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Fog data analytics for IoT applicati...

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Fog data analytics for IoT applicationsnext generation process model with state of the art technologies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fog data analytics for IoT applicationsedited by Sudeep Tanwar.
其他題名:	next generation process model with state of the art technologies /
其他作者:	Tanwar, Sudeep.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xv, 497 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Internet of things.
電子資源:	https://doi.org/10.1007/978-981-15-6044-6
ISBN:	9789811560446$q(electronic bk.)

Fog data analytics for IoT applicationsnext generation process model with state of the art technologies /
Fog data analytics for IoT applicationsnext generation process model with state of the art technologies /[electronic resource] :edited by Sudeep Tanwar. - Singapore :Springer Singapore :2020. - xv, 497 p. :ill., digital ;24 cm. - Studies in big data,v.762197-6503 ;. - Studies in big data ;v.1..

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.

ISBN: 9789811560446$q(electronic bk.)

Standard No.: 10.1007/978-981-15-6044-6doiSubjects--Topical Terms:

670954
Internet of things.

LC Class. No.: TK5105.8857 / .F64 2020

Dewey Class. No.: 004.678

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505 0 $a 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.
520 $a 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.
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