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DOI: 10.14569/IJACSA.2023.0140381
PDF

An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

Author 1: Hai-yu Zhang

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

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Abstract: The low-cost and convenient feature of Wireless Sensor Networks (WSNs) has made them popular in many sectors over the last decade. The WSNs are now widely used as a result of recent advancements in low-power communication and being energy-efficient. The WSNs typically use batteries to power sensor nodes. The finite stored energy in batteries and the hassle of battery replacement have led to a critical focus on energy efficiency for WSNs. Clustering and data aggregation are the most efficient methods to address the energy concerns of WSNs. This paper comprehensively reviews several uneven clustering methods and compares the various uneven clustering algorithms. The methods are described in terms of their goals, attributes, categories, advantages and disadvantages. Probabilistic clustering is used when there is a need of simplicity and speed. As a result, this study compared all these types of protocols based on their clustering properties, CHs properties, and on the type of clustering process; and current research gap effective techniques are also addressed.

Keywords: Wireless sensor networks; data aggregation; uneven clustering; energy-efficient; review

Hai-yu Zhang. “An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.3 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140381

@article{Zhang2023,
title = {An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140381},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140381},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Hai-yu Zhang}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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