Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

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Published: Jun 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.7043
Keywords:
Wireless Sensor Networks (WSNs), Clustering techniques, Energy Efficiency, Classical Methods, Optimization Methods, Machine Learning Methods

Main Article Content

Monica Shivaji Gunjal
Research Scholar, 1Department of Electronics & Telecommunication Engineering, Dr.D.Y.Patil Institute of Technology,Pimpri, Pune, Maharashtra
Pramodkumar H. Kulkarni
Department of Electronics & Telecommunication Engineering, Dr.D.Y.Patil Institute of Technology,Pimpri, Pune, Maharashtra

Abstract

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering.

Article Details

How to Cite
Gunjal, M. S. ., & Kulkarni, P. H. . (2023). Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 345–356. https://doi.org/10.17762/ijritcc.v11i5s.7043
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

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