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2021 | OriginalPaper | Chapter

Next-Generation WSN for Environmental Monitoring Employing Big Data Analytics, Machine Learning and Artificial Intelligence

Authors : Rumana Abdul Jalil Shaikh, Harikumar Naidu, Piyush A. Kokate

Published in: Evolutionary Computing and Mobile Sustainable Networks

Publisher: Springer Singapore

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Abstract

A worldwide network of wireless sensors is used to monitor dynamic environmental changes with respect to time. Therefore, the data provided by these sensor networks are crucial for collecting specific information; hence data analytics is essential in such networks. For effective utilization of gathered data, big data analytics can be one of the prominent solutions since the data plays an important part in machine learning allowing the WSN to adapt the dynamic changes in environment to save cost and efforts of redesigning the present WSN. In this paper we present the advances of WSN to further develop the next-generation wireless sensor network by employing software-defined network (SDN), big data analytics, machine learning and artificial intelligence tool along with its benefits and challenges. We also discuss the software-defined wireless sensor network (SDWSN) and the possibility of application of artificial intelligence in it to meet the challenges of SDWSN and its advantages. And finally, we have discussed different problems associated with WSN network specifically for environmental monitoring and their respective solutions using different machine learning paradigms and how efficiently the adoption of big data analytics in ML and AI plays an important role to serve the improved performance requirements.

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Title: Next-Generation WSN for Environmental Monitoring Employing Big Data Analytics, Machine Learning and Artificial Intelligence
Authors: Rumana Abdul Jalil Shaikh
Harikumar Naidu
Piyush A. Kokate
Publisher: Springer Singapore
Book: Evolutionary Computing and Mobile Sustainable Networks
Print ISBN: 978-981-15-5257-1

Electronic ISBN: 978-981-15-5258-8

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-5258-8_20

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