Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / Hiren Kumar Thakkar, Chinmaya Kumar Dehury, Prasan Kumar Sahoo, Bharadwaj Veeravalli, editors.
Thakkar, Hiren Kumar, editor.; Dehury, Chinmaya Kumar, editor.; Sahoo, Prasan Kumar, editor.; Veeravalli, Bharadwaj, editor.
2023
QA76.585
Available Online

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Title Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / Hiren Kumar Thakkar, Chinmaya Kumar Dehury, Prasan Kumar Sahoo, Bharadwaj Veeravalli, editors.
ISBN 9783031180347 (electronic bk.)
3031180348 (electronic bk.)
9783031180330
303118033X
DOI https://doi.org/10.1007/978-3-031-18034-7
Published Cham, Switzerland : Springer, 2023.
Language English
Description 1 online resource : illustrations (black and white, and color).
Item Number 10.1007/978-3-031-18034-7 doi
Call Number QA76.585
Dewey Decimal Classification 004.6782
Summary This book covers the relationship of recent technologies (such as Blockchain, IoT, and 5G) with the cloud computing as well as fog computing, and mobile edge computing. The relationship will not be limited to only architecture proposal, trends, and technical advancements. However, the book also explores the possibility of predictive analytics in cloud computing with respect to Blockchain, IoT, and 5G. The recent advancements in the internet-supported distributed computing i.e. cloud computing, has made it possible to process the bulk amount of data in a parallel and distributed. This has made it a lucrative technology to process the data generated from technologies such as Blockchain, IoT, and 5G. However, there are several issues a Cloud Service Provider (CSP) encounters, such as Blockchain security in cloud, IoT elasticity and scalability management in cloud, Service Level Agreement (SLA) compliances for 5G, Resource management, Load balancing, and Fault-tolerance. This edited book will discuss the aforementioned issues in connection with Blockchain, IoT, and 5G. Moreover, the book discusses how the cloud computing is not sufficient and one needs to use fog computing, and edge computing to efficiently process the data generated from IoT, and 5G. Moreover, the book shows how smart city, smart healthcare system, and smart communities are few of the most relevant IoT applications where fog computing plays a significant role. The book discusses the limitation of fog computing and the need for the edge computing to further reduce the network latency to process streaming data from IoT devices. The book also explores power of predictive analytics of Blockchain, IoT, and 5G data in cloud computing with its sister technologies. Since, the amount of resources increases day-by day, artificial intelligence (AI) tools are becoming more popular due to their capability which can be used in solving wide variety of issues, such as minimize the energy consumption of physical servers, optimize the service cost, improve the quality of experience, increase the service availability, efficiently handle the huge data flow, manages the large number of IoT devices, etc.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Thakkar, Hiren Kumar, editor.
Dehury, Chinmaya Kumar, editor.
Sahoo, Prasan Kumar, editor.
Veeravalli, Bharadwaj, editor.
Available in Other Form Predictive analytics in cloud, fog, and edge computing.
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Chapter. 1. Collaboration of IoT and Cloud Computing towards Healthcare Security
Chapter. 2. Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare
Chapter. 3.The Role of Blockchain in Cloud Computing
Chapter. 4. Analysis and Prediction of Plant Growth in a Cloud-based Smart Sensor Controlled Environment
Chapter. 5. Cloud-based IoT controlled System Model for Plant Disease Monitoring
Chapter. 6
Design and Usage of a Digital E- Pharmacy Application Framework
Chapter. 7. Serverless Data Pipelines for IoT data analytics: A cloud vendors perspective and solutions
Chapter. 8. Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction
Chapter. 9. Impact of 5G technologies on cloud analytics
Chapter. 10. IoT based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers
Chapter. 11. A Workload-aware Data Placement Scheme for Hadoop-enabled MapReduce Cloud Data Center
Chapter. 12. 5G Enabled Smart City using Cloud Environment.

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