000			01869nam a22002657a 4500
005			20260724122826.0
008			260724s2025 |||||||| |||| 00| 0 eng d
020			_a9781394287031
040			_cPK-LaUMT
082			_a006.31 _bAIB-
245	0	0	_aAI-based advanced optimization techniques for edge computing / _cedited by Mohit Kumar . . . [et al.]
260			_aHoboken : _bJohn Wiley & Sons, Inc., _c2025
300			_axvii, 460 p.
490			_aAdvances in learning analytics for intelligent cloud-IoT systems
500			_aIncludes bibliographical references and index.
520			_aThe book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field. This book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime.
546			_aEng
650			_aEdge computing-Technological innovations _914151
650			_aMachine learning
700	0		_aMohit Kumar _914152
700	1		_aSrivastava, Gautam _914153
700	1		_aSingh, Ashutosh Kumar _914154
700	1		_aDubey, Kalka _914155
942			_cBK
999			_c141635 _d141635