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Pal S , Savaglio C , Minerva R IoT Edge Intelligence 2024
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This book explores fundamental and advanced concepts related to the AI-enabled Edge Technology paradigm, also known as Edge Intelligence, within the framework of the Internet of Things (IoT). Expanding the application of Edge computing is increasingly necessary. This can involve exploring automated, intelligent computational learning theorems, and ANN-oriented, trustworthy machine learning perspectives to enhance computational intelligence. The book functions as a valuable resource for professionals in the sector and also acts as a comprehensive learning tool for newcomers in the field of AI-enabled Edge Technologies and their applications, covering both fundamental and advanced concepts. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT edge-cloud ecosystem and to implement cyber-physical pervasive infrastructure solutions. The book will help readers understand the design architecture and AI algorithms and learn analytics through IoT edge, device-edge and the state-of-the-art in cloud-IoT countermeasures. The book is a valuable reference for anyone doing undergraduate or postgraduate studies, conducting research, or working in the computer science, information technology, electronics engineering, and complicated mathematical modeling domains. Edge Intelligence (EI) has been defined as the confluence of edge computing and Artificial Intelligence (AI). An edge intelligence system (EIS)—a system that supports and utilizes edge intelligence—draws from these fields and from many other fields, depending on the driving of applications. As such, edge intelligence systems also inherit the modeling, simulation, and evaluation challenges from their driving applications. The development and proliferation of EIS have resulted in a plethora of new applications and solutions to challenging problems—including, but certainly not limited to, smart home systems, autonomous vehicle fleets, intelligent manufacturing systems, natural disaster detection systems, and smart agriculture. Such deployments range from having homogenous devices connected over homogeneous networks executing simple Machine Learning (ML) algorithms to heterogeneous, dynamic networks of heterogeneous devices with a range of capabilities executing complex or optimized ML algorithms. Whether simple or complex, the design, development, and modeling of edge intelligence systems requires the integration of different physical and functional components. Contents: Part I. Architecture, Systems, and Services Modeling, Simulating, and Evaluating Complex End-to-End Edge Intelligence Systems Exploring Edge AI Inference in Heterogeneous Environments: Requirements, Challenges, and Solutions Artificial Intelligence-Enabled Edge Computing: Necessity of Next Generation Future Computing System Artificial Intelligence-Based IoT-Edge Environment for Industry 5.0 Service Provisioning at the Edge: An AI Approach Based on Policies Unsupervised Time Series Anomaly Detection for Edge Computing Applications: A Review Part II. Security and Privacy Paradigm Secure, Trusted, Privacy-Protected Data Exchange in an Edge-Cloud Continuum Environment Security, Privacy, Trust, and Provenance Issues in Internet of Things–Based Edge Environment Secure Neural Network Inference for Edge Intelligence: Implications of Bandwidth and Energy Constraints Part III. Applications
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Pal S., Savaglio C., Minerva R. IoT Edge Intelligence 2024.pdf
