Edge Computational Intelligence for AI-Enabled IoT Systems

The book discusses the major contributions in the Edge AI domain of IoT systems: heterogeneous micro clusters employed for processing data and for exploiting adopted AI algorithms for the predictive analysis and or prescription.

Shrikaant Kulkarni has 37 years of teaching and research experience at both undergraduate and postgraduate levels. Presently he is a Professor in the Department of Civil Engineering, Padm. Dr. V. B. Kolte College of Engineering, Malkapur, India. He has published over 60 research papers in national and international journals and conferences Jaiprakash Narain Dwivedi is currently working as an Associate Professor, ECE Department, University Institute of Engineering, Chandigarh University, Mohali, Punjab, India. His interest in research includes machine learning, artificial neural network, pattern recognition, classification, CNN, DNN, deep learning and signal processing. Dinda Pramanta is an Assistant Professor and a committee member of Mathematical-Data Science-AI Educational Program on Kyushu Institute of Information Sciences from 2021. His research interests include spiking neural networks, hardware, and AI for educational purposes. Yuichiro Tanaka is an Assistant Professor with Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Japan. His research interests include soft computing, neural networks, hardware, and home service robots. He is a member of IEEE and JNNS.

I. Computational Intelligence: Edge AI Services 1. Edge Computational Intelligence: Fundamentals, Trends, and Applications 2. Securing IoT Services Using Artificial Intelligence in Edge Computing 3. Computational-Based Edge AI Services and Challenges II. Computational Intelligence: Edge AI security and Privacy 4. Security and Privacy in Edge AI: Challenges and Concerns 5. A Study of Edge Computing-Enabled Metaverse Ecosystem 6. Sustainable Communication-Efficient Edge AI: Algorithms and Systems III. Computational Intelligence: Edge Computing and AI Applications 7. Machine Learning-Based Hybrid Technique for Securing Edge Computing 8. A Study of Secure Deployment of Mobile Services in Edge Computing 9. AI-Enabled Novel Applications in Edge Computing for IoT Services 10. Application of Edge AI in Biomedicine IV. Computational Intelligence: IoT Systems 11. Artificial Intelligence and Soft Computing-Driven Evolutionary Computation Algorithms for Solving Unconstrained Nonlinear Problems 12. UAV-Enabled Mobile Edge Computing for IoT Applications 13. Internet of Things Enabled Software-Defined Networks 14. Smart and Sustainable Energy-Efficient Wireless Sensor Network: Design and Techniques