LEADER 04111nam 22005895 450 001 9910416084103321 005 20200825094902.0 010 $a981-15-6044-7 024 7 $a10.1007/978-981-15-6044-6 035 $a(CKB)4100000011401175 035 $a(MiAaPQ)EBC6318838 035 $a(DE-He213)978-981-15-6044-6 035 $a(PPN)250212994 035 $a(EXLCZ)994100000011401175 100 $a20200825d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFog Data Analytics for IoT Applications$b[electronic resource] $eNext Generation Process Model with State of the Art Technologies /$fedited by Sudeep Tanwar 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (501 pages) 225 1 $aStudies in Big Data,$x2197-6503 ;$v76 311 $a981-15-6043-9 327 $aIntroduction -- Introduction to Fog data analytics for IoT applications -- Fog Data Analytics: Systematic Computational Classification and Procedural Paradigm -- Fog Computing: Building a Road to IoT with Fog Analytics -- Data Collection in Fog Data Analytics -- Mobile FOG Architecture Assisted Continuous Acquisition of Fetal ECG Data for Efficient Prediction -- Proposed Framework for Fog Computing to Improve Quality-of-Service in IoT applications -- Fog Data Based Statistical Analysis to Check Effects of Yajna and Mantra Science: Next Generation Health Practices -- Process Model for Fog Data Analytics for IoT Applications -- Medical Analytics Based on Artificial Neural Networks Using Cognitive Internet of Things. 330 $aThis book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDA in IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy. 410 0$aStudies in Big Data,$x2197-6503 ;$v76 606 $aComputational intelligence 606 $aBig data 606 $aApplication software 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aBig Data/Analytics$3https://scigraph.springernature.com/ontologies/product-market-codes/522070 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 615 0$aComputational intelligence. 615 0$aBig data. 615 0$aApplication software. 615 14$aComputational Intelligence. 615 24$aBig Data. 615 24$aBig Data/Analytics. 615 24$aInformation Systems Applications (incl. Internet). 676 $a004.678 702 $aTanwar$b Sudeep$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416084103321 996 $aFog Data Analytics for IoT Applications$92066353 997 $aUNINA