Secure Edge and Fog Computing Enabled AI for IoT and Smart Cities : Includes selected Papers from International Conference on Advanced Computing & Next-Generation Communication (ICACNGC 2022) (Eai/springer Innovations in Communication and Computing) (2024)

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Secure Edge and Fog Computing Enabled AI for IoT and Smart Cities : Includes selected Papers from International Conference on Advanced Computing & Next-Generation Communication (ICACNGC 2022) (Eai/springer Innovations in Communication and Computing) (2024)

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  • 製本 Hardcover:ハードカバー版/ページ数 256 p.
  • 言語 ENG
  • 商品コード 9783031510960
  • DDC分類 621.382

Full Description

This book gathers recent research in security and privacy to discuss, evaluate, and improve the novel approaches of data protection in IoT and edge and fog computing. The primary focus of the book addresses security mechanisms in IoT and edge/ fog computing, advanced secure deployments for large scaled edge/ fog computing, and new efficient data security strategy of IoT and edge/ fog computing. The book lays a foundation of the core concepts and principles of IoT and 5G security, walking the reader through the fundamental ideas. This book is aimed at researchers, graduate students, and engineers in the fields of secure IoT and edge/ fog computing. The book also presents selected papers from International Conference on Advanced Computing & Next-Generation Communication (ICACNGC 2022).

Contents

Part 1: AI Enabled Smart City IoT System using Edge /Fog Computing.- Chapter 1. Multi-level edge computing system for autonomous vehicles.- Chapter 2. UAVs based edge computing system for smart city applications.- Chapter 3. Organization of Smart City Services Based on Microservice Architecture.- Chapter 4. Pseudo-Random Error-Correcting Codes in Network Coding.- Chapter 5. Proactive management in Smart City: transport convoys.- Chapter 6. Federated Learning for Linux Malware Detection: An Experimental Study.- Chapter 7. Delay prediction in M2M networks using Deep Learning approach.- Chapter 8. Energy-Efficient Beam Shaping in MIMO System Using Machine Learning.- Chapter 9. Channel Cluster Configuration Selection Method for IEEE 802.11 Networks Planning.- Chapter 10. Service migration algorithm for UAVs recharge zones in future 6G network.- Chapter 11. FedBA: Non-IID Federated Learning Framework in UAV Networks.- Part 2: Fog/Edge Computing Security Issues.- Chapter 12. Big Data Analytics for Secure Edge-based Manufacturing Internet of Things (MIoT).- Chapter 13. Artificial Intelligence-Based Secure Edge Computing Systems for IoTDs and Smart Cities: A Survey.- Chapter 14. Machine Learning Techniques for Secure Edge SDN.- Chapter 15.  Machine Learning-Based Identity and Access Management for Cloud Security.- Chapter 16. Spatial Data of Smart Cities: Trust.- Chapter 17. Smart City Infrastructure Projects: Spatial Data of Risks.- Chapter 18. A Comparative Analysis of Blockchain-Based Authentication Models for IoT Networks.- Chapter 19. Development of determining a wireless client location method in the IEEE 802.11 network in order to ensure the IT infrastructure security.

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