Edge of Intelligence

Shubham Mahajan, PhD, 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, PhD, 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, PhD, 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 (IIoT), efficient, explainable, and Edge AI, blockchain, digital twins, cloud-native and edge computing, green and generative AI, and quantum computing.

• Preface xvii1 A Review on Computational Optimization Strategies and Collaborative Techniques of Vehicular Task Offloading in the Era of Internet of Vehicles and 6G 1Aishwarya R., V. Vetriselvi and Meignanamoorthi D.1.1 Introduction 21.2 Computational Optimization Strategies 71.3 Collaborative Techniques 291.4 Security 341.5 Challenges and Future Research Directions 391.6 Conclusion 412 A Study on EDGE AI Application in Crop Monitoring 51N.A. Natraj, Pethuru Raj, M. Karpagam and S. Gunanandhini2.1 Introduction 522.2 Crop Monitoring AI Basics 572.3 AI Applications in Crop Monitoring 602.4 Challenges and Possible Future Paths of AI in Crop Monitoring 652.5 Conclusion 703 A Survey on Reconfigurable Co-Processors Computing Linear Transformations 73Atri Sanyal and Amitabha Sinha3.1 Different Linear Transforms 743.2 Reconfigurable Computing 753.3 Field Programmable Gate Array 783.4 Survey of Existing Work 823.5 Performance Comparison of Different Reconfigurable Co-Processors Implementing Linear Transformation(s) 863.6 Conclusions and Future Work 884 Conversational AI Model for Effective Responses with Augmented Retrieval (CAMERA) Based Chatbot on NVIDIA Jetson Nano 93Kiran Jot Singh, Divneet Singh Kapoor, Amit Singh Bora, Khushal Thakur and Anshul Sharma4.1 Introduction 944.2 Background 1004.3 Literature Review 1034.4 Proposed Framework 1084.5 Results 1124.6 Conclusion and Future Scope 1155 Edge Computing in Educational Technology: The Power of Edge AI for Dynamic and Personalized Learning 121Ganeshayya Shidaganti, V. Aditya Raj, V.R. Monish Raman and Shubeeksh Kumaran5.1 Introduction: Unveiling the Potential of Edge AI in Educational Technologies 1225.2 Challenges of Traditional Education in the Digital Age 1225.3 Edge Computing and AI: Revolutionizing Educational Dynamics 1265.4.1 Methodology 1325.5 Benefits of the Edge AI for Learning 1395.6 Discussions on Edge AI for Education 1425.7 Ethical Considerations in Edge AI for Educational Settings 1445.8 Future of Education with Edge AI 1475.9 Conclusion 1496 Edge Computing Revolution: Unleashing Artificial Intelligence Potential in the World of Edge Intelligence 153Saravanan Chandrasekaran, S. Athinarayanan, M. Masthan, Anmol Kakkar, Pranav Bhatnagar and Abdul Samad6.1 Introduction 1546.2 Definitions 1576.3 Concepts and Architecture 1606.4 Algorithms for Artificial Intelligence in Edge Computing 1636.5 Optimization of Edge Devices Using a Class of Neural Networks 1666.6 Bio-Inspired Algorithms for Edge Computing 1696.7 Real-Time Intelligence-Based Edge Device 1726.8 Conclusion 1827 Ensuring Privacy and Security in Machine Learning: A Novel Approach to Efficient Data Removal 193Velammal B. L. and Aarthy N.7.1 Introduction 1937.2 Related Works 1957.3 Objectives 1977.4 System Design 1987.5 Experimental Results 2067.6 Conclusion and Future Scope 2128 Federated Learning in Secure Smart City Sensing: Challenges and Opportunities 215Monika Gandhi, Sushil Kumar Singh, Ravikumar R. N. and Krunal Vaghela8.1 Introduction 2168.2 Related Work 2188.3 Federated Learning-Based Smart Cities Sensing Architecture for IoT-Enabled Smart Cities Sensing 2328.4 Open Issues, Related Challenges and Opportunities 2398.5 Conclusions 2469 Fusion of Blockchain and Edge Computing for Seamless Convergence 253Indu Bala9.1 Introduction to Blockchain and Edge Computing 2549.2 Key Components of Blockchain and Edge Integration 2589.3 Challenges and Opportunities in Integration 2619.4 Security Considerations in a Converged Environment 2649.5 Use Cases and Applications 2659.6 Benefits of Blockchain and Edge Integration 2689.7 Regulatory and Compliance Issues 2699.8 Future Trends and Innovations 2719.9 Recommendations 2749.10 Conclusion 27610 Industry Adapting the Machine Learning Scenario in Recruitment and Selection of Employees 279Megha Ojha, Vinay Kandpal, Archana Singh and Amar Kumar Mishra10.1 Introduction 28010.2 Evolution of Machine Learning in Recruitment 28010.3 Methodological Insights and Study Contexts 28110.4 Ensuring Reliability and Replicability 28310.5 Ethical Implications of ML in Hiring 28810.6 Addressing Ethical Concerns in Real-World Applications 28910.7 Ensuring Data Privacy in ML Models for Hiring 28910.8 Areas for Future Research in ML for Hiring 30111 Machine Learning for Nano Process Optimization 307Manjushree Nayak and A. Sai Satya Narayana11.1 Introduction 30811.2 Literature Review 30911.3 Conclusion 32312 Quantum Computing for Cryptography: An Extensive Survey 327Soma Debnath and Avishake Adhikary12.1 Introduction 32812.2 Related Works 33612.3 Statistical Analysis 33912.4 Comparative Analysis 34112.5 Conclusion and Future Scope 34513 Role of Blockchain Technology in e-HRM in the Era of Artificial Intelligence: Focus on the Indian Market 351Archana Singh, Girish Lakhera, Megha Ojha and Amar Kumar Mishra13.1 Introduction 35113.2 Literature Review 35413.3 Blockchains for Business and EHRM 35713.4 Case Studies 36113.6 Ethical Implications of Implementing Blockchain in HRM 36213.7 Conclusion 36414 Smart City Innovations and IoT as a Frontier of AI at the Edge of Intelligence 369Priya Soni14.1 Introduction: Smart City Innovations and Internet of Things for Data Analytics 37014.2 Concept of Smart Cities and the Significance of Data-Driven Decision-Making 37114.3 Fundamental Components of Data Analytics in Smart Cities 37314.4 Advanced-Data Analytics Techniques 37514.5 Uses of IoT-Enabled Data Analytics in Smart Cities 38114.6 Challenges and Considerations 38214.7 Future Prospects and Emerging Trends of Smart City Innovations and Internet of Things (IoT) for Data Analytics 38414.8 Indian Case Studies: Successful Implementations on Smart City Innovations and Internet of Things for Data Analytics 38614.9 Conclusion 38815 Synergies Unleashed: The Convergence of AI and Edge Computing in Transformative Technologies 391R. Shobarani, P. Dhivya, G. Savitha, S. Santhi, K. Surya Prakhash and R. Kavitha15.1 Introduction 39215.2 Related Study 39515.3 Reduction of Latency 40015.4 Bandwidth Efficiency 40115.5 Privacy and Security 40215.6 Real-Time Decision-Making: Decision Made with an Example 40515.7 Distributed Architecture: Decentralized Processing Occurs with an Example 40715.8 Edge Computing Use Cases 41015.9 Challenges and Advancements 41215.10 Future Trends 42315.11 Conclusion 430References 431Index 433