000			04051nam a22003015i 4500
001			402118
003			DE-He213
005			20240514120049.0
007			cr nn 008mamaa
008			240319s2024 sz | o |||| 0|eng d
020			_a9783031510977
024	7		_a10.1007/978-3-031-51097-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5101-5105.9 _b2024 EB
245	0	0	_aSecure Edge and Fog Computing Enabled AI for IoT and Smart Cities : _bIncludes selected Papers from International Conference on Advanced Computing & Next-Generation Communication (ICACNGC 2022) _cedited by Ahmed A Abd El-Latif, Lo'ai Tawalbeh, Yassine Maleh, Brij B Gupta
250			_a1st ed. 2024.
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aEAI/Springer Innovations in Communication and Computing _x2522-8609
505	0		_aPart 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.
520			_aThis 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). Discusses, evaluates, and improves approaches in data protections in IoT and edge/ fog computing; Lays a foundation of the core concepts and principles of IoT and 5G security for edge/ fog computing; Includes selected papers from ICACNGC 2022.
942			_2lcc _cLE
988			_aSpringer_Engineering_2024
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-51097-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
999			_c402118 _d402118