LEADER 04539nam 22006375 450 001 9910484253003321 005 20200703194055.0 010 $a3-662-57277-X 024 7 $a10.1007/978-3-662-57277-1 035 $a(CKB)3850000000033227 035 $a(DE-He213)978-3-662-57277-1 035 $a(MiAaPQ)EBC5923866 035 $a(PPN)227402405 035 $a(EXLCZ)993850000000033227 100 $a20180522d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Intelligence in Sensor Networks$b[electronic resource] /$fedited by Bijan Bihari Mishra, Satchidanand Dehuri, Bijaya Ketan Panigrahi, Ajit Kumar Nayak, Bhabani Shankar Prasad Mishra, Himansu Das 205 $a1st ed. 2019. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2019. 215 $a1 online resource (XIV, 488 p. 196 illus., 132 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v776 311 $a3-662-57275-3 320 $aIncludes bibliographical references. 327 $aDistributed Query Processing Optimization in Wireless Sensor Network Using Arti?cial Immune System -- Computational Intelligence Techniques for Localization in Static and Dynamic Wireless Sensor Networks- A Review -- Nature Inspired Algorithm Approach for the Development of an Energy Aware Model for Sensor Network -- Routing Protocols -- Distance based Enhanced Threshold Sensitive Stable Election routing Protocol for Heterogeneous Wireless Sensor Network. 330 $aThis book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v776 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aElectrical engineering 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aElectrical engineering. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aCommunications Engineering, Networks. 676 $a006.3019 702 $aMishra$b Bijan Bihari$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDehuri$b Satchidanand$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPanigrahi$b Bijaya Ketan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNayak$b Ajit Kumar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMishra$b Bhabani Shankar Prasad$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDas$b Himansu$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484253003321 996 $aComputational Intelligence in Sensor Networks$92844813 997 $aUNINA