Shubham Mahajan & Sathyan Munirathinam 
Edge of Intelligence [PDF ebook] 
Exploring the Frontiers of AI at the Edge

Preface xvii

1 A Review on Computational Optimization Strategies and Collaborative Techniques of Vehicular Task Offloading in the Era of Internet of Vehicles and 6G 1
Aishwarya R., V. Vetriselvi and Meignanamoorthi D.

1.1 Introduction 2

1.2 Computational Optimization Strategies 7

1.3 Collaborative Techniques 29

1.4 Security 34

1.5 Challenges and Future Research Directions 39

1.6 Conclusion 41

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

2.2 Crop Monitoring AI Basics 57

2.3 AI Applications in Crop Monitoring 60

2.4 Challenges and Possible Future Paths of AI in Crop Monitoring 65

2.5 Conclusion 70

3 A Survey on Reconfigurable Co-Processors Computing Linear Transformations 73
Atri Sanyal and Amitabha Sinha

3.1 Different Linear Transforms 74

3.2 Reconfigurable Computing 75

3.3 Field Programmable Gate Array 78

3.4 Survey of Existing Work 82

3.5 Performance Comparison of Different Reconfigurable Co-Processors Implementing Linear Transformation(s) 86

3.6 Conclusions and Future Work 88

4 Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Based Chatbot on NVIDIA Jetson Nano 93
Kiran Jot Singh, Divneet Singh Kapoor, Amit Singh Bora, Khushal Thakur and Anshul Sharma

4.1 Introduction 94

4.2 Background 100

4.3 Literature Review 103

4.4 Proposed Framework 108

4.5 Results 112

4.6 Conclusion and Future Scope 115

5 Edge Computing in Educational Technology: The Power of Edge AI for Dynamic and Personalized Learning 121
Ganeshayya Shidaganti, V. Aditya Raj, V.R. Monish Raman and Shubeeksh Kumaran

5.1 Introduction: Unveiling the Potential of Edge AI in Educational Technologies 122

5.2 Challenges of Traditional Education in the Digital Age 122

5.3 Edge Computing and AI: Revolutionizing Educational Dynamics 126

5.4.1 Methodology 132

5.5 Benefits of the Edge AI for Learning 139

5.6 Discussions on Edge AI for Education 142

5.7 Ethical Considerations in Edge AI for Educational Settings 144

5.8 Future of Education with Edge AI 147

5.9 Conclusion 149

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

6.2 Definitions 157

6.3 Concepts and Architecture 160

6.4 Algorithms for Artificial Intelligence in Edge Computing 163

6.5 Optimization of Edge Devices Using a Class of Neural Networks 166

6.6 Bio-Inspired Algorithms for Edge Computing 169

6.7 Real-Time Intelligence-Based Edge Device 172

6.8 Conclusion 182

7 Ensuring Privacy and Security in Machine Learning: A Novel Approach to Efficient Data Removal 193
Velammal B. L. and Aarthy N.

7.1 Introduction 193

7.2 Related Works 195

7.3 Objectives 197

7.4 System Design 198

7.5 Experimental Results 206

7.6 Conclusion and Future Scope 212

8 Federated Learning in Secure Smart City Sensing: Challenges and Opportunities 215
Monika Gandhi, Sushil Kumar Singh, Ravikumar R. N. and Krunal Vaghela

8.1 Introduction 216

8.2 Related Work 218

8.3 Federated Learning-Based Smart Cities Sensing Architecture for Io T-Enabled Smart Cities Sensing 232

8.4 Open Issues, Related Challenges and Opportunities 239

8.5 Conclusions 246

9 Fusion of Blockchain and Edge Computing for Seamless Convergence 253
Indu Bala

9.1 Introduction to Blockchain and Edge Computing 254

9.2 Key Components of Blockchain and Edge Integration 258

9.3 Challenges and Opportunities in Integration 261

9.4 Security Considerations in a Converged Environment 264

9.5 Use Cases and Applications 265

9.6 Benefits of Blockchain and Edge Integration 268

9.7 Regulatory and Compliance Issues 269

9.8 Future Trends and Innovations 271

9.9 Recommendations 274

9.10 Conclusion 276

10 Industry Adapting the Machine Learning Scenario in Recruitment and Selection of Employees 279
Megha Ojha, Vinay Kandpal, Archana Singh and Amar Kumar Mishra

10.1 Introduction 280

10.2 Evolution of Machine Learning in Recruitment 280

10.3 Methodological Insights and Study Contexts 281

10.4 Ensuring Reliability and Replicability 283

10.5 Ethical Implications of ML in Hiring 288

10.6 Addressing Ethical Concerns in Real-World Applications 289

10.7 Ensuring Data Privacy in ML Models for Hiring 289

10.8 Areas for Future Research in ML for Hiring 301

11 Machine Learning for Nano Process Optimization 307
Manjushree Nayak and A. Sai Satya Narayana

11.1 Introduction 308

11.2 Literature Review 309

11.3 Conclusion 323

12 Quantum Computing for Cryptography: An Extensive Survey 327

Soma Debnath and Avishake Adhikary

12.1 Introduction 328

12.2 Related Works 336

12.3 Statistical Analysis 339

12.4 Comparative Analysis 341

12.5 Conclusion and Future Scope 345

13 Role of Blockchain Technology in e-HRM in the Era of Artificial Intelligence: Focus on the Indian Market 351
Archana Singh, Girish Lakhera, Megha Ojha and Amar Kumar Mishra

13.1 Introduction 351

13.2 Literature Review 354

13.3 Blockchains for Business and EHRM 357

13.4 Case Studies 361

13.6 Ethical Implications of Implementing Blockchain in HRM 362

13.7 Conclusion 364

14 Smart City Innovations and Io T as a Frontier of AI at the Edge of Intelligence 369
Priya Soni

14.1 Introduction: Smart City Innovations and Internet of Things for Data Analytics 370

14.2 Concept of Smart Cities and the Significance of Data-Driven Decision-Making 371

14.3 Fundamental Components of Data Analytics in Smart Cities 373

14.4 Advanced-Data Analytics Techniques 375

14.5 Uses of Io T-Enabled Data Analytics in Smart Cities 381

14.6 Challenges and Considerations 382

14.7 Future Prospects and Emerging Trends of Smart City Innovations and Internet of Things (Io T) for Data Analytics 384

14.8 Indian Case Studies: Successful Implementations on Smart City Innovations and Internet of Things for Data Analytics 386

14.9 Conclusion 388

15 Synergies Unleashed: The Convergence of AI and Edge Computing in Transformative Technologies 391
R. Shobarani, P. Dhivya, G. Savitha, S. Santhi, K. Surya Prakhash and R. Kavitha

15.1 Introduction 392

15.2 Related Study 395

15.3 Reduction of Latency 400

15.4 Bandwidth Efficiency 401

15.5 Privacy and Security 402

15.6 Real-Time Decision-Making: Decision Made with an Example 405

15.7 Distributed Architecture: Decentralized Processing Occurs with an Example 407

15.8 Edge Computing Use Cases 410

15.9 Challenges and Advancements 412

15.10 Future Trends 423

15.11 Conclusion 430

References 431

Index 433

Shubham Mahajan, Ph D, is an assistant professor at Amity University, Haryana with a remarkable track record in the field of artificial intelligence and image processing. He has published over 77 articles in peer-reviewed journals and conferences, as well as eleven Indian, one Australian, and one German patent. His research includes video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.
Sathyan Munirathinam, Ph D, is a senior manager on the Customer Service Data and Diagnostics team for the ASML Corporation with over 24 years of experience in business intelligence and 17 years in the semiconductor industry. His responsibilities involve developing and executing a roadmap for data and diagnostics innovation for customer service engineers, aiming to transition equipment from unscheduled downtime to scheduled maintenance. In addition to this role, he has authored numerous papers and participated in numerous international conferences.
Pethuru Raj, Ph D, is a chief architect in the Edge AI division of Reliance Jio Platforms Ltd., Bangalore. with over 23 years of IT industry and 9 years of research experience. He has been granted two international research fellowships from the Japan Society for the Promotion of Science and the Japan Science and Technology Agency. His research interests include the industrial Internet of Things (IIo T), efficient, explainable, and Edge AI, blockchain, digital twins, cloud-native and edge computing, green and generative AI, and quantum computing.