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020			_a9789811521256
024	7		_a10.1007/978-981-15-2125-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aTK7872.D48 _b2020 EB
245	0	0	_aNature Inspired Computing for Wireless Sensor Networks _cedited by Debashis De, Amartya Mukherjee, Santosh Kumar Das, Nilanjan Dey
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint Springer _c2020
300			_a1 recurso en línea (XII, 341 páginas) _b 85 ilustraciones, 54 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aSpringer Tracts in Nature-Inspired Computing _x2524-552X
505	0		_aWireless Sensor Network: Applications, Challenges and Algorithms -- Section 1: Bio-Inspired Optimization -- A GA based Fault-Aware Routing Algorithm for Wireless Sensor Networks -- GA based Fault Diagnosis Technique for Enhancing Network Lifetime of Wireless Sensor Network -- A GA based Intelligent Traffic Management Technique for Wireless Body Area Sensor Networks -- Fault Diagnosis in Wireless Sensor Networks using a Neural Network Constructed by Deep Learning Technique -- Section 2: Swarm Optimization -- Intelligent Routing in Wireless Sensor Network based on African Buffalo Optimization -- Robust Estimation of Feedback System's Parameter in Wireless Sensor Network using Distributed Particle Swarm Optimization -- On the Development of Energy Efficient Distributed Source Localization Algorithm in Wireless Sensor Networks using Modified Swarm Intelligence -- Swarm Intelligence Approach for Ad-Hoc & Sensor Networks -- Section 3: Multi-Objective Optimization -- A Comparensive Survey of Intelligent-based Hierarchical Routing Protocols for Wireless Sensor Networks -- A Qualitative Survey on Sensor Node Deployment, Load Balancing & Energy Utilization in Sensor Network -- Bio-Inspired Algorithm for Multi-Objective Optimization in Wireless Sensor Network -- TLBO based Multi-objective Optimization System in Wireless Sensor Networks -- Nature Inspired Algorithms for Reliable, Low-Latency Communication in Wireless Sensor Networks for Pervasive Healthcare Applications.
520	3		_aThis book presents nature inspired computing applications for the wireless sensor network (WSN). Although the use of WSN is increasing rapidly, it has a number of limitations in the context of battery issue, distraction, low communication speed, and security. This means there is a need for innovative intelligent algorithms to address these issues. The book is divided into three sections and also includes an introductory chapter providing an overview of WSN and its various applications and algorithms as well as the associated challenges. Section 1 describes bio-inspired optimization algorithms, such as genetic algorithms (GA), artificial neural networks (ANN) and artificial immune systems (AIS) in the contexts of fault analysis and diagnosis, and traffic management. Section 2 highlights swarm optimization techniques, such as African buffalo optimization (ABO), particle swarm optimization (PSO), and modified swarm intelligence technique for solving the problems of routing, network parameters optimization, and energy estimation. Lastly, Section 3 explores multi-objective optimization techniques using GA, PSO, ANN, teaching-learning-based optimization (TLBO), and combinations of the algorithms presented. As such, the book provides efficient and optimal solutions for WSN problems based on nature-inspired algorithms.
988			_aPrimersemestre_2020_Engineering
650		7	_2embne _aRedes de sensores inalámbricas _9441179
700	1		_aDe, Debashis _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMukherjee, Amartya _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aKumar Das, Santosh _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aDey, Nilanjan, _d1984- _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _998032
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789811521249
776	0	8	_iPrinted edition: _z9789811521263
776	0	8	_iPrinted edition: _z9789811521270
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-2125-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eb _zSI