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Edge of Intelligence

Exploring the Frontiers of AI at the Edge

Edited Shubham Mahajan, Sathyan Munirathinam and Pethuru Raj
Copyright: 2025   |   Expected Pub Date:2025/04/30
ISBN: 9781394314379  |  Hardcover  |  
458 pages

One Line Description
The book offers cutting-edge insights and practical applications for Edge AI, making it essential for anyone looking to stay ahead in the rapidly evolving landscape of artificial intelligence and Edge computing.

Audience
Engineers, data scientists, IT professionals, researchers, and academics in the fields of artificial intelligence, computer science, and telecommunications, as well as industry professionals in sectors such as the automotive, agriculture, education, and urban planning industries

Description
Edge of Intelligence: Exploring the Frontiers of AI at the Edge examines the transformative potential of edge AI, showcasing how artificial intelligence is being seamlessly integrated with Edge computing to revolutionize various industries. This book offers a comprehensive overview of the latest research, trends, and practical applications of Edge AI, providing readers with valuable insights into how this cutting-edge technology is enhancing efficiency, reducing latency, and enabling real-time decision-making. From optimizing vehicular networks in the era of 6G to the innovative use of AI in crop monitoring and educational technology, this book covers a broad spectrum of topics, making it an essential read for anyone interested in the future of AI and Edge computing.
Featuring contributions from leading experts and researchers, Edge of Intelligence highlights real-world examples and case studies that demonstrate the practical implementation of edge AI in diverse sectors such as smart cities, recruitment, and nano-process optimization. The book also addresses critical issues related to privacy, security, and the fusion of blockchain with edge computing, providing a holistic view of the challenges and opportunities in this rapidly evolving field.

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Author / Editor Details
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.

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Table of 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
2. A Study on EDGE AI Application in Crop Monitoring
N.A. Natraj, Pethuru Raj, M. Karpagam and S. Gunanandhini
2.1 Introduction
2.1.1 The Dynamic Evolution of Precision Agriculture
2.1.2 The Impact of AI on Crop Monitoring
2.1.3 AI’s “Edge” and Its Importance in Agriculture
2.2 Crop Monitoring AI Basics
2.2.1 Using Intelligent Patterns for Maximum Benefit: A Machine Learning Algorithm Study
2.2.2 Computer Vision in Precision Agriculture
2.2.3 Sensor Integration: Precision Agriculture’s Nervous System
2.2.4 Importance of Data: Fuelling AI
2.2.5 Real-Time Intelligence for Edge Learning and Adaptation
2.3 AI Applications in Crop Monitoring
2.3.1 Precision Farming: Nurturing Each Plant with Precision
2.3.2 Disease Detection: An Early Warning System for Crop Health
2.3.3 Yield Prediction: Anticipating Harvests with Precision
2.3.4 Resource Optimization: Balancing Efficiency and Sustainability
2.3.5 Edge Computing: Enhancing Efficiency in the Field
2.4 Challenges and Possible Future Paths of AI in Crop Monitoring
2.4.1 Challenges and Limitations
2.4.2 Future Scope of AI in Crop Monitoring
2.5 Conclusion
References
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
3.2.3.1 On-the-Fly Reconfigurability
3.2.3.2 Partial Programmability
3.2.3.3 Externally-Visible Internal State
3.3 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
3.4 Survey of Existing Work
3.5 Performance Comparison of Different Reconfigurable Co-Processors Implementing Linear Transformation(s)
3.6 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
5.2.5 Teacher Workload
5.2.6 Lack of Timely Feedback
5.3 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
5.4 Enhancing Education Through Video Lecture Summarization: An Exemplary Scenario
5.4.1 Methodology
5.5 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
5.6 Discussions on Edge AI for Education
5.6.1 Case Study: ASUS – Learning on the Edge
5.7 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
5.8 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
5.9 Conclusion
References
6. Edge Computing Revolution: Unleashing Artificial Intelligence Potential in the World of Edge Intelligence
Saravanan Chandrasekaran, S. Athinarayanan, M. Masthan, Anmol Kakkar, Pranav Bhatnagar and Abdul Samad
6.1 Introduction
6.1.1 Motivation for Edge Intelligence
6.2 Definitions
6.2.1 Edge Computing
6.2.2 Challenges in Edge Computing
6.2.2.1 Network Connectivity and Reliability
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
6.3 Concepts and Architecture
6.3.1 Comparison Between EI and Cloud-Centric AI Models
6.4 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)
6.5 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
6.6 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
6.7 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
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
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
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
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
11. Machine Learning for Nano Process Optimization
Manjushree Nayak and A. Sai Satya Narayana
Introduction
Literature Review
Conclusion
References
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
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
13.2.3 Transparency
13.2.4 Cryptographic Security
13.2.5 Immutability
13.2.6 Consensus Mechanisms
13.2.7 Decentralized Identity and Credential Verification
13.2.8 Smart Contracts in HRM
13.2.9 Blockchain-Based Employee Data Management
13.3 Blockchains for Business and EHRM
13.3.1 Payroll Processing
13.3.2 Data Protection and Cyber Attacks
13.3.3 Performance Management
13.3.4 E-Recruitment System Design
13.3.5 Renowned DLT Applications
13.3.6 Incident Logging and Reporting
13.3.7 Employees Assistance Program
13.3.8 Identity Registry
13.4 Case Studies
13.4.1 Barriers to Adoption and Technological Challenges in the Indian Business Environment
13.5 Integration of Blockchain with Industry 4.0 Technologies in HRM
13.6 Ethical Implications of Implementing Blockchain in HRM
13.6.1 Case Studies and Examples
13.6.2 Mitigating Ethical Concerns
13.7 Conclusion
References
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
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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