Edge Artificial Intelligence
Algorithms, Applications, Challenges and Ethical Issues
- 1st Edition - August 1, 2025
- Imprint: Elsevier
- Authors: Parikshit Narendra Mahalle, Mandar Pramod Diwakar, Vijaykumar Raghunath Ghule, Yashwant Sudhakar Ingle
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 6 7 2 0 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 6 7 2 1 - 5
Edge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the archit… Read more
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Request a sales quoteEdge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the architecture, algorithms, and applications of Edge AI, offering insights into regulation and governance. Real-world case studies and practical examples are included, providing readers with the knowledge and tools to harness the transformative power of Edge AI. This book also addresses the ethical considerations and regulatory aspects of deploying AI at the edge.
In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.
In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.
- Offers a comprehensive overview of edge computing that covers everything from the fundamentals to advanced techniques
- Explores a wide range of real-world applications of Edge AI, from smart homes to healthcare and autonomous vehicles, providing practical insights and use cases
- Examines the ethical considerations and regulatory aspects of Edge AI, helping readers navigate the responsible use of this technology
Academics and researchers specializing in AI, machine learning and edge computing
1. Introduction
1.1 AI and Edge AI
1.2 Internet of Things to Internet of Everything
1.3 Internet of Things and Edge Computing
1.4 Edge devices and architectures
1.5 Cloud-Based AI and Edge AI
1.6 Summary References
2. Fundamentals of Machine Learning and Deep Learning
2.1 Brief Introduction to Machine Learning and Deep Learning
2.2 Supervised, Unsupervised, and Reinforcement Learning
2.3 Neural Networks and Deep Learning Architectures
2.4 Summary References
3.Edge AI Hardware
3.1 Overview
3.2 Fundamentals of Wireless Communication
3.3 Hardware Platforms for Edge AI
3.4 Embedded Systems and Microcontrollers
3.5 HPC Platform
3.6 Summary References
4. Edge AI Algorithms and Techniques
4.1 Pre-processing and Feature Extraction
4.2 Model Compression
4.3 Optimization for Edge Devices
4.4 Transfer Learning
4.5 Domain Adaptation
4.6 Federated Learning
4.7 Collaborative Edge AI
4.8 Summary References
5. Edge AI Software Development
5.1 Programming Languages and Frameworks for Edge AI
5.2 Model Deployment and Inference on Edge Devices
5.3 Edge-to-Cloud Integration and Hybrid Architectures
5.4 Security and Privacy Considerations
5.5 Summary References
6. Edge AI Applications
6.1 Smart Home and Internet of Things (IoT)
6.2 Autonomous Vehicles and Robotics
6.3 Healthcare and Remote Monitoring
6.4 Industrial Automation and Predictive Maintenance
6.5 Summary References
7. Edge AI Challenges and Future Directions
7.1 Edge AI Deployment and Scalability
7.2 Integrated Sensing and Communication
7.3 Energy Efficiency and Resource Constraints
7.4 Edge-to-Edge Collaboration and Coordination
7.5 Emerging Trends and Research Directions
7.6 Summary References
8. Case Studies and Practical Examples
8.1 Case Studies - Edge AI Deployments
8.2 Implementation Details, Challenges, and Lessons Learned
8.3 Summary References
9. Ethical Considerations in Edge AI
9.1 Bias and Fairness in Edge AI Systems
9.2 Privacy and Data Protection Issues
9.3 Transparency and Interpret-Ability Challenges
9.4 Regulation and Governance of Edge AI
9.5 Summary
10. Conclusion and Future Outlook
10.1 Recap of Key Concepts and Insights
10.2 Summary of Potential Future Developments in Edge AI
10.3 Final Thoughts and Recommendations
10.4 Research Opportunities
1.1 AI and Edge AI
1.2 Internet of Things to Internet of Everything
1.3 Internet of Things and Edge Computing
1.4 Edge devices and architectures
1.5 Cloud-Based AI and Edge AI
1.6 Summary References
2. Fundamentals of Machine Learning and Deep Learning
2.1 Brief Introduction to Machine Learning and Deep Learning
2.2 Supervised, Unsupervised, and Reinforcement Learning
2.3 Neural Networks and Deep Learning Architectures
2.4 Summary References
3.Edge AI Hardware
3.1 Overview
3.2 Fundamentals of Wireless Communication
3.3 Hardware Platforms for Edge AI
3.4 Embedded Systems and Microcontrollers
3.5 HPC Platform
3.6 Summary References
4. Edge AI Algorithms and Techniques
4.1 Pre-processing and Feature Extraction
4.2 Model Compression
4.3 Optimization for Edge Devices
4.4 Transfer Learning
4.5 Domain Adaptation
4.6 Federated Learning
4.7 Collaborative Edge AI
4.8 Summary References
5. Edge AI Software Development
5.1 Programming Languages and Frameworks for Edge AI
5.2 Model Deployment and Inference on Edge Devices
5.3 Edge-to-Cloud Integration and Hybrid Architectures
5.4 Security and Privacy Considerations
5.5 Summary References
6. Edge AI Applications
6.1 Smart Home and Internet of Things (IoT)
6.2 Autonomous Vehicles and Robotics
6.3 Healthcare and Remote Monitoring
6.4 Industrial Automation and Predictive Maintenance
6.5 Summary References
7. Edge AI Challenges and Future Directions
7.1 Edge AI Deployment and Scalability
7.2 Integrated Sensing and Communication
7.3 Energy Efficiency and Resource Constraints
7.4 Edge-to-Edge Collaboration and Coordination
7.5 Emerging Trends and Research Directions
7.6 Summary References
8. Case Studies and Practical Examples
8.1 Case Studies - Edge AI Deployments
8.2 Implementation Details, Challenges, and Lessons Learned
8.3 Summary References
9. Ethical Considerations in Edge AI
9.1 Bias and Fairness in Edge AI Systems
9.2 Privacy and Data Protection Issues
9.3 Transparency and Interpret-Ability Challenges
9.4 Regulation and Governance of Edge AI
9.5 Summary
10. Conclusion and Future Outlook
10.1 Recap of Key Concepts and Insights
10.2 Summary of Potential Future Developments in Edge AI
10.3 Final Thoughts and Recommendations
10.4 Research Opportunities
- Edition: 1
- Published: August 1, 2025
- No. of pages (Paperback): 220
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780443267208
- eBook ISBN: 9780443267215
PM
Parikshit Narendra Mahalle
Dr Parikshit is a senior member IEEE and is Professor, Dean Research and Development and Head - Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India. He completed his Ph. D from Aalborg University, Denmark and continued as Post Doc Researcher at CMI, Copenhagen, Denmark. He has 23 + years of teaching and research experience. He is an ex-member of the Board of Studies in Computer Engineering, Ex-Chairman Information Technology, Savitribai Phule Pune University and various Universities and autonomous colleges across India. He has 15 patents, 200+ research publications (Google Scholar citations-2950 plus, H index-25 and Scopus Citations are 1550 plus with H index -18, Web of Science citations are 438 with H index - 10) and authored/edited 56 books with Springer, CRC Press, Cambridge University Press, etc. He is editor in chief for IGI Global International Journal of Rough Sets and Data Analysis, Inter-science International Journal of Grid and Utility Computing, member-Editorial Review Board for IGI Global – International Journal of Ambient Computing and Intelligence and reviewer for various journals and conferences of the repute. His research interests are Machine Learning, Data Science, Algorithms, Internet of Things, Identity Management and Security. He is guiding 8 PhD students in the area of IoT and machine learning and SIX students have successfully defended their PhD under his supervision from SPPU. He is also the recipient of “Best Faculty Award” by Sinhgad Institutes and Cognizant Technologies Solutions. He has delivered 200 plus lectures at national and international level.
Affiliations and expertise
Professor, Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, IndiaMD
Mandar Pramod Diwakar
Mr. Mandar Pramod Diwakar is presently working at VIIT, Pune as Assistant Professor in Department of Artificial Intelligence and Data Science. Author has 5 years’ experience in Teaching and 2 years industrial experience. Author is pursuing his PhD from Savitribai Phule Pune University and completed his M.E Computer Engineering from Smt. Kashibai Navale College of Engineering, SPPU, Pune. Author has 2 Indian Utility patents published, 2 important Software Copyrights and 1 literary research Copyrights registered. Author has specialization in AI, Machine Learning, data Science, Data Visualization & Deep Learning
Affiliations and expertise
Assistant Professor, Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra, IndiaVG
Vijaykumar Raghunath Ghule
Assistant Professor, Department of Artificial intelligence and Data Science, Vishwakarma Institute of Information Technology, Survey No. 3/4, Kondhwa (Budruk)Pune – 411048, Maharashtra (India).
Mr. Vijaykumar Raghunath Ghule is presently working as Assistant Professor in Department of Artificial Intelligence and Data Science at BRACTS’s VIIT, Pune. Author has 15 years of experience in Teaching and 1 year industrial experience. Author has completed his M.Tech ( Computer Science & Engineering) from J.N.T.U. Hyderabad, India . Author has specialization in AI, Machine Learning, data Science.
Affiliations and expertise
Assistant Professor, Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, Maharashtra, IndiaYI
Yashwant Sudhakar Ingle
Yashwant Sudhakar Ingle is presently working at VIIT, Pune as Assistant Professor in Department of Artificial Intelligence and Data Science. Author has 12 years’ experience in Teaching and 3 yrs Industry Experience. Author is pursuing his PhD from Savitribai Phule Pune University and completed his M.Tech CSE from Visvesvaraya National Institute of Technology, Nagpur, BE CSE from Amravati University. Author has 1 US Patent published, 15 Indian Utility patents Published ,3 Design Patents granted, 3 important Software Copyrights and 2 literary research Copyrights registered. He also has 25+ publications in SCOPUS, WEB OF SCIENCE journals, IEEE & SPRINGER International Conferences and received 4 Best paper Awards in RACE National Conference 2021. Author is life member of ISTE, VNIT Alumni association and member of ACM. Author regularly teach AI honour course and AI regular course, Soft Computing and Optimization algorithms to Third year and final year of Computer Engineering. Author has undergone vigorous trainings in FDPs, Professional courses on subject Matter AI.
Affiliations and expertise
Assistant Professor, Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India