04111nam 22005895 450 991041608410332120200825094902.0981-15-6044-710.1007/978-981-15-6044-6(CKB)4100000011401175(MiAaPQ)EBC6318838(DE-He213)978-981-15-6044-6(PPN)250212994(EXLCZ)99410000001140117520200825d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierFog Data Analytics for IoT Applications[electronic resource] Next Generation Process Model with State of the Art Technologies /edited by Sudeep Tanwar1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (501 pages)Studies in Big Data,2197-6503 ;76981-15-6043-9 Introduction -- 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.This 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.Studies in Big Data,2197-6503 ;76Computational intelligenceBig dataApplication softwareComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Big Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Computational intelligence.Big data.Application software.Computational Intelligence.Big Data.Big Data/Analytics.Information Systems Applications (incl. Internet).004.678Tanwar Sudeepedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910416084103321Fog Data Analytics for IoT Applications2066353UNINA