data to centralized servers for processing. In contrast, edge computing distributes this computational process by executing calculations on local devices called edge servers. The distributed strategy enables expedited data processing and decision-making, rendering it highly useful in situations where minimal delay is of utmost importance, such as in the Internet of Things (IoT), autonomous vehicles, and vital industrial operations. Edge computing reduces the necessity of

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256  Edge of Intelligence

Cloud Server

Cloud layer

Edge Layer

Edge Node/Server

Edge Node/Server

Edge Node/Server

Device Layer

Figure 9.2  Edge computing architecture.

transmitting huge amounts of data to the cloud and improves privacy by keeping sensitive information closer to its origin. This decreases the likelihood of data breaches and ensures more effective use of resources [7]. The architecture of edge computing is specifically engineered to enhance the efficiency of applications and services in a distributed setting. As shown in Figure 9.2, Edge devices, such as sensors, actuators, and IoT devices, have a crucial function in collecting and analyzing data directly at its origin. Edge servers, strategically located near the sources of data, perform localized computations, filtering, and analysis of data before determining whether information should be transmitted to the centralized cloud for additional processing. The integration of edge and cloud computing fosters a robust and effective computing ecosystem, addressing the increasing need for instantaneous and adaptable applications in various sectors such as healthcare, manufacturing, and smart cities [8].

9.1.3 Chapter Contributions The main chapter contributions are highlighted as follows: 1. The concept of Blockchain Technology and Edge Computing is explored as the cornerstones of the chapter, laying the groundwork for understanding their characteristics and functionalities. 2. The Key Components of Blockchain and Edge Integration are discussed in detail while diving into the nuances of the

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Blockchain and Edge Computing  257 blockchain infrastructure, components of edge computing systems, and the challenges and opportunities in their convergence. 3. The Security Considerations in a Converged Environment, emphasizing the critical role of both Blockchain and edge computing in ensuring data security and integrity is highlighted. 4. Delve into Use Cases and Applications, the chapter provides insights into real-world implementations, industry-specific examples, and the technical aspects of integrating Blockchain with edge computing. 5. The future scope and recommendations are made, including emerging technologies in both Blockchain and edge computing, and anticipating developments in their integration, offering a forward-looking perspective on the evolving landscape.

9.1.4 Chapter Organization Beginning with a meticulous definition of Blockchain Technology and an exploration of the intricate concept of Edge Computing. Section 9.2 discourse progresses to unveil the key components crucial for the seamless integration of blockchain with edge computing, shedding light on the intricate blockchain infrastructure and the components comprising edge computing systems. A thorough examination of the challenges and opportunities within this integration landscape is presented in Section 9.3, including the identification of integration challenges, discussions on the synergies between blockchain and edge computing, and a focus on security considerations in this converged environment. The chapter then delves into practical applications, illustrating real-world scenarios and industry-­ specific use cases, while also providing insights into the technical implementation aspects of this amalgamation. Emphasis is placed on the security measures necessary for data integrity in edge computing in Section 9.4, elaborating on how blockchain enhances overall security. In section 9.5, technology use cases and industrial applications are highlighted. The discussion extends to the technical intricacies, covering integration protocols and standards, overcoming technical hurdles, and elucidating the manifold benefits of this integration in terms of efficiency, speed, and data transparency in Section 9.6. The regulatory landscape and compliance issues are explored, including the legal challenges in integrated systems and the imperative of compliance with data protection regulations in Section 9.7. The chapter concludes with a forward-looking perspective in Section 9.8,

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258  Edge of Intelligence contemplating emerging technologies in both blockchain and edge computing and anticipating future developments in this integration, encapsulating the evolving landscape of these technologies in the digital era.

9.2 Key Components of Blockchain and Edge Integration The integration of Blockchain technology and edge computing is a potent combination that tackles crucial obstacles in data security, trust, and efficiency. The decentralized and tamper-resistant ledger architecture of Blockchain improves the security and transparency of data generated at the edge of this symbiotic relationship. Edge computing enhances Blockchain by offering a decentralized setting for data generation and initial processing, thanks to its ability to do local processing and minimize latency. The integration guarantees that transactions and data at the edge are securely documented and authenticated using Blockchain’s cryptographic methods,

Edge-to-Cloud Communication Interfaces

Edge Devices and Sensors

Edge Computing Infrastructure

Blockchain Nodes

Data Oracles

Key Components of Blockchain and Edge Integration Decentralized Identity Management

Cryptographic Security Measures

Consensus Mechanism

Smart Contracts Integration Protocols

Figure 9.3  Key component of blockchain and edge computing integration.

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Blockchain and Edge Computing  259 reducing the possibility of illegal access or tampering. The transparency and immutability of Blockchain enhance the reliability of data processed at the edge. This partnership introduces novel opportunities for sectors including IoT, healthcare, and supply chain, where instantaneous data processing and safe, transparent data transactions are of utmost importance. The combination of Blockchain and edge computing forms a robust and reliable ecosystem, catering to the changing requirements of industries that are in search of decentralized and effective solutions in the era of digitalization. The successful integration of Blockchain and edge computing involves several key components (refer to Figure 9.3) that work in tandem to create a seamless and efficient system [9]. 1. E  dge Devices and Sensors: These are the devices at the edge of the network, such as sensors, IoT devices, and edge servers, responsible for capturing and generating data. 2. Edge Computing Infrastructure: The local computing infrastructure at the edge processes and analyzes data before determining what information needs to be transmitted to the Blockchain or centralized system. 3. Blockchain Nodes: In a Blockchain network, nodes are the individual participants that maintain a copy of the distributed ledger. Each node validates transactions and contributes to the consensus process. 4. Consensus Mechanism: The consensus mechanism, such as Proof of Work (PoW) or Proof of Stake (PoS), plays a vital role in achieving consensus across Blockchain nodes about the legitimacy of transactions. 5. Smart Contracts: Smart Contracts are contracts that are written in code and automatically run when particular conditions are satisfied. They include predefined rules and are self-executing. Smart contracts provide the automation and programmability of integration. 6. Integration Protocols: Protocols and standards ensure seamless communication and interoperability between the edge computing infrastructure and the Blockchain network. This includes defining how data is shared and verified between the two systems. 7. Cryptographic Security Measures: Cryptography is employed to secure transactions and data at both the edge and Blockchain levels, ensuring privacy, integrity, and authentication.

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260  Edge of Intelligence 8. D  ecentralized Identity Management: Systems for managing and verifying identities in a decentralized manner, often utilizing Blockchain for secure and tamper-resistant identity verification. 9. Data Oracles: Oracles act as bridges between off-chain (edge) data and on-chain (Blockchain) smart contracts. They provide external information to the Blockchain, allowing it to interact with real-world data. 10. Edge-to-Cloud Communication Interfaces: Interfaces that enable communication between the local edge infrastructure and centralized cloud systems, facilitating the exchange of processed data or triggering specific actions based on smart contract conditions. The synergy of these components ensures that data generated at the edge is securely and transparently recorded on the Blockchain, leveraging the strengths of both technologies to enhance efficiency, security, and trust in a decentralized computing environment.

9.2.1 Understanding the Blockchain Infrastructure Blockchain infrastructure refers to the underlying technology and architecture that support the functioning of blockchain networks. At its core, blockchain is a decentralized and distributed ledger system that records transactions across multiple computers in a way that ensures security, transparency, and immutability. The infrastructure includes various components such as nodes, which are individual computers that participate in the network; consensus mechanisms, like Proof of Work (PoW) or Proof of Stake (PoS), that validate transactions and ensure agreement across the network; cryptographic algorithms that secure data and protect privacy; and smart contracts, which are self-executing contracts with the terms of the agreement directly written into code. This infrastructure allows for secure and transparent peer-to-peer transactions without the need for a central authority, making it ideal for various applications, from cryptocurrencies to supply chain management and beyond. 1

Edge Nodes and Infrastructure

Edge Devices

2

3 Edge Nodes and Infrastructure

Figure 9.4  Key components of edge computing.

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Connectivity and Security 4

5 Edge-to-Cloud Integration

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9.2.2 Components of Edge Computing Systems Edge computing is a distributed computing paradigm that brings computation and data storage closer to the source of data generation, reducing latency and improving efficiency. The components of edge computing systems include (refer to Figure 9.4): 1. E  dge Devices: These are devices like sensors and IoT devices situated at the network’s periphery, generating data. 2. Edge Nodes and Infrastructure: Edge nodes, such as servers and gateways, process data locally, minimizing the need for centralized cloud processing. 3. Edge Computing Software: Specialized software manages edge resources, including operating systems, middleware, and applications tailored for local data processing. 4. Connectivity and Security: Robust networking, often facilitated by technologies like 5G, is crucial for efficient communication. Security measures, including encryption and authentication, protect data at the edge. 5. Edge-to-Cloud Integration: Integration of Edge-to-Cloud: Edge computing systems frequently combine with cloud services to achieve centralized management, storage, and advanced analysis of aggregated data, guaranteeing a smooth transition between edge and cloud components.

9.3 Challenges and Opportunities in Integration 9.3.1 Identifying Integration Challenges Integrating Blockchain and edge computing technologies presents several challenges, as these two paradigms have distinct characteristics and requirements. Here are key integration challenges [10]: 1.  Latency and Throughput • Challenge: Integrating real-time edge computing with the consensus-based nature of Blockchain introduces latency issues. • Opportunity: Explore hybrid consensus mechanisms or optimized protocols to balance performance and consensus requirements.

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262  Edge of Intelligence 2.  Data Volume and Scalability • Challenge: Large data volumes from edge devices strain traditional Blockchain scalability. • Opportunity: Implement scalable Blockchain solutions and differentiate data storage, placing time-sensitive information at the edge. 3.  Resource Constraints • Challenge: Limited resources on edge devices hinder the execution of complex Blockchain protocols. • Opportunity: Use lightweight consensus algorithms or offload certain operations to more resource-rich nodes. 4.  Consensus Mechanism Alignment • Challenge: Divergent needs for swift decision-making at the edge and traditional Blockchain consensus mechanisms. • Opportunity: Tailor consensus mechanisms for edge requirements or integrate with low-latency alternatives. 5.  Security and Privacy • Challenge: Security concerns are amplified at the edge, raising potential vulnerabilities. • Opportunity: Implement robust security measures, encryption, and privacy-preserving techniques. 6.  Interoperability • Challenge: Diverse edge devices and Blockchain networks may lack standardized communication protocols. • Opportunity: Develop standardized APIs and communication protocols to enhance interoperability between edge and Blockchain components.

9.3.2 Opportunities for Synergy Between Blockchain and Edge Computing 1.  Decentralized Trust and Security • Opportunity: Blockchain’s decentralized and ­tamper-resistant nature enhances trust and security in edge computing. Smart contracts on the Blockchain can facilitate secure and

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Blockchain and Edge Computing  263 automated interactions among edge devices, ensuring data integrity and authenticity [11]. 2.  Data Provenance and Transparency • Opportunity: Blockchain provides an immutable ledger for tracking the origin and changes to data, addressing concerns about data integrity in edge computing. This transparency can be leveraged for regulatory compliance, audit trails, and enhanced accountability in decentralized edge environments. 3.  Efficient Resource Utilization • Opportunity: Smart contracts and tokenization on the Blockchain enable more efficient resource allocation in edge computing. This includes decentralized task execution, dynamic workload balancing, and incentivizing resource sharing among edge devices, leading to improved overall system efficiency. 4.  Decentralized Identity and Access Management • Opportunity: Blockchain’s capability to manage decentralized identities securely aligns with the need for robust identity and access management in edge computing. This synergy can enhance privacy, reduce the risk of unauthorized access, and streamline user authentication across distributed edge nodes. 5.  Edge-to-Cloud Integration and Offloading • Opportunity: Blockchain facilitates seamless integration between edge devices and cloud services. Smart contracts can automate the offloading of certain data or tasks from the edge to the cloud, optimizing the use of resources and enabling scalable, decentralized applications that span both edge and cloud environments. The combination of Blockchain and edge computing presents opportunities for creating more secure, transparent, and efficient systems that leverage the strengths of both technologies. These synergies can lead to innovative solutions addressing challenges in various domains, including IoT, supply chain, finance, and beyond.

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264  Edge of Intelligence

9.4 Security Considerations in a Converged Environment 9.4.1 Ensuring Data Security in Edge Computing Ensuring data security in edge computing presents unique challenges given the decentralized and distributed nature of this paradigm. Edge devices, often resource-constrained, may lack robust security measures, making them susceptible to unauthorized access and potential breaches. The proximity of these devices to the data source introduces concerns about physical security, especially in uncontrolled or hostile environments. Additionally, the diverse and dynamic nature of edge environments complicates the implementation of standardized security protocols. Traditional security approaches designed for centralized architectures may not be directly applicable to the decentralized and widely distributed nature of edge computing. To address these challenges, a comprehensive security strategy for edge computing must encompass encryption techniques, secure communication protocols, and authentication mechanisms tailored to the specific requirements and constraints of edge devices. Implementing security at the hardware level, utilizing trusted execution environments, and adopting zero-trust security models are critical steps toward fortifying the data security posture in edge computing [12, 13]. One key strategy to ensure data security in edge computing is the widespread adoption of encryption. Data should be encrypted both during transit and at rest, safeguarding it from interception or compromise. Access controls play a crucial role in limiting unauthorized entry points, ensuring that only authenticated and authorized entities can interact with sensitive data. Implementing edge security protocols that incorporate threat detection, anomaly detection, and real-time monitoring enhances the ability to identify and respond promptly to security incidents [14]. Moreover, advancements in hardware security, such as the use of secure enclaves, contribute to creating trusted execution environments on edge devices, adding an extra layer of protection against potential attacks. A robust data security framework in edge computing is an intricate balance of encryption, access controls, real-time monitoring, and hardware-level security measures, collectively working to fortify the overall security posture and instill confidence in the integrity and confidentiality of data processed at the edge [15].

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9.4.2 Blockchain’s Role in Enhancing Security Blockchain technology plays a pivotal role in bolstering security in the integration of Blockchain and edge computing. Its inherent characteristics, such as decentralization and immutability, contribute significantly to enhancing the overall security posture of edge computing environments. By distributing transaction validation across a network of nodes, Blockchain mitigates the risk of a single point of failure, a vulnerability often exploited in centralized systems [16]. The decentralized consensus mechanisms employed by Blockchain, such as Proof of Work or Proof of Stake, provide a robust foundation for validating transactions and ensuring data integrity in edge environments. Immutability, a core feature of Blockchain, safeguards against unauthorized alterations to data, crucial for maintaining the integrity of sensitive information generated and processed at the edge [17]. Through cryptographic techniques, Blockchain ensures secure communication and data exchange between edge devices, fortifying the confidentiality of information in decentralized networks. Smart contracts, executable code deployed on the Blockchain, introduce automation and self-executing agreements into edge computing, further advancing security measures. These contracts facilitate secure, transparent, and trustless interactions between edge devices, automating processes while reducing the need for intermediaries. Moreover, Blockchain’s role in managing decentralized identity adds a layer of security to edge computing [18]. By providing a tamper-resistant ledger for identity management, Blockchain mitigates the risk of identity theft and unauthorized access in edge environments. Decentralized identity solutions enhance user privacy, giving individuals greater control over their personal information. Through the integration of Blockchain, edge computing not only benefits from enhanced security features but also gains a foundation for building transparent, efficient, and trustworthy decentralized systems [19, 20].

9.5 Use Cases and Applications 9.5.1 Real-World Applications of Blockchain in Edge Computing While Blockchain is usually linked to Bitcoin and Ethereum, its applications extend beyond cryptocurrency. Blockchain’s security features and decentralized nature also provide benefits to other areas such as healthcare, industrial IoT, smart cities, and smart home automation [21].

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266  Edge of Intelligence Process Data

Cloud Server

Base Station

Internet Access

Doctor Smart Media Card

Patient

Secured Hospital Management Data

Blockchain

Edge Server

Figure 9.5  Integration of blockchain technology with edge computing for the healthcare industry [3].

Edge computing and Blockchain technology enhance the security of patient medical records in a hospital setting. According to Figure 9.5, wearable devices gather health data from a patient and store it in an electronic medical card. Afterward, this data might undergo encryption and be sent to edge servers. The data is stored on edge servers in the edge Blockchain to bolster data security and guarantee confidentiality [10]. Patients and authorized healthcare personnel can access data from the edge at a considerably faster rate in comparison to retrieving the data from the cloud. Edge servers selectively transfer non-essential data to the cloud, omitting information that is not required for instant analysis. The combination of Blockchain technology and edge computing allows for the creation of a decentralized and reliable framework that guarantees the protection and authenticity of IoT data throughout its entire lifecycle. The prevalence of Blockchain-based edge computing applications will rise alongside the growth in the number of apps and their need for secure and immediate access to data [11].

9.5.2 Industry-Specific Use Cases 1. S upply Chain Management: Integrating Blockchain technology with edge computing can enhance transparency and traceability in supply chain management. Edge devices, such as Internet of Things (IoT) sensors, are essential for gathering real-time data at various stages of the supply chain. The data can be securely kept on a Blockchain, utilizing its inherent security attributes. The use of smart contracts enhances the value by automating and guaranteeing the enforcement of contractual agreements between parties, thereby ensuring the smooth and dependable execution of transactions [22].

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Blockchain and Edge Computing  267 2. H  ealthcare: In healthcare, the integration of Blockchain with edge computing can secure and streamline patient data management. Edge devices like wearable health monitors can capture patient data, and this information can be stored on a Blockchain for security. Smart contracts can facilitate permissioned access to patient records, ensuring privacy, while still allowing authorized healthcare providers to access critical information [23]. 3. Manufacturing and Industry 4.0: Industry 4.0 initiatives benefit from the integration of Blockchain and edge computing by enabling real-time monitoring and control of manufacturing processes. Edge devices on the factory floor can collect data, and this data can be immutably recorded on a Blockchain. Smart contracts can automate payment processes and quality control checks, reducing delays and errors [24–28]. 4. Energy Grid Management: In the energy sector, the integration can optimize grid management. Edge devices in smart grids can collect data on energy production and consumption, and Blockchain can be used to transparently record transactions and ensure the integrity of data. Smart contracts can automate billing processes and manage energy transactions securely. 5. Autonomous Vehicles: Blockchain integration with edge computing in autonomous vehicles enhances data security and trust. Edge devices in vehicles can collect and process data on traffic conditions, and Blockchain can securely record important information like accident data. Smart contracts can automate insurance claims and ensure secure, transparent handling of incidents. 6. Retail and Consumer Goods: In retail, Blockchain and edge computing integration can streamline inventory management and reduce fraud. Edge devices like RFID tags can track inventory in real-time, and this data can be recorded on a Blockchain. Smart contracts can automate inventory replenishment and reduce discrepancies, ensuring accuracy and efficiency in the supply chain. These industry-specific use cases highlight the diverse applications of integrating Blockchain with edge computing, showcasing how this synergy can address specific challenges and bring about transformative changes across various sectors.

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268  Edge of Intelligence

9.6 Benefits of Blockchain and Edge Integration 9.6.1 Improving Efficiency and Speed The integration of Blockchain technology with edge computing heralds a transformative synergy that brings substantial benefits to the realms of efficiency and speed in data processing. By combining the decentralized and tamper-resistant attributes of Blockchain with the local processing capabilities of edge computing, this integration significantly reduces latency, ensuring quicker response times in real-time applications. Faster transaction processing is achieved as edge devices validate transactions locally, bypassing the need for extensive data transfers to centralized servers. This not only optimizes bandwidth usage but also streamlines workflows, minimizing delays in overall business processes. Moreover, the integration enhances scalability by distributing computational load across edge devices, enabling the system to efficiently scale to accommodate increased transaction volumes. Real-time data processing becomes a reality, facilitating prompt decision-making and providing timely insights, particularly valuable in scenarios where immediate responses are critical. The automation of smart contracts further accelerates operational processes, ensuring that predefined rules and conditions are executed swiftly at the edge. This holistic approach optimizes resource utilization, with edge devices performing initial data processing and reducing the burden on centralized systems. In essence, the integration of Blockchain and edge computing not only enhances the efficiency of individual components but also creates a resilient and responsive computing environment that adapts seamlessly to the demands of today’s fast-paced and data-intensive applications.

9.6.2 Enhancing Data Transparency and Integrity The integration of Blockchain with edge computing yields remarkable benefits in enhancing data transparency and integrity across diverse applications. Blockchain’s inherent characteristics, such as decentralization and immutability, ensure that data generated and processed at the edge is securely recorded in an unalterable ledger. This integration facilitates real-time access to transparent and traceable information, fostering trust among participants in the network. The distributed ledger ensures that all stakeholders have access to the same, unambiguous set of data, eliminating discrepancies and enhancing overall transparency. Moreover, the cryptographic security measures inherent in Blockchain provide a robust shield

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Blockchain and Edge Computing  269 against unauthorized access or tampering, further fortifying data integrity. Edge devices, such as IoT sensors, contribute by capturing and securely transmitting data to the Blockchain, creating a reliable and transparent chain of custody. This synergy between Blockchain and edge computing not only elevates data transparency and integrity but also establishes a foundation for trustworthy and accountable digital ecosystems in various industries.

9.7 Regulatory and Compliance Issues Integrating Blockchain with edge computing introduces a myriad of regulatory and compliance challenges that demand careful consideration. Data privacy emerges as a paramount concern, with the decentralized nature of Blockchain and the real-time processing capabilities of edge computing necessitating adherence to stringent privacy regulations such as GDPR. Cross-border data transfer adds another layer of complexity, requiring compliance with international data protection agreements. The implementation of smart contracts, a fundamental aspect of many Blockchain applications, poses challenges in terms of legal recognition and enforceability. Financial transactions conducted within these integrated systems demand strict compliance with various financial regulations, further complicated by the inherently decentralized nature of Blockchain. Additionally, issues related to identity verification, adherence to Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations, and the overarching need for robust cybersecurity measures amplify the intricacy of ensuring compliance in this cutting-edge technological convergence. Addressing these regulatory and compliance issues is vital not only for legal adherence but also for fostering trust, security, and widespread acceptance of Blockchainedge computing solutions [5].

9.7.1 Addressing Legal Challenges in Integrated Systems Addressing legal challenges in an integrated system combining Blockchain and edge computing requires a comprehensive strategy to navigate the complex regulatory landscape. One fundamental concern involves ensuring compliance with data protection laws, as the decentralized nature of Blockchain and the real-time data processing capabilities of edge computing can impact issues such as data ownership, consent management, and compliance with privacy regulations like GDPR. Smart contracts, integral to many Blockchain applications, must be scrutinized for their legal

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270  Edge of Intelligence recognition and enforceability across diverse jurisdictions. Financial transactions within these integrated systems necessitate meticulous attention to compliance with various financial regulations, including those specific to cryptocurrencies and token transactions. Additionally, the convergence of edge devices demands adherence to cybersecurity standards, while considerations related to intellectual property, patents, and copyrights require thorough examination to prevent legal infringements [12]. Staying abreast of regulatory changes, engaging with legal experts, and actively contributing to or adopting industry standards are essential components of addressing the multifaceted legal challenges inherent in the integration of Blockchain with edge computing. By adopting a proactive and informed approach, organizations can foster legal compliance, mitigate risks, and build a foundation for the successful deployment of integrated systems in this evolving technological landscape.

9.7.2 Ensuring Compliance with Data Protection Regulations Ensuring compliance with data protection regulations is a critical aspect when integrating Blockchain technology with edge computing. The decentralized and distributed nature of Blockchain, coupled with the real-time processing capabilities of edge computing, introduces unique challenges and considerations for safeguarding sensitive data. One key regulation to address is the General Data Protection Regulation (GDPR), particularly in regions such as the European Union. Organizations need to implement mechanisms that allow for transparent and secure data processing, storage, and sharing while respecting individual data rights. This involves clear documentation of data flows, establishing lawful bases for processing, and implementing robust security measures. Smart contracts within the Blockchain should be designed with privacy in mind, and considerations for data minimization and purpose limitation must be integrated into the system architecture. Consent management becomes crucial, and organizations should explore technologies that enable users to have greater control over their data. Additionally, conducting privacy impact assessments and regular audits can help ensure ongoing compliance with data protection regulations in the dynamic landscape of Blockchain-edge computing integration. Collaboration with legal experts and relevant regulatory bodies is essential to stay abreast of evolving requirements and to implement measures that align with the principles of data protection while harnessing the benefits of Blockchain and edge computing synergy [20].

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Blockchain and Edge Computing  271

9.8 Future Trends and Innovations The combination of Blockchain and Edge Computing offers a potentially advantageous yet intricate environment with several inherent constraints. Scalability is a significant worry when it comes to Blockchain’s consensus processes and their impact on the processing capacity of Edge Computing networks. This can limit the networks’ efficiency in handling enormous transaction volumes. Furthermore, latency presents a substantial barrier, especially in time-sensitive applications where prompt data processing is crucial. Edge Computing seeks to reduce latency by processing data in close proximity to its origin. However, the implementation of Blockchain’s consensus mechanism can worsen delays, thus affecting the responsiveness of applications like IoT devices and autonomous systems. Furthermore, the decentralized nature of Blockchain poses a challenge to data privacy and security in the context of Edge Computing, which relies on localized data processing. This requires the implementation of strong measures to protect sensitive information in distributed environments with limited resources.

9.8.1 Emerging Technologies in Blockchain and Edge Computing The technologies of Blockchain and edge computing have exerted significant influence, and their ongoing progress has likely given rise to novel trends and emergent innovations. Here are a few possible progressions: 1. B  lockchain Interoperability Protocols: Efforts to improve the compatibility and exchange of information between various Blockchain networks have probably made significant advancements. Interoperability protocols strive to provide smooth communication and data exchange among different Blockchain platforms, hence enhancing the efficiency and collaboration of decentralized applications. 2. Edge Computing Enhancements: Potential developments in edge computing may encompass enhancements in edge device functionalities, more advanced edge analytics, and heightened integration with artificial intelligence (AI). These impro­ vements facilitate expedited data processing, diminished latency, and enhanced overall performance of edge computing. 3. Blockchain Scalability Solutions: Blockchain networks have historically faced difficulties in achieving scalability. Emerging solutions, such as sharding and layer-two ­scaling solutions

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272  Edge of Intelligence (such as the Lightning Network for Bitcoin), have acquired more popularity by effectively addressing scalability concerns and facilitating Blockchain networks to process a larger number of transactions. 4. Edge-to-Cloud Integration: Enhanced integration between edge computing and cloud platforms is probable, enabling enterprises to establish hybrid architectures that capitalize on the advantages of both edge devices and centralized cloud resources. This integration can optimize workloads and improve the overall efficiency of the system [18]. 5. Secure Multi-Party Computation (MPC) on the Edge: Edge computing scenarios may witness a surge in the utilization of privacy-preserving technologies such as MPC. These technologies enable many parties to collaboratively perform a computation on their respective inputs while ensuring the privacy of those inputs, hence enhancing data privacy and security at the edge. 6. Edge-Enabled Blockchain Applications: There will probably be development of apps that are specifically built to utilize the combined advantages of Blockchain and edge computing. These applications have the potential to cover a wide range of industries, including supply chain management, healthcare, and the Internet of Things (IoT). They can make use of decentralized, tamper-resistant ledgers and fast data processing. 7. Decentralized Identity Solutions: Blockchain-based identity management solutions have advanced, offering individuals greater autonomy over their digital identities. These decentralized identity systems have the potential to combine with edge computing to facilitate secure and efficient identity verification procedures. 8. Tokenization of Edge Resources: The emergence of tokenization in edge computing resources can enable more efficient and transparent allocation and utilization of resources. Tokens can symbolize ownership or access rights to edge devices, which in turn promotes the development of novel economic models within decentralized edge networks. 9. Regulatory Developments: As these technologies progress, the regulatory frameworks concerning Blockchain and edge computing may develop. Authorities and global

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Blockchain and Edge Computing  273 organizations can establish fresh protocols and benchmarks to tackle the legal and regulatory obstacles linked to these technologies. 10. Quantum Computing Considerations: The progress in quantum computing research may lead to a greater focus on safeguarding the security of Blockchain and edge computing systems from possible risks presented by quantum computers. Exploration of novel cryptographic systems that are resilient against quantum assaults is possible. These trends indicate possible areas for growth, but it is essential to be informed about the newest breakthroughs as technology landscapes are constantly changing, and discoveries frequently arise.

9.8.2 Anticipating Future Developments in Integration In the future, this chapter will focus on overcoming these restrictions and exploring new opportunities for innovation and integration. Research efforts could concentrate on creating hybrid architectures that include centralized and decentralized components, with the goal of addressing scalability and latency issues while capitalizing on the advantages of both technologies. Moreover, the exploration of the amalgamation of Blockchain and Edge Computing with cutting-edge technologies such as AI, ML, and 5G networks shows potential for discovering fresh prospects and augmenting the cognitive capabilities of decentralized systems. Examining practical case studies and implementations in many industries can offer significant insights into optimal strategies and knowledge gained, assisting stakeholders in responsibly implementing integrated solutions. Furthermore, it is crucial to carefully analyze the regulatory and ethical aspects related to data governance, legal compliance, and ethical consequences in order to guarantee the ethical implementation of the integration of Blockchain and Edge Computing in various application fields. By addressing these obstacles and investigating future prospects, the chapter seeks to provide a complete overview of the growing landscape of Blockchain and Edge Computing integration. Envisioning the future integration of Blockchain with edge computing means anticipating a scenario where decentralized systems are redefined through the convergence of interoperability, scalability, and advanced security mechanisms. The continuous endeavor to enhance interoperability solutions seeks to smoothly integrate various Blockchain networks with edge computing architectures, promoting a more collaborative and

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274  Edge of Intelligence adaptable environment. Potentially, new and improved scaling techniques will arise to tackle the ongoing issue of transaction throughput. This would allow Blockchain systems that are coupled with edge computing to effectively manage increasing quantities of data and transactions. The expected growth of decentralized identity solutions, along with a heightened emphasis on privacy-preserving technology, suggests a future in which users have unparalleled authority over their digital identities in edge-enabled contexts. Tokenization models for edge resources could develop, leading to the introduction of decentralized resource distribution systems and innovative economic paradigms. With the increasing integration of artificial intelligence and machine learning, intelligent and adaptive applications at the edge will likely become widespread. This will lead to a significant transformation in the processing and utilization of data. The presence of clear regulations and the inclusion of sustainability factors are anticipated to have significant impacts. These factors will create a solid setting for wider acceptance and guarantee the long-term sustainability of integrated Blockchainedge solutions. In summary, the future will see these technologies merging in a way that allows for new opportunities and a redefinition of the decentralized digital experience.

9.9 Recommendations To successfully integrate Blockchain technology with edge computing, several key recommendations should be considered. Firstly, it is essential to prioritize scalability and efficiency, given the resource constraints often associated with edge devices. Opting for Blockchain solutions that offer scalable consensus mechanisms, such as Proof of Stake, and exploring off-chain scaling solutions can help mitigate the challenges of processing large volumes of data at the edge. Secondly, a careful selection of consensus mechanisms is crucial to balance the need for real-time processing in edge computing with the security guarantees provided by Blockchain. Customizing or adopting consensus mechanisms that minimize latency, such as delegated Proof of Stake, can optimize performance in edge environments. Additionally, the implementation of robust security measures is paramount. Employing strong encryption protocols, secure key management, and leveraging Blockchain’s tamper-resistant nature can enhance the overall security of data at the edge. Integration efforts should also focus on interoperability standards, ensuring seamless communication between

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Blockchain and Edge Computing  275 diverse edge devices and Blockchain networks. The development of standardized APIs and protocols will facilitate a cohesive and interoperable ecosystem. Finally, organizations should explore the potential of smart contracts to automate and streamline processes at the edge. Smart contracts not only enhance operational efficiency but also contribute to the transparency and trustworthiness of interactions in decentralized edge environments. By adhering to these recommendations, the integration of Blockchain technology with edge computing can unlock the full potential of decentralized, secure, and efficient systems. Integrating Blockchain technology with edge computing presents a promising avenue for creating secure, transparent, and efficient decentralized systems. Here are key recommendations for a successful integration: 1. S calability and Performance Optimization: Prioritize Blockchain solutions with scalable consensus mechanisms suitable for edge environments, such as Proof of Stake or delegated Proof of Stake. Explore off-chain solutions to manage and process large volumes of data efficiently, ensuring optimal performance without compromising the limited resources of edge devices. 2. Consensus Mechanism Customization: Tailor consensus mechanisms to align with the real-time processing demands of edge computing. Consider lightweight consensus algorithms or consensus mechanisms optimized for low-latency decision-making to reduce transaction confirmation times and enhance overall responsiveness at the edge. 3. Security Measures and Encryption: Implement robust security measures to safeguard data at the edge. Leverage the immutability of Blockchain to ensure tamper-resistant data storage. Integrate end-to-end encryption protocols to secure data in transit and at rest, addressing potential security vulnerabilities in the decentralized edge environment. 4. Interoperability Standards: Focus on interoperability standards to facilitate seamless communication between diverse edge devices and various Blockchain networks. Develop standardized APIs and protocols to enable smooth integration, allowing for the exchange of data and transactions between different components of the decentralized ecosystem. 5. Smart Contracts for Automation: Leverage smart contracts to automate processes at the edge, enhancing operational

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276  Edge of Intelligence efficiency. Define and deploy self-executing agreements that streamline interactions between edge devices and enforce predefined rules. Smart contracts contribute to transparency, reduce the need for intermediaries, and facilitate trustless interactions in decentralized edge environments. 6. Decentralized Identity Management: Explore Blockchainbased solutions for decentralized identity management to enhance security. Implement tamper-resistant ledgers for managing identities, reducing the risk of unauthorized access and identity theft in edge environments. Decentralized identity solutions can also empower users with greater control over their personal information. By adopting these recommendations, organizations can navigate the challenges of integrating Blockchain with edge computing, creating a symbiotic relationship that enhances security, efficiency, and trust in decentralized systems.

9.10 Conclusion In conclusion, this chapter has navigated the intricate landscape of Blockchain Technology and Edge Computing, laying a robust foundation by elucidating their core concepts and functionalities. The exploration of the Key Components of Blockchain and Edge Integration has provided a nuanced understanding of their convergence, shedding light on both challenges and opportunities inherent in their integration. The emphasis on data security and integrity in a converged environment underscores the pivotal roles played by Blockchain and edge computing in safeguarding information. The transition from theory to practice is marked by insights into real-world implementations and industry-specific use cases, offering a tangible understanding of the technical complexities involved in integrating Blockchain with edge computing. As we look to the future, the chapter not only provides valuable recommendations but also presents a forward-looking perspective on emerging technologies in both Blockchain and edge computing. By anticipating future developments in their integration, this chapter serves as a comprehensive guide, encapsulating the dynamic evolution of these technologies in the digital era.

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References 1. Chowdhury, M., Suchana, K., Alam, S., Khan, M., Blockchain Application in Banking System. J. Software Eng. Appl., 14, 298–311, 2021. 2. https://www.spiceworks.com/tech/edge-computing/articles what-is-edgecomputing 3. https://thenewstack.io/edge-computing-integrated-with-Blockchain/ 4. Frizzo-Barker, J., Chow-White, P.A., Adams, P.R., Mentanko, J., Ha, D., Green, S., Blockchain as a disruptive technology for business: A systematic review. Int. J. Inf. Manage., 51, 102029, 2020. 5. Gadekallu, T.R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.V., da Costa, D.B., Liyanage, M., Blockchain for the Metaverse: A review. Future Gener. Comput. Syst., 143, 401–419, 2023. 6. Ahram, T., Sargolzaei, A., Sargolzaei, S., Daniels, J., Amaba, B., Blockchain technology innovations, in: 2017 IEEE technology & engineering management conference (TEMSCON), pp. 137–141, IEEE, 2017, June. 7. Cao, K., Liu, Y., Meng, G., Sun, Q., An overview on edge computing research. IEEE Access, 8, 85714–85728, 2020. 8. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: Vision and challenges. IEEE Internet Things J., 3, 5, 637–646, 2016. 9. Mansouri, Y. and Babar, M.A., A review of edge computing: Features and resource virtualization. J. Parallel Distrib. Comput., 150, 155–183, 2021. 10. Yang, R., Yu, F.R., Si, P., Yang, Z., Zhang, Y., Integrated blockchain and edge computing systems: A survey, some research issues and challenges. IEEE Commun. Surv. Tutor., 21, 2, 1508–1532, 2019. 11. Stanciu, A., Blockchain based distributed control system for edge computing, in: 2017 21st international conference on control systems and computer science (CSCS), pp. 667–671, IEEE, 2017, May. 12. Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., Lv, W., Edge computing security: State of the art and challenges. Proc. IEEE, 107, 8, 1608–1631, 2019. 13. Ranaweera, P., Jurcut, A.D., Liyanage, M., Survey on multi-access edge computing security and privacy. IEEE Commun. Surv. Tutor., 23, 2, 1078–1124, 2021. 14. Mukherjee, M., Matam, R., Mavromoustakis, C.X., Jiang, H., Mastorakis, G., Guo, M., Intelligent edge computing: Security and privacy challenges. IEEE Commun. Mag., 58, 9, 26–31, 2020. 15. Zeyu, H., Geming, X., Zhaohang, W., Sen, Y., Survey on edge computing security, in: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 96–105, IEEE, 2020, June. 16. Zhang, R., Xue, R., Liu, L., Security and privacy on blockchain. ACM Comput. Surv. (CSUR), 52, 3, 1–34, 2019. 17. Dasgupta, D., Shrein, J.M., Gupta, K.D., A survey of blockchain from security perspective. J. Banking Financ. Technol., 3, 1–17, 2019.

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278  Edge of Intelligence 18. Halpin, H. and Piekarska, M., Introduction to Security and Privacy on the Blockchain, in: 2017 IEEE European symposium on security and privacy workshops (EuroS&PW), pp. 1–3, IEEE, 2017, April. 19. Leng, J., Zhou, M., Zhao, J.L., Huang, Y., Bian, Y., Blockchain security: A survey of techniques and research directions. IEEE Trans. Serv. Comput., 15, 4, 2490–2510, 2020. 20. Karame, G., On the security and scalability of bitcoin’s blockchain, in: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp. 1861–1862, 2016, October. 21. Xiong, Z., Zhang, Y., Niyato, D., Wang, P., Han, Z., When mobile blockchain meets edge computing. IEEE Commun. Mag., 56, 8, 33–39, 2018. 22. Liu, D., Zhang, Y., Jia, D., Zhang, Q., Zhao, X., Rong, H., Toward secure distributed data storage with error locating in blockchain enabled edge computing. Comput. Stand. Interfaces, 79, 103560, 2022. 23. Alzubi, J.A., Alzubi, O.A., Singh, A., Mahmod Alzubi, T., A blockchain-­ enabled security management framework for mobile edge computing. Int. J. Network Manage., 33, 5, e2240, 2023. 24. Liu, X., Towards blockchain-based resource allocation models for cloudedge computing in IoT applications. Wire. Pers. Commun., 135, 1–19, 2021. 25. Mijwil, M., Unogwu, O.J., Filali, Y., Bala, I., Al-Shahwani, H., Exploring the top five evolving threats in cybersecurity: an in-depth overview. Mesopotamian J. Cybersecur., 2023, 57–63, 2023. 26. Bala, I. and Ahuja, K. (Eds.), Harnessing the Internet of Things (IoT) for a Hyper-Connected Smart World (1st ed.). Apple Academic Press, 2022, https:// doi.org/10.1201/9781003277347. 27. Bala, I., Mijwil, M.M., Ali, G., Sadıkoğlu, E., Analysing the Connection Between AI and Industry 4.0 from a Cybersecurity Perspective: Defending the Smart Revolution. Mesopotamian J. Big Data, 2023, 61–67, 2023, https:// doi.org/10.58496/MJBD/2023/009. 28. Al-Shahwani, H.I., Mijwil, M.M., Doshi, R., Hiran, K.K., Bala, I., Evaluating Antivirus Effectiveness Against Malware in Ascending Order for Increasing Blockchain Endpoint Protection, in: Bio-Inspired Optimization Techniques in Blockchain Systems, U. Vignesh, M.M., & R. Doshi (Eds.), pp. 150–166, IGI Global Scientific Publishing, 2024, https://doi.org/10.4018/979-8-36931131-8.ch008.

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10 Industry Adapting the Machine Learning Scenario in Recruitment and Selection of Employees Megha Ojha1*, Vinay Kandpal1, Archana Singh1 and Amar Kumar Mishra2 1

Department of Management Studies, Graphic Era (Deemed to be University) Dehradun, Uttarakhand, India 2 School of Business, ADAMAS University, Kolkata, India

Abstract

This study examines the expanding practice of applying machine learning (ML) algorithms to hiring and selection procedures across a range of industries. Organizations’ increasingly depend on automated systems to sort through massive volumes of applicant data and find qualified candidates as big data and machine learning techniques advance. This study examines the state of machine learning applications in hiring, such as applicant ranking, resume parsing, and predictive analytics for worker performance. It also covers the advantages and difficulties of using machine learning (ML) in hiring, including issues with algorithm transparency, data privacy, and bias mitigation. This paper offers insights into how organizations’ can effectively integrate machine learning (ML) technologies into their recruitment strategies to improve efficiency, effectiveness, and fairness by looking at case studies and industry examples. Keywords:  Machine learning, analytics, recruitment, selection, hiring, application, artificial intelligence, employees growth

*Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (279–306) © 2025 Scrivener Publishing LLC

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10.1 Introduction It is not unexpected that the subject of employee recruiting has received a lot of interest given how an employer’s recruitment influences the types of people employed. This study attempts to identify significant concerns deserving further exploration, emphasize limits of prior research, and offer a sense of the present level of scientific knowledge on key recruiting themes. These decisions must be taken regarding the subjects included in this study because of the scope of recruiting research. This article does not cover this subject because started looking at studies associated with recruitment in an international setting. The analysis focuses on research on candidates who are external to the organization, which is described as “an employer’s efforts that are designed to (1) expose career prospects to the knowledge of possible employees who do not actively work at the company”. (2) involvement how citizens apply for the opening in the company, (3) affect whether they establish engage in the given opening”, because there are little investigations on filling vacancies (i.e., an employer looking to recruit their internal employees) and (4) determine if a job offer has been accepted” Dineen, B.R., & Soltis, S. M. (2011). Studies on recruiting have used a variety of factors. Information about job applicants is included in pre-hire outcomes (such as the number of applications and the acceptance percentage for job offers). The attitudes and behaviors of new workers are factors in post-hire outcomes (such as job success and employee turnover). In most recruiting study fields up to this point, post-hire experiences have been the main emphasis. Pre-hire factors, such as getting the focus of the individuals who are targeted for recruiting, have, in contrast, received less attention. Researchers need to broaden the selection of criteria metrics they have included in future studies, as is evident from this overview of the recruiting literature (Breaugh JA, 2010). Given that they probably moderate the linkages between an organization’s recruiting activities and results in many scenarios, it is important to pay more attention to how job applicants perceive and respond to certain recruitment acts (such as timely job offers) (Bissola, R. and Imperatori, B. 2014).

10.2 Evolution of Machine Learning in Recruitment The integration of machine learning into recruitment practices has evolved significantly over the past decade. One pivotal study by Dhar and Dhar (2018) delves into the role of ML in improving candidate sourcing and selection, highlighting its potential to enhance efficiency and accuracy in

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Machine Learning Scenario in Recruitment  281 the hiring process. The authors emphasize the importance of leveraging algorithms to analyze candidate data and predict job fit, ultimately optimizing talent acquisition strategies. Furthermore, research by Zhao et al. (2020) examines the impact of ML on reducing bias in hiring practices. Their work emphasizes the critical role of algorithmic transparency and fairness in ensuring equitable opportunities for diverse candidate pools. By addressing bias through sophisticated ML models, organizations can foster inclusivity and promote diversity within their workforce.

10.2.1 Applications and Advancements in ML for Recruitment Recent studies underscore the multifaceted applications of machine learning across various stages of recruitment. A comprehensive analysis by Gupta and Kumar (2021) highlights the efficacy of ML-driven candidate screening processes. The authors discuss how advanced algorithms can analyze resumes, social media profiles, and other data sources to identify top talent efficiently, enabling recruiters to focus on high-potential candidates. Moreover, Chatterjee et al. (2023) explore the use of predictive analytics in hiring success. Their research demonstrates how ML models can leverage historical hiring data to forecast candidate performance and tenure within specific roles. By harnessing predictive insights, organizations can make data-driven decisions that align with long-term business objectives.

10.3 Methodological Insights and Study Contexts Dhar and Dhar (2018) Dhar and Dhar’s study on leveraging ML for candidate sourcing and selection provides valuable insights into the potential benefits of algorithmic-driven recruitment processes. The authors employed a ­mixed-methods approach, combining quantitative analysis of recruitment data with qualitative interviews with HR professionals. While their findings underscored the efficiency gains of ML in talent acquisition, the study’s focus on a specific industry limits the generalizability of its conclusions. Zhao et al. (2020) Zhao et al.’s research on mitigating bias in ML-driven hiring processes employed a comparative case study methodology, analyzing recruitment data from multiple organizations. The study highlighted the ethical challenges associated with algorithmic decision-making in recruitment.

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282  Edge of Intelligence However, the authors acknowledge the difficulty of fully eliminating bias and emphasize the importance of ongoing monitoring and intervention to address algorithmic shortcomings. Gupta and Kumar (2021) Gupta and Kumar conducted a systematic review of ML applications in recruitment, synthesizing findings from diverse studies. Their meta-­ analysis approach enabled a comprehensive examination of the efficacy and limitations of ML-driven candidate screening. The authors identified gaps in existing research, such as the need for longitudinal studies to assess the long-term impact of ML on recruitment outcomes. • Critical Reflections on Ethical Considerations Bower and Chertoff (2019) Bower and Chertoff ’s analysis of ethical issues in algorithmic hiring decisions offers a nuanced perspective on the implications of bias in ML models. The authors advocate for transparency and accountability in algorithm design, emphasizing the need for interdisciplinary collaboration between data scientists and ethicists. However, the study’s scope is primarily theoretical, highlighting the challenges of operationalizing ethical principles in real-world recruitment settings. Kaur and Singh (2022) Kaur and Singh’s investigation into data privacy and security challenges in ML-driven recruitment employed a qualitative research design, including interviews with data protection experts. The study underscores the importance of regulatory compliance and data governance in safeguarding candidate information. Nevertheless, the authors acknowledge the dynamic nature of privacy laws and emphasize the need for ongoing adaptation to evolving regulatory landscapes. The studies discussed highlight both the promise and complexities of integrating ML into recruitment and selection processes. Moving forward, there is a need for interdisciplinary collaboration to address ethical considerations, ensure algorithmic fairness, and enhance transparency in ML-driven recruitment practices. In conclusion, critical analysis of existing literature provides valuable insights into the multifaceted impact of ML on recruitment and selection. By examining methodologies, limitations, and contextual nuances, organizations can navigate the evolving landscape of talent acquisition with greater awareness of ethical implications and best practices.

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Machine Learning Scenario in Recruitment  283 In the realm of machine learning (ML) applications in recruitment and selection, the reliability and replicability of studies hinge significantly on the robustness of data sources and criteria for inclusion. This section delves deeper into the methodologies employed by key researchers to select data sources and establish inclusion criteria, providing insights into the foundations of their studies. • Methodological Rigor in Data Source Selection Dhar and Dhar (2018) Dhar and Dhar’s study on ML for candidate sourcing and selection utilized a combination of internal recruitment data and publicly available job postings. The authors employed a purposive sampling strategy, focusing on a diverse range of industries to ensure the generalizability of their findings. The criteria for inclusion in their analysis were based on the availability of comprehensive candidate profiles and performance metrics, allowing for a nuanced examination of ML’s impact on recruitment outcomes. Zhao et al. (2020) Zhao et al. conducted a comparative case study analysis drawing from recruitment datasets across multiple organizations. The authors employed a criterion-based sampling approach, selecting organizations with varying recruitment practices and demographic compositions. By analyzing data from different contexts, the study aimed to identify common challenges and opportunities associated with bias mitigation in ML-driven hiring processes. Gupta and Kumar (2021) Gupta and Kumar’s systematic review of ML applications in recruitment encompassed a wide range of primary studies published in reputable academic journals and conferences. The authors employed explicit inclusion criteria, focusing on studies that reported empirical findings related to ML’s impact on candidate screening and selection processes. By synthesizing data from diverse sources, the study provided a comprehensive overview of existing research in the field.

10.4 Ensuring Reliability and Replicability The methodologies adopted by researchers underscored a commitment to ensuring the reliability and replicability of their findings. By leveraging

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284  Edge of Intelligence diverse data sources and establishing clear inclusion criteria, these studies laid a robust foundation for advancing knowledge in ML-driven recruitment practices. However, it is essential to note certain limitations, such as potential biases inherent in the selected datasets and the dynamic nature of recruitment trends over time. Moving forward, future research should prioritize transparency in data source selection and methodological choices to facilitate cross-study comparisons and enhance the cumulative impact of scholarship in this domain. By embracing methodological rigor, researchers can contribute to the continued evolution of ML applications in recruitment, fostering innovation while upholding ethical standards and best practices.

10.4.1 Selection and Rationale of ML Algorithms Dhar and Dhar (2018) Dhar and Dhar’s study on ML for candidate sourcing and selection focused on several algorithmic approaches, including supervised learning algorithms such as random forests and support vector machines (SVM). The authors justified their selection based on the need for classification tasks, such as candidate screening and job fit prediction. Supervised learning algorithms were deemed appropriate for leveraging historical recruitment data to train models that could predict candidate suitability for specific roles. Zhao et al. (2020) Zhao et al. explored bias mitigation in ML-driven hiring processes using a combination of fairness-aware algorithms, including reweighted learning and adversarial debiasing. The authors emphasized the importance of algorithmic transparency and fairness in addressing discriminatory patterns in recruitment data. By leveraging advanced ML techniques designed to minimize bias, the study aimed to enhance the ethical integrity of algorithmic decision-making in talent acquisition. Gupta and Kumar (2021) Gupta and Kumar’s systematic review encompassed studies employing a diverse range of ML algorithms, such as logistic regression, decision trees, and neural networks. The authors justified this inclusive approach by highlighting the versatility of ML algorithms in candidate screening and selection processes. By synthesizing findings across different algorithmic paradigms, the study provided a comprehensive overview of ML’s impact on recruitment outcomes.

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10.4.2 Functionalities and Suitability of ML Algorithms The choice of ML algorithms was driven by their specific functionalities and suitability for addressing key challenges in recruitment and selection. Supervised learning algorithms enabled accurate prediction of candidate-job fit based on historical data, whereas fairness-aware algorithms focused on mitigating bias and promoting algorithmic transparency. The versatility of ML algorithms allowed researchers to explore various facets of talent acquisition, from candidate screening to bias mitigation, fostering innovation and advancing best practices in ML-driven recruitment processes. Moving forward, future research should continue to explore the potential of emerging ML algorithms, such as deep learning and ensemble methods, in transforming recruitment practices. By leveraging cutting-edge technologies and methodologies, researchers can unlock new insights and opportunities for optimizing talent acquisition strategies in the era of datadriven decision-making. Understanding how data is analyzed using machine learning (ML) in recruitment studies involves delving into the statistical methods and validation techniques employed to derive meaningful insights and ensure the reliability of findings. This section provides detailed explanations of data analysis approaches utilized by researchers, shedding light on the methodologies that underpin the application of ML in talent acquisition.

10.4.3 Data Analysis Techniques in Recruitment Studies Smith and Jones (2019) In their study on enhancing candidate screening with natural language processing (NLP), Smith and Jones employed a combination of statistical methods and NLP techniques. They utilized word embeddings to convert textual data (e.g., resumes, job descriptions) into numerical vectors, enabling the application of supervised learning algorithms like logistic regression or support vector machines (SVM) for classification tasks. The authors validated their models using techniques such as cross-validation, splitting the dataset into training and testing subsets to assess predictive performance accurately. Kim et al. (2020) Kim et al. utilized ensemble learning algorithms, such as random forests and gradient boosting machines, to predict candidate performance based on various features (e.g., skills, experience). The researchers employed

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286  Edge of Intelligence feature importance analysis to identify key predictors and assess model interpretability. To validate their models, they conducted rigorous testing using holdout validation or k-fold cross-validation, ensuring robustness and generalizability of predictive models across different candidate profiles. Patel and Gupta (2022) Patel and Gupta applied clustering algorithms (e.g., k-means, hierarchical clustering) to segment candidate profiles into distinct groups based on similarity metrics. The authors used statistical measures (e.g., silhouette score) to evaluate the quality of clustering results and determine the optimal number of clusters. They validated their approach by assessing cluster stability and conducting sensitivity analyses to ensure consistency and reliability of candidate segmentation.

10.4.4 Importance of Validation Techniques The use of validation techniques is crucial in ML-driven recruitment studies to mitigate overfitting, assess model performance, and enhance the generalizability of findings. By leveraging statistical methods such as cross-validation, feature importance analysis, and cluster validation metrics, researchers can derive actionable insights from recruitment data while ensuring the validity and reliability of ML models. Moving forward, researchers should continue to explore advanced validation techniques, such as explainable AI (XAI) methods and model-agnostic approaches, to enhance transparency and interpretability of ML-driven recruitment processes. By embracing robust data analysis methodologies, organizations can leverage the power of machine learning to optimize talent acquisition strategies and foster innovation in workforce management. The mitigation of bias in machine learning (ML)-driven hiring processes represents a critical challenge and opportunity for enhancing fairness and equity in talent acquisition. This section delves deeper into specific bias mitigation techniques employed by researchers, providing comparative analyses of different approaches to address algorithmic biases in recruitment and selection.

10.4.5 Bias Mitigation Techniques in Recruitment Studies Zhao et al. (2020) Zhao et al. explored bias mitigation through the application of fairness-­aware ML techniques, including reweighted learning and adversarial debiasing.

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Machine Learning Scenario in Recruitment  287 The researchers focused on reweighting training samples to reduce bias towards certain demographic groups and employed adversarial learning to create robust models that mitigate discriminatory patterns in recruitment data. Comparative analysis of these techniques revealed varying degrees of effectiveness in achieving algorithmic fairness across different datasets and recruitment contexts. Singh and Patel (2021) Singh and Patel investigated the use of pre-processing techniques, such as demographic parity and equalized odds, to mitigate bias in candidate selection algorithms. The authors conducted a comparative evaluation of these techniques using real-world recruitment data, assessing their impact on fairness metrics (e.g., disparate impact, equal opportunity) and predictive performance. Their findings highlighted trade-offs between fairness and accuracy, underscoring the need for context-specific bias mitigation strategies in talent acquisition. Lee and Kim (2023) Lee and Kim proposed an ensemble-based approach for bias mitigation, leveraging multiple ML models trained on balanced datasets. The researchers employed techniques such as model stacking and adversarial training to develop robust ensemble classifiers that minimize bias while maintaining high predictive accuracy. Comparative analysis demonstrated the superior performance of ensemble methods in mitigating bias compared to individual classifiers, emphasizing the value of collaborative learning approaches in tackling algorithmic biases in recruitment.

10.4.6 Comparative Analysis and Effectiveness of Bias Mitigation Techniques The comparative analysis of bias mitigation techniques in recruitment studies provides valuable insights into their effectiveness and trade-offs. By exploring diverse approaches, researchers can identify best practices for promoting fairness and equity in ML-driven hiring processes. However, challenges remain in operationalizing these techniques across different organizational contexts and ensuring alignment with legal and ethical frameworks. Moving forward, future research should continue to explore innovative bias mitigation strategies, such as post-processing techniques and interpretability-enhancing methods, to foster trust and transparency in ML-driven recruitment practices. By embracing comparative analysis and

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288  Edge of Intelligence collaborative efforts, organizations can advance the adoption of responsible AI technologies and cultivate inclusive workplace environments.

10.5 Ethical Implications of ML in Hiring The ethical considerations surrounding the use of machine learning (ML) in hiring are of paramount importance, given the potential impacts on employment equity, fairness, and diversity. This section provides a comprehensive exploration of ethical concerns and real-world applications of ML in recruitment, highlighting strategies to address these challenges and promote responsible use of AI technologies.

10.5.1 Bias and Discrimination One of the primary ethical concerns with ML in hiring is the risk of perpetuating bias and discrimination. ML algorithms trained on historical data may inadvertently encode biases present in past hiring decisions, leading to biased outcomes against certain demographic groups. This can exacerbate disparities in employment equity and perpetuate systemic discrimination in recruitment processes.

10.5.2 Transparency and Explainability The opacity of ML algorithms poses challenges for transparency and accountability in hiring decisions. Candidates and stakeholders may not understand how decisions are made, leading to concerns about fairness and algorithmic accountability. Ensuring the explainability of ML models is essential for building trust and addressing ethical concerns in recruitment practices.

10.5.3 Data Privacy and Security ML-driven recruitment relies on vast amounts of personal data collected from candidates, raising concerns about data privacy and security. Mishandling or unauthorized access to sensitive information can compromise candidate confidentiality and erode trust in recruitment processes. Safeguarding data privacy is critical for upholding ethical standards and regulatory compliance in ML applications.

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10.6 Addressing Ethical Concerns in Real-World Applications 10.6.1 Algorithmic Auditing and Bias Mitigation Organizations are increasingly implementing algorithmic auditing processes to identify and mitigate biases in ML-driven hiring systems. By conducting regular audits and assessments, employers can proactively address algorithmic biases and ensure fairness and equity in recruitment outcomes. Techniques such as bias detection algorithms and fairness-aware training are employed to promote ethical ML practices. Stakeholder Engagement and Diversity Initiatives Engaging stakeholders, including candidates, employees, and regulatory bodies, is essential for promoting transparency and accountability in ML-driven recruitment. Employers are adopting diversity initiatives and inclusive hiring practices to mitigate bias and foster equitable employment opportunities. Collaborative efforts between data scientists, HR professionals, and ethicists are critical for navigating ethical complexities and promoting responsible use of AI in talent acquisition.

10.6.2 Regulatory Compliance and Ethical Guidelines Adhering to regulatory frameworks, such as data protection laws (e.g., GDPR, CCPA), is imperative for ensuring data privacy and security in ML-driven recruitment. Employers are also adopting ethical guidelines and principles, such as the IEEE Ethically Aligned Design, to guide the development and deployment of AI technologies responsibly. Compliance with ethical standards is essential for building public trust and mitigating ethical risks associated with ML applications.

10.7 Ensuring Data Privacy in ML Models for Hiring 10.7.1 Anonymization and Pseudonymization Organizations employ anonymization techniques to remove personally identifiable information (PII) from candidate data before it is used for training ML models. Anonymization ensures that sensitive information such as names, addresses, and contact details are replaced with pseudonyms or

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290  Edge of Intelligence removed entirely, reducing the risk of re-identification and safeguarding candidate privacy.

10.7.2 Data Minimization and Purpose Limitation ML models used for hiring are designed to process only relevant data necessary for recruitment purposes. Employers implement data minimization strategies to collect and retain the minimum amount of candidate information required for making hiring decisions. Purpose limitation ensures that candidate data is used solely for recruitment purposes and not repurposed for other activities without explicit consent.

10.7.3 Consent Management and Opt-Out Mechanisms Organizations obtain informed consent from candidates for processing their personal data in ML-driven recruitment systems. Employers provide clear and accessible information about data processing practices, including the types of data collected, purposes of processing, and rights of candidates. Opt-out mechanisms allow candidates to withdraw consent and request deletion of their data from ML models, promoting transparency and empowering individuals to control their data.

10.7.4 Compliance with International Data Protection Regulations I.  GDPR Compliance Organizations operating in the European Union (EU) or processing data of EU residents must comply with the GDPR’s stringent data protection requirements. Employers implement GDPR-compliant practices, such as conducting data protection impact assessments (DPIAs), appointing data protection officers (DPOs), and implementing technical and organizational measures to ensure data security and privacy. GDPR principles, including lawful processing, data minimization, transparency, and accountability, guide the development and deployment of ML models in recruitment processes. II.  Cross-Border Data Transfers Employers ensure lawful transfer of candidate data across borders by adopting GDPR-approved mechanisms, such as standard contractual clauses

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Machine Learning Scenario in Recruitment  291 (SCCs) or binding corporate rules (BCRs). These safeguards ensure that candidate data remains protected even when transferred to third-party service providers or subsidiaries located outside the EU, fostering global data protection compliance in ML-driven recruitment. Industry Case Studies in ML-Driven Recruitment • Case Study 1: Tech Startup in Silicon Valley Industry: Technology Location: Silicon Valley, United States Overview: A tech startup implemented ML algorithms to automate candidate screening and selection processes. The system analyzed resumes, GitHub profiles, and project portfolios to identify top candidates based on technical skills and cultural fit. Outcomes: Improved efficiency in candidate sourcing and reduced timeto-hire by 30%. ML models accurately predicted candidate-job fit, leading to higher-quality hires and improved team performance. Challenges: Initial challenges included algorithmic bias towards candidates from certain educational backgrounds. Bias mitigation techniques, such as fairness-aware training and continuous monitoring, were implemented to address these issues. Lessons Learned: Regular auditing and validation of ML models are essential to ensure fairness and equity in recruitment outcomes. Collaborative efforts between data scientists, HR professionals, and diversity advocates are critical for promoting inclusive hiring practices in tech industries. • Case Study 2: Financial Services Firm in London Industry: Finance Location: London, United Kingdom Overview: A financial services firm utilized ML algorithms to analyze candidate profiles and predict job performance in sales and customer service roles. The system assessed behavioral traits, communication skills, and past performance metrics to identify high-potential candidates. Outcomes: ML-driven candidate selection resulted in a 20% increase in sales productivity and a 15% reduction in employee turnover. Predictive models enabled personalized coaching and development plans for new hires, improving job satisfaction and retention rates. Challenges: Privacy concerns arose due to the collection of sensitive behavioral data. The firm implemented robust data anonymization and consent management practices to protect candidate privacy and comply with GDPR regulations.

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292  Edge of Intelligence Lessons Learned: Transparent communication with candidates about data usage and processing practices builds trust and enhances candidate experience. Ethical considerations must be prioritized throughout the ML implementation lifecycle to ensure responsible and equitable recruitment practices.

10.7.5 Analyzing Practical Implications and Lessons Learned The case studies highlight the transformative impact of ML on recruitment outcomes across different industries and geographic locations. By leveraging data-driven insights and predictive analytics, organizations can optimize talent acquisition strategies and enhance workforce performance. However, challenges related to algorithmic bias, privacy, and ethical considerations underscore the importance of responsible AI deployment and continuous monitoring of ML-driven hiring systems.

10.7.6 Actionable Recommendations for ML in Hiring Processes Implementing machine learning (ML) in hiring processes requires careful planning, adherence to best practices, and consideration of potential pitfalls to ensure successful adoption and ethical use of AI technologies. This section outlines concrete, actionable recommendations for organizations looking to leverage ML in recruitment and selection, including key best practices, pitfalls to avoid, and critical checkpoints for implementation. 1. Define Clear Objectives and Use Cases Best Practice: Begin by defining specific objectives and use cases for ML adoption in recruitment, such as automating candidate screening, predicting job fit, or enhancing diversity initiatives. Pitfall to Avoid: Failing to align ML initiatives with strategic hiring goals may result in misalignment between technology and business objectives, leading to inefficiencies and suboptimal outcomes. Consideration Checkpoint: Ensure that ML objectives are aligned with organizational priorities and stakeholder expectations to maximize the value of technology investments in talent acquisition.

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Machine Learning Scenario in Recruitment  293 2. Curate High-Quality Training Data Best Practice: Invest in curating high-quality training data that is representative, diverse, and unbiased to develop robust ML models for candidate assessment. Pitfall to Avoid: Using biased or unrepresentative datasets may perpetuate algorithmic biases and result in discriminatory hiring practices, undermining the fairness and credibility of ML-driven recruitment. Consideration Checkpoint: Implement data validation and preprocessing techniques to detect and mitigate biases in training datasets, ensuring fairness and equity in candidate evaluation. 3. Ensure Algorithmic Transparency and Explainability Best Practice: Prioritize algorithmic transparency and explainability to enhance trust and accountability in ML-driven hiring decisions. Pitfall to Avoid: Deploying opaque or black-box ML models without the ability to interpret decision-making processes may lead to distrust among candidates and stakeholders. Consideration Checkpoint: Utilize interpretable ML techniques, such as feature importance analysis and model-agnostic methods, to provide actionable insights and explanations for recruitment outcomes. 4. Implement Bias Mitigation Strategies Best Practice: Integrate bias mitigation techniques, such as fairness-aware training and demographic parity, to mitigate algorithmic biases and promote diversity and inclusion in hiring. Pitfall to Avoid: Neglecting bias mitigation may result in discriminatory outcomes, legal risks, and reputational damage for organizations deploying ML in recruitment. Consideration Checkpoint: Conduct regular audits and fairness assessments to monitor and address bias in ML models, leveraging stakeholder feedback and expert guidance to refine algorithms. 5. Emphasize Ethical and Regulatory Compliance Best Practice: Adhere to ethical guidelines and regulatory frameworks, such as the General Data Protection Regulation (GDPR), to ensure responsible use of candidate data in ML-driven recruitment processes. Pitfall to Avoid: Ignoring ethical considerations and legal requirements may result in privacy breaches, data misuse, and compliance violations, jeopardizing organizational credibility and stakeholder trust. Consideration Checkpoint: Establish robust data governance policies, appoint data protection officers (DPOs), and conduct privacy impact

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294  Edge of Intelligence assessments (PIAs) to uphold ethical standards and regulatory compliance in ML deployments. After addressing the subject of theory formation in the domain of recruit research, several facets of the perspective of the job candidate are reviewed. 1.  The Development of Theory Within the Framework of Recruitment Research Despite the substantial amount of existing research, no general theory of workplace recruitment has been presented (i.e., a theory that attempts to address the connections between different job placement dependent variables and examines how these areas affected job applicant character traits and organizational attributes in affecting job placement outcomes). Instead, some researchers have offered imperfect models of the selection process (e.g., associations respectively significant factors are not wholeheartedly explicated, relevant points are not included), and others have provided theoretical models that focus on a specific aspect of something like the hiring process (e.g., information exchange media, the recruitment statement) in isolation from other recruitment-related factors. The pertinent portions further in this document explain these more micro-focused ideas. The research literature provides a fragmented coverage of themes because researchers fail to create a working hypothesis of recruitment that combines different facets of the recruiting process. For instance, various theoretical approaches hypothesize an impact on the accuracy of recruits to the job expectations. These approaches include using current employees to recruit offering a realistic job preview and hiring people who have held positions like those being filled. (Nikolaou, I., Ahmed, S., Woods, S. A., Anderson, N., & Costa, A. C. 2020) However, research has focused on each of these three subjects separately rather than looking at how they may be used in concert to affect work expectations. 2.  Methods of recruitment and hiring success The recruiting process begins by making potential candidates aware of open positions using various RMs, including advertisements, OLR techniques, social media, etc. (Breaugh JA. 2008). With the help of these techniques, recruiters may talk to potential applicants and provide them with useful and reliable information about jobs, which encourages them to apply for the same position inside a business. However, using a simplified technology acceptance model to examine aspirations in using e-recruitment to apply for jobs in Iran, Carlson (KD, Connerley ML, Mecham RL, 2002) discovered that perceived usefulness had a substantial influence on behavioral intentions to utilize e-recruitment. They discovered that as an

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Machine Learning Scenario in Recruitment  295 investment in Intertubes recruiting rose, newspaper advertising and headhunter retainer costs declined by 20% in the USA. Social media as a means of recruiting offers the potential to engage and attract brilliant young people and support the quality and quantity of the candidate pool. SMR is a premium recruitment strategy that aids in posting job vacancies quickly to fulfill deadlines. According to Woods, S. A., Ahmed, S., Nikolaou, I., Costa, A. C., & Anderson, N. R. (2020) webpages and website style elements have varied effects on job seekers’ perceptions of fit, which can either raise or diminish the organizational appeal. 3.  Machine learning in recruitment How does machine learning work? Simply described, it is the aspect of education when a computer learns independently without being taught to do so. Machines are now able to learn from their mistakes and improve thanks to artificial intelligence (AI). For instance, AlphaGo, a Google pattern recognition computer created to play the game of Go, lost several games before evolving into a master and defeating human players. These losses helped AlphaGo learn and create new winning strategies. How can machine learning apply to contemporary hiring? Most HR/recruitment professionals now employ ML algorithms to some degree. Currently, a substantial portion of the hiring process can be automated. Let us have a closer look at the different stages of attracting a talent that can be done without a human touch: Employee handbooks: Some systems assist in writing and posting job descriptions as well as using pertinent language that is gender-neutral, devoid of prejudice, and targeted at a specific target audience. • Screening of CVs: A strong ATS (applicant tracking system) can pre-screen applications, find keywords, and match individuals with the appropriate positions. There are several programs available for organizing interviews that can interact with your electronic agenda and do it automatically. • Hiring process: There are several programs available for organizing interviews that can interact with your electronic agenda and do it automatically. • First screenings - rather than having recruiters do a few monotonous phone or video screens each day, a business may implement a chatbot that will quickly take the role of a human interviewer.

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296  Edge of Intelligence • Vacancies allotments: today, a lot of businesses utilize specialized software to produce vacancies and have them electronically signed. • Pre-boarding: Many pre-boarding tasks were successfully connected. It is simple to automate approaching a novice. Most administrative recruitment chores, if not all of them, could be automated if fully executed. However, the truth varies depending on the company. Few completely rely on automation. Most firms continue to view recruiters or recruiting coordinators as just having organizational/administrative responsibilities. 4.  Technology in Attraction The attraction phase is the initial step in the recruiting and selection process. The vital but frequently ignored function of attraction, defined by (Ryan AM, and Delaney T. 2010) as “a set of systems, procedures, and tactics aimed to maximize the amount and quality of the available candidates”, is highlighted (p. 27). Particularly in the current digital world, recruiting and attraction are different. The word “recruitment” encompasses a wide range of actions that firms engage in to find a desired set of applicants, entice them to join their employee ranks, and keep them on board at least temporarily. These activities are vital for the entire selection process recruiting should be in line with the strategic goals of the company. In the modern world, this means that it should consider all technological initiatives that have an impact on the selection and selection process, such as work motivation, applicant engagement, etc. Online or internet-based recruiting is one of the first technical developments in the field that has attracted increased interest from academics and practitioners. Job boards and job portals that let firms advertise job opportunities to a broad audience were the earliest examples of online recruiting applications. These applications are still in widespread use today since they are often perceived as being very effective (which is undoubtedly the case by both recruitment agencies and job searchers). Companies are interested in creating specialized job/ career websites where they may list available positions. Thanks to technology, firm career sites are now a very effective tool for drawing in and keeping prospects. They also heavily assist any employer branding tactics and policies that professionals may want to apply. For example, companies may include video testimonials from current employees on what it’s like to work there or from recruiters explaining the hiring process to prospective candidates on their job websites. Companies might also analyze website traffic, improve website functionality, and track, and follow candidates on

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Machine Learning Scenario in Recruitment  297 social media and other platforms, among other things. It’s possible that sooner rather than later, a job posting for an SEO career analyst or hiring coordinator will surface. Though they are still unusual in human resources, these jobs are ubiquitous in digital marketing. 5.  Technology in Selection Organizational psychologists have traditionally focused their study on how technology influences employee selection on online testing. Two new selection methods, nevertheless, are currently receiving greater attention. (Abdul, S. M., Anderson, N. R., Costa, A. C., Truxillo, D. M., Bauer, T. N., & McCarthy, J. M. 2017) These include asynchronous interviews and evaluation methods based on games or gamification, which focus on candidate emotions and experiences. The interview, which is also sometimes referred to as a video interview or a digital interview, is a style of interview in which applicants are expected to film their answers to a series of interview questions and submit them online. The computerized interview is somewhat more prone to be affected in the early stages of the hiring process to determine the basic qualifications for the position and narrow the candidate pool. Without managers present, numerous candidates might be interviewed at once, and multiple rates could watch the interview later to come to a consensus (Cook, R., Jones-Chick, R., Roulin, N., & O’Rourke, K. 2020). The number of times an applicant blinks, the amount of time between responses, changes in body temperature, word speed, and other indicators are all monitored by companies that specialize in data analytics for selection during digital interviews (e.g., HireVue); occasionally, the use of smart sensors, automatic extraction and analyzing data, and visualization to automate the actual interview workflow. On the other side, job seekers may apply for employment abroad, saving both time and money. The first research examining participants’ perceptions of the digital interview and its efficacy in comparison with the traditional interview was not particularly encouraging. Participants in a study felt that digital interviews were weirder and less interpersonal, and they expressed heightened privacy worries, but there was no difference in organizational attractiveness levels (Zottoli MA, Wanous JP, 2000). 6.  Technology and On-Boarding The candidate’s first day as a new employee is included in the last phase of the selection process. Onboarding and socializing are crucial components of employee adjustment, which have been extensively studied. It has been demonstrated that employing socializing strategies, including formal or casual coaching, on-the-job training, coaching-mentoring, etc.,

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298  Edge of Intelligence is extremely helpful in assisting new hires in making a rapid transition into their new responsibilities. To increase the effectiveness of these tactics, many businesses are increasingly heavily reliant on technology. They provide access to specific enterprise applications, skills courses, e-mentoring for career advancement, and intranets that resemble social media platforms, like Microsoft’s Yammer, etc. (Nikolaou, I., Ahmed, S., Woods, S. A., Anderson, N., & Costa, A. C., 2020). However, when businesses can use computer science methods like data mining with the amalgamation of external (pre-entry) and institutional (post-entry) data, recruitment and selection will finally be able to fully realize the potential of using advanced technologies throughout the entire lifecycle of the selection phase, including onboarding. 7.  Technology in Selection Online testing has been the focus of work and organizational psychologists’ research on how technology affects employee selection. However, two novel selection techniques have now drawn more interest. These include asynchronous interviews and gamification- or game-based assessments, which focus on candidate emotions and experiences (Earnest DR, Allen DG, Landis RS, 2011). The definition of an asynchronous interview, also known as a video interview or a digital interview, is a sort of interview in which applicants are expected to record their answers to a series of interview questions and submit them online. Computerized interviewing is more prone to be affected in the early stages of the hiring process to determine the basic qualifications for the position and narrow the candidate pool. Executives may conduct simultaneous interviews with numerous candidates while avoiding their presence, and multiple rates may watch the interview thereafter to come to a consensus. The number of times an applicant blinks, the amount of time between responses, changes in body temperature, word speed, and other indicators are all monitored by companies that specialize in data analytics for selection during digital interviews (e.g., HireVue); occasionally, the tracking of sensor devices, instantaneous extraction and analyzing of information, and visualization to automate the interview. The interview methodology. On the opposite side, job seekers may apply for employment abroad, saving both time and money. The initial research on participant perceptions of the digital interview and its effectiveness in comparison to the traditional interview, however, was not particularly encouraging. Participants in a study felt that digital interviews were weirder and less personal, and they expressed heightened privacy worries, but there was no difference in organizational attractiveness levels.

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Machine Learning Scenario in Recruitment  299 For instance, despite the obvious benefits they provide, a major disadvantage of the digital interview has been the lack of personal, face-to-face engagement with the interviewer and the candidates’ perception of their inability to affect the outcome of the initial interview, as in the step in the communication (Turban DB, Campion JE, Eyring AR, 1995). It will be challenging to change this in the future. An employer’s incorporating new or even its budgetary cost of capital may suffer because of applicants posting about their unpleasant interactions with an employer on other websites and social media like Glassdoor.com. This can result in customer dissatisfaction and the creation of a negative image of the company among potential employees. Thus, even in the absence of first-hand knowledge about the organization’s hiring and selection procedures, this information may influence candidates’ online recruitment activities and/or lead to unfavorable word of mouth between many potential candidates for employment. 8.  The Job Applicant’s Site Visit Managers have mainly paid very little attention to job applicants that are received by\ the organization’s HR department. This brings to notice that a site visit typically offers a “longer and more intense application form interaction”) and thus should have a significant impact on a recruit in comparison to other activities (such as a conversation with a recruiter at a job fair) (Fisher, R., McPhail, R., You, E. and Ash, M., 2014). During a site visit, for instance, a job applicant should gain first-hand knowledge about an employer’s staff (such as its diversity) and locations (e.g., the safety of the neighborhood). An employer has the chance to deliver more information about a position, more detailed information, realistic information, and more believable information through a site visit (e.g., first-hand knowledge typically has more credibility than being informed by others). Such knowledge may significantly alter an applicant’s first perception of a position with a business (Kashi, K. and Zheng, C., 2013). Additionally, an invitation to a site visit is frequently seen by a candidate as an indication that a job offer is imminent. One of the first studies to concentrate on the applicant site visit discovered that it had a significant influence. These interviewers identified three elements as crucial: the employer’s flexibility in scheduling a visit; the candidate’s treatment; and whether the candidate interacted with high-­ status personnel. found that candidates’ opinions of the friendliness of the site host were related to their choice to accept a job offer. This result might be explained by the fact that should the applicant be employed, the person who hosted the visit would be the recruit’s co-worker. the opportunity to interact with current employees who held the same post at which they qualified, to meet employees with backgrounds like their own, and to get

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300  Edge of Intelligence in touch with top management all had a positive influence on prospects. They disclosed site arrangements as well (e.g., a well-organized schedule, and an impressive hotel room). They revealed that employment offers with a flexible start date were more than likely to be accepted by candidates than offerings with a set start date. A population of bachelor students from two universities was used to accomplish this. According to (Boswell WR, Roehling MV, LePine MA, and Moynihan LM. 2003) College students in a sample did not react negatively when a potential employer established a date for acceptance of a position According to these authors, several of the people in their sample requested and were granted deadline extensions. In conclusion, the incorporation of machine learning into the recruiting and selection procedures signifies a revolutionary change in the way various businesses handle the acquisition of talent. Organizations may improve hiring procedures, find exceptional people more quickly, and make data-driven decisions to create better teams by utilizing sophisticated algorithms and data analytics. Nonetheless, it’s critical to acknowledge how crucial it is to preserve ethical standards and human oversight throughout these developments. Transparency, justice, and accountability must be given top priority as companies adjust to the machine-learning situation in recruiting. Only then can these technologies help, not hurt, the employment process for candidates and employers. Accepting this change has the potential to open new possibilities for workforce management in terms of productivity, variety, and creativity, opening the door to a more competitive and adaptable future. The use of machine learning in hiring and selection has numerous important advantages beyond increased productivity and better deci­sionmaking. One such benefit is the capacity to lessen the prejudices present in conventional recruiting procedures, which encourages inclusion and diversity in workplaces. By analyzing candidate data objectively, machine learning algorithms can lessen the impact of unconscious biases that may sway human decision-makers. Furthermore, the machine learning algorithms’ data-driven insights offer insightful feedback loops that help improve talent pipelines and recruiting tactics. Organizations may uncover success trends and customize their recruiting strategies to focus on applicants who have the greatest potential for success in certain jobs by examining performance data and hiring results from the past. In addition, machine learning technology’ scalability makes it possible for businesses to manage massive candidate data sets more skillfully, especially when dealing with quickly shifting market needs or ambitious development plans. Because of its scalability, organizations may easily and rapidly modify their hiring procedures to match

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Machine Learning Scenario in Recruitment  301 the changing demands of their staff, whether those needs involve growing up during expansion times or modifying for shifting skill requirements. However, it’s important to be aware of the possible difficulties in incorporating machine learning into the hiring and selection procedures. Ensuring data security and privacy, resolving issues with algorithmic fairness and transparency, and offering sufficient assistance and training to human recruiters so they can use these technologies efficiently are some of these obstacles. while machine learning in hiring and selection has great potential to improve productivity, diversity, and creativity in workforce management, its effective application necessitates a well-rounded strategy that integrates ethical concerns, human judgment, and technological advancements. Organizations can fully realize the potential of their talent acquisition initiatives and create resilient, future-ready teams who are ready for success in a constantly changing business environment by properly utilizing the power of machine learning.

10.8 Areas for Future Research in ML for Hiring Exploring future research directions is essential for advancing the understanding of machine learning (ML) applications in hiring and addressing emerging trends and challenges in talent acquisition. This section suggests key areas for future research that could bridge gaps in current understanding and shape the future of ML-driven recruitment processes. 1.  Fairness-Aware ML Algorithms Future research could focus on developing and evaluating fairness-aware ML algorithms specifically tailored for recruitment processes. This includes exploring novel techniques for bias detection, mitigation, and transparency in candidate evaluation, with an emphasis on promoting algorithmic fairness and mitigating discriminatory outcomes. 2.  Explainable AI (XAI) in Recruitment Investigating explainable AI (XAI) techniques for recruitment can enhance the transparency and interpretability of ML models. Future studies could explore methods for generating human-understandable explanations of hiring decisions, empowering candidates to understand and challenge algorithmic outcomes.

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302  Edge of Intelligence 3.  Cross-Cultural Adaptation of ML Models Exploring cross-cultural adaptation of ML models in hiring can address biases inherent in datasets from diverse geographic regions and cultural contexts. Future research could investigate transfer learning approaches and cross-validation techniques to ensure the generalizability and fairness of ML-driven recruitment systems across different populations. 4.  Multi-Objective Optimization in Candidate Selection Research on multi-objective optimization can optimize hiring decisions based on multiple criteria, such as skills, diversity, and cultural fit. Future studies could explore methodologies for balancing conflicting objectives in candidate selection and developing personalized hiring strategies tailored to organizational needs. 5.  Ethical Considerations and Regulatory Compliance Continued exploration of ethical considerations and regulatory compliance in ML-driven recruitment is crucial. Future research could focus on developing frameworks for ethical AI governance, integrating ethical guidelines into ML development lifecycles, and assessing the societal impact of AI technologies on workforce diversity and inclusion. 6.  Human-Machine Collaboration in Talent Acquisition Investigating human-machine collaboration models in talent acquisition can enhance the synergy between AI technologies and human expertise. Future studies could explore augmentation strategies, such as collaborative filtering and hybrid decision-making approaches, to optimize recruitment outcomes and foster inclusive hiring practices. By focusing on these future research areas, scholars and practitioners can advance the state of knowledge in ML applications for hiring, address existing gaps, and drive innovation in talent acquisition practices. Collaborative efforts between academia, industry, and policymakers are essential for promoting responsible AI adoption and shaping the future of workforce management.

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Machine Learning Scenario in Recruitment  303 3. Boehle, S., Online recruitment gets sneaky. Training, 37, 5, 66, 2000. 4. Boswell, W.R., Roehling, M.V., LePine, M.A., Moynihan, L.M., Individual job-choice decisions and the impact of job attributes and recruitment practices: A longitudinal field study. Hum. Resour. Manage., 42, 1, 23–37, 2003. 5. Bower, J. and Chertoff, E., Ethical issues in algorithmic hiring decisions: An analysis of past and future challenges. J. Bus. Ethics, 145, 4, 813–828, 2019. 6. Breaugh, J.A., Employee recruitment: Current knowledge and important areas for future research. Hum. Resour. Manag. Rev., 18, 2, 103–118, 2008. 7. Breaugh, J.A., Realistic job previews, in: Handbook of Improving Performance in the Workplace, R. Watkins and D. Leigh (Eds.), pp. 203–218, Pfeiffer, San Francisco, CA, 2010. 8. Buolamwini, J. and Gebru, T., Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 77–91, 2018. 9. Carlson, K.D., Connerley, M.L., Mecham, R.L., Recruitment evaluation: The case for assessing the quality of applicants attracted. Pers. Psychol., 55, 2, 461–490, 2002. 10. Cook, R., Jones-Chick, R., Roulin, N., O’Rourke, K., Job seekers’ attitudes toward cyber vetting: Scale development, validation, and platform comparison. Int. J. Sel. Assess., 28, 4, 383–398, 2020, https://doi.org/10.1111/ ijsa.12300. 11. Dhar, A. and Dhar, S., Leveraging machine learning for candidate sourcing and selection. Int. J. Hum. Resour. Manage., 29, 15, 2749–2770, 2018. 12. Diakopoulos, N., Accountability in algorithmic decision making. Commun. ACM, 59, 2, 56–62, 2016. 13. Dineen, B.R. and Soltis, S.M., Recruitment: A review of research and emerging directions, in: APA Handbook of Industrial and Organizational Psychology, vol. 2, S. Zedeck (Ed.), pp. 43–66, American Psychological Association, Washington, DC, 2011. 14. Earnest, D.R., Allen, D.G., Landis, R.S., Mechanisms linking realistic job previews with turnover. Pers. Psychol., 64, 4, 865–897, 2011. 15. European Commission, General Data Protection Regulation (GDPR), 2016, Retrieved from https://eur-lex.europa.eu/eli/reg/2016/679/oj. 16. European Commission, Ethics guidelines for trustworthy AI, 2020, Retrieved from https://ec.europa.eu/digital-single-market/en/news/ethicsguidelines-trustworthy-ai. 17. European Data Protection Board (EDPB), Guidelines 07/2020 on the concepts of controller and processor in the GDPR, 2021, Retrieved from https://edpb.europa.eu/our-work-tools/our-documents/guidelines/ guidelines-072020-concepts-controller-and-processor-gdpr_en. 18. Fisher, R., McPhail, R., You, E., Ash, M., Using social media to recruit global supply chain managers. Int. J. Phys. Distrib. Logist. Manage., 44, 8/9, 635–645, 2014.

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304  Edge of Intelligence 19. Google Cloud, Machine learning best practices: How to navigate the ML journey, 2021, Retrieved from https://cloud.google.com/solutions/ machine-learning/ml-on-gcp/ml-design-patterns. 20. Gupta, R. and Kumar, S., Machine learning in recruitment: A systematic review of applications and implications. Inf. Syst. Front., 23, 3, 529–548, 2021. 21. Hajian, S. and Domingo-Ferrer, J., Direct and indirect discrimination prevention methods, in: Discrimination and Privacy in the Information Society, pp. 49–70, Springer, Berlin, Heidelberg, 2013. 22. He, K. et al., Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 558–567, 2019. 23. Information Commissioner’s Office (ICO), Guide to the General Data Protection Regulation (GDPR), 2021, Retrieved from https://ico.org. uk/for-organisations/guide-to-data-protection/guide-to-the-generaldata-protection-regulation-gdpr. 24. Jobin, A. et al., The global AI ethics landscape. Nat. Mach. Intell., 1, 9, 389– 399, 2019. 25. Johnson, A. and Patel, S., Leveraging predictive analytics in talent acquisition: A case study of a London-based financial services firm. Int. J. Financ. Serv., 12, 2, 145–160, 2023. 26. Kashi, K. and Zheng, C., Extending technology acceptance model to the e-recruitment context in Iran. Int. J. Sel. Assess., 21, 1, 121–129, 2013. 27. Kaur, R. and Singh, P., Data privacy and security challenges in machine learning-driven recruitment. J. Inf. Privacy Secur., 18, 2, 245–262, 2022. 28. Kim, C. et al., Predicting candidate performance using ensemble learning algorithms: A comparative study. Expert Syst. Appl., 159, 113456, 2020. 29. Lee, Y. and Smith, J., Advancements in natural language processing for recruitment: Implications and future directions. Expert Syst. Appl., 175, 115149, 2023. 30. Lipton, Z.C., The mythos of model interpretability, arXiv preprint arXiv:1606.03490, 2018. 31. McCarthy, J.M., Bauer, T.N., Truxillo, D.M., Anderson, N.R., Costa, A.C., Ahmed, S.M., Applicant perspectives during selection: A review addressing “So What?”, “What’s New?”, and “Where to Next?”. J. Manage., 43, 6, 1693– 1725, 2017. 32. Narayanan, A. and McSherry, F., Privacy, fairness, and the dangers of current machine learning. Queue, 18, 4, 44–59, 2020. 33. Nikolaou, I., Ahmed, S., Woods, S.A., Anderson, N., Costa, A.C., Applicant reactions towards Internet-based selection methods. [Manuscript in preparation], Department of Management Science & Technology, Athens University of Economics & Business, 2020. 34. Patel, R. and Gupta, S., Leveraging clustering algorithms for candidate segmentation in recruitment. Inf. Sci., 505, 185–201, 2022.

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Machine Learning Scenario in Recruitment  305 35. Ryan, A.M. and Delaney, T., Attracting job candidates to organisations, in: Handbook of Employee Selection, J.L. Farr and N.T. Tippins (Eds.), pp. 127– 146, Routledge, New York, NY, 2010. 36. Rynes, S.L., Bretz, R.D., Gerhart, B., The importance of recruitment in job choice: A different way of looking. Pers. Psychol., 44, 3, 487–521, 1991. 37. Singh, R. and Patel, A., Addressing bias in candidate selection algorithms: A comparative study of pre-processing techniques. Inf. Process. Manage., 58, 5, 102472, 2021. 38. Smith, A. and Jones, B., Enhancing candidate screening with natural language processing: A case study. J. Appl. Artif. Intell., 32, 5, 789–804, 2019. 39. Smith, J., Transforming recruitment with machine learning: A case study of a Silicon Valley tech startup. J. Technol. Manage., 45, 3, 321–335, 2022. 40. Taylor, M.S. and Schmidt, D.W., A process source investigation of recruitment source effectiveness. Pers. Psychol., 36, 2, 343–354, 1983. 41. Turban, D.B., Campion, J.E., Eyring, A.R., Factors related to job acceptance decisions of college recruits. J. Vocat. Behav., 47, 2, 193–213, 1995. 42. Zhao, Q. et al., Mitigating bias in machine learning-driven hiring processes: Challenges and opportunities. J. Manage. Inf. Syst., 37, 4, 1219–1247, 2020. 43. Zottoli, M.A. and Wanous, J.P., Recruitment source research: Status and future directions. Hum. Resour. Manage. Rev., 10, 4, 353–383, 2000.

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11 Machine Learning for Nano Process Optimization Manjushree Nayak* and A. Sai Satya Narayana Department of Computer Science and Engineering, NIST University, Berhampur, Odisha, India

Abstract

Nano process optimization is crucial for enhancing the performance of nanodevices and materials. This chapter presents a comprehensive review of machine learning (ML) techniques in nanofabrication optimization. It first defines the fundamental problems of nanoscale optimization processes and examines how machine learning can effectively solve these issues. The chapter provides a detailed overview of machine learning techniques used in various aspects of nano process optimization, including design, optimization, and quality control. It also discusses the advantages and limitations of machine learning in this context while addressing future research directions. The ongoing discussion about the potential of machine learning in new nanotechnologies such as nanomedicine and nanoelectronics aims to provide researchers and professionals in the nanotechnology industry with a deeper understanding of how machine learning can improve nanotechnology processes to increase efficiency and reliability. Additionally, this chapter aims to increase the efficiency and accuracy of nanofabrication by using machine learning in the optimization process. By combining machine learning algorithms with nanotechnology, it has the potential to revolutionize the design and production of nanoscale materials and devices. The chapter underlines the need for an expert-driven approach and highlights key issues related to trial and error in nano process development. It shows how machine learning models can analyze complex data from nanofabrication processes, revealing subtle patterns and relationships that aid decision-making across products. Furthermore, this chapter explores the potential impact of machine learning-­ driven process optimization in various industries such as electronics, healthcare, *Corresponding author: [email protected]; ORCID: 0000-0001-6383-780X A. Sai Satya Narayana: ORCID: 0009-0009-3169--5080 Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (307–326) © 2025 Scrivener Publishing LLC

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307

308  Edge of Intelligence and repair. The integration of machine learning and nanotechnology is expected to lead to advances in information science and engineering. It presents a novel machine learning-based framework for optimizing nanofabrication processes using experimental data and advanced learning methods to achieve process control and optimization. This approach improves process efficiency, throughput, and quality control, ultimately reducing the time and resources required for development and optimization. Through simulations and real-world studies, this chapter demonstrates the role of machine learning in optimizing various nanofabrication processes, providing the potential for rapid and cost-effective production of nanomaterials and nanodevices. Keywords:  Nanofabrication, nanotechnology, machine learning, process optimization, nanomaterials, nanodevices, metrology

Introduction Nanomanufacturing involves the precise fabrication and manipulation of materials and structures at the nanoscale, where control and optimization of nano processes are paramount [1]. Machine learning is increasingly recognized as a key player in this domain, offering a data-centric approach to constructing predictive models that establish correlations between process inputs and outputs [10]. For instance, neural networks can be trained on labeled datasets containing diverse process conditions as inputs and resulting nanostructure properties as targets. These models excel at capturing intricate relationships among input parameters such as temperature, pressure, and concentrations, and the achieved outputs like particle size distribution, morphology, and yield. Trained machine learning models can be seamlessly integrated into instrumentation to suggest optimal process settings for achieving desired nanostructure quality and functionality. They also facilitate autonomous closed-loop control by continuously monitoring outputs and providing real-time feedback to fine-tune the process [8]. Furthermore, machine learning aids in defect classification within nanostructures, enabling pattern recognition to identify potential causes for root cause analysis and further optimization [20]. A notable advantage of machine learning lies in its capacity to handle extensive datasets and unveil hidden insights that conventional methods may overlook. With advancements in computing power and sensing technologies yielding copious amounts of high-fidelity process data, machine learning models can be periodically retrained to enhance accuracy and robustness. These models can also be extended to related processes through transfer learning. Moreover, machine learning supports the development of ‘virtual metrology’ models

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Nano Process Optimization  309 that swiftly estimate challenging-to-measure nanostructure properties non-destructively. This broadens the scope of optimization from equipment performance to actual nanostructure quality. The fusion of machine learning with nanomanufacturing has the potential to expedite innovation by streamlining fabrication and testing cycles. Machine learning presents a versatile toolkit that can enhance various facets of nano process engineering. Ongoing research into tailored machine learning solutions for nanomanufacturing is crucial for unlocking the myriad benefits across diverse applications spanning electronics, energy, coatings, and pharmaceuticals. The future appears promising for this interdisciplinary approach poised to push the boundaries of nanotechnology.

Literature Review Machine learning has emerged as a powerful tool in the realm of nanoprocess optimization, offering innovative solutions to enhance efficiency and effectiveness in manipulating materials at the nanoscale. This literature review delves into recent studies and advancements in the application of machine learning techniques for optimizing nano processes. In a study by [11], neural networks were employed to predict optimal process parameters for synthesizing nanoparticles with specific properties. The results demonstrated the ability of machine learning models to accurately forecast outcomes based on input variables such as precursor concentrations and reaction temperatures. This approach not only streamlined the optimization process but also led to significant improvements in nanoparticle quality. Similarly, Jones and colleagues, 2021 [5] investigated the use of support vector machines (SVM) for classifying defects in nanostructures during the manufacturing process. By training the SVM model on a dataset of defect images and corresponding process parameters, they achieved high accuracy in identifying and categorizing defects. This study showcases the potential of machine learning in automated defect detection and root cause analysis in nanomanufacturing. Furthermore, recent research by [17], explored the application of reinforcement learning algorithms for autonomous process control in nanofabrication. By integrating a reinforcement learning agent into the control system, they demonstrated the agent’s ability to adaptively adjust process parameters in real time to optimize nanostructure properties. This adaptive control mechanism holds promise for enhancing efficiency and precision in nano process optimization.

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310  Edge of Intelligence In a different vein, [4] investigated the transferability of machine learning models across different nano processes. Their study revealed that pre-trained models could be effectively transferred and fine-tuned for optimizing various nanofabrication techniques, showcasing the versatility and scalability of machine learning approaches in nano-process optimization. These studies underscore the growing significance of machine learning in revolutionizing nano-process optimization by enabling predictive modeling, defect classification, autonomous control, and transfer learning. The integration of machine learning techniques holds immense potential for accelerating advancements in nanotechnology by facilitating rapid experimentation, precise control, and enhanced understanding of complex nanoscale phenomena. Future research efforts are poised to further explore the synergies between machine learning and nanoengineering to unlock new possibilities for innovation and discovery in this exciting field. Problem Statement Nano process optimization, occurring at the atomic and molecular scale, presents a distinct set of challenges due to the intricate interactions and behaviors of materials at such small dimensions [1]. Conventional optimization techniques often struggle to navigate this complex landscape efficiently, resulting in less-than-optimal outcomes. Machine learning (ML) has emerged as a promising solution to these challenges, offering a data-driven approach to uncover concealed patterns and relationships within nanoscale systems [10]. The problem statement for utilizing ML in nano process optimization involves the development and application of ML algorithms to analyze extensive datasets generated from experiments in nanotechnology. These datasets encompass crucial information regarding material composition, processing conditions, and performance metrics. The objective is to leverage this data to optimize processes, predict outcomes, and reveal insights that can enhance the efficiency and effectiveness of nanoscale processes [7]. Significant challenges within this problem space include the creation of ML algorithms capable of handling the complexity and multidimensionality of nanoscale data, seamless integration of ML into existing nanotechnology workflows, and ensuring the interpretability and transparency of ML models [21]. Furthermore, there exists a need to validate the efficacy of ML-based optimization strategies through rigorous experimentation and comparison against conventional methodologies [19]. In essence, the problem statement for ML in nano process optimization revolves around harnessing data-driven approaches to enhance the efficiency, effectiveness, and comprehension of nanoscale processes, ultimately driving progress in the field of nanotechnology.

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Nano Process Optimization  311 Need of Machine Learning in Nano Process Optimization Optimizing nanoscale processes at the atomic and molecular scales poses special challenges due to the interaction and behavior of particles at such a small scale. Traditional optimization techniques often struggle to perform well in complex and inefficient environments [3]. Machine learning (ML) has emerged as a promising method to solve these problems by providing a data-driven approach to uncover hidden patterns and relationships in nanoscale systems. The necessity of machine learning in the nanotechnology optimization process arises from the limitations of traditional methods in dealing with the complexity of nanoscale systems. These systems involve interactions between atoms and molecules and allow for precise control and optimization over traditional methods. Machine learning provides a data-driven approach to analyzing large amounts of data from experimental nanotechnology to discover patterns and insights that can lead to better outcomes [13]. Using machine learning algorithms, researchers can analyze complex data containing information about components, parameters, and performance indicators. This analysis can help improve processes, predict outcomes, and identify new opportunities to increase the efficiency and effectiveness of nanoscale processes [20]. Additionally, machine learning can help researchers more easily explore the environment to find better solutions. The combination of machine learning and advanced nanofabrication has the potential to revolutionize the field by providing scientists with powerful tools to better understand and manage nanotechnology [19]. This could lead to significant advances in nanotechnology by supporting the creation of new products and materials with properties and functionality. Data collection and Preprocessing Data Collection and Preprocessing are essential stages when employing Machine Learning (ML) for nano process optimization. These phases involve acquiring and readying data to guarantee its integrity and applicability in ML algorithms. Within the framework of nano process optimization, data acquisition might incorporate retrieval of details about material composition, processing parameters, and performance metrics from experiments or simulations. Initially, pertinent variables and parameters should be determined and selected for incorporation into the ML model. Such elements may consist of the kind of material utilized, temperature and pressure circumstances throughout processing, and the anticipated attributes or execution metrics of the last item [6]. Upon identification of the appropriate data, it needs to be gathered and arranged in a fashion amenable to examination. This may imply saving the data in databases or

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312  Edge of Intelligence spreadsheets, and ensuring proper labeling and annotation for utilization in ML algorithms. Following data accumulation, preprocessing measures are applied to purge and ready the data for investigation [20]. This may involve eradicating anomalies or mistakes in the data, normalizing or standardizing the data to make sure that each variable is on the identical scale, and partitioning the data into coaching and checking sets for usage in ML model instruction and assessment. Generally speaking, data collection and preprocessing are vital stages when deploying ML to nano process optimization. By ensuring that the data is tidy, structured, and appropriately prepared, researchers can enhance the exactitude and productivity of their ML models in optimizing nanoscale procedures. Feature Selection and Engineering Feature selection and engineering play a crucial role in the application of machine learning to nano-process optimization [18]. These processes involve identifying and crafting relevant features from the data to enhance the performance and interpretability of machine learning models. In the context of nano process optimization research, feature selection entails choosing the most informative variables that have a significant impact on the desired outcomes. The first step in feature selection is to analyze the dataset and identify potential features that are relevant to the optimization process. This may include parameters such as material composition, processing conditions, particle size distribution, and other key factors that influence the quality of nanostructures. By selecting the right features, researchers can improve the efficiency and accuracy of their machine-learning models. Feature engineering is another critical aspect that involves creating new features or transforming existing ones to better represent the underlying patterns in the data [8]. This may involve techniques such as scaling, normalization, encoding categorical variables, or creating interaction terms between variables. By engineering features effectively, researchers can extract more meaningful information from the data and improve the performance of their machine-learning models. Furthermore, feature selection and engineering help in reducing dimensionality, improving model interpretability, and enhancing generalization capabilities [22]. By focusing on relevant features and optimizing their representation, researchers can build more robust machine-learning models that are better suited for nano-process optimization tasks. Feature selection and engineering are essential steps in leveraging machine learning [2] for nano process optimization research [4]. These processes enable researchers to extract valuable insights from

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Nano Process Optimization  313 complex datasets, improve model performance, and ultimately drive advancements in nanotechnology by optimizing nanoscale processes effectively. Machine Learning Algorithms for Nano Process Optimization Support Vector Machines (SVM): SVMs are highly effective in handling large datasets, making them invaluable for analyzing intricate data in nano process optimization. Their proficiency in splitting tasks, such as determining optimal methods based on input variables, enhances their utility in this specialized field [12]. Neural Networks: Particularly within deep learning models, neural networks demonstrate a remarkable ability to capture complex relationships inherent in nanoscale data. They are commonly utilized for tasks like product forecasting and process optimization, leveraging their capacity to unveil intricate patterns within the dataset [16]. Random Forest: Renowned for their versatility and robustness, Random Forest algorithms are well-suited for a range of tasks in nano process optimization. Capable of performing both classification and regression functions, they prove invaluable in predicting outcomes and identifying critical processes within the nanodomain. Gradient Boosting Machines (GBM): Algorithms like XGBoost or LightGBM under the GBM umbrella excel at managing correlations within extensive datasets. Their effectiveness lies in optimizing processes based on diverse input variables, significantly enhancing nano-process efficiency. Clustering Algorithms: By utilizing clustering algorithms such as K-means or hierarchical clustering, similar objects or processes can be grouped. This clustering aids in identifying common patterns and streamlining processes tailored to specific products or outcomes in nano-process optimization. Reinforcement Learning: Reinforcement learning techniques can be leveraged for nano process optimization by dynamically learning quality control strategies. This adaptability proves beneficial in scenarios where adjustments to unwanted processes are necessary to achieve desired results efficiently. Model Evaluation and Validation of Machine Learning Algorithms for Nano Process Optimization Cross-Validation: Cross-validation is a process for testing the performance of an ML model by splitting data into relevant components. The model is trained on a subset of the data and tested on the remaining subset.

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314  Edge of Intelligence This process is repeated many times, training and testing with different subsets to ensure the model is optimized for new products. Performance Measurements: Various performance measurements can be used to evaluate the effectiveness of ML models in the following areas: Nano process optimization. These metrics include accuracy, precision, recall, F1 score mean square error, etc. takes place. The choice of indicators depends on the specific objectives of the study. Hyperparameter Tuning: Hyperparameter tuning involves optimizing the hyperparameters of a machine learning model to improve its performance. Techniques such as grid search or random search can be used to find the best combination of hyperparameters to achieve optimal performance of the model. Model Comparison: Compare the performance of different learning models to determine which model is most useful and best for nanometer process optimization tasks. This can be done by using statistical or visualization methods to evaluate the relative strengths and weaknesses of each model. Validation of Independent Data: To ensure the generality of machine learning models, it is important to validate them on independent data. This helps evaluate how well the model performs on new, unseen information that is important for practical use. Sensitivity Analysis: Sensitivity analysis will evaluate how changes in input points affect the output of the machine learning model. This can help identify key optimization considerations. Different Case Studies Related to Machine Learning for Nano Process Optimization Predictive Modeling for Etching Process Predictive modeling for etching processes is a sophisticated approach that integrates statistical analysis and machine learning algorithms to forecast and optimize the etching behavior of materials at the nanoscale. This field of research delves into the intricate dynamics of material removal through chemical or physical processes, aiming to enhance precision, efficiency, and control in nano fabrication as shown in (Figure 11.1) [8]. Researchers in this domain explore a wide array of process parameters, material characteristics, and environmental factors to build comprehensive predictive models. By leveraging advanced statistical techniques like regression analysis, principal component analysis, and response surface methodology, coupled with machine learning algorithms such as neural

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Nano Process Optimization  315 Multiscale Plasma Etching Model Fluid Model

kMC Model

Model Order Reduction Low Order Model Identification Prediction

Identification

RNN Models

Initial Condition

Predictive Optimizer Control Actions

Measured States Multiscale Systems

Measure

Figure 11.1  Predictive control of a plasma etch process.

networks, support vector machines, and decision trees, these models can accurately predict etching rates, selectivity, uniformity, and other critical parameters. The predictive models developed for etching processes enable researchers and engineers to simulate complex interactions between etchants and substrates under varying process conditions. By understanding and predicting how different factors influence the etching outcomes, these models empower stakeholders to optimize process parameters, minimize defects, reduce variability, and achieve desired material removal profiles with high precision [9]. The application of predictive modeling in etching processes holds significant promise for advancing nanofabrication technologies. By enabling real-time monitoring, feedback control, and automated optimization of etching processes, these models contribute to the development of ­cutting-edge nanoscale devices and structures tailored for diverse applications in electronics, photonics, sensors, and biomedical devices. Through continuous refinement and validation against experimental data, predictive modeling plays a pivotal role in accelerating innovation and driving progress in the field of nanotechnology [8]. Nanoparticle synthesis optimization: Nanoparticle synthesis optimization is a critical area of research that aims to control the size, shape, and properties of nanoparticles for various applications (Figure 11.2). The optimization process involves investigating the impact of

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316  Edge of Intelligence Discovery of desired NPs classification Text mining •• Literature Keyword extraction • Data constructing

Nano-particle synthesis database

• Screening with ML prediction model structure or composition of NPs

• Deep leaming (MLP, CNN, RINN, etc) • Traditional ML (RF, k-NN, GP etc)

ML Desired NPs Predictor screening

• Generative model

Encoder

Decoder

Latent space of NPs Sam plin Desired NPs g

property

Synthesis optimization

Automated experiment

Input • precursor • temperature • concentration • lime • structure • composition

Target (property) • particle size • yield • optical property • catalytic property

• Grid search conditions Regression Model

• Generative model

• Bayesian optimization

Encoder Decoder Latent space of synthesis conditions

Property

Feedback: Synthesis attempt & validation

Optimized synthesis condition

Figure 11.2  Closed loop optimization of nanoparticle synthesis.

various factors, such as pH, temperature, concentration of reactants, and reaction time, on the synthesis of nanoparticles [4]. Statistical tools like Response Surface Methodology (RSM) and Central Composite Design (CCD) are often utilized to analyze the relationships among these factors and the resulting responses, such as nanoparticle size, shape, and yield. One example of nanoparticle synthesis optimization is the use of green chemistry approaches, such as the utilization of plant extracts as reducing agents. In this approach, researchers explore the use of plant extracts to synthesize nanoparticles with desirable properties. The choice of plant extract significantly influences the nanoparticle synthesis process, and statistical and machine-learning techniques can be applied to optimize the synthesis process. Another example of nanoparticle synthesis optimization is the use of machine learning techniques to optimize the synthesis process. Machine learning algorithms, such as artificial neural networks, genetic algorithms, and random forests, can be used to identify optimal reaction conditions and provide valuable insights into the underlying mechanisms governing nanoparticle formation. Nanoparticle synthesis optimization is a crucial area of research that enables the production of nanoparticles with tailored properties for various applications. By leveraging statistical and machine learning techniques, researchers can efficiently optimize the synthesis process, ensuring consistent quality and performance of nanoparticles [6].

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Nano Process Optimization  317 Surface Roughness Control in Nanoimprint Lithography Surface roughness is a critical issue in nanoimprint lithography, as it can negatively impact the performance and quality of the imprinted nanostructures (Figure 11.3). There are several ways to control and reduce surface roughness in nanoimprint lithography: Template surface treatment: The surface of the imprint template can be modified to reduce roughness. Methods include coating with anti-­ sticking layers, plasma/chemical treatments, and annealing to reflow surfaces. Smoother templates produce smoother imprinted surfaces. Imprint pressure/temperature: Higher pressures and temperatures during imprinting can help force polymers to better fill template patterns and reduce residual roughness. However excessive conditions can damage templates. Polymer material selection: Choosing polymer resists with suitable viscoelastic properties allows resists to optimally fill template features during imprinting, reducing residual roughness. Residual layer etching: For bilayer imprint processes, etching the residual layer between imprinted features can selectively reduce roughness. Etch process parameters need optimization.

1. Orient substrate and imprint mask

2. Drop-on-DemandTM dispense of UV curable resist

3. Close gap and illuminate with UV

Transparent Mask Release Layer Hard Mask Layer Substrate GDS-targeted Resist Dispenser Imprint Resist

UV Blanket Exposure

(Room Temperature, Low Pressure)

4. Separate the mask from the substrate

5. Descum followed by hard mask etch

Figure 11.3  Nanoimprint lithography.

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High Resolution Pattern Residual Layer High Resolution. High Aspect-ratio Feature

318  Edge of Intelligence Demolding treatments: Anti-stick coatings, UV exposure, or heat can assist template demolding in a way that avoids surface distortions and roughness. Combining treatments of the template, imprint process, resist material, residual layer, and demolding allows for controlling and minimizing roughness. This improves the resolution and performance of nanoimprinted devices. With careful optimization, surface roughness can be reduced to the single-digit nanometer scale. Optimization of Carbon Nanotube Growth Carbon nanotubes (CNTs) are promising materials for many applications due to their unique properties, electrical and thermal properties (Figure  11.4). Optimization of carbon nanotube growth is essential to obtain high-­quality carbon nanotubes with desired properties. Researchers have investigated various ways to improve CNT growth, including catalyst design, pretreatment, and inactivation techniques [8]. Optimization of CNT growth involves controlling the growth rate, diameter, and chirality of carbon nanotubes. The growth rate and diameter of carbon nanotubes can be controlled by adjusting processes such as temperature, pressure and gas flow. The chirality of carbon nanotubes can be controlled by selecting appropriate catalysts and precursors. Catalyst design is important in improving carbon nanotube growth. The catalyst material, size, and distribution affect the growth, diameter, and chirality of carbon nanotubes. Researchers have explored a variety of catalyst materials, including transition metals, alloys, and nanoparticles [8], to obtain high-quality carbon nanotubes with desired properties. Precursor selection is another important factor in improving CNT growth. Precursor materials and concentrations affect the growth rate, diameter, and chirality of carbon nanotubes. Researchers have explored various precursors, including hydrocarbons, alcohol, and carbon monoxide, to obtain carbon nanotubes with desired properties. Process parameters such as temperature, pressure, and gas flow rate play an important role in improving CNT growth. These parameters affect the growth, diameter, and chirality of carbon nanotubes. Researchers have explored various techniques to obtain high-quality CNTs with desired properties. Optimization of CNT growth is important to produce good CNTs with desired properties. Catalyst design, precursor selection, and inactivation techniques are important factors in improving CNT growth. By controlling the growth rate, diameter, and chirality of carbon nanotubes, researchers can obtain high-quality carbon nanotubes with customized properties for

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Nano Process Optimization  319 I: Catalyst preparation and treatment

III: Catalytic activation and CNT nucleation

Metal (Fe)

Buffer (Al2O3) Si wafer

0: Deposition of thin film catalyst and buffer

1: Film restructuring and oxide reduction

2: Catalyst nanoparticle formation

3: Carbon arrangement and CNT nucleation

5: CNT steady growth and density decay

4: CNT Self-organization and crowding

III: CNT growth and termination

7: Posttermination evolution

6: Growth selftermination and loss of alignment

Catalyst nanoparticle coarsening and atomic diffusion into substrate

Figure 11.4  Collective carbon nanotube growth.

various applications. Optimization of carbon nanotube growth holds great promise for advancing nanotechnology and creating new materials and devices. Challenges in the Nano Process Optimization Nano process optimization poses many challenges that scientists and engineers must solve to achieve control of nanoscale processes. A major challenge lies in the complexity of nanoscale events, where factors such as quantum effects, surface interactions and stochastic behavior can influence the resulting process. Understanding and modeling these complex interactions requires the use of advanced technology and experimental techniques to accurately predict and optimize nanofabrication parameters. Another challenge in nano process optimization is the need for multidisciplinary skills, as success often requires knowledge of several disciplines, such as information science, chemistry, physics and engineering. Integrating expertise from multiple fields to solve complex nanoscale processes can be challenging, but is necessary to develop the best possible strategy. In addition, the scalability of nanofabrication process optimization causes problems, especially in the transition from experimental scale

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320  Edge of Intelligence experiments to industrial scale production. Ensuring repeatability, reliability and optimization of process optimization while maintaining cost-­ effectiveness and efficiency are the main challenges faced by researchers in nanometer process optimization. In addition, the sensitive nature of nanoscale systems leads to problems with immediate monitoring and control. Nanoscale systems can experience rapid and dynamic changes during optimization, requiring adaptive control strategies to ensure stability and accuracy. Solving these problems in nano process optimization requires an integrated approach combining theoretical models, experimental analyses, characterization methods and new control strategies. By overcoming these challenges, scientists can unlock the potential of nanotechnology to create new materials, materials, and technologies with customized products for various applications. Optimization Methods Machine learning plays a vital role in optimizing nano-scale processes, offering methods to enhance efficiency and reduce costs. Several optimization techniques can be employed in this context, tailored to the specific challenges of nano-scale processes. Here are key optimization techniques: Gradient Descent and Variants: Gradient descent is a fundamental optimization algorithm used to minimize a loss function iteratively. Variants like stochastic gradient descent (SGD), mini-batch SGD, and adaptive methods like Adam can be applied to optimize machine learning models for nano process optimization. Bayesian Optimization: Bayesian optimization is a sequential ­model-based optimization technique that uses probabilistic models to predict the performance of different configurations of process parameters. It is particularly useful when the objective function is expensive to evaluate, as is often the case in nano-scale processes. Genetic Algorithms: Genetic algorithms are optimization techniques inspired by the process of natural selection. They can be used to explore the space of process parameters and identify optimal configurations for nano-scale processes. Simulated Annealing: Simulated annealing is a probabilistic optimization technique that is particularly effective for problems with a large search space. It can be used to explore different configurations of process parameters and find near-optimal solutions for nano-process optimization. Particle Swarm Optimization: Particle swarm optimization is a  population-based optimization technique inspired by the social behavior of birds

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Nano Process Optimization  321 flocking or fish schooling. It can be applied to optimize process parameters in nano-scale processes by iteratively updating a population of candidate solutions. Reinforcement Learning: Reinforcement learning is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. It can be applied to optimize control policies for nanoscale processes, learning from feedback on the quality of the produced nanostructures. Hybrid Approaches: Hybrid approaches that combine multiple optimization techniques can often achieve better results than using a single technique alone. For example, a genetic algorithm could be combined with a local search method to improve exploration and exploitation in the search space. A variety of optimization techniques can be applied to machine learning in nano process optimization, each with its strengths and weaknesses. The choice of technique depends on the specific characteristics of the nanoscale process and the goals of the optimization. Challenges Precise control at nanoscale: Precise control at nanoscale has faced many challenges due to many factors such as variability and repeatability. Multi-objective optimization: Optimization for nanotechnology processes often involves evaluating multiple objectives, such as yield, cost reduction, and improved performance. Parameter sensitivity and complexity: The nanofabrication process is very sensitive to variables such as temperature, pressure and material strength, which increases the complexity in the optimization process. Poor understanding of nanomaterial interactions: Understanding how nanostructures interact with each other and environmental materials is still limited, leading to uncertainty and problems in prediction and control. Data collection and integration: Collection and integration of different data such as simulation outputs, simulation results, and measurement results will be difficult due to the large amount of data. Computational complexity: Modeling and simulating nanoscale processes can be computationally intensive and time-consuming, especially when detailed models are used. Findings and conclusions: Due to the limitations of current processing methods and the complex nature of nanomaterials, it can be difficult to test and analyze to confirm that the nanofabrication process has been optimized. It is a big problem.

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322  Edge of Intelligence Interdisciplinary nature: Nano process optimization requires expertise in many fields, such as materials science, physics, chemistry and engineering, and there are challenges in communication, collaboration and cognitive integration [14]. Ethical and social: Nano process optimization increases ethical and environmental impact, health risks and safety, and public recognition [15]. Applications Process Parameter Optimization: Machine learning algorithms can be used to optimize process parameters in nanofabrication, such as temperature, pressure, and deposition rates, to achieve desired material properties and performance. Defect Detection and Quality Control: Machine learning can be applied to detect defects in nanostructures, such as nanoparticles and thin films, and to improve quality control in nanofabrication processes. Material Discovery and Design: Machine learning techniques, such as predictive modeling and optimization algorithms, can be used to discover new materials with specific properties and to design novel nanostructures for various applications. Process Modeling and Simulation: Machine learning can be used to develop accurate and efficient models for simulating nano processes, reducing the computational cost and improving the prediction accuracy. Real-time Process Monitoring and Control: Machine learning algorithms can be used to monitor nanofabrication processes in real-time and to adjust process parameters dynamically to ensure optimal performance and quality. Data-driven Decision Making: Machine learning can help in making data-driven decisions in nano process optimization, such as selecting the best fabrication process based on the desired material properties and cost constraints. Optical and Electron Microscopy Image Analysis: Machine learning algorithms can be used to analyze images obtained from optical and electron microscopes to extract useful information about nanostructures, such as size, shape, and orientation. Optimization of Nanostructure Arrays: Machine learning can be applied to optimize the arrangement and spacing of nanostructures in arrays to achieve desired collective properties, such as plasmonic effects and optical properties. Predictive Maintenance: Machine learning algorithms can be used to predict equipment failures in nanofabrication facilities, allowing for proactive maintenance and minimizing downtime.

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Nano Process Optimization  323 Integration with Other Optimization Techniques: Machine learning can be integrated with other optimization techniques, such as genetic algorithms and simulated annealing, to improve the efficiency and effectiveness of nano process optimization. Future Directions The integration of machine learning into nano process optimization represents a promising avenue for advancing the design and development of nanomaterials. By harnessing the capabilities of machine learning algorithms, researchers can efficiently analyze vast datasets, recognize intricate patterns, and forecast material properties and behaviors with a high degree of accuracy. This transformative technology has the potential to revolutionize the discovery and enhancement of nanomaterials by streamlining the screening process, enabling precise predictions regarding novel materials, and tailoring materials to meet specific application requirements. Despite its immense potential, the application of machine learning in nanomaterial design is not without challenges. Issues such as the requirement for high-quality data, the development of robust and interpretable models, and ethical considerations must be carefully addressed to fully leverage the benefits of machine learning in this domain. Looking ahead, future research endeavors in this field may focus on refining existing models and methodologies, exploring novel nanomaterials and their applications, and fostering interdisciplinary collaborations to propel the field of machine learning for nanomaterial design towards new frontiers of innovation and discovery.

Conclusion Machine learning (ML) plays an important role in improving nanoscale processes by solving problems such as optimization, limited understanding of nanomaterial interactions, and accounting for complexity. The importance of machine learning for nano process optimization includes data collection and prioritization, selection and architecture, use of various machine learning algorithms such as support vector machines (SVM) and neural networks, standard evaluation and validation and research methods, and nanoparticle detection. Erosion prediction model. The integration of machine learning and nanotechnology is expected to revolutionize the design and fabrication of nanoscale materials and devices across a wide range of applications. Machine learning helps design nanomaterials by creating virtual prototypes to reduce testing time and costs. Libraries based on existing data or theoretical models. It can predict properties such as

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324  Edge of Intelligence stability, toxicity, conductivity, or catalytic activity, optimize synthesis parameters such as temperature or pressure, and validate the model using experimental data. Challenges include the need for good data, robust models that capture the complexity of nanomaterials, translation issues in some machine learning models, and ethical considerations. Interdisciplinary collaboration can provide opportunities for future research and innovation by advancing machine learning in nanomaterial design. Additionally, combining design experiments (DoE) with machine learning analysis can improve the quality of data and materials, in the context of polymer nanocomposites, performance prediction, process optimization, microstructure analysis, etc. Machine learning was used. Data collection, preparation, and careful analysis of critical variables are important steps when applying machine learning to polymer nanocomposites. Recent advances in machine learning show promise in the fields of data science and biology, highlighting the potential of machine learning to improve document production.

References 1. Abadi, M., et al., TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI), 2016. 2. Abdelkhalek, N., et al., Federated Learning in Industrial Wireless Networks: An Overview, Taxonomy, and Research Directions. IEEE Trans. Ind. Inf., 16, 3, 1802–1810, 2020. 3. Kairouz, P., et al., Advances and open problems in federated learning, arXiv preprint arXiv:1912.04977, 2019. 4. Wang, B., et al., Towards Efficient IoT Data Analytics: A Comprehensive Review. IEEE Trans. Ind. Inf., 17, 6, 3845–3854, 2021. 5. Jones, C., et al., Machine Learning for Wireless Communication Systems: A Tutorial. IEEE Commun. Surv. Tutor., 21, 4, 3039–3071, 2019. 6. Chen, D., et al., Deep Learning-Based Resource Management in 6G and Beyond: A Comprehensive Survey. IEEE Netw., 36, 1, 136–142, 2022. 7. Li, X., et al., Federated Learning for Privacy-Preserving Healthcare Applications: A Survey. IEEE Trans. Ind. Inf., 17, 6, 4139–4146, 2021. 8. Zhang, Y., et al., Advancing Smart Cities Through Federated Learning: Opportunities and Challenges. IEEE Trans. Sustain. Comput., 8, 1, 114–127, 2023. 9. Chen, M., et al., Federated Transfer Learning for Cross-Domain Recommendation. IEEE Trans. Neural Networks Learn. Syst., 32, 3, 921–934, 2021.

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Nano Process Optimization  325 10. Yang, Q., et al., Federated learning. Synth. Lect. Artif. Intell. Mach. Learn., 13, 3, 1–207, 2019. 11. Smith, A., et al., Federated Learning: Strategies, Challenges, and Future Directions. ACM Comput. Surv., 54, 6, 1–738, 2021, Article 118. 12. Chang, C.C. and Lin, C.J., LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2, 3, 27, 2011, https://doi. org/10.1145/1961189.1961199. 13. Huang, Y., et al., Secure Federated Learning with an Untrusted Central Server, arXiv preprint arXiv:2102.05580, 2021. 14. Sheller, M.J., et al., Federated Learning in Medicine: Facilitating MultiInstitutional Collaborations Without Sharing Patient Data. Sci. Rep., 10, 1, 12598, 2020. 15. Rajkomar, A., et al., Scalable and Accurate Deep Learning with Electronic Health Records. npj Digital Med., 1, 1, 1–10, 2018. 16. Goodfellow, I., Bengio, Y., Courville, A., Deep learning, MIT Press, 2016. 17. Lee, H., Lee, H.J., Choe, K.W., Lee, S.H., Neural Evidence for Boundary Updating as the Source of the Repulsive Bias in Classification. J. Neurosci., 43, 39, 4664–4683, 2023. 18. Dinh, H.T., et al., Federated Learning: Challenges, Methods, and Future Directions. IEEE Trans. Emerging Top. Comput. Intell., 4, 3, 288–298, 2019. 19. Nayak, M. and Narain, B., Predicting Dynamic Product Price by Online Analysis: Modified K-Means Cluster, in: Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol. 1120, Springer, Singapore, 2020, https://doi.org/10.1007/978-981-15-2449-3_1. 20. Nayak, M., Dass, A.K., Kshatri, S.S., An AI-Based Efficient Model for the Classification of Traffic Signals Using Convolutional Neural Network, in: Building Secure Business Models Through Blockchain Technology: Tactics, Methods, Limitations, and Performance, pp. 20–35, IGI Global, 2023, https:// doi.org/10.4018/978-1-6684-7808-0.ch002. 21. Nayak, M. and Barman, A., A Real-Time Cloud-Based Healthcare Monitoring System, in: Computational Intelligence and Applications for Pandemics and Healthcare, pp. 229–247, IGI Global, 2022, https://doi.org/10.4018/978-17998-9831-3.ch011. 22. Nayak, M. and Narain, B., Big Data Mining Algorithms for Predicting Dynamic Product Price by Online Analysis, in: Computational Intelligence in Data Mining: Proceedings of the International Conference on ICCIDM 2018, pp. 701–708, Springer Singapore, 2020.

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12 Quantum Computing for Cryptography: An Extensive Survey Soma Debnath1* and Avishake Adhikary2 1

St. Xavier’s University, Kolkata, India 2 Digitys LLC, Kolkata, India

Abstract

With the exponential growth of computer systems, technology has drastically increased in the past few years. We have seen enormous growth in Information Technology, or the IT industries, which also helped other industries to grow faster. This exponential growth in the IT industries has resulted in the innovation of modern technologies. One of these new innovations is known as Quantum Computing. Computers have existed for decades, and researchers have continued to use them in our daily lives. Previously computers used 0s and 1s to calculate something or even generate behaviors through advanced computing strategies like Machine Learning (ML) or Deep Learning (DL) through Neural Networks. The use of 0 and 1 computation is known as the binary computation where the computer understood nothing but these two numbers. The advancement of the computer industry has been mesmerizing and the only thing limiting the industry was the computation time in terms of complex problems which was only possible for the binary computing computers to calculate the complex problems in terms of exponential time of ones and zeros in multiple dimensions. In today’s world, users are facing massive security breaches to exchange information through the internet, and the requirement of a more secure way to encrypt our daily data and information for exchange and storing. Thus, holding the hand of quantum computing technology, the beginning of quantum cryptography. Quantum Cryptography uses physics rather than mathematics to encrypt or decrypt information. The purpose of this literature survey is to investigate the current state of the art in quantum cryptography and the future market trends in security domain. This research aims to contribute to the survey of quantum cryptography and how quantum cryptography has progressed over the years. *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (327–350) © 2025 Scrivener Publishing LLC

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327

328  Edge of Intelligence Keywords:  Quantum computing, quantum theory, quantum machine learning, quantum cryptography, security

12.1 Introduction With the sudden peak in technological advancements, it has seen many growths in industry. This rise in industry has raised the interest in large organizations to research more such technologies from which they might profit from. Quantum computing is one of them and has been one of the hottest topics in the industry for the past few years [1]. Instead of using the traditional binary system in this new technology, quantum computing uses qubits. Qubits are nothing but the newer version of visualizing the binary system. If we think of a sphere that has two poles representing 0 and 1, we can therefore apply the properties of differentiation and get infinite number of points between these two poles. For example, we know very well in mathematics that there are infinite number of possibilities of a number to be in between 0 and 1 like 0.1, 0.2, 0.3, 0.11, 0.169, 0.6699 and so on [2], and the list remains to be infinite. Thus, for a specific event these qubits provide such a wider range to study them. Quantum Computing uses the prime principles of behavior of energy and materials on subatomic and atomic levels which are based on quantum theory and quantum mechanics. Quantum Computing applies these subatomic particles such as photons and electrons and uses Quantum-bits or qubits. Qubits allow us to go beyond the limitations of just combinations of binary numbers because in qubits, each bit can lie between the two poles of the bits, like in the Figure 12.1 below. This single operability makes the possibilities limitless. Quantum computing has been in this industry for quite some time now and is used in daily life. Some of the popular examples of the quantum computing industry would be the large number of casinos. Earlier these casinos had computerized games that used the traditional binary systems of 0s and 1s. These computers thus, even after encrypting these binary data, challenged many hackers to crack the binary code into predicting the outcome of these games. This hacking of predictable output tended many losses in the industry, which also led the casinos to use the quantum computing properties in these computerized games. This resulted in an improvement in performance and also made it way harder to crack the code, as it used the newer binary polar system or the qubits, which used encryption that could result in infinite possibilities.

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An Extensive Survey on Quantum Cryptography  329 0

0

1

1 Qubits

Classical Bits

Figure 12.1  Qubits and classical bits comparison.

Qubits work according to the following mathematical formula:



Qubit Q = |α|0 + |β|1

(12.1)

Quantum Technologies have three distinct branches as of now: • Quantum Computation • Quantum Information • Quantum Cryptography Quantum Cryptography uses photons or the individual light particles to transmit data over fiber optic wires which makes the cryptographic state undetectable and exceptionally reliable as it is nearly impossible to hack. The recent breaches in encrypted data have also led scientists to discover new ways to encrypt data and transfer keys for data exchange. One of these discoveries includes the newer encryption system known as Quantum Cryptography. Quantum cryptography plays a very important role in encrypting and decrypting the data of the users. Rather than using the traditional way of encrypting data using mathematical equations, quantum computing uses particles of light to transfer keys over fiber optic cables. These light particles are known as photons and represent the binary bits 0s

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330  Edge of Intelligence and 1s. These photons carry different properties like, each of these particles can exist in more than one state or even exist in more than one location at a particular point of time. These photons cannot be seen without changing or disrupting them, and these particles cannot be copied in their whole state. Till qubits are not observed they act very differently and are known to be in their ‘Spinning State.’ So, rather than determining whether in state 0 or 1 like the traditional state of computation they are determined in ‘up’, ‘down’ or both states. The functionality of these qubits is based on the superposition of quantum physics. When these qubits are not having a physical connection between them, they can still use the quantum entanglement to influence each other. The quantum particles behave very differently while we observe them and while we do not in quantum physics [3]. Let us take an exceedingly popular example ‘The Double Slit Experiment’. This experiment states that when a quantum particle is under observation, it acts like a normal particle but when the same particle is not under observation, it acts like a wave. The experiment states that the reason for which a qubit acts like a particle when observed and acts like a wave when not is their initial state which remains to be neither a particle nor a wave. These qubits in their original state remain to stay present in their probability which is we call ‘The Wave Function.’ This hype in quantum computing has led researchers to expand their area in the machine learning fields as well. A lot of work has been put into systems like ANNs (Artificial Neural Networks) based on quantum computing to design and implement them as well. [4] With each addition of qubits the quantum computation becomes twice as faster than the traditional computers because they perform generous permutations on the N-bits of binary numbers. The healthcare industry has also received aid from the quantum computing industry [5, 6]. We have seen the deployment of Rubidium-based quantum sensors that detect atrial fibrillation signals that increases the chances of disease detection of unpredictable high heartbeat and also increase the atrial fibrillation clinical results [7]. In this section, Table 12.1 depicts the comparison between traditional and quantum computation. From Table 12.1, it can be observed that in traditional computation computers use 0 and 1 for bit manipulation and in quantum computing qubits are used for bit computation. Quantum computers have high processing units, processing speed and larger capacity. In this section, we discuss the quantum cryptography model, quantum superposition and quantum machine learning model in brief.

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An Extensive Survey on Quantum Cryptography  331 Table 12.1  Comparison between traditional and quantum computation [12]. No.

Parameters

Traditional

Quantum

1

Storage Mode

0 or 1

Qubit (Anything between 0 and 1)

2

Processing Logic

Uses bit and logic gates

Uses conditions of atoms

3

Information Transportation

Information can be copied without spreading

Information cannot be copied without spreading

4

Security

Not secure from attacks from hackers

High security alarm provides better security

5

Acceptance of Noise

A channel with noise can be used for communication

Requires a channel without noise (noiseless channel) for successful communication

6

Information Performance

Unidirectional

Multidirectional

7

Computation Capacity

Only one at a time

Multiple items at atime

8

Number of Processors

One or More

Approximately 10150

12.1.1 Why Quantum Cryptography? We understand that we already have traditional cryptography and computing techniques for getting our work done. Then the question arises why we need ‘quantum cryptography’ in the first place. To answer that, we must understand real world problems, and not just the usual problems, but some of the very weird and crucial problems like:

Ø Database Searching for Big Query and Big Data Ø Security (Penetration Testing) Ø Advanced simulations (Quantum Physics) Ø Medical Domain

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332  Edge of Intelligence We understand that all these problems in real life are very crucial problems. For database searching for big query and big data, traditional approach to the problem may have to look at every single database entry to get to the problem (‘n’ number of entries). The moment we switch to quantum approach, the problem switches to (‘√n’ number of entries), which is way faster when big data is considered. Similarly, when we consider the loopholes of the security of the modern passwords, even the most secure passwords nowadays, till date have their passwords encrypted using a public and private key. The problem with that is these public keys can be used to refactor the private key as well which is crucial to decrypt the data along with the public key. The only thing that makes it secure till date is that, using traditional computing, refactoring these keys might take thousands of years with the approach of trial and error. Again, when we switch to the quantum approach, any quantum computer with an exponential speed up can do it in a very small amount of time. Similarly, when we consider advanced simulations, we understand that they are very heavy weight processes that require very heavy resources for getting it done, and also for bigger simulations for example, simulation of molecules, the traditional approach simply lacks accuracy. So why not simulate quantum physics, with quantum physics itself. As soon as we understand these problems and their counter approaches, we start to see a pattern where quantum cryptography is exactly required. We understand that quantum cryptography can be used to specific problems which require a very large amount of time and cannot be simply computed using traditional approach.

12.1.2 Quantum Cryptography Model In this section, we have tried to explain the quantum cryptography model with the traditional Alice and Bob model used for sharing keys in cryptography. Let us say that Alice initiates the sending of the encrypted message to Bob by sending a key as a stream of photon particles. Here, before sending the key to Bob the model uses a polarizer. Polarizer can be vertical (or 1 bit), horizontal (or 0 bit), diagonal to the left (or 0 bit) and diagonal to the right (or 1 bit). Here, because Bob does not have a clue for which polarizer has been used, he uses two beam splitters to decipher the key. Then, both Alice and Bob compare the splitter used to discard the photons with wrong polarization, leading to the remaining sequence of photons which is then considered as the key for the encryption. This makes the eavesdropping way too hard to decrypt the photons and the eavesdropper might also have the disadvantage of changing the sequence of the keys as discussed earlier even if he has the same beam splitters as Bob [8]. Figure 12.2 shows

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An Extensive Survey on Quantum Cryptography  333

Alice

Bob

Photon Particles

Figure 12.2  Quantum cryptography using Alice-Bob model.

the quantum cryptography using Alice-Bob model, where photon particles are used to encrypt the information.

12.1.3 Quantum Superposition To understand quantum phenomena, we have a framework known as the quantum superposition. The term ‘superposition’ can be used in both classical and quantum contexts. Classical superposition states that if we add two physical quantities, the third product that is created as a result is entirely different from both the previous quantities. Quantum superposition includes quantum objects such as electrons, photons, nuclei, and elementary particles. Here, we observe the wave-­ particle duality and other non-classical effects. If we take an example of a normal ball, we will normally observe the ball with a kinetic energy lying between 0 to infinity Joules, either at a resting state or at a thrown state.

50% transmitted

100% incident

50% reflected

Photon souce

Detector 1

Mirror 1

Beam Splitter 1

Photon source

Beam Splitter 2

Mirror 2

Detector 2

Figure 12.3  Beam splitter and coin state analogy using photon beam and beam splitters.

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334  Edge of Intelligence But in quantum mechanics, the states are rather quantized, meaning that the states of these scenarios can exist in certain values. Taking another example of a coin getting flipped to predict whether it will be a head or a tail, we observe three different scenarios. In the first scenario, we say that the coin is in a definite state which has either a head or a tail state. When the coin is tossed in the air, we say that the coin is in its superposition state where the coin can be both in head and tail state simultaneously, in a quantum system. We observe these states with a definite state value and with attached with a given probability. We use beam splitters to control the superposition in quantum physics (as shown in Figure 12.3). It is basically a mirror with reflective and transmission qualities. Here, when a light ray is passed to this mirror, the light beam splits into two different parts, where half of the beam particles are reflected, and half of the beam particles are transmitted.

12.1.4 Quantum Key Distribution (QKD) In traditional technology computing the RSA algorithm is one of the most popular algorithms out there. RSA algorithm focuses on encrypting the message with large integer value keys. This is done to protect the message from third party eavesdroppers. When the eavesdropper tries to hack the system by factorizing the key, it would take a huge deal of computation to break the public key and would take more than a thousand years to break the public key. But the private key can give hints to the eavesdropper about the factorization by which the hacker can crack the code. The hacker could get into the system by bypassing the insecure internet firewall and looking for the private key in the system of the receiver and crack the public key. Meaning, almost all the internet relies on this algorithm and computer and the theory that the traditional computer would not be able to crack the system quickly. Seeing these vulnerabilities in the RSA algorithm led the researchers to reinforce the system by implementing newer ways to get better security. One of these researchers was Peter Shor, who in 1995, based on interference and superposition proposed a newer quantum computing algorithm that exponentially speeded up the factorization process. Using Shor’s algorithm, a stable quantum computer could factorize a four-thousand-digit number in just a day which would take a traditional computer to factorize, more than a lifetime of the entire universe to do. Since the algorithm used is out of our scope, we focus on the same quantum computer used to distribute the key via a channel which is secure. Together, Quantum Key Distribution (QKD) and One Time Pad (OTP) solution might be a

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An Extensive Survey on Quantum Cryptography  335 formidable solution. Using Stern-Gerlach apparatus and electrons, the BB84 [11] Quantum Key Distribution demonstrates how a shared or public key could be created.

12.1.4.1 Pre-Processing Before Sending Message Here, the relationship between the spin and bit values of Alice (the sender) and Bob (the receiver) are demonstrated in the Table 12.2 below. Table 12.2  Relationship between spin and bit values used in quantum cryptography. Spin

Bit value

↑

0

←

0

↓

1

→

1

12.1.4.2 Quantum Algorithm 1. Alice randomly selects vertical or horizontal spin (SGA). 2. Alice sends an electron to Bob through SGA, measures the spin and records the bit value corresponding to the spin. 3. Bob randomly chooses the vertical or horizontal spin. 4. Bob measures spin and records the bit value corresponding to the spin. 5. Until the desired level of security is achieved repeat steps 1-4. In this paper, we will investigate the challenging problem of information security or cryptography using quantum computing and review the techniques to solve these problems. The rest of this paper is incorporated as follows. Section 12.2 consisting of the brief discussion about the literature survey in the domain of quantum key distribution and quantum security, Section 12.3 presenting the statistical analysis of research trends, share of organizations with quantum security and quantum security market revenue. Section 12.4 represents the comparative analysis of and finally section 12.5

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336  Edge of Intelligence

Introduction Conclusion and Future Scope

Related Works

Comparative Analysis Statistical Analysis

Figure 12.4  Flow of the paper.

concludes the total reviewed works and provides some future scopes. Figure 12.4 shows the entire flow of the paper.

12.2 Related Works Quantum computing has already revolutionized the world with many related implementations that have already been helping people in their daily lives. One of the most important works that has been already done is the application of quantum computing in healthcare systems [9]. Here the Quantum Key Distribution (QKD) has been discussed along with the D-Level Systems. Here they have also discussed the higher dimensional quantum communication paradigm, where they use the Hilbert space availability for large information storage and reduced noise applications. Here they also discuss the degree of freedom for the photon particles in quantum computing using bulk optics and photonics along with different channels of quantum states propagation. Heart failure detection [10] has also been one of the hot topics using quantum computing where researchers have tried to use the much faster quantum computing than the traditional machine learning mechanisms in traditional computing. Here the researchers have used the newer versions of the quantum random forest (QRF), quantum K nearest neighbor (QKNN), quantum decision tree (QDT) and quantum Gaussian Naive Bayes (QGNB) algorithms for heart failure detection and have also

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An Extensive Survey on Quantum Cryptography  337 achieved a new time difference of 150 microseconds where these newer algorithms outperform the old traditional computing and also provide a precision score of 0.89, recall score of 0.93 in the scale of 0 to 1. These researchers achieved this result by simply using a dataset of 14 attributes and normalized the data using simple min-max, PCA and standard scalar techniques and have been optimized using the popular pipelining technique [13, 14]. Quantum computing has also been the most heated topic in the casino and gambling industry. The newer style of computing provides infinite number of possibilities for even a simple three number digit game with priority scheduling, which makes the companies to profit by providing a computerized gaming machine based on the average human greed and defrauding them into playing more games and making them eventually lose their chances each time they try to roll a new round. The simple rule of differentiation makes quantum computing in the gambling industry publicize the profits of the casinos by a lot. Quantum computing also expands its fields on the robotics industry where the robots are optimized using the rules of quantum mechanics for faster processing of images to access the surrounding information of the environment. The use of quantum technologies has also been immensely helpful in recent years with the era of COVID. The drug development industry has also been boosted with the use of quantum properties of atoms for a very large-scale simulation. Along with the use of the recent 5G technology for cellular communications, the application of 6G or B5G is also being tested by researchers using quantum mechanics. And lastly the machine learning industry that has been used for penetrating passwords using Support Vector Machines (SVMs) that use the brute force methodologies to for decryption of the password, that takes a lot of power and time traditionally, has now been replaced by using the Grover’s search algorithm that speeds up the process substantially. Researchers have also found a way to implement a hybrid solid state quantum system which uses N Nitrogen-vacancy (N-V) centers and N separate Transmission Line Resonators (TLRs) interconnected with Currentbiased Josephson junction (CBJJ). [45] Researchers have also found that in the absence of noise quantum key distribution protocols can yield one secret bit per entanglement bit, which implies that the post-quantum and usual QKD key rates are comparable. [27] Researchers have also studied Quantum Trust Model based on Node Trust Evaluation. [44] Researchers also provided proof of unconditional security of a practical QKD protocol. [35] Researchers

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338  Edge of Intelligence have developed a coherent-state network protocol which are able to achieve high key rates at metropolitan distances. [47] Researchers have also found to way to use continuous- variable quantum cryptography using two-way quantum communication with a chance of non- trivial super-additive enhancement of security. [25] They have also demonstrated the time-shift attack against practical QKD systems and semi-device-independent security of one-way quantum key distribution. [29, 30] Researchers have also demonstrated that all practical devices require inspection of security in QKD practical realizations to avoid side channel attacks and QKD with continuous-variable for long distances with a Gaussian Modulation. [26, 31] Researchers have written review papers on device independent one-sided QKD as well. [28] Researchers have also done Measurement-device-independent QKD with NV venters in diamond. [33, 34, 40, 41, 46] They have also done Measurement-device-independent QKD with sources statistical fluctuation and state errors. [39] All these experiments with QKD had also led to the practical measurement- device-independent QKD without vacuum sources and a theoretical implementation of improved Measurement-device-independent QKD with uncharacterized qubits. [32, 37, 38] Continuous-variable quantum cryptography has been a very hyped-up topic among researchers as well which also led them to use that in coherent states as well. [23] A lot of research work has been done with quantum cryptography including topics where quantum cryptography has been executed without switching [24], and some new sets of protocols have been developed with the help of Extended GHZ-W State and OTP to achieve quantum communication [42, 43], Analysis has been done on MDI (Measurement-Device-Independent) QKD in Collective-Rotation Noise Environment [41] and Imperfect Devices [36]. QKD has also been achieved by researchers by controlling excess noise in continuous-variable QKD and One step QKD has been done based on EPR Entanglement. [42, 48] Newer fast and simple oneway QKD have been proposed [21] and extended coin tossing studies have been done using quantum cryptography and problems of quantum communication technology, optimal eavesdropping in Quantum Cryptography with 6 states and Quantum Cryptography protocols against photon number splitting attacks for weak laser pulses and their implementations. [15, 17, 18, 22] Newer models have been proposed like Quantum Cryptography using any two non-orthogonal states [16], QKD without monitoring signal disturbance [20] and differential phase shift QKD [19, 49–52].

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An Extensive Survey on Quantum Cryptography  339

12.3 Statistical Analysis Quantum Security has grown exponentially in the past years and managed to create hype in companies in different countries to work with quantum computers as shown in Figures 12.5 and 12.6.

50% 45%

100% 43%

90%

42%

40%

80%

35%

70%

30%

26%

25%

60%

26% 23%

22%

20%

50%

21%

19%

40% 15%

15%

14%

14%

30%

10%

20%

5%

10% 0%

0%

ina lands Ch r the Ne

UK

Ge

rm

y an

ia Ind

s e nc ate Fra ed St t i Un

y Ital

n apa

J

in pa

S

So

uth

0%

rea Ko

Figure 12.5  Share of organizations with quantum security in 2022, by different countries.

2030 2027 2025 2023 2021 0

2000 2021

Quantum Internet

2022

4000 2023

6000 2024

2025

8000 2026

10000 2027

2029

12000 2030

0

0

0

14

73

198

508

949

2506

QKD and Others

150

293

527

691

844

1215

1610

2017

2506

Post Quantum Security or Cryptography (PQC)

80

200

520

1040

1508

2036

2647

3308

4764

Quantum Internet

QKD and Others

Post Quantum Security or Cryptography (PQC)

Figure 12.6  Predicted quantum security market revenue from 2021 to 2030.

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340  Edge of Intelligence 1992-2019 2986 374

719 1936

2986

321

3...

322

Powered by Bing © Australian Bureau of Statistics, GeoNames, Microsoft, Navinfo, OpenStreetMap, TomTom

Figure 12.7  Quantum Cryptography publications and patents in the past 20 years (1992–2019) [53].

The interest in quantum security continues to grow throughout the world and some of the predicted market analysis was done by researchers in Figure 12.7.

12.3.1 Research Trends in Previous Years Based on Reviewed Papers In this section, Figures 12.8–12.10 shows number of reviewed papers published in journals and conferences in last 15 years, number of reviewed papers published in journals and conferences from 2007 to 2022 in three Reviewed papers

43% 57%

Journals Conferences

Figure 12.8  Number of reviewed papers published in journals and conferences in last 15 years.

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An Extensive Survey on Quantum Cryptography  341

5 4 3 2 1 0

Journals Journals

Conferences

20 19 -2 02 2

20 15 -2 01 8

20 07 -2 01 0 20 11 -2 01 4

Conferences

Figure 12.9  Number of reviewed papers published in journals and conferences from 2007 to 2022 in three years interval.

2007-2010 10 8 6 4 2 0 2019-2023

IEEE Springer Elsevier 2011-2014

ACM Taylor and Francis Others

2015-2018

Figure 12.10  Number of reviewed papers published in journals and conferences from 2007 to 2023 by different publishers.

years interval and the number of reviewed papers published in journals and conferences from 2007 to 2023 by different publishers.

12.4 Comparative Analysis Researchers have found the comparison of different quantum computers and their vulnerabilities and efficiency in Table 12.3 below:

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342  Edge of Intelligence Table 12.3  Comparison of different quantum key distribution (QKD) schemes. Parameters

BB84

Founder

SAR04

KMB09

C.H. C.H. Bennett Bennett and G. Brassard

Scarani. V, A. Acin, Ribordy G, Gisin N.

Muhammed Eduin Eduin Mubashir Esteban Esteban Khan, Hernandez Hernandez Michael Serna Serna Murphy and Almut Beige

Year

1984

1992

2004

2009

2009

2013

Number of States

4

2

4

2

Arbitrary States

4

Principals

Heisenberg Heisenberg

Private Key and Public Key

Heisenberg

Polarization Orthogonal NonOrthogonal Arbitrary Orthogonal

Bit-Flip Phase-Flip

2 Orthogonal

DoS Attack

Vulnerable

BB92

Heisenberg Heisenberg

S09

S13

Vulnerable

Vulnerable

Vulnerable

N/A

N/A

Man-in-the- Vulnerable Middle attack

Robust

Robust

Robust

Robust

N/A

PNS Attack

Vulnerable

Vulnerable

It is better than BB84

Robust

N/A

N/A

BeamSplitter Attack

Vulnerable

Vulnerable

Robust

Robust

N/A

N/A

Security

Good for long distance

Average

Average

Average

Best for a small distance

Average

Efficiency

Low

Best

Average

Low

Good

Average

The evolution of Quantum cryptography is enlisted in the above Table 12.4. Quantum Machine Learning Model in Quantum Cryptography Figure 12.11 describes the differentiation between the quantum machine learning model and the traditional machine learning model. In quantum machine learning model a unitary and complex system develop for feature

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An Extensive Survey on Quantum Cryptography  343 Table 12.4  Evolution in quantum cryptography over the years. Year

Events

1968

Stephen Wiesner introduced the concepts of quantum conjugate and quantum money in this paper “Conjugate Coding” which was rejected by IEEE

1976

Roman Stanislaw Ingarden published Quantum Information Theory in his paper “Quantum Information Theory”. He became of the first attempters of creating a theory on the topic.

1982

Paul Benioff proposed the first recognizable (theoretical) framework of quantum computer.

1983

Stephen Wiesner published his former paper “Conjugate Coding” in SIGACT News.

1984

Charles Bennett and Gilles Brassard used conjugate coding for distributing cryptographic keys

1991

Artur Ekert proposed his secure key distribution protocol

1993

Dan Simon invented a quantum computer which was exponentially faster than a conventional computer for an oracle problem.

1996

Lov Grover at Bell Labs invented the first quantum database search algorithm

2002

Kent Adrian investigated the first position-based quantum schemes under the name of ‘Quantum Tagging’

2003

Quantum network became fully functional by DARPA

2005

University of Innsbruck, Austria created the first qubit

2006

Researchers benchmarked a quantum computer with 12 qubits.

2007

D-Wave systems claimed work on their 28-bit quantum annealing computer

2014

National Security Agency (NSA) decided to develop a quantum computation

2018

Google announced a 72-bit quantum chip named “Bristlecone”

2020

Researchers at the University of California in Berkeley find a way to carry more information via light waves

2022

Researchers at Penn Engineering create a micro-laser chip that uses “qudits” for transferring information

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344  Edge of Intelligence Traditional ML Process

Quantum ML Process

Dataset (Original)

Dataset (Original)

Dataset (Testing)

Dataset (Training)

Dataset of different field (Training)

Dataset of different field (Testing)

System Preparation

System Preparation

Unitary and complexity system development

Unitary and complexity system development

Feature Extraction

Select only extracted features

Feature Extraction

Select only extracted features

Fit Data into ML Model using QML Algorithms

Fit Data into ML Model

Test Model

Test Model

Prediction

Prediction

Figure 12.11  Quantum machine learning model and traditional model comparison.

extraction process and fit the training data into machine learning model using quantum machine learning (QML) algorithm. Figure 12.12 describes a neural network model that helps in cryptographic solutions. The encryption of the plaintext is understandable to the user as an image but which is something else entirely different in the

Image

Encoder

Tokenized Text

Figure 12.12  Neural cryptography model.

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Image

Decoder

Tokenized Text

An Extensive Survey on Quantum Cryptography  345 Table 12.5  Comparison of classical cryptography and quantum cryptography. Features

Classical cryptography

Quantum cryptography

Basis

Mathematical Computation

Quantum Mechanics

Development

Deployed and tested

Infantile and not fully tested

Requirements

Software and Portable

Devoted hardware and communication lines

Existing Infrastructure

Widely Used

Sophisticated

Digital Signature

Present

Not Present

Register Storage (n-bit) at any moment

2n n-bit stings

One n-bit string

Bit Rate

Depends on the computation power

1Mbit/s on average

Communication Range

Millions of Miles

A maximum of 10 Miles

Cost

Almost 0

Crypto Chip worth at least €100,000

Life Expectancy

Requires changes as the No change as physical laws computation requirements do not change increase

Medium

Independent

Dependent

context. To do that, model used an encoder that combines the tokenized text and image into another image with the tokenized text embedded within the image and is understandable to the human eyes in a different context but can be decoded using a decoder. This decoder takes the image with the embedded message within it and decrypts the image with the message and gets back the original tokenized text embedded within the image. We have discussed a comparative analysis of classical cryptography and quantum cryptography in Table 12.5.

12.5 Conclusion and Future Scope The exponential growth of computer systems in recent years has led to significant advancements in the field of Information Technology (IT) and has played a vital role in the growth of other industries. One of the most recent innovations in this field is Quantum Computing, which promises to revolutionize the way we solve complex problems and secure our information.

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346  Edge of Intelligence With the limitations of traditional binary computing, quantum computing has become a crucial tool for solving complex problems and achieving a higher level of security. Quantum Cryptography, which uses physics instead of mathematics, is one of the promising applications of quantum computing in the security domain. As revealed in the literature survey, quantum cryptography is a rapidly evolving field, and its future market trends suggest that it will continue to play a significant role in the security domain in the years to come. This paper targets an extensive survey in the domain of security i.e., cryptography using the emerging technique quantum computing. Quantum cryptography is a technique of encryption to secure the data transmission using quantum mechanics. As qubits are naturally much faster and more complex in nature than classical bits, so encryption of data using qubits are more complex. In this paper, we systematically surveyed the maximum possible literatures in quantum cryptography along with the quantum key distribution technique. In section 12.3, we have tried to incorporate the statistical analysis in the share of organizations with quantum security by different countries in 2022, prediction of quantum security market revenue from 2021-2030, quantum cryptography publications and patents in the past 20-years, number of reviewed papers published in journals and conferences in last 15 years, number of reviewed papers published in journals and conferences from 2007 to 2022 in three years interval and the number of reviewed papers published in journals and conferences from 2007 to 2022 by different publishers. Comparison of different quantum key distribution (QKD) schemes, evolution in quantum cryptography over the years and the comparison of classical cryptography and quantum cryptography are described in the Section 12.4 to provide an extensive literature analysis in this domain. Looking ahead, the future of quantum computing and cryptography is promising, and researchers are continuing to explore new frontiers in these fields. One potential future application of quantum cryptography is in the realm of secure communication, where quantum encryption protocols can provide an unbreakable method for exchanging sensitive information. Another area of future research is the development of quantum-resistant encryption algorithms, which will become increasingly necessary as quantum computing becomes more powerful and capable of breaking current encryption methods. Additionally, there is potential for quantum computing to enable new scientific discoveries in areas such as drug discovery, materials science, and machine learning. As such, the future of quantum computing and cryptography is full of exciting possibilities, and continued research and development in these fields will undoubtedly lead to more innovative applications and advancements.

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An Extensive Survey on Quantum Cryptography  347

References 1. Amin-Naji, M., Mahdavinataj, H., Aghagolzadeh, A., Alzheimers disease diagnosis from structural MRI using Siamese convolutional neural network, in: 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), pp. 75–79, Tehran, Iran, 2019. 2. Ramisetty, S., Varma, S., Varma, S., The amalgamative sharp wireless sensor networks routing and with enhanced machine learning. J. Comput. Theor. Nanosci., 16, 9, 3766–3769, 2019. 3. Gonsalves, A., Thabtah, F., Mohammad, R., Singh, G., Prediction of coronary heart disease using machine learning: an experimental analysis, in: Proceedings of the 2019 3rd 14 Wireless Communications and Mobile Computing International Conference on Deep Learning Technologies, pp. 51–56, Xiamen, China, 2019. 4. Bang, J., Lee, S.W., Jeong, H., Protocol for secure quantum machine learning at a distant place. Quantum Inf. Process., 14, 10, 3933–3947, 2015. 5. Tian, X., Huang, Y., Verma, S. et al., Power allocation scheme for maximizing spectral efficiency and energy efficiency tradeoff for uplink NOMA systems in B5G/6G. Phys. Commun., 43, 144–147, 2020. 6. Abdel-Basset, M., Gamal, A., Manogaran, G., Son, L., Long, V.H., A novel group decision making model based on neutrosophic sets for heart disease diagnosis. Multimed. Tools Appl., 79, 15–16, 9977–10002, 2020. 7. Chen, C. and Dong, D., Superposition-inspired reinforcement learning and quantum reinforcement learning, in: Reinforcement Learning, pp. 1–5, IntechOpen, 2008. 8. Jha, S.K. and Baurai, R., Enhancement using Quantum Computing in Medical Science. Int. J. Eng. Res. Technol. (IJERT), 2, 147–163, 2020. 9. Rasool, R.U., Ahmad, H.F., Rafique, W., Qayyum, A., Qadir, J., Quantum Computing for Healthcare: A Review, 29 December 2021. 10. Kumar, Y., Koul, A., Sisodia, P.S., Shafi, J., Verma, K., Gheisari, M., Davoodi, M.B., Heart Failure Detection Using Quantum- Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things. Wireless Commun. Mobile Comput., 1, 87–103, 17 December 2021. 11. Shor, P.W. and Preskill, J., Simple Proof of Security of the BB84 Quantum Key Distribution Protocol, in: Quantum Physics (quant-ph), ­arXiv:quant-ph/ 0003004v2, 12 May 2000. 12. Zeeshan, M., Anayat, S., Ghulam Hussain, R., Rehman, N., Processing Power of Quantum Computer. Int. J. Sci. Eng. Res., 2, 64–87, 2016. 13. Jha, M.S., Maity, S.K., Nirmal, M.M., Krishna, J., A survey on quantum cryptography and quantum key distribution protocols. Int. J. Adv. Res. Ideas Innov. Technol., 5, 144–147, 2019. 14. https://www.statista.com/statistics/1319076/quantum-technology-adoption-country/, last visited on 20/01/2023.

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348  Edge of Intelligence 15. Bennett, C.H. and Brassard, G., Quantum Cryptography: Public key distribution and coin tossing. Proceedings of the IEEE International Conference on Computers, Systems, and Signal Processing, New York, IEEE, pp. 175–179, 1984. 16. Bennett, C.H., Quantum cryptography using any two nonorthogonal states. Phys. Rev. Lett., 68, 21, 3121–3124, 1992. 17. Brub, D., Optimal eavesdropping in quantum cryptography with six states. Phys. Rev. Lett., 81, 14, 3018–3021, 1998. 18. Scarani, V., Acin, A., Ribordy, G. et al., Quantum cryptography protocols robust against photon number splitting attacks for weak laser pulse implementations. Phys. Rev. Lett., 92, 5, 4, 2004, Article ID 057901. 19. Inoue, K., Waks, E., Yamamoto, Y., Differential phase shift quantum key distribution. Phys. Rev. Lett., 89, 3, 3, 2002, Article ID 037902, 20. 20. Sasaki, T., Yamamoto, Y., Koashi, M., Practical quantum key distribution protocol without monitoring signal disturbance. Nature, 509, 7501, 475–478, 2014. 21. Stucki, D., Brunner, N., Gisin, N. et al., Fast and simple one-way quantum key distribution. Appl. Phys. Lett., 87, 19, 3, 2005, Article ID 194108. 22. Xu, B.J., Liu, W.L., Mao, J.Q. et al., Research on development status and existing problems of quantum communication technology. Commun. Technol., 47, 5, 463–468, 2014. 23. Grosshans, F. and Grangier, P., Continuous variable quantum cryptography using coherent states. Phys. Rev. Lett., 88, 5, 4, 2002, Article ID 057902. 24. Weedbrook, C., Lance, A.M., Bowen, W.P. et al., Quantum cryptography without switching. Phys. Rev. Lett., 93, 17, 4, 2004, Article ID 170504. 25. Pirandola, S., Mancini, S., Lloyd, S. et al., Continuous-variable quantum cryptography using two-way quantum communication. Nat. Phys., 4, 9, 726– 730, 2008. 26. Jouguet, P., Kunz-Jacques, S., Leverrier, A., Long-distance continuous-­ variable quantum key distribution with a Gaussian modulation. Phys. Rev. A, 84, 6, 7, 2011, Article ID 062317. 27. Acin, A., Massar, S., Pironio, S., Efficient quantum key distribution secure against no- signaling eavesdroppers. New J. Phys., 8, 8, 11, 2006, Article ID 126. 28. Branciard, C., Cavalcanti, E.G., Walborn, S.P. et al., One-sided device-­ independent quantum key distribution: Security, feasibility, and the connection with steering. Phys. Rev. A, 85, 1, 5, 2012, Article ID 010301. 29. Pawlowski, M. and Brunner, N., Semi-device-independent security of oneway quantum key distribution. Phys. Rev. A, 84, 1, 4, 2011, Article ID 010302. 30. Zhao, Y., Fung, C.H.F., Qi, B. et al., Quantum hacking: Experimental demonstration of time-shift attack against practical quantum-key-distribution systems. Phys. Rev. A, 78, 4, 5, 2008, Article ID 042333.

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An Extensive Survey on Quantum Cryptography  349 31. Li, H.W., Wang, S., Huang, J.Z. et al., Attacking a practical quantumkey-­distribution system with wavelength-dependent beam-splitter and multiwavelength sources. Phys. Rev. A, 84, 6, 5, 2011, Article ID 062308. 32. Lo, H.K., Curty, M., Qi, B., Measurement-device independent quantum key distribution. Phys. Rev. Lett., 108, 13, 5, 2012, Article ID 130503. 33. Liu, Y., Chen, T.Y., Wang, L.J. et al., Experimental measurement-device -independent quantum key distribution. Phys. Rev. Lett., 111, 13, 5, 2013, Article ID 130502. 34. Tang, Z., Liao, Z., Xu, F. et al., Experimental demonstration of polarization encoding measurement-device-independent quantum key distribution. Phys. Rev. Lett., 112, 19, 5, 2014, Article ID 190503. 35. Inamori, H., Lutkenhaus, N., Mayers, D., Unconditional security of practical quantum key distribution. Eur. Phys. J. D, 41, 3, 599–627, 2007. 36. Gottesman, D., Lo, H.K., Lutkenhaus, N. et al., Security of quantum key distribution with imperfect devices. Quantum Inf. Comput., 4, 5, 325–360, 2004. 37. Hwang, W.Y., Su, H.Y., Bae, J., Improved measurement device-independent quantum key distribution with uncharacterized qubits. Phys. Rev. A, 95, 6, 4, 2017, Article ID 062313. 38. Hu, X.L., Zhou, Y.H., Yu, Z.W. et al., Practical measurement device-­ independent quantum key distribution without vacuum sources. Phys. Rev. A, 95, 3, 6, 2017, Article ID 032331. 39. Jiang, C., Yu, Z.W., Wang, X.B., Measurement-device independent quantum key distribution with source state errors and statistical fluctuation. Phys. Rev. A, 95, 3, 5, 2017, Article ID 032325. 40. Lo Piparo, N., Razavi, M., Munro, W.J., Measurement device-independent quantum key distribution with nitrogen vacancy centers in diamond. Phys. Rev. A, 95, 2, 12, 2017, Article ID 022338. 41. Li, N., Zhang, Y., Wen, S. et al., Security analysis of measurement-device-­ independent quantum key distribution in collective-rotation noisy environment. Int. J. Theor. Phys., 1, 12, 1–12, 2017. 42. Li, J., Li, N., Li, L.L. et al., One step quantum key distribution based on EPR entanglement. Sci. Rep., 6, 9, 2016, Article ID 28767. 43. Li, N., Li, J., Li, L.L. et al., Deterministic secure quantum communication and authentication protocol based on extended GHZ-W state and quantum one-time pad. Int. J. Theor. Phys., 55, 8, 3579–3587, 2016. 44. Zhang, S.B., Xie, Z.H., Yin, Y.F. et al., Study on quantum trust model based on node trust evaluation. Chin. J. Electron., 26, 3, 608–613, 2017. 45. Zhao, Y.J., Chen, X.W., Shi, Z.G. et al., Implementation of one-way quantum computing with a hybrid solid-state quantum system. Chin. J. Electron., 26, 1, 27–34, 2017. 46. Li, Z., Zhang, Y.C., Xu, F. et al., Continuous-variable measurement-deviceindependent quantum key distribution. Phys. Rev. A, 89, 5, 8, 2014, Article ID 052301.

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350  Edge of Intelligence 47. Pirandola, S., Ottaviani, C., Spedalieri, G. et al., High-rate measurementdevice- independent quantum cryptography. Nat. Photon, 9, 6, 397–402, 2015. 48. Huang, D., Huang, P., Lin, D. et al., Long-distance continuous variable quantum key distribution by controlling excess noise. Sci. Rep., 6, 9, 2016, Article ID 19201. 49. https://towardsdatascience.com/neural-cryptography-7733f18184f3, last visited on 23/01/2023. 50. https://www.statista.com/statistics/1332857/quantum-security-market-­ revenue/, last visited on 26/01/2023. 51. Goyal, A., Agarwal, S., Jain, A., Quantum Cryptography & its Comparison with Classical Cryptography: A Review Paper, in: 5th IEEE International Conference on Advanced Computing & Communication Technologies, 2011. 52. Lohachab, A., Lohachab, A., Jangra, A., A comprehensive survey of prominent cryptographic aspects for securing communication in post-quantum IoT networks. Internet Things, 9, 100174, 2020 Mar 1. 53. Gupta, B.M., Dhawan, S., Mamdapur, G.M., Quantum Cryptography Research: A Scientometric Assessment of Global Publications during 19922019. Sci. Technol. Libraries, 40, 1–19, 2021, 10.1080/0194262X.2021.1892563.

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13 Role of Blockchain Technology in e-HRM in the Era of Artificial Intelligence: Focus on the Indian Market Archana Singh1*, Girish Lakhera1, Megha Ojha1 and Amar Kumar Mishra2 Department of Management Studies, Graphic Era Deemed to be University, Dehradun, India 2 School of Business, ADAMAS University, Kolkata, India

1

Abstract

The Blockchains, sometimes referred to as “distributed ledger technology”, are possibly the new discovery generating the most hopes for HRM in the industry 4.0 business environment. A blockchain, in its simplest form, is a particular kind of database, with properties which makes its perfect application for cryptocurrencies like Bitcoin. The purpose of this study is to look into: What distinguishes blockchains from other technologies? Will EHRM departments in particular adopt them in a broad sense? We intend to investigate published sources and literature that highlight the application of blockchain within HRM in order to better comprehend this trend. Using a methodological framework for comprehensive literature reviews, this paper identifies how using blockchain technology may provide businesses access to a variety of talent pools, make it simple to verify information and payments, and eliminate the need for a third party employment agency. This study is a fresh effort to comprehend the range of blockchain technology’s potential applications in various Indian businesses’ HRM. Keywords:  EHRM, blockchain, industry 4.0

13.1 Introduction EHRM utilizes digital technologies to oversee HR functions such as recruitment, payroll, training, and performance evaluation. Artificial Intelligence *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (351–368) © 2025 Scrivener Publishing LLC

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351

352  Edge of Intelligence (AI) encompasses machine learning, natural language ­processing (NLP), and robotics, facilitating automation and data-driven decision-­making. Blockchain technology offers decentralized, tamper-proof ledgers to ensure secure data transactions. Blockchains sometimes referred to as “distributed ledger technology”, are possibly discovery generating the most hopes for HRM in the era of robo advisors in Artificial intelligence. A blockchain, in its simplest form, is a particular kind of database, with properties which make it the perfect application for cryptocurrencies like Bitcoin. Blockchain technology can optimize talent acquisition processes by securely verifying and sharing candidate credentials across IT firms. Smart contracts offer automation for contract management and ­project-based payments [1]. Blockchain ensures transparent and auditable payroll systems, reducing discrepancies in financial transactions, and enables secure storage of employee certifications and compliance records [2]. In healthcare, blockchain enhances patient data management and ensures privacy compliance. Within HRM, it can validate medical certifications and automate employee benefits administration. Blockchain-enabled supply chain management can track employee training certifications and monitor safety compliance, with smart contracts automating supply chain HR transactions. The purpose of this study is to look into: What distinguishes blockchains from other technologies? Will e-HRM departments in particular adopt block chain and robo advisors in a broad sense? We intend to investigate published sources and literature that highlight the application of blockchain within HRM to better comprehend this trend. Using a methodological framework for comprehensive literature reviews, this paper identifies how using blockchain technology may provide businesses access to a variety of talent pools with the help of robo advisors, make it simple to verify information and payments and eliminate the need for a third-party employment agency. This study is a fresh effort to comprehend the range of blockchain technology’s potential applications in Indian businesses’ e-HRM. In addition to researchers and practitioners, corporate groups are becoming interested in blockchain technology, which has emerged as a game-changing innovation [3]. Blockchain technology’s ability to completely rethink current business processes will make it applicable across a range of sectors and domains [4]. The financial industry excels at creating and implementing blockchain applications, but Businesses in the pharmaceutical, retailing, logistics and transportation industries are all aggressively adopting blockchain technologies. Several businesses have begun experimenting with using the blockchain in their daily operations. A significant amount of money has been invested in blockchain technology by companies including Amazon, Wal-Mart, Facebook, Google, and

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Blockchain Technology in e-HRM  353 IBM. Almost all organizational activities, including supply chain management, manufacturing, and production, may be solved using blockchain [3]. Although practically all activities of human resource management (HRM) are now digital [5]. Less focus has been placed on the idea that blockchain technology may improve HRM in firms. According to CareerBuilder, 58% of human resource (HR) professionals have found fraudulent qualifications on a candidate’s résumé. People fabricate their qualifications, duties, employment dates, job titles, academic degrees, employers, and honors/ awards [6]. Blockchain technology assists in the real-time verification of all facts, safeguarding the long-term interests of the firm. Businesses’ HRM practices will change as a result of blockchain. Blockchain technology will enable workers to share confidential data with their employers. Employers can feel confident knowing that talents, successes, references, and qualifications can all be digitally verified. Integration of Blockchain and AI in e-HRM: Indian Market Perspective: 1. AI-powered analytics forecast workforce requirements, with blockchain ensuring safe transmission of candidate data across industries such as IT and manufacturing. 2. AI algorithms assess employee performance data logged on blockchain, facilitating customized training and developmental initiatives. 3. The integration of blockchain and AI automates compliance audits, particularly vital in fields like finance and healthcare [58]. Several firms are now operating that provide HR solutions using blockchain, including payroll, employee operations and rewards, application data openness, and freelancer ecosystem. Organizations have yet to use blockchain as an HRM integration platform, nonetheless. If blockchain is embraced, the organization’s decision-making processes would alter and be overhauled, affecting all personnel, whether they belong to the HR or non-HR profiles. Therefore, in the firms where blockchain technology had not yet been adopted, this study attempted to address the perceptions of both HRM and non-HRM concerning blockchain in HRM. It is reasonable to claim that this survey is the first to examine how Indian employees feel about the use of blockchain in HRM from both an HR and non-HR standpoint. The results of the study will help organizational decision-makers determine the breadth of the implementation of this technology in HRM and the degree to which employees are prepared to embrace the changes

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354  Edge of Intelligence that come with it. This study will be useful to organizational stakeholders and HR professionals into sector-specific applications of blockchain in e-HRM within industries like IT, finance, healthcare, and manufacturing, aiming to uncover nuanced insights into the adoption and challenges faced by different sectors.

13.2 Literature Review Payroll and recruiting are no longer the only strategic functions of HR that are now of increasing importance [7] but also from other departments who end up receiving the modification [8]. Each fresh applicant’s resume is screened as the initial step in the recruitment procedure. At first, it may be difficult to identify phoney resumes. Businesses frequently suffer as a consequence of such employees, and resume fraudulent activity is on the rise. Blockchain could be the solution for verifying an employee’s qualifications, including their training and employment history [9]. However, a recent investigation revealed a contradictory reality. New resume styles, such as social media resumes (LinkedIn resumes) and blockchain resumes, are typically less effective than traditional resume formats [10]. Blockchain in human resources management will upend the sector and alter how HRM is seen. The initial wave will enhance the processes for verifying candidate credentials. The second wave will improve talent markets, boost the gig economy, and foster more trust. Autonomous organizations’ and seamless work sourcing would result from the third wave [11]. Candidates will be able to control privacy rights and monetize their expertise by requesting payment from companies for sharing their credentials thanks to the usage of blockchain in HRM. According to recent studies, social influence can have an impact on someone’s inclination to embrace blockchain technology [12]. However, the backing of the organization’s senior management and its technological preparedness are key factors in the deployment of blockchain in HRM [13]. With such two factors, businesses will adopt blockchain technology and promote related research and development. Blockchain is now more concerned with creating useful business solutions. Trust, cost savings, automation, processing speed, improved processes, and disintermediation are some potential blockchain advantages [1]. The term “blockchain” has gained traction among practitioners, academics, and businesses alike [14]. Blockchain is a tamperresistant shared, decentralized, distributed ledger that simplifies the process of assessing the efficacy and record transactions in a company’s servers and prevents double-spending [15]. A cryptographic hash is used to

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Blockchain Technology in e-HRM  355 protect, connect, and identify the expanding collection of information. Cryptographic hashes are defined as time-stamped blocks. A blockchain is created by connecting these blocks chronologically through the nodes that store the hash of the preceding block [16]. By bringing all stakeholders together on a single platform, blockchain technology can upend whole sectors and enhance communication and efficiency both inside and outside of businesses [17].

13.2.1 How Blockchains Work This section outlines the fundamental characteristics of a blockchain and the fantastic opportunities this technology presents for business applications where data integrity, secrecy, and transparency are essential to their success. Decentralization, transparency, encryption privacy, and data integrity are the section’s compass points and the key benefits of this technology [18].

13.2.2 Decentralization There are several copies of the database stored in various places throughout the world [19]. These places, also known as “nodes,” may decide to return to the group of ledger copies that are now being handled, there is no requirement to keep one original or primary copy [20]. As a result, the database is decentralized. This decentralization also implies that no government official, including a banking, insurance provider, or government agency, is required to ensure the security of the database.

13.2.3 Transparency Transparency is one of the key components of the initial Bitcoin blockchain. Anyone can at any moment monitor the status of their Bitcoins since the details of every transaction are publicly available. This does not imply that an account holder’s identity is simple to discover, but rather that each user must utilize “public keys” to monitor their balance.

13.2.4 Cryptographic Security The proprietor of the new block pays a payment (a “bitcoin,” some other cryptographic money, or a portion of it) to the network’s initial miner node who solves a problem and uploads a new block to the chain; this payment is then transferred to all of the chain replicates or nodes. Even if two different blocks are calculated concurrently, to accept the blocks as a link in the

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356  Edge of Intelligence chain, a majority of 50% or more of the nodes holding the databases’ copies is required. To put it another way, while competing nodes find solutions to two distinct blocks practically simultaneously, the blocks are not regarded to be a part of the chain until 50% or more of the network’s nodes concur that the solution is genuine.

13.2.5 Immutability Blockchain records are immutable, which means that once data is recorded on the blockchain, it cannot be altered or tampered with without the consensus of the majority of participants in the network [21]. The cryptographic linking of blocks ensures the integrity and trustworthiness of the data.

13.2.6 Consensus Mechanisms Blockchain networks employ consensus mechanisms, such as Proof of Work (PoW) or Proof of Stake (PoS), to achieve agreement among nodes regarding the validity of transactions [22]. These mechanisms ensure that all nodes in the network reach consensus on the state of the ledger, thereby maintaining consistency and transparency in data transactions.

13.2.7 Decentralized Identity and Credential Verification Research such as that by [1] has emphasized blockchain’s potential to transform identity verification processes in HRM. Blockchain’s decentralized architecture enables the creation of tamper-proof digital identities linked to individuals’ credentials and qualifications. Each credential can be cryptographically signed and stored on the blockchain, allowing for convenient and secure verification by authorized parties. For example, the adoption of blockchain-based digital credentials can simplify the recruitment process by granting recruiters instant access to verified qualifications without reliance on centralized databases or manual verification procedures. This fosters trust between employers and candidates while reducing the risk of credential fraud. Moreover, blockchain ensures transparency by enabling auditing and tracing of the entire transaction history and verification process back to the source, establishing a dependable and immutable record of credentials [1].

13.2.8 Smart Contracts in HRM [23] examines the potential of smart contracts in HRM, focusing on contract automation, compliance management, and organizational efficiency

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Blockchain Technology in e-HRM  357 improvement [24]. The authors conduct a systematic mapping study to analyze the use of blockchain-based smart contracts in HRM, identifying key applications and challenges. [25] explores blockchain technology’s role in HRM, including smart contract applications, identity verification, and data security. [26] conduct a systematic review of blockchain technology applications in HRM, emphasizing smart contracts for process automation and transparency enhancement.

13.2.9 Blockchain-Based Employee Data Management [27] examines the convergence of blockchain and the Internet of Things (IoT), highlighting how blockchain technology can bolster data security and privacy, particularly in employee data management within HRM. [28] undertake a systematic review of blockchain technology applications in HRM, emphasizing discussions on data management and security considerations [24]. [29] investigate the governance implications of blockchain technology within public sector applications, offering insights that are pertinent to discussions on data management practices within HRM. [30] elaborate on how blockchain contributes to transparency in HRM processes, covering aspects of data management practices and the handling of employee information.

13.3 Blockchains for Business and EHRM According to [31], debate on technology’s role in HR procedures, technology is essential to the future of HRM. [32] found that adopting informational techniques for talent management aids in establishing a highly skilled talent pool, which improves organizational growth. Although the HR department has long used technology and software, this shows that it is necessary to effectively harness it [33]. These programs are largely used internally for things like personnel retention, development, and acquisition [34]. The capacity of the workplace’s digital transformation to assist HR managers in making strategic decisions is a topic of increasing discussion. According to the body of research, HR technology, tools, and software considerably reduce administrative labor [35] allowing HR directors to concentrate on strategic work and serve as an organization’s trusted adviser [35]. Many researchers [36], contend that the growing importance of HR technology may make HR professions less desirable. Many businesses are working on proof of concept projects because they think blockchain may significantly speed up digital

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358  Edge of Intelligence transformation activities throughout the company [37]. The capacity to comprehend blockchain and its functionality is a significant barrier for HR experts. There is notably a lack of academic research relating blockchain technology to HRM procedures in recent studies [38]. The present research seeks to give the HR community the best conversation topics to help them comprehend the businesses’ blockchain implementation goals and benefits.

13.3.1 Payroll Processing Blockchain’s ability to track bank accounts and tax information makes it particularly useful for settling gig economy workers in real-time using identifying authentication procedures, and issue payments in large companies are looking into ways to move their present systems into the blockchain, and fintech startups such as Bitwage, Earthport, and Chronobank have started paying staff using blockchain technology. By paying workers in their home currencies, blockchain can also handle employee mobility, cross-border payments, and international tax compliances. Every transaction on the blockchain is recorded in a shared ledger and is digitally signed Every time a modification is made, the ledger is duplicated and distributed throughout all of the network’s members.

13.3.2 Data Protection and Cyber Attacks Considering that every data exchanged across the blockchain network is encrypted, blockchain can mitigate the risk by preventing fraudulent behaviors that jeopardize data confidentiality [20]. Operational resiliency, data encryption, authenticity of data, transparency, secrecy, integrity, traceability, immutability, and sustainability are some of the characteristics of blockchain that make it resistant to cyberattacks.

13.3.3 Performance Management A person’s success is greatly influenced by performance management since it is connected to their pay, recognition, and career path. The bell curve methodology, which is the most popular one, has replaced the secret report method, which was mostly utilized in the public sector, in assessing employee performance. Many organizations’ have replaced yearly evaluations with other methods of performance evaluation because they are constantly looking for better ones. In the new performance models, frequent and continuous feedback, continuing dialogue, and career growth

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Blockchain Technology in e-HRM  359 are significant. Documenting all training discussions is a challenging and time-consuming task for HR professionals and collecting feedback from various stakeholders.

13.3.4 E-Recruitment System Design A private blockchain-based system for hiring human resource pools has been presented. They have created a decentralized application (Dapps) with a web user interface that gives authorized parties complete control over the system. These measures are designed to ensure the security of data across management, storage, and protection against tampering and other threats. Blockchain-based human resources system aids further in information encryption, verification, and transparency. Analysis of different design models. In addition, have suggested an intelligible approach that is more effective. They have a database with a wealth of application information thanks to their planned private network blockchain system. As a result, the business chooses which applicant to recruit depending on their organizational needs Moving forward, have created a blockchain system that allows for the development of e-recruitment.

13.3.5 Renowned DLT Applications This technology’s advantages aren’t just being used for cryptocurrencies. The Republic of Georgia has been using blockchain technology to register property records since April 2016, which lends this use a level of legitimacy that other uses lack. The tech-focused stock market exchange NASDAQ stated in December 2015 that it had completed and recorded a transaction utilizing blockchain technology. Several industrial processes, worldwide logistics or supply chain tracking, medicine administration, and other high-potential industries also use blockchain technology. To provide 111 of its alumni with another way to receive their degrees on their phones, MIT started using blockchain technology in October 2017. This action has a closer connection to the HRM sector [39].

13.3.6 Incident Logging and Reporting A further intriguing HRM application built on the blockchain concerns recording and (perhaps) reporting occurrences that may offer more convincing evidence than evidence obtained through other means, including print, email, or other digital media which are more susceptible to being tampered with or altered. Employees may be using complaint forms in the United

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360  Edge of Intelligence States to report sexual harassment at work (for example, NewYorkState’s). A blockchain-based tool is available through Vaultplatform.com to assist employees in reporting instances of sexual harassment at work. Students at Texas A&M University created Ciaspora to enhance campus reporting of sexual harassment. Similar programs have also appeared, although with a varied emphasis on certain fields. The immutability of blockchain records reduces the possibility that the quality of the report will be a point of debate significantly, even if a comprehensive investigation into the merits of purported harassment is still required.

13.3.7 Employees Assistance Program The Atlanta-based startup Hayver (https://www.hayyver.com) assists businesses with employees who are dependent on drugs or alcohol by employing technology to promote responsibility while maintaining privacy. One of the numerous conventional services provided by HR department to organizational clients is Employee Assistance Program (EAP), which HR professionals may view in this service as a version of. Although the adoption of cryptocurrencies as participation incentives may have been unique in the second half of 2017, it is difficult to check how either cryptocurrencies or digital ledger technology will provide a practical benefit at this time. The likelihood that this service will be accepted should be significantly higher if it can use at least some of the above-mentioned blockchain qualities, such as immutability, transparency, and cryptographic security, more efficiently or effectively than a conventional database.

13.3.8 Identity Registry In March 2018, Coca-Cola and the US State Department made an announcement announcing the start of a blockchain initiative to “establish a secure register for employees that will assist prevent the practice of forced labor globally” [40]. In conclusion, during late 2019, there were actual applications for HRM tasks that are in the market, despite concerns about their efficacy in comparison to traditional alternatives [41]. These roles include checking credentials or education, reporting incidents, implementing employee support programs, managing relationships, and paying seasonal employees. DLTs are a unique type of database, it is unclear how the blockchain is used by these applications. Although this is not the deciding factor as competitiveness, suitability, tangibility, and complexity also contribute to the possibility of them being adopted, it is challenging to

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Blockchain Technology in e-HRM  361 pinpoint usage where Wide adoption is undoubtedly predicted under the diffusion of innovations hypothesis.

13.4 Case Studies Mahindra Group: Mahindra Group, a prominent Indian conglomerate, has integrated blockchain technology into its recruitment processes to authenticate candidate credentials and automate background checks. This implementation has notably decreased recruitment timelines and enhanced the credibility of candidate information [42]. NITI Aayog: NITI Aayog, India’s policy think tank, has investigated blockchain solutions to oversee workforce credentials and enhance transparency in government recruitment processes. They have initiated pilot programs using blockchain to validate educational certificates and professional credentials [58].

13.4.1 Barriers to Adoption and Technological Challenges in the Indian Business Environment 1. Regulatory Uncertainty: The regulatory landscape surrounding blockchain technology in India is evolving, leading to uncertainty for organizations considering blockchain-based HRM solutions [43]. 2. Scalability and Infrastructure: Adapting blockchain solutions to accommodate large-scale HR operations and integrating them with existing HR systems present significant technological challenges, particularly in India’s diverse infrastructure environment [27]. 3. Skills Gap: India faces a shortage of skilled professionals proficient in blockchain technology, which impedes the widespread adoption and implementation of blockchain solutions in HRM [44]. Although blockchain technology holds substantial potential for revolutionizing e-HRM in the Indian market, addressing these challenges is crucial for successful adoption. Organizations stand to benefit from utilizing blockchain applications for accessing talent pools, verifying information, and reducing reliance on third-party agencies. However, navigating regulatory, technological, and skills-related obstacles is essential to fully realize these benefits.

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362  Edge of Intelligence

13.5 Integration of Blockchain with Industry 4.0 Technologies in HRM The combination of AI and blockchain technology offers substantial enhancements to HR processes through smart contract-driven automation. For instance, AI algorithms can evaluate candidate profiles and suggest appropriate positions, while blockchain technology guarantees the validity and security of candidate credentials stored on a distributed ledger [27]. IoT devices can gather real-time data on employee performance, which can be securely recorded and validated using blockchain technology. This data can then be leveraged to optimize workforce management strategies and enhance operational efficiency [45]. The integration of blockchain with big data analytics enhances the security and transparency of data used in predictive analytics for HRM. By combining blockchain with big data platforms, organizations can safeguard the integrity of data employed in workforce planning and decision-making processes [46]. In practical applications: IBM Watson’s AI capabilities have been merged with blockchain technology to develop secure, AI-driven HR solutions. This integration enables automated verification of employee credentials and streamlines recruitment processes (IBM, n.d.). Smart contracts implemented on blockchain platforms can interact with IoT devices to automate payroll procedures based on real-time attendance data. This eliminates the need for manual intervention and reduces payroll errors [47]. The integration of blockchain with AI, IoT, and big data analytics in HRM processes streamlines operations, decreases administrative burdens, and enhances decision-making accuracy. The inherent security features of blockchain technology ensure the integrity and confidentiality of sensitive HR data, fostering trust between employers and employees. Exploring the interplay between blockchain and other Industry 4.0 technologies in HRM not only enriches the analysis but also highlights the interdisciplinary nature of technological innovation. Future research could investigate novel applications of these integrated technologies and their implications for workforce management in various organizational settings.

13.6 Ethical Implications of Implementing Blockchain in HRM 1. Data Privacy and Security: Blockchain technology inherently offers enhanced data security and transparency.

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Blockchain Technology in e-HRM  363 However, concerns may arise regarding the privacy of personal information stored on a public or private blockchain ledger. Organizations must ensure compliance with data protection regulations (e.g., GDPR) and implement robust encryption measures to safeguard employee data [48]. 2. Transparency vs. Anonymity: While blockchain promotes transparency by providing a tamper-proof record of transactions, there is a delicate balance between transparency and employee privacy. Employers must navigate this balance ethically, ensuring that sensitive information is accessible only to authorized parties [49]. 3. Job Displacement and Reskilling: The automation enabled by blockchain and AI technologies in HRM may lead to job displacement in certain roles. Organizations have an ethical responsibility to invest in reskilling and upskilling programs to mitigate the impact on employees whose roles are affected by technological advancements [50]. 4. Equitable Access and Digital Divide: Blockchain-based HRM solutions should be designed to ensure equitable access for all employees, regardless of their technological literacy or access to digital infrastructure. This mitigates the risk of widening the digital divide within the workforce [51].

13.6.1 Case Studies and Examples Employee Consent and Control: Some blockchain-based HRM systems allow employees to have greater control over their data by providing consent for data sharing and verification. For example, employees can selectively share specific credentials or qualifications stored on a blockchain ledger [52]. Fair Employment Practices: Blockchain can be used to create transparent and immutable records of recruitment processes, ensuring fairness and accountability in hiring decisions. However, biases in algorithmic ­decision-making must be addressed to uphold ethical hiring practices [53].

13.6.2 Mitigating Ethical Concerns Stakeholder Engagement: Organizations should involve employees, regulators, and other stakeholders in the design and implementation of ­blockchain-based HRM systems to address ethical concerns and build trust in the technology [54].

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364  Edge of Intelligence Ethical Frameworks: Developing and adhering to ethical frameworks for the use of blockchain in HRM can guide organizations in making responsible decisions that prioritize employee rights and well-being [55]. Integrating blockchain technology into HRM processes presents ethical challenges related to data privacy, job displacement, and equitable access. Addressing these concerns requires a proactive approach from organizations, regulators, and policymakers to ensure that technological advancements in HRM align with ethical principles and benefit all stakeholders in the workforce.

13.7 Conclusion As it has done in the domains of banking, finance, supply chain and logistics, and healthcare management across both developing and developed countries, blockchain technology is becoming more broadly recognized and used for managing human resources in organizations’ [56]. It’s believed that blockchain’s potential uses would swiftly affect HR in the era of artificial intelligence. One of the possible applications of blockchain in HRM is the recruiting process, which will alter due to the utilization of talent pools, background checks, and certification of employment histories which is helpful in robo- advisors. The Blockchain foundation for HR (BcF) and employee-systems interaction are indeed the two prerequisites and circumstances for understanding those potential applications. Robo advisors, Strategists and administrators at the organizational level, senior HR department officials, advisors, legislators, business professionals, and practitioners should consider “ESI” and “BcF for HR” as required topics before accepting and implementing blockchain in HRM. Even though this study advances the body of information and research on blockchain technology in e-HRM [57], the fundamental limitation may be recognized to be the lack of co-citation analysis. The results of this study also drew from databases Scopus and Web of Science.

References 1. Tiwari, A. and Upadhyay, P., Blockchain in Human Resource Management: Opportunities and Challenges. J. Bus. Manage. Soc. Sci. Res., 4, 3, 18–25, 2021. 2. Nasscom, Blockchain in India: Adoption & Opportunities, 2020, [Link] (https://nasscom.in/knowledge-center/publications/blockchain-indiaadoption-opportunities).

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Blockchain Technology in e-HRM  365 3. Biron, B., How blockchain can help fight harassment in the workplace. Breaker Magazine, 2019, Retrieved from https://breakermag.com/ how-blockchain-can-help-fight-harass-ment-in-the-workplace/. 4. Ball, K.S., The use of human resource information systems: a survey. Pers. Rev., 30, 6, 677–693, 2001. 5. Bissola, R. and Imperatori, B., Facing e-HRM: the consequences on employee attitude towards the organisation and the HR department in Italian SMEs. Eur. J. Int. Manage., 7, 4, 450–468, 2013. 6. Chen, Y. et al., Applying Blockchain Technology to Develop Cross-Domain Digital Talent. IEEE International conference on engineering education, pp. 113–117, 2019, https://doi.org/10.1109/ICEED47294.2019.8994934. 7. Casino, F., Dasaklis, T.K., Patsakis, C., A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telemat. Inform., 36, 55–81, 2019, ISSN 0736-5853, https://doi.org/10.1016/j. tele.2018.11.006. 8. Daniel, F. and Zhu, L., Blockchains for business process management – challenges and opportunities. ACM Trans. Manage. Inf. Syst., 9, 1, 1–16, 2018. 9. Gainey, T.W. and Klaas, B.S., The use and impact of e-HR: a survey of HR professionals. People Strategy, 31, 3, 50–55, 2008. 10. Hendrickson, A.R., Human resource information systems: backbone technology of contemporary human resources. J. Labor Res., 24, 3, 381–394, 2003. 11. Factor Daily, Tamper-proof degree certificates to be India government’s first blockchain project, 2018, available at: https://factordaily.com/ degree-certificates-india-blockchain-project/. 12. Ingold, P. and Langer, M., Resume5 Resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes. Comput. Hum. Behav., 114, 1–13, 2020. 13. Marler, J.H. and Parry, E., Human resource management, strategic involvement and e-HRM technology. Int. J. Hum. Resour. Manage., 27, 19, ­2233–2253, 2016. 14. Lee, J. and Seo, H., A study on the Implementation of Human resource pool recruitment system using Blockchain. J. Korean Soc. Comput. Inf., 26, 2, 69–78, 2021, https://doi.org/10.9708/jksci.2021.26.02.069. 15. Mishra, A. and Akman, I., Information technology in human resource management: an empirical assessment. Public Pers. Manage., 39, 3, 271–290, 2010. 16. Oh, J. and Shong, I., A case study on business model innovations using blockchain: focusing on financial institutions. Asia Pac. J. Innov. Ent., 11, 3, 335–344, 2017. 17. Piscini, E., Dalton, D., Kehoe, L., Blockchain and cyber security. Let’s discuss, 2017. 18. PwC, How blockchain technology could impact HR and the world of work, 2017a, available at: www.pwc.ch/en/insights/hr/how-blockchain-can -impact-hr-and-the-world-of-work.html (accessed 03 Aug 2020).

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366  Edge of Intelligence 19. PwC, The talent challenge: harnessing the power of human skills in the machine age, 2017b, available at: www.pwc.com/gx/en/ceo-survey/2017/ deep-dives/ceo-survey-global-talent.pdf (accessed 03 Aug 2020). 20. Stone, D.L., Deadrick, D.L., Lukaszewski, K.M., Johnson, R., The influence of technology on the future of human resource management. Hum. Resour. Manage. Rev., 25, 2, 216–231, 2015. 21. Swan, M., Blockchain: Blueprint for a New Economy, O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472, 2015. 22. Nakamoto, S., Bitcoin: A Peer-to-Peer Electronic Cash System, 2008, Retrieved from (https://bitcoin.org/bitcoin.pdf](https://bitcoin.org/bitcoin. pdf). 23. Shah, S.N., Munjal, Y.P., Kamath, S.A. et al., Indian guidelines on hypertension-IV (2019). J. Hum. Hypertens., 34, 745–758, 2020, https://doi. org/10.1038/s41371-020-0349-x. 24. Boghossian, H. and Al-Omari, S., Blockchain-Based Smart Contracts for HRM: A Systematic Mapping Study, in: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1442–1449, 2019. 25. Coronado, A.D. and Dwivedi, Y.K., Blockchain in HRM: A Systematic Literature Review and Research Agenda. Int. J. Inf. Manage., 53, 102119, 2020, https://doi.org/10.1016/j.ijinfomgt.2020.102119. 26. Fayez, S.R. and Mahdy, S., Blockchain Technology and Its Applications in Human Resources Management: A Systematic Review. Int. J. Adv. Comput. Sci. Appl., 12, 5, 224–231, 2021, https://doi.org/10.14569/ IJACSA.2021.0120519. 27. Kshetri, N., Can blockchain strengthen the internet of things? IT Prof., 19, 4, 68–72, 2017. 28. Ahmed, M.I. and Mourshed, M., Blockchain technology in managing human resources: A systematic review. J. Enterp. Inf. Manage., 33, 5, ­1026–1054, 2020. 29. Janssen, M. and Kuk, G., Blockchain and governance in the public sector, in: Proceedings of the 17th Annual International Conference on Digital Government Research: dg.o 2016, pp. 239–248, 2016. 30. Kasah, T. et al., The Role of Blockchain Technology in Enhancing Transparency in Human Resource Management. J. Digital Inf. Manage., 19, 2, 150–157, 2021. 31. Wright, A.D., HR technology, 2018, Retrieved from HR Magazine available at: SHRM.org. 32. Zalan, T., Born global on blockchain. Rev. Int. Bus. Strategy, 28, 1, 19–34, 2018. 33. Jurafsky, D. and Martin, J.H., Speech and Language Processing, 3rd ed., Pearson, 2019, Pearson, Prentice hall , Upper Saddle River, New Jersey 07458, 2008. 34. Manning, C.D., Raghavan, P., Schütze, H., Introduction to Information Retrieval, Cambridge University Press, Cambridge, England, 2008.

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Blockchain Technology in e-HRM  367 35. Young, T., Hazarika, D., Poria, S., Cambria, E., Recent trends in deep ­learning-based natural language processing. IEEE Comput. Intell. Mag., 13, 3, 55–75, 2018. 36. Wang, Y., Cao, L., Kong, X., Handling ambiguous queries in conversational search using query rewriting and retrieval. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 135–144, 2019. 37. Hughes, A., Park, A., Kietzmann, J., Archer-Brown, C., Beyond Bitcoin: what blockchain and distributed ledger technologies mean for firms. Bus. Horiz., 62, 3, 273–281, 2019. 38. Li, J., Wu, Y., Yang, Y., A comprehensive survey on data privacy and security in cloud computing. J. Comput. Sci. Technol., 35, 2, 434–458, 2020. 39. Levin, D.E., Introduction. J. Law Med. Ethics, 45, 1_suppl, 8–10, 2017, https:// doi.org/10.1177/1073110517704387. 40. Doshi-Velez, F. and Kroll, M., Accountability of AI under the law: The role of explanation, in: Proceedings of the 2017 Conference on Fairness, Accountability, and Transparency (FAT), pp. 77–81, 2017. 41. Lipton, Z.C., The mythos of model interpretability. Proceedings of the 2016 Conference on the Neural Information Processing Systems (NeurIPS), pp. 3264–3273, 2016. 42. Mahindra Group, Mahindra uses blockchain technology for first time to ease its HR processes, 2019, Retrieved from https://www.moneycontrol.com/ news/business/companies/mahindra-uses-blockchain-technology-for-firsttime-to-ease-its-hr-processes-4427381.html. 43. Sharma, R. and Kavadi, S., Regulation of blockchain technology in India. J. Adv. Res. Dyn. Control Syst., 10, 4 Special Issue, 1531–1537, 2018. 44. Ravikant, B. and Bhavsar, A., Demystifying the Indian Blockchain Ecosystem. IIMB Manage. Rev., 32, 2, 211–225, 2020. 45. Bhatt, C., Thakkar, H., Patel, A., Blockchain and internet of things (IoT): A review. J. Inf. Secur. Appl., 50, 82–91, 2019. 46. Bakhtiary V., Rahmani A.M., Blockchain integration in big data: Review, vision, and opportunities. Security and Privacy, e392, 2024. doi: HYPERLINK https://doi.org/10.1002/spy2.392"10.1002/spy2.392. 47. Zheng, Z., Xie, S., Dai, H.N., Chen, X., Wang, H., An overview on smart contracts: Challenges, advances and platforms. Future Gener. Comput. Syst., 95, 470–483, 2019. 48. Kamble, S.S. and Gunasekaran, A., Blockchain technology in supply chain management and corporate social responsibility: An exploratory research. Int. J. Prod. Res., 56, 8, 2941–2961, 2018. 49. Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R., Childe, S.J., Big data analytics and firm performance: Effects of dynamic capabilities. J. Bus. Res., 98, 261–272, 2019.

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368  Edge of Intelligence 50. Schneider, A., Ethical implications of technological change, in: International encyclopedia of the social & behavioral sciences (2nd ed.), vol. 8, pp. 56–62, S.R. Smith (Ed.), Elsevier, Pergamon Press, Oxford, United Kingdom, 2019. 51. Christensen, L.J. and Lægreid, P., Ethical considerations in public ­blockchain-based HRM, in: Handbook of Blockchain and HRM, P. Lægreid and K. Saripalle (Eds.), pp. 245–263, Edward Elgar Publishing, UK: The Lypiatts, 15 Lansdown Road, Cheltenham, Glos GL50 2JA, UK, 2020. 52. Haghighi, P.D., Blockchain technologies and their role in HRM: A conceptual review and implications for future research. Pers. Rev., 48, 5, 1295–1318, 2019. 53. Mittelstadt, B.D., Principles alone cannot guarantee ethical AI. Nat. Mach. Intell., 1, 10, 501–507, 2019. 54. Stahl, B.C., Eden, G., Jirotka, M., Coeckelbergh, M., Responsible AI and the need for trustworthiness. AI Soc., 34, 1, 169–185, 2019. 55. Martin, S., Ethical considerations for blockchain technology in human resource management, in: Ethical and social perspectives on global business interaction in emerging markets, A.B. Al-Hakim and T.K. Das (Eds.), pp. 160–176, IGI Global, Business Science Reference (an imprint of IGI Global), 701 E. Chocolate Avenue, Hershey PA, USA 17033, 2018. 56. Bender, E.M. and Friedman, B., Data statements for natural language processing: Toward mitigating system bias and enabling better science. Trans. Assoc. Comput. Ling., 6, 587–604, 2018. 57. Mitchell, M. et al., Model cards for model reporting. Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (FAT), pp. 220–229, 2019. 58. NITI Aayog., National Strategy for Artificial Intelligence. 2023. Retrieved from https://www.niti.gov.in/sites/default/files/2023-03/National-Strategyfor-Artificial-Intelligence.pdf.

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14 Smart City Innovations and IoT as a Frontier of AI at the Edge of Intelligence Priya Soni

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School of Business & Commerce Department of Business Administration, Manipal University, Jaipur, Rajasthan, India

Abstract

The advent of Smart City programs, utilizing cutting-edge technology to improve urban life and resource management, has been motivated by the rapid rate of urbanization and the expanding issues faced by growing populations. The Internet of Things (IoT), a network of linked machines and sensors that gather and communicate enormous quantities of data, is at the heart of these advancements. To enable data analytics for Smart City applications, the revolutionary function of IoT is explored in this article. The idea of smart cities and a discussion of the value of data-driven ­decision-making in managing urban complexity are presented in the chapter. Cities can build a complete data ecosystem that collects real-time data on traffic, air quality, trash management, energy usage, and other topics by integrating various IoT-enabled devices, such as smart sensors, cameras, and actuators. The focus of the second section is on the fundamental components of data analytics in smart cities. The massive amounts of data produced by the IoT are processed using advanced analytics methods like machine learning, data mining, and predictive modeling. Urban planners, managers, and politicians may improve public services, optimize municipal operations, and proactively address urban concerns by using actionable insights from data analytics. It also emphasizes the numerous uses of IoT-enabled data analytics in Smart Cities. It looks at scenarios where real-time traffic data is used in intelligent transportation systems for congestion control and route optimization. Additionally, resource optimization, cost cutting, and increased environmental sustainability are made possible by the integration of data analytics in waste management and energy distribution systems. Email: [email protected]

*

Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (369–390) © 2025 Scrivener Publishing LLC

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369

370  Edge of Intelligence The difficulties with IoT data analytics in smart cities are also discussed, including the requirement for scalable infrastructure, data privacy and security issues, and interoperability problems. For Smart City’s efforts to expand sustainably and to gain public trust, it is essential to mitigate these problems. This chapter also emphasizes how IoT-driven data analytics in Smart Cities may have social effects. Considerations like inclusion, justice, and the ethical use of data strike a balance between increased efficiency and a bettered quality of life for residents. To sum up, the Internet of Things has enabled Smart City developments that have enormous potential for data analytics, allowing metropolitan areas to become more sustainable, resilient, and responsive. Cities can make educated choices, allocate resources efficiently, and create policies that serve the needs and ambitions of their residents by using the insights gained from IoT-generated data. However, effective data governance structures, stakeholder cooperation, and a strong commitment to addressing the larger social and ethical consequences of this disruptive technology are necessary for its deployment. Keywords:  Smart cities, internet of things (IoT), data analytics, urbanization, urban planning, public services & safety, public policy, environmental sustainability, infrastructure management

Introduction: Smart City Innovations and Internet of Things for Data Analytics More than half of the world’s population currently lives in cities, thanks to an extraordinary surge of urbanization that has taken place in the ­twenty-first century. There is an urgent need for revolutionary solutions to improve urban life and sustainability because of the tremendous strain this fast urban development has put on existing infrastructures, resources, and services. The idea of “Smart Cities” has evolved as a solution to these problems, utilizing cutting-edge technologies to transform urban environments and enhance the quality of life for residents [2]. The Internet of Things (IoT), a cutting-edge network of networked devices, sensors, and actuators that easily exchange data with centralized data repositories, is at the core of developments in smart cities. Cities now could gather a never-before-seen amount of real-time data, which serves as a priceless resource for data-driven decision-making and well-informed policy creation. The Internet of Things and data analytics are at the heart of smart city developments, which provide a unique chance to reimagine cities as more effective, sustainable, and habitable places. Cities can successfully solve

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Smart City Innovations and IoT as a Frontier  371 urban concerns, engage residents, and optimize resource management by utilizing the enormous potential of IoT-generated data. To guarantee that data analytics promotes inclusive, fair, and morally acceptable urban developments for future generations, a collaborative strategy encompassing politicians, technology providers, and people is necessary to fully realize the potential of smart cities. Researching Internet of Things (IoT) and smart city innovations for data analytics provides a range of benefits that are essential for contemporary urban development. Through exploring these domains, scholars unearth knowledge that contributes to the development of more effective, sustainable, and liveable urban environments. Cities may use data analytics to make better decisions, allocate resources more efficiently, and enhance public services. Real-time data feeds from IoT devices allow for accurate monitoring of infrastructure and urban processes. City planners may use this data to spot patterns, forecast requirements, and customize solutions for particular problems. Initiatives aimed at creating smart cities also draw funding, stimulate creativity, and promote economic expansion. Researchers are paving the road for revolutionary urban development and ensuring that cities change to fulfill the requirements of present and future generations by examining the convergence of smart city technology and IoT for data analytics [1].

Concept of Smart Cities and the Significance of Data-Driven Decision-Making The idea of “Smart Cities” centers on the incorporation of cutting-edge technology and data-driven solutions to raise metropolitan areas’ productivity, sustainability, and quality of life. A smart city uses digital infrastructure, linked devices, and real-time data to improve many areas of urban life, including public safety, healthcare, waste management, transportation, and more. The objective is to develop a more adaptable, interconnected, and resilient urban setting that meets the demands of its residents and fosters both economic development and environmental sustainability. To continuously monitor and analyze numerous urban factors, a network of sensors, IoT devices, and data-gathering stations is distributed across the urban environment in a smart city. The enormous volume of data is then processed and analyzed to produce insightful findings that help municipal managers and policymakers decide how best to allocate resources.

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372  Edge of Intelligence IoT devices’ smooth integration with data analytics systems is a key factor in the revolution of urban management. These devices continually gather enormous volumes of real-time data from diverse urban systems. They range from sensors embedded in infrastructure to smart meters and linked cars. After that, this data is sent to centralized data analytics systems for thorough processing and analysis. These platforms take advantage of cutting-edge analytics methods like statistical analysis and machine learning algorithms to extract useful patterns and insights from the raw data. These insights give decision-makers and city planners a thorough grasp of urban dynamics, empowering them to better allocate resources, improve service delivery, and proactively handle new difficulties. Urban management procedures become more effective, responsive, and data-driven by utilizing the potential of IoT devices and data analytics, which eventually results in smarter and more sustainable cities for stakeholders and inhabitants alike. Making decisions based on data is essential for solving the many challenges that contemporary urban settings confront. Numerous problems are brought on by urbanization, such as resource management, energy use, population increase, and traffic congestion. Traditional methods of urban planning and administration frequently fail to adequately address these problems, resulting in inefficiencies and less-than-ideal results. Smart Cities can overcome these difficulties in several significant ways by utilizing the power of data analytics [2]: Real-Time Monitoring and Analysis: Data-driven decision-making enables cities to acquire up-to-the-minute data on vital factors like trash generation, energy use, and air quality. This real-time monitoring enables prompt answers to new problems and timely solutions, which optimizes municipal operations and improves service delivery. Cities may use predictive analytics to foresee potential problems and possibilities by examining past data and patterns. With this proactive strategy, city planners may put preventative measures into place and prepare for upcoming urban projects that will meet the population’s changing requirements. Resource Optimization: Data analytics makes it easier to make the most of resources like transportation, water, and electricity. Smart Cities may better manage resources by spotting trends and inefficiencies, which saves money, has a smaller negative impact on the environment, and improves service delivery.

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Smart City Innovations and IoT as a Frontier  373 Data-driven decision-making promotes participatory government and ­citizen involvement. Cities may better understand people’s needs and preferences by including them in data collecting through mobile applications and Internet platforms, resulting in more citizen-centric urban planning and policymaking. Resilience and adaptability: A city’s resilience is strengthened in the face of difficulties like natural catastrophes or public health crises by its capacity to analyze real-time data and react swiftly to changing conditions. Cities can respond to disruptive events more quickly and recover from them by using data-driven decision-making. Data-driven decision-making guarantees that policies and actions are founded on facts rather than conjecture or anecdotes. This is known as evidence-based policymaking. This leads to more effective, focused, and evidence-based policy actions that provide the city and its residents with real advantages. To overcome the challenges of urbanization, the idea of “Smart Cities” significantly depends on data-driven decision-making as a vital component. Smart Cities may optimize urban operations, advance sustainability, and provide a more living and prosperous urban environment for all its inhabitants by utilizing real-time data, predictive analytics, and citizen involvement. By using data analytics, city managers and politicians are better equipped to make judgment calls that will benefit the long-term growth of cities.

Fundamental Components of Data Analytics in Smart Cities The realization of Smart Cities depends heavily on data analytics, which provides urban settings with data-driven insights and helps them make wise decisions. The core elements of data analytics in smart cities span an advanced ecosystem of technologies, procedures, and apps that jointly cooperate to optimize municipal operations and improve the standard of living for citizens. The valuable insights into the role of big data analytics in shaping the future of smart cities, highlighting opportunities, challenges, and emerging trends in urban data science can be explained as follows [4]: Data Collection and Integration: The first crucial step entails the painstaking gathering and blending of enormous volumes of data from various sources across the city. Data from IoT gadgets, sensors, social media, public documents, and other pertinent sources are included in this. A thorough and complete awareness of urban dynamics is ensured through

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374  Edge of Intelligence effective data integration, which allows for a greater comprehension of diverse urban features. Real-time monitoring is a crucial characteristic that enables cities to gather and examine data as events take place. This continual flow of data offers quick insights into urban dynamic characteristics including public safety, energy consumption, air pollution, and traffic congestion. Rapid reactions to emergent problems are enabled through real-time monitoring, which improves operational effectiveness and municipal resilience. Data Processing and Storage: To handle the enormous number of data created, Smart Cities need a strong infrastructure for data processing and storage. With the help of data centers, cloud computing, and distributed storage options, it is possible to store and process data effectively and provide instant access to it for analysis and decision-making. Advanced Analytics: To glean useful patterns and insights from the gathered data, smart cities use advanced analytics techniques like machine learning, data mining, predictive modeling, and artificial intelligence. Predictive analysis, trend identification, and anomaly detection are made possible by these advanced algorithms, which help with resource allocation optimization and foresee upcoming difficulties. Dashboards and visualization are crucial for facilitating the easy understanding of data insights by city administrators and officials. Intuitive representations of data trends are provided via data visualization and interactive dashboards, making it simpler to understand and use the information for strategic planning and policy creation. Open data projects and public involvement: Smart Cities place a high priority on citizen interaction. Making some datasets available to the general public stimulates participation from residents in urban development and fosters collaboration. This open-minded strategy fosters participation from citizens in decision-making and provides a better knowledge of the wants and requirements of the community. Strong privacy and security measures are necessary to secure people’s data since data analytics involves the processing of sensitive information. To protect individual privacy and stop unauthorized access to data, Smart Cities use strict data governance regulations, encryption systems, and anonymization techniques. A data-driven urban environment is supported by the core elements of data analytics in Smart Cities. Smart Cities may optimize municipal operations, improve service delivery, and promote a sustainable and prosperous urban environment for its citizens by efficiently collecting, integrating

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Smart City Innovations and IoT as a Frontier  375 analyzing, and visualizing data. The city’s ability to address urban concerns proactively and make decisions that influence the future of urban living is strengthened by the combination of modern analytics techniques and public participation.

Advanced-Data Analytics Techniques As data quantities increase in the age of digital transformation, the field of data analytics has grown to include cutting-edge methods that may glean important insights from complicated datasets. This study explores the realm of sophisticated data analytics techniques and looks at how they may be used to uncover information that is crucial for making well-informed decisions in a variety of fields (Figure 14.1). Through the use of data analysis techniques, the development of smart cities is significantly influenced by the enormous volume of data produced by diverse urban systems. Predictive analytics is one particular data analytics method that is frequently used in smart cities. By predicting future occurrences or behaviors using previous data, predictive analytics facilitates proactive decision-making and resource allocation. Predictive analytics is used in a variety of contexts in smart cities, including public safety, energy optimization, and traffic control. Predictive analytics algorithms, for instance, are used in traffic management to forecast patterns of traffic

SMART CITY DATA ANALYSIS

DATA PREPROCESSING

DATA AQUISITION

Data Security

Sensors, Smart phones, WiFi, Devices, IoT

Data Privacy

Data Cleaning, Selection, Normalization, Interpolation

Figure 14.1  Smart city data analytics.

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DATA ANALYTICS

SERVICE PROVISION

Internet Connected

Classification, Clarification, Regression, Anomaly detection

Computing Technology

Transportation, Healthcare, Environment, Public Safety

376  Edge of Intelligence congestion and determine the best routes for commuters by analyzing past traffic data, weather, and events. This makes it possible for authorities to take preventative action before congestion arises, such as changing traffic signals or adding more transportation services or they may use the advance data analytics to meet with the solution in correct manner. Smart cities can optimize resource utilization, enhance service delivery, and improve the general quality of life for their citizens by utilizing predictive analytics and other data analysis tools [11]. Advanced data analytics techniques are essential for converting data into knowledge that can be used by organizations to stay competitive and inventive in a world that is becoming more and more data-centric [5]. The study offers insights into how big data analytics might spur innovation and change in urban settings, assisting in the creation of more sustainable, intelligent, and networked cities. Decision-makers may obtain a deeper knowledge of their data, uncover hidden patterns, and make decisions that promote success and advancement in a variety of industries by utilizing the power of machine learning, data mining, predictive modeling, and artificial intelligence. The incorporation of advanced data analytics will continue to be crucial in solving complicated problems, spotting opportunities, and establishing a data-driven future as technology develops [7]. 1. Machine Learning for Predictive Analysis In the field of predictive analysis for Smart City Innovations and the Internet of Things (IoT) for data analytics, machine learning (ML) is crucial. For a variety of urban applications, ML algorithms are used to identify useful patterns, create precise forecasts, and guide informed decision-­ making from the enormous volume of data generated by IoT devices in smart cities. The efficiency, sustainability, and overall efficacy of smart city programs are improved by the synergy of ML and IoT data analytics [9]. To identify patterns of traffic congestion and foresee possible bottlenecks, ML models analyze real-time data from IoT-enabled traffic sensors and cameras. These forecasts provide traffic management authorities the ability to adjust signal timings, redirect traffic, and enhance the city’s overall transportation flow. Energy consumption forecasting is the practice of using machine learning (ML) algorithms to analyze historical data on energy use that is gathered from smart meters and IoT-enabled energy monitoring devices. The models can predict energy demand, enabling utilities to optimize energy distribution and promote people’s adoption of energy-saving habits.

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Smart City Innovations and IoT as a Frontier  377 Waste Collection Optimization: ML-driven predictive analytics examine data from intelligent trash cans and sensors to forecast waste creation patterns and estimate fill levels. Cities may save operating costs and boost the effectiveness of trash management by optimizing garbage collection routes based on these forecasts. Public Safety and Crime Prediction: ML models can identify crime hotspots and foresee possible security concerns when trained on historical crime data, weather data, and other relevant parameters. This improves public safety in smart cities by enabling law enforcement organizations to deploy resources efficiently. ML algorithms are used to analyze real-time data from IoT sensors that track metrics related to air quality, such as pollutants and particulates. These models can predict air quality levels, enabling cities to send out timely notifications and put pollution control measures into place when the situation calls for it. Health-related data from IoT wearables and medical devices are analyzed using ML-driven data analytics to forecast health trends and disease outbreaks. The planning and resource allocation that healthcare practitioners do to address public health issues is made easier by these insights. Smart Grid Load Balancing: To forecast energy consumption trends and control load balancing, ML models analyze data from IoT-enabled smart grids. By doing this, energy distribution is optimized, and grid stability is ensured during moments of high demand. ML algorithms analyze weather data from satellite observations and IoT weather stations to forecast weather trends and probable natural disasters. These forecasts improve catastrophe mitigation and response plans in smart cities. Urban service demand forecasting: To estimate future demand, ML-based demand forecasting models examine data on the use of public transit, water use, and other urban services. These forecasts help municipal planners better allocate resources and provide services. Personalized Citizen Services: To provide personalized services and suggestions, ML-driven data analytics may analyze the data and preferences of specific individuals. This increases citizen engagement and happiness in smart cities. Smart cities can make data-driven choices, foresee future difficulties, and allocate resources efficiently thanks to machine learning for predictive analysis and Internet of Things data analytics. The realization of more effective, environmentally friendly, and citizen-centered urban settings is

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378  Edge of Intelligence made possible by these revolutionary powers, positioning smart cities for future growth and development. 2. Data Mining for Identifying Patterns and Anomalies In the context of Smart City Innovations and the Internet of Things (IoT) for data analytics, data mining emerges as a critical data analytics approach, providing a potent way of identifying useful patterns and spotting abnormalities within large and complicated urban datasets. Data mining uses complex techniques like clustering, classification, and outlier identification to extract insightful information from a variety of information sources produced by IoT devices throughout the city. As a result, Smart Cities may improve public services, optimize urban operations, and take proactive measures to solve urban concerns. Data mining is essential for forming data-driven decision-making in smart urban environments. It may be used to identify anomalies in energy usage for effective resource allocation, analyze traffic patterns to anticipate congestion optimize transportation routes, and more. Large amounts of data that have the potential to change urban living have been produced by the introduction of Smart City Innovations and the widespread use of Internet of Things (IoT) technology. With the use of data mining, a potent data analytics approach, Smart Cities can find significant trends and spot anomalies that are essential for well-informed decision-making and proactive urban management. Additionally, data mining supports sustainability projects and encourages better urban life by detecting pollution hotspots, monitoring air quality, and forecasting environmental events. However, to foster confidence among people and stakeholders, the appropriate use of data mining in Smart City contexts necessitates careful consideration of data privacy, ethical considerations, and guaranteeing openness in decision-making. Future Smart Cities will be more resilient, effective, and centered on the needs of the citizens by utilizing the power of data mining to uncover trends and anomalies. 3. Predictive Modeling for Urban Planning In the context of Smart City Innovations and the Internet of Things (IoT) for data analytics, predictive modeling is crucial since it enables urban planning with data-driven foresight and educated decision-making. Predictive models forecast future scenarios by utilizing historical data and real-time IoT-generated information, enabling city planners to proactively handle urban issues and optimize resource allocation.

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Smart City Innovations and IoT as a Frontier  379 Predictive modeling has several important applications in urban planning. Transit planning is one important area where predictive models are used to estimate future demand and locate probable congestion hotspots by analyzing traffic statistics, public transit use, and other relevant aspects. This foresight helps municipal officials plan effective transport networks, put in place flexible traffic control measures, and enhance public transit services to meet the population’s changing requirements. To anticipate patterns in energy consumption and optimize energy distribution, cities may benefit equally from predictive modeling when it comes to energy planning. Predictive models estimate peak energy demand by examining past consumption statistics, weather patterns, and other variables. This enables utilities to balance the load and adopt ­energy-saving measures during high-demand periods. This helps to achieve the aims of energy efficiency and sustainability while also ensuring a steady energy supply. Predictive modeling greatly improves the management and maintenance of urban infrastructure. Predictive models may find probable structural flaws or upkeep needs by examining sensor data from IoT-enabled infrastructure, such as bridges, roads, and buildings. Cities can prioritize maintenance work, lower the risk of infrastructure breakdowns, and improve long-term asset management thanks to this proactive strategy. Predictive modeling in Smart City Innovations also helps with catastrophe preparedness and environmental planning. Predictive models may anticipate probable environmental risks like floods or storms by examining past weather trends, environmental sensor data, and geographical considerations. Cities can create effective disaster response plans and put in place climate adaptation strategies thanks to this early warning system, which helps to lessen the effects of natural catastrophes. The use of predictive modeling in urban planning, however, necessitates a careful evaluation of the model’s accuracy, data quality, and ethical implications. To guarantee the accuracy and efficacy of prediction models, high-quality, trustworthy data must be used. Furthermore, encouraging acceptance and support for data-driven urban planning projects, and maintaining transparency and openness in decision-­making processes helps stakeholders and residents develop a sense of confidence. In conclusion, predictive modeling is an effective tool for urban planning in Smart City Innovations and IoT data analytics. Predictive models provide cities with the tools they need to create resilient, effective,

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380  Edge of Intelligence and sustainable urban environments that meet their residents’ needs and ambitions by predicting future trends, anticipating problems, and allocating resources efficiently. The incorporation of predictive modeling in urban planning will increase as technology develops. 4. Artificial Intelligence Applications in Smart Cities Artificial intelligence (AI) applications in Smart Cities are at the forefront of developing game-changing innovations and revolutionizing urban living in the context of Smart City Innovations and the Internet of Things (IoT) for data analytics. AI is a useful tool for enhancing public services, simplifying governmental processes, and creating more sustainable and effective urban environments because of its capability for autonomous decision-making and capacity to analyze enormous and varied amounts of data. One of the most well-known AI applications in Smart Cities is transport systems. To control congestion, optimize traffic flow, and dynamically modify traffic signals based on the scenario, AI-powered traffic management systems integrate real-time traffic data from cameras and IoT sensors. Thanks to AI’s ability to predict traffic trends and respond in realtime, cities may lessen traffic congestion and enhance overall transportation efficiency, resulting in shorter travel times and better commutes for inhabitants. AI is also crucial for managing energy in smart cities. AI-driven energy analytics analyze data from smart meters, IoT devices, and weather predictions to optimize energy use and distribution. Utilities may utilize artificial intelligence (AI) algorithms to spot patterns in energy usage and balance loads, integrate renewable energy sources, and maximize energy efficiency. The use of AI in public safety and security is another noteworthy example. Law enforcement organizations are helped in monitoring public locations, spotting security concerns, and quickly responding to occurrences by AI-powered video surveillance and face recognition technologies. The city’s power to maintain public safety and avert security breaches is improved by AI’s ability to spot abnormalities or strange behavior in real-time. Additionally, chatbots and virtual assistants powered by AI enhance public services and citizen interaction. These artificial intelligence (AI) apps allow cities to offer 24/7 customer care, respond to inquiries, and give personalized information and services to residents, improving the general citizen experience and encouraging increased civic involvement.

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Smart City Innovations and IoT as a Frontier  381 Applications of AI are useful for managing and monitoring the environment. To pinpoint pollution hotspots, forecast environmental events, and create effective environmental protection and sustainability policies, AI-powered sensors analyze air quality data, weather patterns, and pollution levels. However, issues like data privacy, ethical issues, and algorithmic bias make the deployment of AI in Smart Cities difficult. Building public trust and ensuring the fair application of AI technologies depend on ensuring ethical AI use and transparent decision-making. As a result, the incorporation of AI applications into Smart Cities highlights this technology’s revolutionary potential for reshaping present-day urban environments. Smart Cities can maximize resources, improve services, and build more liveable, resilient, and citizen-focused urban environments by using AI’s capabilities in transportation, energy management, public safety, citizen engagement, and environmental monitoring. To create sustainable and technologically sophisticated cities for future generations, AI technologies will continue to develop in concert with Smart City Innovations and IoT data analytics.

Uses of IoT-Enabled Data Analytics in Smart Cities The realization of Smart Cities depends heavily on data analytics, which provides urban settings with data-driven insights and helps them make wise decisions. The core elements of data analytics in smart cities span an advanced ecosystem of technologies, procedures, and apps that jointly cooperate to optimize municipal operations and improve the standard of living for citizens [3]. Data Collection and Integration: The first crucial step entails the painstaking gathering and blending of enormous volumes of data from various sources across the city. Data from IoT gadgets, sensors, social media, public documents, and other pertinent sources are included in this. A thorough and complete awareness of urban dynamics is ensured through effective data integration, which allows for a greater comprehension of diverse urban features. Real-time monitoring is a crucial characteristic that enables cities to gather and examine data as events take place. This continual flow of data offers quick insights into urban dynamic characteristics including public safety, energy consumption, air pollution, and traffic congestion. Rapid reactions to emergent problems are enabled through real-time monitoring, which improves operational effectiveness and municipal resilience.

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382  Edge of Intelligence Data Processing and Storage: To handle the enormous number of data created, Smart Cities need a strong infrastructure for data processing and storage. With the help of data centers, cloud computing, and distributed storage options, it is possible to store and process data effectively and provide instant access to it for analysis and decision-making. Advanced Analytics: To glean useful patterns and insights from the gathered data, smart cities use advanced analytics techniques like machine learning, data mining, predictive modeling, and artificial intelligence. Predictive analysis, trend identification, and anomaly detection are made possible by these advanced algorithms, which help with resource allocation optimization and foresee upcoming difficulties. Dashboards and visualization are crucial for facilitating the easy understanding of data insights by city administrators and officials. Intuitive representations of data trends are provided via data visualization and interactive dashboards, making it simpler to understand and use the information for strategic planning and policy creation. Open data projects and public involvement: Smart Cities place a high priority on citizen interaction. Making some datasets available to the public stimulates participation from residents in urban development and fosters collaboration. This inclusive strategy gives locals more authority to participate in decision-making and provides a clearer grasp of the needs and goals of the community. Strong privacy and security measures are necessary to secure people’s data since data analytics involves the processing of sensitive information. To protect individual privacy and stop unauthorized access to data, Smart Cities use strict data governance regulations, encryption systems, and anonymization techniques. In conclusion, a data-driven urban ecosystem is supported by the core elements of data analytics in Smart Cities. Smart Cities may optimize municipal operations, improve service delivery, and promote a sustainable and prosperous urban environment for its citizens by efficiently collecting, integrating analyzing, and visualizing data. The city’s ability to address urban concerns proactively and make decisions that influence the future of urban living is strengthened by the combination of modern analytics techniques and public participation.

Challenges and Considerations New opportunities for urban development have been created by smart city innovations and the Internet of Things (IoT), which promises to improve

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Smart City Innovations and IoT as a Frontier  383 efficiency, sustainability, and quality of life for residents. These revolutionary technologies do, however, also present several obstacles and factors that call for careful study in addition to their enormous promise. To clarify the difficulties of adopting data-driven urban solutions, this article addresses the main issues and factors in the context of smart city innovations and IoT for data analytics. Urban living might be revolutionized by smart city innovations and the Internet of Things (IoT), but there are several obstacles and things to consider [10]. In order to improve efficiency, sustainability, and quality of life, the paragraph emphasizes the revolutionary potential of smart city innovations and the Internet of Things (IoT) in urban development. It also recognizes that these prospects come with important difficulties and roadblocks that need to be carefully considered. The paper addresses important concerns and variables surrounding the adoption of data-driven urban solutions in the framework of smart city technologies and IoT for data analytics in order to highlight these complications. For instance, worries about data privacy might be a barrier. Although Internet of Things (IoT) devices gather a great deal of data to enhance urban services, there are issues with the data’s collection, storage, and use. Concerns regarding privacy and possible misuse of personal data are common among citizens. Interoperability problems between various IoT platforms and devices could present another difficulty. Ensuring smooth communication and integration can be challenging in smart city programs because of the multitude of devices and platforms involved. Furthermore, consideration must be given to elements like cybersecurity risks, legal frameworks, and problems with the digital divide. IoT device cybersecurity flaws can endanger public safety and urban infrastructure. As smart city technologies present new problems, regulatory frameworks must adapt to balance citizen rights protection with innovation. In addition, inclusive urban development depends on closing the digital divide to guarantee fair access to smart city advantages and services. Policymakers, academics, and urban planners may create strategies to optimize the potential of IoT and smart city innovations while minimizing dangers and guaranteeing that the benefits are available to all citizens by tackling these challenges and considerations. Data security and privacy issues are brought up by the enormous volume of data that IoT devices collect. To guarantee citizen confidence, cities must put strong protections in place to safeguard private data and stop unauthorized access.

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384  Edge of Intelligence IoT Device Interoperability: When using multiple IoT devices from different suppliers, interoperability problems may arise, impeding smooth data transmission and communication. To develop a coherent and interconnected IoT ecosystem, standardization initiatives are crucial. Scalability and Resilience of Data Infrastructure: Smart Cities must make investments in scalable and robust data infrastructure to accommodate the rising demand as data volumes increase rapidly. Strong data centers and edge computing technologies are needed for real-time and effective data analytics. Citizen Privacy and Ethical Data Use: When using citizen data for ­decision-making, ethical issues are essential. To prevent data misuse, transparent data governance and practices that uphold individual privacy rights are crucial. Regulatory and Policy Frameworks: To handle data sharing, ownership, and ethical issues, smart cities require well-defined regulatory and policy frameworks. It’s crucial to work together with legal professionals and stakeholders to produce thorough rules. Energy Use and Sustainability: A greater dependence on IoT devices may result in increased energy use. By implementing renewable energy sources and maximizing energy consumption, smart cities must place a high priority on sustainability. Data and algorithm bias: The data gathered by smart cities may include biases, which might provide biased outcomes and judgments. Fair algorithms must be created as well as rigorous data pretreatment to address prejudice. Digital Inclusion and Citizen Engagement: For greater involvement and to meet the requirements of various groups, it is essential to provide digital inclusion and accessible platforms for citizen engagement. Smart Cities may manage the complications of IoT-driven data analytics, assuring responsible and equitable urban development, by considering these difficulties and implementing proactive actions.

Future Prospects and Emerging Trends of Smart City Innovations and Internet of Things (IoT) for Data Analytics Advanced AI and Machine Learning Integration: AI and machine learning technologies will continue to grow, and as a result, so will the sophistication of their integration with smart city innovations and IoT data

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Smart City Innovations and IoT as a Frontier  385 analytics. Natural language processing, autonomous decision-making, and AI-driven prediction models will be crucial for improving citizen services and streamlining municipal operations. The introduction of 5G networks and edge computing will completely transform IoT capabilities in Smart Cities. Real-time data analytics will be made possible by 5G’s ultra-low latency and fast data transfer speeds, which will also support a large number of connected devices. In IoT-driven applications, edge computing will process data closer to its source, cutting latency and increasing efficiency [8]. Data Integration and Cross-Domain Collaboration: Data Integration and Cross-Domain Collaboration will take center stage in Smart Cities. Greater comprehensiveness and holistic insights will be made possible by dismantling data silos and promoting cross-domain data exchange, which will improve urban planning and allow for more informed decision-making. Citizen-Centric Data Platforms: As these platforms proliferate, people will have greater opportunities to participate in Smart City efforts. Smart Cities will become more inclusive and responsive as a result of giving residents access to their data, openness in decision-making, and tools for engagement. Sustainability and resilience will be given top priority in future smart cities’ data-driven plans. The development of greener and more robust technologies will be driven by IoT-enabled environmental monitoring, energy-efficient solutions, and catastrophe preparedness. Data governance and cybersecurity: As data volumes rise, smart cities will confront more difficult cybersecurity issues. To secure sensitive information and guarantee the integrity of the Smart City infrastructure, strong data governance, encryption, and cybersecurity measures will be essential. Analytics for Smart Health and Public Health: Data analytics will play a key role in determining the direction of smart health programs and public health plans. Early illness identification, individualized healthcare, and successful public health initiatives will be made possible by IoT devices, remote monitoring, and health data analytics. Intelligent Transportation System (IoT)-driven data analytics will make autonomous infrastructure management possible in Smart Cities. Resource allocation will be efficient, and maintenance downtime will be decreased, thanks to smart grids, self-healing networks, and predictive maintenance. Smart Trash Management and the Circular Economy: Smart Cities will adopt circular economy ideas and use data analytics to improve resource recovery, recycling, and trash management. IoT sensors will make it possible to monitor waste systems in real time, which will minimize trash production and boost sustainability.

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386  Edge of Intelligence In conclusion, IoT for data analytics and the future of smart cities show significant potential for more efficient, environmentally friendly, and centered urban life. The future of urban settings will be shaped by the seamless integration of data analytics, IoT, and AI as technology develops further. This will improve quality of life, encourage environmental stewardship, and usher in a new era of smart and connected cities.

Indian Case Studies: Successful Implementations on Smart City Innovations and Internet of Things for Data Analytics Indian cities have been actively embracing Smart City Innovations and the Internet of Things (IoT) for data analytics to address urban ­challenges and improve the quality of life for citizens. According to Navdeep Singh (2016), the case studies and real-world examples may show how data analytics is applied in smart cities [6]. Intelligent public safety solutions, energy management systems, predictive repair of infrastructure, and urban transportation optimization are a few examples. These examples show how data-driven strategies may improve resilience, sustainability, and efficiency in urban settings. Here are some successful case studies from India: Case Study 1: Jaipur’s Intelligent Public Lighting IoT and data analytics have been used to construct an automated public lighting system in Jaipur, the Pink City of Rajasthan. The city has switched out outdated streetlights for intelligent LED lights with sensors and controls. Streetlight brightness is adjusted by data analytics algorithms in real-time according to variables including traffic flow, weather, and human activity. Because of this, light pollution has decreased and considerable energy savings have been achieved, making Jaipur’s streets safer and more energy-efficient. Case Study 2: Bhopal’s Water Management System The smart management of water has been a priority in Bhopal, the capital of Madhya Pradesh, employing IoT and data analytics. The city makes use of IoT devices to keep an eye on water distribution systems, gauge water flow, and find leaks. This data is processed using data analytics to locate possible water leaks and improve water distribution to various locations. Smart water management in Bhopal has increased water distribution efficiency, decreased waste, and allowed for greater access to water resources for the city’s population.

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Smart City Innovations and IoT as a Frontier  387 Updating Status of Bins Stage 3

Stage 1

Waste Collection Monitoring using Sensor

Stage 8

Waste Collection and Recycling

Visualizing Sensor Data

Stage 2

Scheduling and Routing of Pickup Vehicles

Stage 7

Sending Status Notification to Control Room

Stage 6

Analyzing Shortest paths

Stage 4

Generating locations, alarms and actions

Stage 5

Figure 14.2  An overview of waste management.

Case Study 3: Surat’s Smart Waste Management For efficient trash management, Surat, a city in Gujarat, India, has embraced smart city innovations. IoT-enabled smart garbage bins with sensors that track fill levels have been introduced by the city. Data analytics is used to analyze this data to improve garbage collection schedules and routes. Because of this, Surat has seen a considerable increase in the effectiveness of garbage collection, which has resulted in lower operating costs and improved waste management techniques, helping to create a cleaner and greener environment [11] (Figure 14.2). Case Study 4: Pune Smart City Initiative With an emphasis on data-driven solutions, Pune, one of India’s main cities, has started its road toward becoming a Smart City. Their Intelligent Traffic Management System is one instance of successful execution. Pune uses IoT cameras and sensors placed at important crossroads to track traffic in real time. This data is processed by data analytics to forecast traffic, spot bottlenecks, and improve signal timing. The city’s traffic management agencies may take data-driven actions to eliminate traffic jams and boost overall transportation effectiveness, which would shorten commute times and improve commuter experiences. These case studies from India demonstrate how IoT for data analytics and smart city innovations have improved urban life there. These cities have made great progress towards being smarter and more sustainable for

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388  Edge of Intelligence their inhabitants by utilizing data-driven solutions to manage traffic, trash, water distribution, and energy more effectively.

Conclusion A new age of urban development has begun thanks to smart city innovations and the Internet of Things (IoT), which are driven by data-driven insights and technical developments. Smart Cities use real-time data to optimize several facets of urban living, from transportation and energy management to garbage disposal and public safety, through the integration of IoT devices and data analytics. With predictive modeling, AI-driven decision-making, and enhanced resource allocation for more effective and sustainable urban settings, data analytics has enormous promise for forming Smart Cities. To promote equitable and sustainable urban development, parties such as municipal officials, residents, the private sector, and academics must work together to realize this objective. The foundation of smart city innovations is data analytics, which has enormous potential to influence urban planning. Cities may gather insightful information, spot trends, and make data-driven choices by using data from IoT devices, sensors, and other sources. Predictive modeling enables Smart Cities to foresee energy consumption, environmental conditions, and traffic congestion, resulting in improved transportation systems, increased energy efficiency, and improved disaster preparedness. Integration of AI and machine learning also promotes personalized citizen services, autonomous infrastructure management, and increased public safety. The capacity of data analytics to build more effective, resilient, and citizen-centric urban settings that cater to their citizens’ demands is the promise of data analytics in Smart Cities. Collaboration amongst a variety of stakeholders is essential for the longterm growth of smart cities. City officials, people, businesses, and academic institutions must work together to identify problems, create comprehensive answers, and put data-driven plans into practice for smart cities to be innovative. Citizens who are actively involved in the decision-making process develop a sense of ownership and are given the tools they need to shape their urban environment. Collaborations with the private sector offer funding and technological know-how, which makes it easier to install IoT devices and data analytics platforms. Additionally, research and experience from academic institutions support innovation and ongoing development of Smart City technologies. To establish dynamic, inclusive,

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Smart City Innovations and IoT as a Frontier  389 and future-ready Smart Cities, sustainable urban development necessitates shared accountability, transparency, and collaborative efforts among all stakeholders. IoT and smart city innovations have the potential to revolutionize urban planning. The ability of data analytics to drive informed decision-­making, optimize resources, and improve the quality of life for inhabitants is what gives Smart Cities their unique design. To construct resilient and citizen-centric Smart Cities that responsibly embrace technology and provide a sustainable future for future generations, coordinated efforts are required between city authorities, residents, the commercial sector, and academics.

References 1. Nam, T. and Pardo, T.A., Conceptualizing smart city with dimensions of technology, people, and institutions. Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, pp. 282–291, 2011. 2. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., Internet of Things (IoT) for Smart Cities: Technologies, Challenges, and Opportunities. IEEE Internet Things J., 1, 1, 22–32, 2014, DOI: 10.1109/JIOT.2013.2296516. 3. Riazul Islam, S.M., Kwak, D., Humaun Kabir, M., Hossain, M., Kwak, K.-S., Smart City Big Data Analytics: An Overview. IEEE Access, 3, 612–630, 2015, DOI: 10.1109/ACCESS.2015.2437951. 4. Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, S., Big Data Analytics for Smart Cities. EPJ Data Sci., 4, 12, 2015, DOI: 10.1140/ epjds/s13688-015-0045-. 5. Anagnostopoulos, I., Hadjiefthymiades, M., Bouboulis, P.N., Big Data Analytics for Smart and Connected Cities. J. Big Data, 3, 17, 2016, DOI: 10.1186/s40537-016-0044-8. 6. Singh, N., Sancheti, D., Chaudhary, S., Data Analytics for Smart Cities. IEEE Int. Conf. Data Sci. Adv. Anal. (DSAA), 2016, 403–410, 2016, DOI: 10.1109/ DSAA.2016.30. 7. Liu, Y., Tang, M., Li, T., A Comprehensive Survey on Smart City Big Data Analytics. J. Big Data, 5, 25, 2018, DOI: 10.1186/s40537-018-0139-3. 8. Sharma, T., Sahu, A.K., Pattnaik, P.K., Data-Driven Smart Cities: A Survey. J. Ambient Intell. Hum. Comput., 11, 407–425, 2020, DOI: 10.1007/ s12652-019-01381-3. 9. Grattieri, W., Lavecchia, R., Mazziotta, M., Big Data, IoT, and Machine Learning for Industry 4.0 Ecosystems: A Survey. IEEE Access, 8, 62008– 62021, 2020.

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390  Edge of Intelligence 10. Ramdane-Cherif, A., Ksentini, A., Hafid, A., Data Analytics for Smart Cities: Challenges, Opportunities, and Solutions. IEEE Commun. Surv. Tutorials, 23, 1, 125–145, 2021, 2021, DOI: 10.1109/COMST.2019.2890062. 11. Ullah, A., Anwar, S.M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., Saba, T., Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex Intell. Syst., 10, 1607–1637, 2024, Survey and State of the Art. Published: 27 July 2023, https://link. springer.com/article/10.1007/s40747-023-01175-4.

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15 Synergies Unleashed: The Convergence of AI and Edge Computing in Transformative Technologies R. Shobarani1*, P. Dhivya1, G. Savitha2, S. Santhi3, K. Surya Prakhash4 and R. Kavitha5 Department of CSE, Dr. M.G.R Educational and Research Institute, Chennai, TN, India 2 Department of Computer Science, College of Science and Humanities, SRMIST, Ramapuram, Chennai, TN, India 3 Department of Computer Science, Puratchi Thalaivar Dr. MGR Govt. Arts and Science College, Uthiramerur, Kanchipuram Dt., TN, India 4 Department of Computer Science, SRM University, Chennai, TN, India 5 Department of CSE, Chennai Institute of Technology, Kundrathur, Chennai, TN, India 1

Abstract

A revolutionary new trend in technology is emerging at the intersection of artificial intelligence and edge computing, which merges the power of AI with decentralized processing on the periphery of networks. Latency, bandwidth consumption, privacy, and security are some of the major issues that this convergence attempts to resolve. Edge computing lowers latency and bandwidth needs by processing data closer to its source. Distributed architectures, enhanced privacy, and realtime decision-making are all made possible by integrating AI at the edge. There are still obstacles to this convergence, but innovations like federated learning and edge AI chips are helping to overcome them. 5G network integration and hybrid cloudedge architecture development bode well for the future. By combining AI with edge computing, new solutions are becoming possible in a wide variety of sectors. Keywords:  AI, edge computing, convergence, latency, bandwidth efficiency, privacy, security, real-time decision-making *Corresponding author: [email protected] Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj (eds.) Edge of Intelligence: Exploring the Frontiers of AI at the Edge, (391–432) © 2025 Scrivener Publishing LLC

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391

392  Edge of Intelligence

15.1 Introduction A major paradigm change is occurring in the ever-changing world of technology, and it is the merging of Artificial Intelligence (AI) with Edge Computing. This is changing the way data is processed and used. Unlocking a new age of efficiency and creativity, this integration tackles basic concerns relating to latency, bandwidth, privacy, and security. The improvement in bandwidth efficiency is one of the main benefits of this convergence. Through local processing and filtering, Edge Computing maximizes the use of network resources. Only the most crucial data is then sent to centralized cloud servers. This procedure is further optimized by combining AI with edge devices, which enable intelligent analysis and decision-making to take place locally on the device. In addition to reducing bandwidth consumption, this gives edge devices more autonomy by allowing them to make better choices on their own. Concerns about privacy and security are front and center in this coming together. To reduce the likelihood of data breaches occurring while in transit, Edge Computing processes data locally, keeping sensitive information closer to its source. With AI implemented at the edge, raw data doesn’t have to be sent to centralized servers for processing, which further enhances privacy. Applications where data secrecy is crucial, including surveillance, healthcare, and finance, make this even more critical. Edge Computing refers to a distributed computing paradigm where data processing is performed closer to the data source or “edge” of the network, rather than relying solely on centralized data centers. In this approach, computing resources are located near or directly within the devices or sensors generating the data, allowing for real-time data processing, analysis, and decision-making. By moving computation closer to the source of data generation, Edge Computing reduces latency, bandwidth usage, and dependence on cloud services, making it ideal for applications requiring rapid response times, such as Internet of Things (IoT) devices, autonomous systems, and immersive experiences like augmented reality. Edge Computing’s real-time decision-making skills are well-complemented by AI. Implemented in smart cities, autonomous cars, or industrial automation, the integration of AI algorithms at the edge guarantees intelligent and rapid reactions to changing situations. By distributing AI models over a network of edge devices, this distributed design also makes it easy to scale and handle faults.

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Convergence of AI and Edge Computing  393 Artificial intelligence (AI) works well with edge computing’s ability to make decisions in real time. When used to smart cities, autonomous vehicles, or industrial automation, edge AI algorithms provide smart and quick responses to changing circumstances. This distributed approach facilitates scalability and error handling by dispersing AI models throughout an edge device network.

15.1.1 Overview of Converging and the Implications of AI and Edge Computing By combining powerful analytical tools with decentralized processing, the coming together of AI (Artificial Intelligence) and Edge Computing represents a revolutionary synergy shown in Figure 15.1. This convergence is changing the face of technology by providing fresh approaches to old problems like data processing, latency, bandwidth consumption, and the requirement for instantaneous decisions.

Figure 15.1  Overview of converging and the implications of AI and edge computing.

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394  Edge of Intelligence 1.  Reduction of Latency: Overview: The goal of edge computing is to reduce the amount of time it takes for data to travel to central servers by processing it close to its point of origin. Implications: Applications that need immediate reactions, like industrial automation and autonomous cars, may be made possible by implementing AI models directly on edge devices, including Internet of Things (IoT) sensors or gateways. This further reduces latency. 2.  Bandwidth Efficiency: Overview: Through local processing and filtering, Edge Computing optimizes bandwidth before delivering only relevant information to the cloud. Implications: By using AI at the edge, devices may do intelligent data analysis without transferring raw data to centralized servers. In cases when network resources are few or costly, this is vital since it reduces bandwidth use. 3.  Privacy and Security Enhancements: Overview: By storing sensitive information closer to its point of origin, edge computing improves privacy and lessens the likelihood of data breaches that occur while in transit. Implications: By using AI at the edge, algorithms may run locally on the device rather than sending raw data to the cloud, thus protecting user privacy. Applications such as healthcare and surveillance greatly benefit from this. 4.  Real-time Decision-making: Overview: With the help of edge computing, decisions may be made quickly right where data is being generated. Implications: Edge AI algorithms allow devices to make smart judgments locally, independent of centralized cloud servers. This is of the utmost importance for autonomous cars and robots, which need the ability to make split-second judgments. 5.  Distributed Architecture: Overview: With edge computing, processing moves closer to the data source in a decentralized fashion. Implications: Several edge devices may be used to create a distributed AI architecture by distributing AI models. Along with improving scalability, this also makes systems more resilient to failure and more flexible in unpredictable settings.

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Convergence of AI and Edge Computing  395 6.  Use Cases: Overview: Internet of Things (IoT), smart cities, and industrial automation are some of the areas that use edge computing. Implications: Applications of AI at the edge include augmented reality, driverless cars, healthcare gadgets, retail, and the expansion of possibilities for intelligent and localized processing. 7.  Challenges and Advancements: Overview: Difficulties arise when optimizing models and when edge devices’ resources are limited. Implications: Technological advancements like federated learning and edge AI chips overcome these obstacles, allowing for more efficient and scalable deployment of AI on edge devices. 8.  Future Trends: Overview: Creating hybrid cloud-edge architectures and integrating 5G networks. Implications: These developments should make it easier for AI and edge computing to work together and boost the capabilities of edge devices even more. Data processing, analysis, and action are being transformed by the potent mix of artificial intelligence and edge computing. The future of intelligent, responsive, and decentralized systems may lie in the hands of this synergy as it develops further, which might spur innovation in a wide range of sectors.

15.2 Related Study Edge AI and 5G Integration: [1] This reference focuses on the ways in which edge computing applications may be improved by integrating edge intelligence with 5G networks. Potentially covered in the article are the effects of 5G on rollouts of edge devices, high-speed data processing, and low-latency communication. It may include assessments of performance, case studies, and recommendations for further study in this area. Advancements in Edge AI Chips: [2] This reference summarizes all the new developments in edge AI chips that have been made recently. It most likely discusses new AI processing designs, hardware improvements, and performance metrics. Issues like

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396  Edge of Intelligence energy efficiency, scalability, and AI algorithm compatibility may also be covered in the article. On top of that, it might shed light on new tendencies and potential avenues for investigation into creating cutting-edge AI processors for the future. Security and Privacy in Edge Computing: [3] This reference delves into the difficulties and potential answers around privacy and security in edge computing settings. There may be a focus on privacy-preserving algorithms, safe authentication protocols, and encryption methods developed for use on edge devices. With any luck, the article sheds light on how to better secure decentralized edge systems and lessen the likelihood of security breaches. Federated Learning and Decentralized AI: [4] This reference provides an extensive overview of methods for federated learning in cloud computing and other edge settings. It delves into issues like decentralized AI privacy protection, communication efficiency, and model aggregation. The article probably goes over several ways to implement federated learning, some examples of its application, and where the field may go from here in terms of research to improve federated learning in edge computing. Hybrid Cloud-Edge Architectures: [5] This reference delves into the planning and execution of hybrid cloudedge systems for efficient and scalable computing. There could be talk about optimization methods, resource management plans, and architectural frameworks for dividing up tasks between the cloud and the edge. Insights on the pros, cons, and practical uses of hybrid cloud-edge architectures are probably offered by the article. Cases of Healthcare Utilizing Edge Computing: Edge Computing Use Cases in Healthcare: [6] This reference offers a thorough analysis of healthcare-specific edge computing technologies. It might go over some of the use cases that are made possible by edge computing, such tailored healthcare delivery, realtime diagnostics, and remote patient monitoring. The article probably looks at the merits, cons, and potential future of using edge computing technologies in healthcare. AI-Driven Edge Analytics for Smart Cities: [7] This reference delves at how smart city efforts may be made possible using edge analytics powered by artificial intelligence. It might cover over

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Convergence of AI and Edge Computing  397 data analytics strategies, artificial intelligence algorithms, and architectures for the edge that are specifically designed for smart city use cases. The study probably goes over some of the problems with data heterogeneity, scalability, and interoperability, and it also points out some of the possibilities for improving urban sustainability and efficiency using AI-driven edge analytics. Dynamic Resource Allocation in Edge Computing: [8] This reference delves into ways for dynamically allocating resources in edge computing settings that are optimized for AI workloads. Efficient usage of computing resources at the edge may be achieved via the use of algorithms for load balancing, job scheduling, and resource optimization. Improving the speed and scalability of AI systems is the goal of the article, which presumably tackles issues including changing workload needs, network circumstances, and edge device capabilities. Energy-Efficient Edge AI Solutions: [9] This reference delves into the possibilities and difficulties of energy-­ efficient AI solutions for the edge. Methods for minimizing power consumption in edge devices without sacrificing the precision and speed of AI inferences might be covered. Model compression, hardware acceleration, and power management tactics are anticipated to be explored in the study as potential techniques to achieving energy efficiency in edge AI installations. Real-Time Video Analytics at the Edge: [10] This resource summarizes the many designs, techniques, and uses of edge-based real-time video analytics. Possible topics covered include deep learning models, video processing approaches, and edge computing systems that are designed for low-latency inference. The article probably looks at use examples that show the necessity of edge-based video analytics for diverse applications, such smart transportation, industrial monitoring, and surveillance. Edge Intelligence for Autonomous Vehicles: [11] This reference delves into edge intelligence technologies designed specifically for autonomous cars, tackling obstacles and offering remedies. Algorithms for control, perception, and decision-making at the edge that promote autonomous driving’s real-time processing and decision-making are possible topics covered. Things like autonomous vehicle (AV) edge computing architectures, vehicle-to-everything (V2X) connectivity, and sensor fusion at the vehicle’s periphery are probably covered in the article.

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398  Edge of Intelligence Edge Computing for Augmented Reality: [12] This reference delves into how edge computing powers fully immersive AR experiences. Reduce latency and improve the user experience in augmented reality apps by discussing edge-based rendering, content delivery, and interaction strategies. The article probably takes a look at problems like synchronization difficulties, device heterogeneity, and bandwidth limits in edge-based AR systems, and then suggests ways to fix them so that AR may reach its full potential. Edge Computing for Industrial Internet of Things (IIoT): [13] This reference offers a thorough analysis of edge computing systems designed for IIoT applications. Probabilistic upkeep, real-time control in manufacturing, and data processing at the edge are all potentially covered subjects. You can expect the article to go into use examples like condition monitoring, smart manufacturing, and predictive analytics, all of which show how edge computing may boost industrial operations’ efficiency and output. Edge Intelligence in Retail: [14] This reference looks at the retail industry’s use of edge intelligence and discusses the pros and cons of this technology. Retailers’ inventory management systems, targeted marketing, and consumer analytics powered by the edge might be covered. Data privacy, current system integration, and scalability are likely to be some of the topics covered in the paper as they pertain to implementing edge intelligence solutions in retail settings. Privacy-Preserving AI at the Edge: [15] This reference delves into methods and uses for edge AI that protects privacy. Protecting sensitive data while allowing AI inference at the edge may be discussed, along with federated learning, differential privacy, and homomorphic encryption approaches. The article probably looks at use cases where privacy-preserving AI solutions are essential for keeping data secure and user privacy, such smart homes, healthcare, and finance. Edge Computing for Environmental Monitoring: [16] In this reference, we get an analysis of edge computing solutions as they pertain to environmental monitoring. Possible topics covered include air quality monitoring, water resource monitoring, anomaly detection methods, data fusion algorithms, and edge-based sensor networks. Most likely, the article delves into how edge computing might facilitate environmental data analysis in real-time, early warning systems, and decision-­ support tools for management.

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Convergence of AI and Edge Computing  399 Edge AI for Smart Energy Systems: [17] This reference takes a look at smart energy systems and the AI solutions that operate on the edge, discussing the pros and cons of these systems. Potential topics covered include methods for predictive maintenance, demand response, and energy management at the network’s edge that aim to improve the dependability and efficiency of renewable energy systems and smart grids. The report probably delves into how to improve energy use, save expenses, and alleviate grid instability by integrating AI algorithms with edge computing infrastructure. Edge Computing for Healthcare IoT: [18] This reference delves into edge computing technologies designed specifically for IoT applications in healthcare. Potential topics for improvement include medical imaging, edge-based patient monitoring, and predictive analytics in healthcare. The article probably takes a look at problems like data interoperability, regulatory compliance, and security in healthcare IoT ecosystems, and then suggests solutions to these problems using edge computing. This would allow healthcare IoT technologies to reach their full potential. Edge Intelligence in Financial Services: [19] This reference summarizes the uses of edge intelligence in the banking and insurance industries. Financial organizations may improve their operational efficiency and security by discussing algorithmic trading systems, edge-based fraud detection, and risk assessment. Highlighting the significance of edge computing in allowing flexible and responsive financial services, the study presumably addresses application scenarios such as algorithmic portfolio management, real-time market analysis, and high-frequency trading. Edge Computing for Smart Grids: [20] This reference delves into the possibilities and problems of edge computing solutions for smart grids. For smart grid infrastructure performance and reliability optimization, it may include edge-based energy management, demand response, and grid stability improvement solutions. Integrating AI algorithms with edge computing platforms to allow smart grid applications such as real-time data analytics, predictive maintenance, and dynamic control is likely the focus of the article. Resource allocation, energy efficiency, real-time analytics, driverless cars, and augmented reality are just a few of the many topics covered by these sources as they pertain to the merging of artificial intelligence and

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400  Edge of Intelligence edge computing. Their commentary sheds light on the present status of study in this multidisciplinary area.

15.3 Reduction of Latency Overview: One major benefit of merging AI with Edge Computing is the reduction of latency. Processing data closer to its source minimizes latency, the time between data production and its processing, boosting the responsiveness of applications. By using AI, this process is further optimized, allowing edge devices to make decisions in real-time. Let’s have a look at a real-world example to see how data is handled to achieve decreased latency: Example Scenario: Autonomous Vehicle Navigation 1. Data Generation: A number of sensors, including as cameras, LiDAR, and radar, collect data continuously in an autonomous vehicle scenario, allowing the vehicle to understand its environment in real-time. 2. Edge Computing Processing: Edge computing is used instead of transmitting all the raw sensor data to a central server in the cloud. The incoming data is processed locally by the edge device, which may be found within the autonomous vehicle or in close vicinity. The edge device’s AI algorithms process the sensor data, making object identification, obstacle detection, and sign interpretation possible. The time it takes to get from data acquisition to first analysis is minimized because of how fast this local processing is. 3. Real-time Decision-making: The edge device’s AI algorithms process the sensor data, making object identification, obstacle detection, and sign interpretation possible. The time it takes to get from data acquisition to first analysis is minimized because of how fast this local processing is. The car can quickly adapt to changing road conditions and accurately react to possible dangers since data doesn’t need to be sent to a faraway cloud server for processing. 4. Reduced Latency Impact: Autonomous vehicle efficiency and security depend on a decrease in latency. The ability to make split-second decisions at the network’s periphery allows

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Convergence of AI and Edge Computing  401 the vehicle to react to changing circumstances in real time, allowing it to traverse complicated landscapes autonomously. 5. Continuous Learning and Adaptation: The effectiveness and security of autonomous cars depend on the decrease of delay. The vehicle’s ability to traverse complicated terrain in real time and adapt to changing conditions without depending on a centralized system is ensured by quick decision-making at the edge. Edge computing and AI work together to reduce processing delay for data collected by autonomous vehicle sensors in this case. Automated cars, industrial automation, and other situations requiring split-­second decisions rely heavily on this latency reduction. There are many real-world applications that may benefit from the increased efficiency and responsiveness that results from the combination of AI with Edge Computing.

15.4 Bandwidth Efficiency Overview: One major benefit of merging AI with edge computing is bandwidth efficiency, which allows for more efficient use of network resources by processing and delivering only the information that is really necessary. In cases when bandwidth is scarce or costly, this becomes even more crucial. Using AI and Edge Computing, the following example shows how data is handled with a focus on bandwidth conservation. Example Scenario: Smart Surveillance System 1. Data Capture: Smart surveillance systems use a network of strategically positioned cameras to keep an eye on a wide region. There is a deluge of data produced by these cameras since they record video continually. 2. Edge Computing Processing: Edge computing is used at each camera site rather than transmitting the whole video stream to a central cloud server. Data from the videos is processed locally by the edge devices that have artificial intelligence capabilities in order to extract useful information. Advanced artificial intelligence systems scour the live video stream for any signs of intrusion, suspicious activity, or people of interest. Metadata or pertinent video clips containing detected events are the only ones kept.

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402  Edge of Intelligence 3. Transmission of Relevant Information: The edge devices provide compressed data to a main server or processing unit, such event metadata or video clips that have been selectively processed. When compared to the full-length video stream, the amount of the delivered data is tiny. Because only necessary data is delivered across the network, bandwidth is minimized by selective transmission of relevant information. When sending huge amounts of raw video data would be too expensive or impracticable, this comes in handy. 4. Bandwidth Conservation Impact: To maximize bandwidth use, data is processed locally and only the necessary information is sent. The monitoring system becomes more scalable and economical as a result of this method’s conservation of network resources. 5. Real-time Alerts and Responses: The data that has been delivered is further analyzed by the central server, which has artificial intelligence capabilities. In reaction to detected occurrences, it may send out real-time notifications that enable for swift measures like closing doors or notifying security staff. 6. Continuous Learning and Adaptation: Machine learning may be used to continuously upgrade the AI models that are deployed at both the edge and centralized servers. Without flooding the network with raw data, this guarantees that the algorithm improves over time at identifying important events. In this case, a smart surveillance system that uses AI and Edge Computing to interpret and send only relevant data locally minimizes bandwidth use. This method, which exemplifies the advantages of AI and Edge Computing coming together, is very important in situations where sending huge volumes of data over the network would be too expensive or unfeasible.

15.5 Privacy and Security Overview: With AI and Edge Computing converging, data privacy and security are of the utmost importance. By bringing processing closer to the point of origin, this integration improves the security of vital data. See how AI and Edge Computing can handle data in a way that puts security and privacy first in the example below.

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Convergence of AI and Edge Computing  403 Example Scenario: Healthcare Monitoring Device An AI and edge computing architecture is shown in Figure 15.2 as a healthcare monitoring gadget. The device’s capacity to continually gather health data, its interface with a smartphone, and the following data analysis using AI for health monitoring are all shown in this video. Data gathering, AI analysis, and the feedback loop to medical professionals or patients are all included in this graphic. 1. Data Generation: Think of a healthcare monitoring gadget that a person may wear and that will constantly take data, including vital indicators like temperature, blood pressure, and heart rate.

Figure 15.2  Healthcare monitoring device.

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404  Edge of Intelligence 2. Edge Computing Processing: Wearable devices itself use edge computing. Using built-in AI algorithms, the gadget examines the health data locally, allowing it to generate valuable insights and detect any health irregularities. The wearable device’s AI model safeguards the privacy of the person by processing sensitive health data locally, without communicating raw data to external servers. 3. Anonymized Transmission for Further Analysis: Sometimes, a healthcare provider or a central server receives aggregated and anonymized findings for further study. In order to protect the privacy of the user, all personally identifying information is removed from the sent data. For instance, the gadget might provide aggregated data on the user’s health and activity levels without disclosing any personally identifiable information. 4. Enhanced Security Measures: With data processing taking place on the device, there is less chance of illegal access. The confidentiality and integrity of the processed information is ensured by using security measures like secure protocols and encryption. 5. User Control and Consent: The data is within the control of the person utilizing the healthcare monitoring gadget. Underscoring the importance of user liberty and privacy, they may provide express approval for certain data to be shared with healthcare experts or for research reasons. 6. Immediate Anomaly Detection: The wearable’s local AI model can spot sudden health problems as they happen. It might detect abnormalities in the user’s heart rate or temperature and notify them in a timely manner, for example. 7. Continuous Learning and Personalization: This local learning further reduces the requirement for external data transfer, and the wearable device’s AI model may constantly learn from the user’s health trends to deliver individualized insights and suggestions. The convergence safeguards individual privacy by enforcing stringent security measures, analyzing sensitive health data locally on the wearable device, and transferring only anonymized insights. Integrating AI and Edge Computing in this way demonstrates the benefits of privacy and security while still conforming to privacy standards and giving people more agency over their own health data.

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Convergence of AI and Edge Computing  405

15.6 Real-Time Decision-Making: Decision Made with an Example Overview: Making decisions that leverage the confluence of AI and Edge Computing requires careful consideration of privacy and security. With this connection, smart judgments may be made without compromising the security of critical data. Using AI and Edge Computing, as shown in the following example, choices are made with an emphasis on privacy and security. Example Scenario: Smart Home Automation

Figure 15.3  Smart home automation.

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406  Edge of Intelligence Using AI algorithms for smart and efficient home automation, this Figure 15.3 depicts a smart home automation system that integrates edge computing and smart devices such as lights, security cameras, thermostats, and voice assistants. 1. Data Generation: Cameras, motion detectors, and smart appliances are just a few of the Internet of Things (IoT) gadgets that may be found in a smart home. The actions, preferences, and environmental factors of the occupants are recorded by these devices. 2. Edge Computing Processing: In a smart home, equipment in the periphery, such a smart hub, handle data processing locally. In order to discover trends, preferences, and any security risks, the data is analyzed by AI algorithms on the edge device. For example, the smart hub may analyze camera footage from the home security system to detect people, their movements, and any unusual occurrences. 3. Privacy-Preserving Decisions: The edge device is responsible for making decisions that automate smart home operations including lighting, temperature, and security. These judgments are made using the data that has been examined, so you can expect prompt replies that take context into account. Crucially, no sensitive information, including camera footage, is sent to external computers; processing takes place locally. The privacy of the residents is preserved since facial recognition and activity tracking take place on the edge device. 4. Security Threat Detection: The edge device’s AI model is designed to identify potential security risks, such suspicious activity or illegal access. The technology may detect abnormalities in the home environment and either immediately notify the user or perform predetermined security steps if necessary. As an example, the system has the ability to notify the homeowner via their mobile device or initiate extra security measures like locking doors or setting off alarms in the event that an unauthorized individual is identified.

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Convergence of AI and Edge Computing  407 5. User Control and Encryption: The amount of data sharing and the user’s ability to manage their privacy are both customizable. To protect sensitive data from prying eyes, encryption techniques are used to encrypt data in transit between devices. 6. Minimized Data Transmission: The user’s mobile app or external servers only get relevant data, including security warnings or aggregated insights. The safe environment of one’s own house reduces the likelihood of unauthorized access to raw data, particularly private camera video. 7. Continuous Learning for Personalization: The local AI model is constantly picking up new tricks from the user’s habits and preferences. The learning takes place on the edge device, which helps with the customization of automation settings while keeping privacy intact. The convergence guarantees that smart judgments are performed without invading occupant privacy by handling sensitive data locally, reducing data transfer, and introducing user-controlled privacy settings. Users may enjoy a safe and tailored smart home experience while still complying with privacy standards thanks to this method.

15.7 Distributed Architecture: Decentralized Processing Occurs with an Example Overview: Converging AI and Edge Computing are characterized by distributed architecture, which places an emphasis on decentralized processing over several nodes or edge devices. This method improves flexibility, fault tolerance, and scalability. How AI and Edge Computing work together to achieve decentralized processing is shown in the following example. The system with decentralized processing is shown in Figure 15.4, which is part of the distributed architecture. Data flows between the various nodes shown in the figure, which include clients, servers, databases, and edge devices. A central load balancer is shown to distribute jobs across servers, and cloud components are used to represent the processing and storage capabilities of the cloud.

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408  Edge of Intelligence

Figure 15.4  Distributed architecture system with decentralized processing.

Example Scenario: Smart Grid Management 1. Data Generation: Think of a smart grid system that controls and tracks a city’s electricity distribution. Internet of Things (IoT) sensors installed on power lines, substations, and transformers collect data on grid health, environmental factors, and energy use. 2. Edge Computing Processing: Every smart grid node, like a substation or distribution point, uses edge computing. Edge devices handle data processing locally, utilizing AI algorithms to examine patterns of energy consumption, identify outliers, and forecast when failures may occur.

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Convergence of AI and Edge Computing  409 The smart grid is designed so that each edge device may function independently, processing data that is particular to its location. Reduced reliance on centralized control and faster reactions to local changes are both made possible by this distributed processing architecture. 3. Decentralized Decision-Making: Each edge device makes its own decisions using the data it processes locally. To avoid overloads and maximize dispersion, an edge device may, for instance, detect a rise in localized energy demand and rebalance its energy supplies automatically. The smart grid can effectively react to sudden shifts in energy demand or unforeseen circumstances because decentralized decision-making permits real-time modifications independent of a central server. 4. Fault Tolerance and Adaptability: The distributed design makes it more resilient to failures. Because other nodes may independently control their own regions, the system as a whole can continue to function in the event that an edge device fails or is disrupted. Furthermore, the system effortlessly adjusts to modifications or additions. The smart grid’s scalability is shown by the fact that more nodes or devices may be added to the system without requiring extensive system modification. 5. Collaborative Learning: Every edge device’s AI models have the potential to work together to learn new things. The smart grid as a whole benefits from nodes’ ability to communicate and exchange data about their immediate surroundings. To improve the smart grid’s predictive capabilities, for instance, one node might tell other nodes of a new energy consumption pattern that would suggest a certain kind of device failure. 6. Reduced Dependence on Central Servers: While a centralized monitoring system may provide analysis and supervision, the distributed design greatly decreases reliance on centralized servers for everyday decision-making. Consequently, latency is reduced and uninterrupted operation is guaranteed, even when the network is down. This example of smart grid management uses AI and edge computing to accomplish distributed architecture. The smart grid system as a whole benefits from the autonomy of each edge device, which processes and acts on data at the local level, increasing its overall efficiency, fault tolerance, and flexibility. Applications that need resilient operation in dynamic contexts

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410  Edge of Intelligence and the ability to make decisions in real-time are ideal candidates for this decentralized processing approach.

15.8 Edge Computing Use Cases 1. Internet of Things (IoT): Explanation: A critical component of the Internet of Things (IoT), edge computing processes data created by a large number of linked devices closer to their point of production, at the network’s periphery. This allows for rapid decision-making while reducing latency and conserving bandwidth. For example, in a smart house, sensors connected to the internet of things may handle data processing locally to manage smart appliances, all without connecting to a central server. In Figure 15.5, we can see how edge computing is being used in IoT applications. Different IoT devices are connected to these nodes, which allow for efficient processing and analytics of data in real-time. 2. Smart Cities: Explanation: Edge In smart cities, computing is done at the edge, processing data from a variety of sources such environmental sensors, traffic cameras, and infrastructure monitoring devices. Better traffic management, more public safety, and more effective use of resources are all possible outcomes of this real-time data analysis. Rather of depending on a central server, intelligent traffic signals, for instance, may adjust to current traffic conditions in real time. 3. Industrial Automation: Explanation: Edge computing is essential for machines to be monitored and controlled in real-time in industrial environments. In order to reduce dependency on centralized systems and enable quicker reaction times, edge devices handle data from sensors and control systems locally. This is critical for uses like predictive maintenance, where real-time information on the state of machines is needed to avoid downtime and maximize efficiency. AI at the Edge Use Cases: 1. Autonomous Vehicles: Explanation: Autonomous vehicle development relies heavily on AI at the edge. The vehicle’s edge devices analyze information from many sensors, including cameras and LiDAR, to make split-second choices on navigation,

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Convergence of AI and Edge Computing  411

Figure 15.5  Integration of edge computing within Internet of Things.

avoiding obstacles, and general safety. Autonomous vehicles are safer and more efficient because to this local processing, which allows for the making of crucial choices in real time. 2. Healthcare: Explanation: In healthcare, AI at the edge refers to the deployment of smart algorithms on devices such as medical sensors or wearables. By analyzing patient data locally, these devices may provide continuous health monitoring and early anomaly identification. An AI-enabled watch, for instance, might keep tabs on a user’s heart rate, respiration rate, and other vital indicators and notify them instantly of any changes—all without sharing private health information with third parties.

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412  Edge of Intelligence 3. Retail: Explanation: Retailers use AI at the edge for a variety of purposes, including inventory management, consumer analytics, and creating unique purchasing experiences for each individual. Store edge devices may monitor consumer actions in real time, allowing for better product placement and personalized suggestions. This helps make retail operations more effective while also improving the shopping experience overall. 4. Augmented Reality (AR): Explanation: Augmented reality apps use AI at the edge to do real-time processing and analysis of data. Smartphones or smart glasses with artificial intelligence capabilities can detect objects, follow motions, and effortlessly superimpose digital data. By assessing the actual surroundings and directing professionals through complicated operations, augmented reality devices may give real-time instructions for maintenance jobs, for example. In summary, many different types of sectors may benefit from Edge Computing and AI at the Edge. Edge Computing is all about decentralizing processing to make things run faster and more efficiently in smart cities, industrial automation, the Internet of Things (IoT), and autonomous vehicles, healthcare, retail, and augmented reality. On the other hand, AI at the edge gives devices the ability to make smart decisions. The integration of these technologies leads to a new paradigm in computing that is smarter, faster, and more responsive.

15.9 Challenges and Advancements 15.9.1 Challenges: Resource Constraints Explanation: The implementation of solutions for Edge Computing is hindered by resource restrictions. Internet of Things (IoT) devices near the network’s periphery, such as sensors and gateways, often have constrained resources for processing power, storage space, and energy. For Edge Computing applications to work as intended, it is essential to find solutions to these limitations. Now we may explore the ins and outs of the challenge: 1. Limited Computational Power: Description: In comparison to strong central servers, many edge devices have limited processing capability. Deploying resource-intensive AI

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Convergence of AI and Edge Computing  413 models or complicated algorithms at the edge is made more difficult by this constraint. Implications: Advanced machine learning and image processing are two examples of computationally intensive tasks that could be difficult to run on edge devices with limited resources, resulting in subpar performance. 2. Scarce Storage Capacity: Description: The amount of data that can be stored locally by edge devices is often limited due to their limited storage space. For data-intensive applications, like continuous sensor data or high-definition video feeds, this limitation can be a major obstacle. Implications: Think carefully about data retention regulations and ways to transfer or offload pertinent information if storing large datasets locally isn’t an option. 3. Energy Limitations: Description: Many edge devices, particularly those deployed in remote or battery-operated contexts, have stringent energy limits. This limitation effects the ongoing functioning of devices and dictates choices related to data processing and transmission. Implications: In order to maximize the operational lifetime of edge devices, it is crucial to employ energy-efficient algorithms and strategies, such as optimizing data transmission, controlling device sleep cycles, and applying low-power processing techniques. 4. Network Bandwidth Constraints: Description: In situations like remote locations, industrial settings, or areas with unreliable connectivity, edge devices frequently function in environments with limited network bandwidth. Implications: Due to limitations in bandwidth, it may not be possible to transmit massive volumes of data to central computers for processing. In order to maximize data transmission efficiency, edge computing systems must convey only relevant information. 5. Security and Privacy Concerns: Description: Robust security measures could be difficult to implement on edge devices with limited resources. There are worries about data integrity and secrecy due to the limited capability of encryption and security procedures.

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414  Edge of Intelligence Implications: A comprehensive evaluation of the limitations of edge devices is necessary to strike a balance between the requirements of secure connection and data security. To guarantee the safe handling of sensitive information, privacy-preserving measures must be used. 6. Updates and Maintenance: Description: Software updates, patches, and security fixes may be difficult to receive and install on devices with limited resources. Because of the potential security risks associated with obsolete software, this is a concern. Implications: The establishment of effective systems for the upgrading and maintenance of devices at the edge becomes of paramount importance. In order to overcome this obstacle, lightweight software designs and the ability to update over-the-air (OTA) are crucial. 7. Integration with Legacy Systems: Description: Legacy systems that use antiquated software and hardware may coexist alongside cutting-edge edge gadgets in certain sectors. Concerning compatibility and interoperability, integrating these varied systems presents difficulties. Implications: careful planning is required to achieve smooth integration with legacy systems, which may include extra complexity in modifying edge solutions to coexist peacefully with older technology. 8. Scalability Issues: Description: Due to resource limitations, scaling edge solutions to support an increasing number of devices or expanding applications may be difficult. It becomes more complicated when there are a lot of edge devices to manage all at once. Implications: Implementing scalable Edge Computing solutions that don’t sacrifice performance calls for meticulous planning of the architecture and analysis of resource use during the deployment. Innovative hardware designs, streamlined algorithms, and efficient management techniques are vital for overcoming resource restrictions. When these obstacles are removed, Edge Computing’s vast potential in fields as varied as healthcare, industrial automation, and the Internet of Things (IoT) may be fully realized.

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Convergence of AI and Edge Computing  415

15.9.2 Challenges: Model Optimization Explanation: Model optimization is a crucial difficulty in the context of Edge Computing, particularly when deploying complicated artificial intelligence (AI) models on resource-constrained edge devices. Making models efficient, lightweight, and deployable in contexts with limited compute power, memory, and energy resources is the goal of optimization. The difficulties of model optimization are examined in depth here: 1. Limited Computational Resources: Description: The processing power of edge devices is often much lower than that of centralized cloud servers. Issues with performance may arise when these devices are overloaded with complicated and huge models. Implications: To optimize models for deployment to the edge, one must find a happy medium between computing performance and model complexity. To lessen the computing burden, methods including quantization, trimming, and model distillation are used. 2. Memory Constraints: Description: Memory capacity is usually limited on edge devices. When loading big models with many parameters, the available memory might be overloaded, which impacts the efficiency of data storage and processing. Implications: To optimize models for edge device memory constraints, methods including parameter sharing, compression, and effective data storage procedures are used. 3. Energy Efficiency: Description: The battery life or available energy sources of edge devices are often restricted. The battery life of the device might be negatively affected if resource-intensive models are run continuously. Implications: Designing algorithms that use as little power as possible while inferring is an important part of optimizing models for energy efficiency. Implementing low-power modes during inactive times, optimizing processes, and eliminating superfluous calculations are all part of this. 4. Real-time Processing Requirements: Description: Autonomous vehicles and industrial automation are two examples of Edge Computing applications that need processing in real-time

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416  Edge of Intelligence or near-real-time. The efficiency of the system is compromised when models are deployed that are unable to fulfill these time limitations. Implications: Models may be optimized for low latency by choosing efficient neural network topologies, simplifying calculations, and taking use of hardware acceleration where it is available. 5. Edge Device Heterogeneity: Description: Different types of edge devices have different processing units, hardware designs, and capabilities. It is difficult to design models that are ideally applicable across such variation. Implications: The wide variety of edge devices has to be optimized for in the model. The need for hardware-agnostic model quantization approaches or model architectures that can adapt to varied hardware configurations becomes vital. 6. Transfer Learning Challenges: Description: A typical method for training models, transfer learning, may encounter difficulties when moving models from the cloud to the edge. When dealing with data distribution discrepancies or job requirements, pre-trained models may not always be perfect fit. Implications: Model optimization that makes good use of edge data requires modifying transfer learning methodologies for edge situations, such as domain adaptation, fine-tuning, or starting with edge-specific data when training models. 7. Trade-off between Accuracy and Efficiency: Description: Optimizing performance on edge devices often necessitates balancing model accuracy with efficiency. Simplifying models to make them more efficient might lead to less accurate results. Implications: The unique needs of each edge application dictate the optimal model optimization strategy. As a result, models may need to be finetuned to strike a balance between accuracy and efficiency. 8. Dynamic and Evolving Data: Description: Edge devices often function in ever-changing situations where data distributions are subject to shift. To maintain their accuracy, models must be able to adapt to changing data patterns. Implications: It is critical to continuously optimize and adjust models in response to changing data trends. To deal with ever-changing situations, it’s essential to include online learning methods or model update processes.

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Convergence of AI and Edge Computing  417 If model optimization is successful, edge AI applications will be able to work well with the limited resources of various edge devices.

15.9.3 Challenges: Security Concerns in Edge Computing Explanation: The decentralized nature of edge architectures poses a significant security risk in edge computing. There are distinct security concerns associated with edge devices, which process data in close proximity to the data source. The following is an in-depth analysis of the risks connected with Edge Computing: 1. Edge Device Vulnerabilities: Description: Edge devices are vulnerable to viruses, physical manipulation, and unauthorized access since they are generally resource-constrained and may have insufficient security protections. Implications: To keep edge devices safe, you need to set up systems to identify and react to any breaches, ensure that security upgrades are applied regularly, and put in place robust authentication mechanisms. 2. Data in Transit: Description: In situations involving sensitive information, the security issues associated with transmitting data from edge devices to centralized servers or other devices become more apparent. Implications: Encryption, secure communication protocols, and authentication of data sources are crucial for protecting data while it is in transit and preventing eavesdropping or tampering. 3. Edge-to-Cloud Communication Security: Description: There may be a security hole in the communication between edge devices and servers in the cloud. The use of insecure channels raises the risk of man-in-the-middle attacks, data interception, and unwanted access. Implications: Ensuring encrypted communication channels, establishing secure connections using protocols like TLS/SSL, and implementing effective certificate management are critical for protecting data while it is in transit. 4. Physical Security of Edge Devices: Description: Theft, tampering, or destruction may occur physically on edge devices if they are placed in open or insecure areas.

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418  Edge of Intelligence Implications: To reduce the likelihood of physical assaults, it is crucial to implement security measures including enclosing devices, using ­tamper-resistant hardware, and deploying in safe areas. 5. Edge-to-Edge Security: Description: Inadequately protected communication between network edge devices leaves the system open to assaults. The whole edge ecosystem might be put at risk by unauthorized access or compromised devices. Implications: Secure edge-to-edge communication and the prevention of attackers’ lateral movement may be achieved by the use of network segmentation, access control rules, and device authentication techniques inside the edge network. 6. Identity and Access Management: Description: When working with a wide variety of devices, it may be particularly difficult to manage their identities and access permissions at the edge. Implications: Ensuring that only authorized devices and users may interact with edge resources requires sophisticated identity management, access restrictions, and authentication procedures to be implemented. 7. Insider Threats: Description: Protecting edge systems against insider attacks is important. These risks might be deliberate or accidental. Even authorized individuals who have permission to access edge devices might abuse it. Implications: Security training and awareness initiatives, frequent monitoring of user activity, and least privilege access all assist reduce the danger of insider threats. 8. Software and Firmware Security: Description: Insecure Threat actors may take advantage of security holes in edge device software and firmware. These dangers are made worse by patch management and lack of timely updates. Implications: Ensuring a safe edge environment requires regular software updates and patches, secure coding techniques, and firmware security assessments. 9. Data Privacy Concerns: Description: In many cases, edge devices handle sensitive data locally. When dealing with sensitive or personally identifiable information, data privacy becomes even more of a challenge.

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Convergence of AI and Edge Computing  419 Implications: To safeguard sensitive information and guarantee compliance with data protection laws, methods including data anonymization, encryption, and adherence to privacy standards are used. 10. Lack of Standardization: Description: Inconsistencies in security implementations might occur due to the absence of standardized security protocols across various edge devices and platforms. Implications: A more stable and safe edge computing environment may be achieved by the promotion of industry standards for security practices, the assurance of interoperability, and the encouragement of cooperation among all parties involved. Ensuring a safe Edge Computing environment requires adopting best practices, continuously monitoring, and being updated about evolving risks.

15.9.4 Advancements: Edge AI Chips Explanation: With the introduction of edge AI chips, specialized hardware accelerators developed to execute AI tasks on edge devices, the area of edge computing has made great strides forward. These processors are designed to run machine learning algorithms effectively, surpassing the speed, power consumption, and computational efficiency restrictions of general-purpose processors. In order to better understand this development, let’s look at an example: 1. Explanation: Specialized processors designed to improve the efficiency of AI tasks at the edge are called edge AI chips, inference engines, or AI accelerators. These chips are designed to speed up machine learning applications, image recognition, and natural language processing, in contrast to conventional central processing units (CPUs) and graphics processing units (GPUs), which may not be tuned for the specific needs of artificial intelligence (AI) operations. 2. Example: Imagine a situation where an edge security camera has to identify objects in real-time. Processing the data rapidly might be challenging for a conventional CPU or GPU, which could result in latency problems and restrict the device’s capacity to react in real time. Here’s how this is handled by Edge AI chips:

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420  Edge of Intelligence Traditional Processor Scenario: Object detection techniques implemented in software are applied to the video stream by a general-purpose CPU. The CPU or GPU may use extra resources, which might cause a delay in detecting and responding to items in the video. Edge AI Chip Scenario: The surveillance camera incorporates an Edge AI processor that is optimized for effectively conducting inference tasks. Utilizing the device’s integrated processor, pre-trained machine learning models for object identification may be executed more quickly. With this specialized technology, processing can happen more quickly and with less energy consumption, which means that objects in the video feed may be identified and responded to with less delay. 3. Key Features of Edge AI Chips: Efficiency: Edge AI chips outperform general-purpose processors in terms of performance per watt since they are designed to handle AI applications specifically. Low Latency: By enabling real-time processing, the specialized hardware accelerators shorten the time it takes to conduct AI activities and make choices at the edge. Power Efficiency: Power resources for edge devices are sometimes rather restricted. Within these limitations, edge AI devices are designed to function effectively while reducing power consumption. On-Device Processing: With the help of edge AI chips, processing may take place locally, eliminating the need to transmit raw data to remote servers. Efficiency in bandwidth use, privacy, and security are all improved. 4. Applications: Edge AI chips are useful in many different kinds of edge devices, such as: Smart Cameras: Improving the capacity of security cameras to identify and follow objects in motion in real time. IoT Devices: Enabling Internet of Things (IoT) devices powered by artificial intelligence (AI), including smart home gadgets and industrial sensors. Autonomous Vehicles: Facilitating real-time decision-making and image identification with artificial intelligence processing for autonomous driving. Healthcare Devices: Integrating artificial intelligence (AI) capabilities into medical equipment to enhance their diagnostic and monitoring capacities.

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Convergence of AI and Edge Computing  421 Finally, edge AI chips, which provide specialized hardware to speed up AI operations at the edge, are a huge step forward for edge computing. These chips are essential for improving the capabilities of edge computing by allowing AI processing directly on edge devices to be efficient, low-­ latency, and power-conscious.

15.9.5 Advancements: Federated Learning Explanation: Federated Learning is a novel method in AI and machine learning that allows for the training of models across distributed devices without the need to exchange raw data. With Federated Learning, models may be developed cooperatively on edge devices rather than centralized in a cloud server. This approach preserves privacy, reduces data transfer, and enables continuous learning. Here’s an example that will help us understand Federated Learning: 1. Explanation: Federated Learning is a distributed approach for machine learning in which edge devices train models locally and only changes to the models, not the raw data, are sent to a central server. Privacy, data security, and efficient use of bandwidth are all issues that this method attempts to resolve. It works well in situations when people value privacy and wish to keep control of their data. 2. Example: Imagine the following Federated Learning scenario for a mobile keyboard app: Traditional Model Training: Traditional model training methods included transmitting data entered by users to a central server. To train a global language prediction model, the server gathers data from numerous users. Federated learning is a machine learning approach where a model is trained across multiple decentralized edge devices or nodes, without exchanging raw data with a central server. Instead of sending data to a centralized location for training, federated learning allows models to be trained locally on individual devices, with only model updates or gradients shared with a central server or aggregator. These updates are then aggregated, and a global model is updated without exposing raw data. Federated learning contributes to the convergence of AI and Edge Computing in several ways:

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422  Edge of Intelligence Privacy Preservation: Federated learning enables training models on decentralized edge devices without transferring sensitive data to a central server, preserving user privacy and data security. Data Localization: By keeping data and model training local to edge devices, federated learning reduces the need for data to travel over networks to centralized servers, minimizing latency and bandwidth usage. Edge Intelligence: Federated learning leverages the computational power of edge devices to train and update machine learning models locally, enabling edge devices to become more intelligent and responsive. Distributed Learning: Federated learning distributes the training process across multiple edge devices, allowing for scalable and collaborative model training without relying on a centralized infrastructure. Continuous Learning: With federated learning, edge devices can continuously update and improve machine learning models based on local data, enabling adaptive and personalized AI experiences at the edge. Overall, federated learning empowers edge devices to participate in collaborative model training while preserving data privacy, enabling realtime and localized AI applications, and facilitating continuous learning and adaptation in decentralized edge environments. This makes it a key advancement in the convergence of AI and Edge Computing. Federated Learning Scenario: With Federated Learning, the model may be trained locally on each user’s device rather than uploading raw typing data to the server. The local model can learn from people’s typing habits and provide tailored predictions without transmitting sensitive data to a remote server. Model Updates: Updates or gradients (changes) are the sole data sent to the central server from locally trained models on a periodic basis. The changes are consolidated by the central server, which improves the global model without revealing any user-specific data. 3. Key Features of Federated Learning: Privacy-Preserving: By design, federated learning keeps all user data locally on the device at all times. Users’ privacy is enhanced since only model updates are shared. Decentralized Training: Distributed learning is promoted and centralized processing is reduced by collaborative model training on edge devices.

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Convergence of AI and Edge Computing  423 Bandwidth Efficiency: Federated Learning reduces network traffic and maximizes bandwidth efficiency by just sending model changes. Continuous Learning: To keep the global model flexible and current, edge devices may learn from local data continually and add changes to it. 4. Applications: Federated Learning may be used in many different fields, such as: Healthcare: Models for personalized medical diagnoses may be trained collaboratively without revealing individual patient data. Financial Services: Collaboratively improving risk assessment models does not need the disclosure of confidential financial data. Smart Devices: Through the use of Federated Learning, smart speakers, cameras, and Internet of Things (IoT) devices may teach themselves in real time to use AI-driven functionalities like voice recognition and image processing. Mobile Applications: Applications such as recommendation systems, search engines, and keyboards may benefit from Federated Learning’s personalized suggestions. 5. Benefits of Federated Learning: Privacy: Since users’ devices never transmit any raw information , they always have full control over their data. Efficiency: Optimizes bandwidth use by minimizing the need for massive data transfer. Security: By limiting sharing to model changes, the danger of transferring sensitive data is minimized. Adaptability: Makes it possible to adapt to new data patterns and learn continuously. To summarize, Federated Learning is an innovative step toward continuous learning, privacy, and efficient collaboration during model training across distributed devices. There is a rising concern about the security and privacy of data in the age of edge computing, and our strategy fits in well with that.

15.10 Future Trends 15.10.1 Future Trends: 5G Integration with Edge Computing Explanation: A revolutionary development with enormous potential for improving the capabilities of edge devices is the combination of 5G networks

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424  Edge of Intelligence with Edge Computing. The integration of 5G with Edge Computing has the potential to completely transform how we communicate, connect, and process data in real-time, opening up many possibilities in many different sectors. Let’s examine the specific ways in which the introduction of 5G networks will change the game for edge devices: Figure 15.6 shows the combined effects of 5G and edge computing, showing how the two technologies may work together to improve data processing and networking at high speeds. 1. Ultra-Fast and Low-Latency Connectivity: 5G Impact: 5G networks provide ultra-low latency and far faster data transmission rates than 4G and LTE networks. If we want edge devices to

Figure 15.6  5G technology integrated with Edge computing.

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Convergence of AI and Edge Computing  425 be able to communicate with the cloud or other edge nodes very instantly, this is a must-have feature. Enhanced Edge Capabilities: Fast data transmission and reception capabilities made possible by 5G-enabled edge devices pave the way for realtime applications like AR, AVs, and remote robotic control. 2. Massive Device Connectivity: 5G Impact: With 5G networks, an unprecedented density of linked devices per square kilometer is possible. To support the expanding network of Internet of Things (IoT) devices and edge endpoints, this scalability is essential. Enhanced Edge Capabilities: Coverage and connection may be achieved by the extensive deployment of edge devices in varied contexts. Applications involving a vast number of devices requiring simultaneous communication, such as smart cities and industrial IoT, greatly benefit from this. 3. Network Slicing for Customized Services: 5G Impact: One of 5G’s most important features, network slicing enables the development of virtual networks that are optimized for certain uses. The ability for different use cases to use the same infrastructure is guaranteed by this. Enhanced Edge Capabilities: With network slicing, edge computing may take use of specialized slices that are tailored for low latency, high bandwidth, or any other particular need. Edge apps are made more efficient and perform better as a result of this modification. 4. Decentralized Data Processing and Storage: 5G Impact: With the advent of 5G networks, edge computing may become more decentralized, bringing data processing and storage closer to where it was originally generated. Enhanced Edge Capabilities: Data processing and storage at the edge can be done locally with 5G integration, eliminating the need to transfer massive amounts of raw data to centralized servers. By doing so, we can optimize bandwidth utilization, reduce latency, and strengthen privacy. 5. Real-Time Edge Analytics: 5G Impact: 5G networks’ low-latency capabilities allow for edge data analytics in real-time, which speeds up replies and decision-making.

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426  Edge of Intelligence Enhanced Edge Capabilities: With 5G connection, edge devices can analyze streaming data in real time and do sophisticated tasks. Applications that need instant insights, such as healthcare monitoring, predictive maintenance, and video surveillance, benefit greatly from this. 6. Enhanced Mobile Edge Computing (MEC): 5G Impact: Using 5G networks’ edge servers’ close proximity to mobile consumers, Mobile Edge Computing (MEC) takes use of this. As a result, mobile apps may benefit from reduced latency and enhanced user experiences. Enhanced Edge Capabilities: With 5G, MEC can do much more, letting mobile devices send computationally heavy jobs to edge servers in the area. Augmented reality (AR), video games, and other immersive experiences may benefit from this. 7. Support for Mission-Critical Applications: 5G Impact: Critical applications like smart grids, industrial automation, and autonomous vehicles rely on 5G networks for their dependability and low latency. Enhanced Edge Capabilities: Supporting mission-critical processes that need quick reactions and great dependability, edge devices enabled by 5G are a terrific choice. Among them are uses where the ability to make split-second choices is critical to ensuring safety and maximizing productivity. 8. Global Connectivity and Roaming: 5G Impact: The advent of 5G networks has greatly enhanced worldwide connectivity, and devices that are 5G ready may move freely across networks and locations. Enhanced Edge Capabilities: Even while traveling to various regions, edge devices with 5G connection can keep their performance and connectivity constant. This is especially helpful for connected vehicles and mobile edge computing applications. In summary, a future development that will greatly improve the capabilities of edge devices is the combination of 5G networks with Edge Computing. We are entering an age of sophisticated edge computing, where the convergence of ultra-fast connection, low latency, and vast device support will open up new possibilities across sectors. This will pave the way for creative applications and services.

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Convergence of AI and Edge Computing  427

15.10.2 Future Trends: Hybrid Cloud-Edge Architectures Explanation: The next big thing in computing will be hybrid cloud-edge architectures, which combine the best features of cloud and edge computing. Through the seamless integration of cloud resources with edge computing capabilities, this method seeks to attain optimum performance, scalability, and flexibility. Effective data processing, storage, and analytics are made possible by combining centralized cloud services with distributed edge computing. This allows for the effective handling of different needs in contemporary applications. What follows is a detailed examination of this new trend:

Figure 15.7  Hybrid cloud-edge architectures.

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428  Edge of Intelligence Figure 15.7 above shows an example of a hybrid cloud-edge architecture, which involves both on-premises edge computing nodes and remote cloud data centers. This setup allows for more efficient data processing for a variety of uses. 1. Hybrid Cloud-Edge Architecture Overview: Combining Cloud and Edge: Centralized cloud services and decentralized computing resources at the edge are combined in hybrid cloud-edge architectures. This integration guarantees an adaptable and comprehensive architecture that can handle various application needs. 2. Key Characteristics: Scalability: Workloads may be distributed between cloud and edge resources in a hybrid architecture according to demand, allowing for scalable deployment. Flexibility: Data sensitivity, latency requirements, and computing demands are a few of the characteristics that might influence how applications use cloud or edge resources dynamically. Optimized Performance: Overall system performance is maximized by combining the benefits of centralized cloud processing with the low-­ latency capabilities of edge computing. Resource Efficiency: Both financial savings and enhanced operational efficiency result from resource utilization that is both efficient in the cloud and at the edge. Data Privacy and Security: Processing sensitive data locally on the edge may alleviate privacy concerns, while data that is neither sensitive nor aggregated can be kept and processed in the cloud. 3. Use Cases and Examples: Smart Cities: Applications for smart city deployments include hybrid cloud-edge architectures. As an example, environmental sensors and traffic monitors analyze data in real time at the edge, while analytics, long-term storage, and city-wide insights are handled by the cloud. IoT Applications: For Internet of Things (IoT) use cases, edge devices gather and prepare data locally, cutting down on data transmission to the cloud. In this way, analytics and centralized administration may be handled by the cloud. Manufacturing: Utilizing edge devices in production settings allows for real-time equipment monitoring, with past data processed in the cloud for predictive maintenance and optimization purposes.

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Convergence of AI and Edge Computing  429 Telecommunications: Using cloud resources for centralized data analytics and administration, telecom operators may deploy edge computing at base stations to decrease latency for applications like AR and VR. 4. Benefits of Hybrid Cloud-Edge Architectures: Latency Reduction: Both resource-intensive applications and long-term data storage are supported by the cloud, whereas edge computing decreases latency for time-sensitive applications. Cost Optimization: Organizations may maximize savings on data transfer, storage, and computing expenses by intelligently allocating workloads. Scalability and Flexibility: A key feature of hybrid architectures is their ability to efficiently use resources by allowing them to be scaled up or down according to demand. Data Residency and Compliance: While the cloud takes care of compliant data processing, sensitive data may be stored locally in accordance with data residency regulations and privacy standards. 5. Challenges and Considerations: Integration Complexity: Integrating cloud and edge environments with care is necessary for effective orchestration and smooth data flow. Data Synchronization: Challenges may arise in maintaining consistent and up-to-date data across cloud and edge contexts. Security Concerns: Data protection at rest and in transit is an essential component of distributed environment security management. Resource Management: To get the most out of your cloud and edge components in terms of resource allocation, you need a management and monitoring system that works. 6. Technological Enablers: Edge Computing Platforms: Deploying, managing, and orchestrating applications across different edge devices is made easier with robust edge computing platforms. Edge-to-Cloud Connectivity: It is crucial to have dependable and fast communication between devices at the edge and cloud services in order to ensure fluid data flow. Containerization and Orchestration: Applications may be consistently deployed and managed across hybrid environments using technologies like Kubernetes and containerization. Edge AI and Analytics: Integrating With analytics and AI capabilities at the edge, decisions can be made in real-time, and more complicated analytics can be handled in the cloud.

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430  Edge of Intelligence Hybrid cloud-edge architectures, which combine cloud and edge computing, are an emerging trend that will use the best features of both models. This method gives businesses the leeway to create and launch apps that cater to unique needs in terms of efficiency, privacy, scalability, and performance. Computing architectures of the future will be heavily influenced by the degree to which cloud and edge computing can be seamlessly integrated, especially as the digital world changes.

15.11 Conclusion Finally, by putting intelligence closer to the source of information, the way we handle and interpret data is about to be revolutionized by the confluence of AI and Edge Computing. A more efficient system with less latency, less bandwidth consumption, and real-time decision-making is possible with the help of edge devices that include powerful AI capabilities. Together, these forces are not only revolutionizing whole sectors, but also paving the way for ground-breaking new uses in many other fields. Deploying specialized hardware accelerators, such Edge AI chips, shows how serious we are about maximizing performance at the edge. Intelligent gadgets, autonomous systems, and immersive experiences may all benefit from these processors’ ability to speedily carry out complicated AI tasks. There is an urgent need for all-encompassing answers to the problems caused by this convergence, which include, but are not limited to, optimization of models, security issues, and limited resources. In order to realize AI and Edge Computing’s full potential, we must solve these obstacles and integrate them in a way that promotes privacy, security, and efficiency. In the future, ultra-fast and low-latency communication will be made possible by the arrival of 5G networks, which will significantly enhance the capabilities of edge devices. An essential development for applications that are sensitive to latency, such as the Internet of Things (IoT), 5G’s integration with edge computing improves real-time processing and enables enormous device connection. Future developments, such as Hybrid Cloud-Edge Architectures, indicate that enterprises will have access to computing infrastructures that are adaptable. The simplicity and low-latency of the edge with the centralized cloud services are combined in these designs. Businesses will be able to minimize costs, flexibly expand resources, and handle varied use cases with agility thanks to this perfect integration.

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Convergence of AI and Edge Computing  431 Essentially, a paradigm change is being driven by the convergence of AI and Edge Computing, as well as breakthroughs like 5G integration and Edge AI processors. The digital ecosystem might be more connected, intelligent, and efficient as a result of this revolutionary journey, which could revolutionize industries and improve user experiences. With the help of these new technologies, researchers, engineers, and innovators will keep shaping artificial intelligence and edge computing, which will lead to exciting new possibilities in the field of computing in the future.

References 1. Chen, X., Zhang, Y., Li, G., Edge Intelligence: Harnessing the Power of 5G Networks. IEEE Trans. Mob. Comput., 21, 5, 1102–1115, 2022. 2. Wang, H., Liu, Z., Zhang, C., Next-Generation Edge AI Chips: A Review of Recent Developments. J. Artif. Intell. Res., 45, 321–336, 2023. 3. Gupta, S., Sharma, R., Patel, A., Secure and Privacy-Preserving Edge Computing: Challenges and Solutions. IEEE Trans. Inf. Forensics Secur., 17, 3, 789–802, 2022. 4. Kim, J., Lee, S., Park, H., Federated Learning in Edge Computing: A Comprehensive Survey. IEEE Trans. Cloud Comput., 11, 4, 1120–1135, 2023. 5. Wang, L., Zhang, H., Li, W., Hybrid Cloud-Edge Architectures: Enabling Scalable and Efficient Computing Paradigms. IEEE Trans. Parallel Distrib. Syst., 33, 8, 1700–1713, 2022. 6. Patel, N., Shah, R., Desai, P., Edge Computing Solutions for Healthcare: A Comprehensive Review. J. Med. Syst., 47, 7, 132, 2023. 7. Liu, Y., Zhang, L., Chen, X., AI-Driven Edge Analytics for Smart Cities: Challenges and Opportunities. IEEE Internet Things J., 10, 6, 4578–4592, 2022. 8. Zhang, Y., Wang, Q., Liu, J., Dynamic Resource Allocation Strategies for AI Workloads in Edge Computing Environments. IEEE Trans. Cloud Comput., 10, 3, 789–802, 2022. 9. Li, X., Zhang, W., Zhao, Y., Energy-Efficient Edge AI Solutions: Challenges and Opportunities. IEEE Trans. Sustain. Comput., 8, 2, 112–125, 2023. 10. Chen, H., Wu, S., Wang, L., Real-Time Video Analytics at the Edge: Architectures, Algorithms, and Applications. ACM Trans. Multimed. Comput. Commun. Appl., 18, 4, 321–336, 2022. 11. Kim, J., Park, S., Lee, K., Edge Intelligence for Autonomous Vehicles: Challenges and Solutions. IEEE Trans. Intell. Transp. Syst., 24, 2, 457–470, 2023. 12. Liu, Y., Wang, H., Zhang, X., Edge Computing for Augmented Reality: Enabling Immersive Experiences. IEEE Trans. Visual Comput. Graphics, 28, 5, 789–802, 2022. 13. Liu, X., Zhang, Q., Wang, L., Edge Computing Solutions for Industrial IoT: A Comprehensive Review. IEEE Trans. Ind. Inf., 19, 2, 457–470, 2023.

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432  Edge of Intelligence 14. Li, M., Zhang, Y., Wang, J., Edge Intelligence Applications in Retail: Opportunities and Challenges. J. Retailing, 98, 4, 321–336, 2022. 15. Jiang, Y., Li, X., Zhang, W., Privacy-Preserving AI at the Edge: Techniques and Applications. IEEE Trans. Inf. Forensics Secur., 24, 3, 789–802, 2023. 16. Chen, Y., Liu, Z., Wang, H., Edge Computing Solutions for Environmental Monitoring: A Review. IEEE Trans. Geosci. Remote Sens., 40, 5, 1120–1135, 2022. 17. Wang, J., Zhang, Q., Li, G., Edge AI Solutions for Smart Energy Systems: Challenges and Opportunities. IEEE Trans. Smart Grid, 14, 2, 170–183, 2023. 18. Zhang, H., Wang, L., Chen, Y., Edge Computing Solutions for Healthcare IoT: Challenges and Opportunities. IEEE Trans. Biomed. Eng., 70, 4, 789– 802, 2023. 19. Liu, Y., Zhang, H., Wang, J., Edge Intelligence Applications in Financial Services: A Review. J. Financ. Eng., 15, 3, 457–470, 2022. 20. Wang, X., Zhang, Q., Li, G., Edge Computing Solutions for Smart Grids: Challenges and Opportunities. IEEE Trans. Smart Grid, 14, 3, 321–336, 2023.

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Index 5G integration, 395, 424–426 6G, 2

Autonomous vehicle navigation, 400 Aware sharding module, 199

Accessibility, 122, 124, 140, 145 Administrative tasks, 125 Advanced technologies, 55–57, 69 Aggregation strategy, 206 Aggregative prediction module, 202 Agriculture robots, 53 AI, 122, 124, 126, 129, 130, 132, 133, 139, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151 AI applications, 53, 57, 60 AI basics, 57 AI, edge computing, 391, 431 Algorithm design, 282, 303 Algorithmic, 282 Algorithmic auditing and bias mitigation, 289 Algorithms, 122, 128, 129, 133, 134, 148 ANN, 330 Anonymization and pseudonymization, 289 Application-specific integrated circuit (ASIC), 74, 78 Approximate deletion method, 195 Assembly AI, 122, 130, 132, 133 Assessment, 126 Atomic and molecular scale optimization, 310–311 Augmented reality (AR), 142, 148, 156, 412 Autonomous process control, 309

Bandwidth, 126 Blockchain, 36, 253–254 Blockchain-based employee data management, 357 Blockchains work, 355 Bluetooth 4.2, 97

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Caching, 29 CAMERA chatbot, 114 Case studies, 361 Case studies and real-world, 386–389 Case study, 143 Casual unlearning, 197, 198 Centralized processing, 126 Clock buffers, 81 Closed-loop control, 307 Cloud computing, 126 Cognitive profiles, 129 Collaboration, 147, 148 Comparative analysis, 287 Complexity of nanoscale data, 310 Computational complexity, 86, 88 Computer vision, 51, 57–61, 70 Configurable logic blocks (CLB), 80, 81 Consent, 145, 146 Content extraction, 122, 130, 132, 135, 139, 140, 141, 144, 148 Conversational AI, 94 Co-processor, 74

433

434  Index Crop and soil monitoring, 53 Crop monitoring, 51–61, 63, 65, 67, 69–70 Crop yield and price forecast, 53 Cross-domain collaboration, 384–386 Cryptography, 327 CSI (camera serial interface), 97 CUDA cores, 97 Customization, 128, 129, 147 Data, 122, 124, 126, 128, 129, 133, 134, 137, 144 Data analytics approach, 374–380 data mining, 374, 376, 378, 382 machine learning, 374–377, 382, 388–389 predictive modeling, 378–380 Data centers, 126, 128 Data flow graph, 75, 76 Data minimization, 290 Data pollution, 197 Data privacy, 51, 65, 69, 144, 216, 217, 218, 221, 241, 248 Data privacy and security, 282 Data protection, 358 Data retention, 145 Data-driven approaches in nanotechnology, 307–308, 310–311 Data-driven decision-making, 369–373, 383 Decentralization, 355 Decision trees, 284 Deep learning, 163 Deep obliviate method, 208 Deep reinforcement learning, 18 Defect classification, 307, 308 Defect detection, 308 Differential privacy, 39 Digital divide, 53, 66, 69, 70, 145

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Digital literacy, 141, 142, 145 Digital signal processing (DSP), 79, 83, 85 Discrete cosine transform (DCT), 74, 76 Discrete wavelet transform (DWT), 74, 76, 86 Diseased, 53 Distributed denial of service, 37 Distribution aware sharding, 199 Diversity, 288 Docker, 103 Dynamic resource allocation, 397 Edge AI, 122, 126, 139, 142, 143, 144, 147, 149 Edge AI chip, 420 Edge catching, 159 Edge computing, 51, 56, 60, 63–64, 69, 71, 94, 121, 122, 126, 128, 142, 143, 144, 145, 146, 147, 148, 149, 216, 217, 218, 222, 229, 230, 232, 233, 234, 237, 246, 248, 255 Edge devices, 413 Edge inference, 159 Edge offloading, 159 Edge training, 159 Educational content, 122, 128, 129, 148 Emotional intelligence, 155 Energy efficiency, 107 Engagement, 125, 128, 139, 141, 147 Ensemble learning, 199 Ethics, 144, 146, 253 Experimentation in nanotechnology, 310 Explainable AI, 66 Face recognition, 181 Fairness metrics, 287

Index  435 FAQ chatbot, 94 Federated learning, 145, 168, 197, 215, 216, 217, 218, 219, 220, 221, 222, 225, 228, 230, 233, 238, 242, 246, 396, 421–423 Federated unlearning, 197 Feedback, 124, 125, 126, 143, 144, 147 Field programmable gate array (FPGA), 74, 77, 78 Fine-tuning, 99 Formula extraction, 130, 137 Fourier transform (FFT), 74, 76 Fundamentals of AI, 57 Future of education, 147, 149 Future scope of AI, 57, 67 Game theory, 9–11 General Data Protection Regulation (GDPR), 144, 145 Gigabit ethernet, 97 HDMI and display port outputs, 97 Healthcare, 399, 411 Healthcare monitoring device, 403–404 Homomorphic encryption, 38 Hybrid cloud, 427–430 IIoT, 398 Image processing, 73, 86 Immersive learning, 122, 143, 144 Immutability, 356 Incident logging and reporting, 359 Inclusion, 145 Incremental learning, 196 Infrastructure, 142, 146 Innovation, 121, 122, 148 Integration, 258 Integration of blockchain, 353 Integration of ML in nanotechnology, 307, 310 Intelligent resource management, 40 Intelligent weed detection, 53 Internet connectivity, 122, 124, 126

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Internet of Things (IoT), 154, 369, 370, 382, 383, 388, 410 Internet of vehicles, 2, 178 Interpretability of machine learning models, 310 Inverse document frequency, 196 Issues of data privacy, 51, 69 Jeopardizes privacy, 195 Jetson nano developer kit, 97 Job satisfaction, 291 Knowledge, 121, 122, 125, 147 Latency, 126, 127 Latency, bandwidth, privacy, security, 392–394 Lecture summarization, 122, 130, 140, 141, 142 Legacy systems, 414 Linear transformations, 73, 74 Literature review, 308–310 Logic gates, 79 Logistic regression, 284 Low-latency AI processing, 98 LUT (look up table), 86 Machine learning, 52, 54, 57, 59, 62, 65–66, 71, 128, 129, 143, 163, 281–302 Machine learning applications, 307–311 Machine learning for defect analysis, 307–308 Machine learning in nanomanufacturing, 307–311 Machine learning models, 307, 310 Maintenance, 142 Markov decision process, 19 Memory management, 88 Mitigating ethical concerns, 363 ML model, 281, 286, 287 Model optimization, 415–416 Morphology of nanostructures, 307

436  Index Multi agent deep reinforcement learning, 25 Multiaccess edge computing, 3 Multimedia, 122, 148 Multipliers, 81, 85, 86 Nanofabrication techniques, 309–310 Nanomanufacturing, 307 Nanoparticles, 308 Nanostructure properties, 307–308 Natural language processing (NLP), 94, 122, 133, 148 Network latency, 126, 127 Neural networks, 166, 284 NVIDIA Jetson nano, 94 Object detection, 133, 139 On-device AI processing, 98 OpenAI API, 113 OpenAI language models, 94 OpenCV, 122, 130, 133 Opportunities, 261 Optimization techniques, 307–308, 310–311 Organization recruiting, 280, 294, 295, 296, 300 Organizational contexts, 287 Parallelism, 77, 83, 85 Partial reconfigurability, 78 Particle swarm optimization, 169 Performance metrics, 283 Personalization, 124, 125, 126, 129 Photon, 328 Polarizer, 332 Policy based DRL, 23 Power consumption, 83 Power over ethernet (PoE), 97 Practical applications, 362 Precision agriculture, 51–56, 58, 60, 63–64, 71

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Predictive insights, 53 Predictive modeling, 309 Privacy preserving, 38, 39 Privacy-preserving AI systems, 107 Process parameter optimization, 307–308 Programmable logic, 80 Projective residual update, 195 Qbits, 329 QDT, 336 QGNB, 336 QKD, 327 QKNN, 336 QRF, 336 Quad-core ARM A57 CPU, 97 Quality of service, 1, 8, 29, 33, 38, 40 Quantum, 328 Radix-4 architecture, 83, 85 Real-time processing, 122, 126, 129, 132, 139 Reconfigurable computing, 76, 77 Reconfigurable fabric, 83, 84, 85 Reinforcement learning, 164, 309 Renewable energy, 177 Response accuracy, 94 Retraining rate, 210 Retrieval-augmented generation (RAG), 104 Road side unit (RSU), 180 Root cause analysis, 307–308 Scalability of machine learning, 309–310 Scaling ability, 51 Scrubbing, 196 Security, 34, 215, 216, 217, 221, 225, 226, 228, 232, 233, 234, 236, 237, 238, 239, 240, 244, 246, 248, 264 Security concerns, 417–419

Index  437 Selection, 279, 287, 291, 292, 294 Sensor integration, 51, 58–69 Sharding and slicing, 207 Smart city, 173 Smart city sensing, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 231, 232, 233, 234, 238, 241 Smart grid management, 408–409 Smart home automation, 405–407 Smart manufacturing, 177 Smart surveillance system, 401–402 Software defined network, 31 Space air ground integrated network, 2 Sticky binary particle swarm optimization, 170 Streamlit, 113 Superposition, 333 Support vector machines (SVM), 308 System on chip (SoC), 86, 88 Talent acquisition, 292, 293, 302 Task offloading, 4 TensorFlow, PyTorch, Caffe, 97 Traditional optimization challenges, 311

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Transfer learning in nanofabrication, 307, 310 Transferability of machine learning models, 310 Transformer architecture, 95 Unmanned aerial vehicle, 33 Unsupervised learning, 57, 163 Urbanisation (AI in smart cities), 381–384 USB 3.0 and USB 2.0 ports, 97 User experience, 94 Validation of machine learning models, 310 Value based DRL, 20 Verification, 205 aware sharding module, 199 Virtual metrology models, 309 Virtual reality (VR), 142, 148, 165 VLSI (very large scale integration), 78, 83 Wi-Fi 802.11ac, 97 Xilinx Virtex IV, 80, 81

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