LS-FCS-DV Hop Least Square Fuzzy Chicken Swarm Optimization-based approach for WSN Localization

LS-FCS-DV Hop Least Square Fuzzy Chicken Swarm Optimization-based approach for WSN Localization
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© 2025 by IJETT Journal
Volume-73 Issue-4
Year of Publication : 2025
Author : Kavitha Narayan B M, Smitha Elsa Peter, Siddesha K
DOI : 10.14445/22315381/IJETT-V73I4P112

How to Cite?
Kavitha Narayan B M, Smitha Elsa Peter, Siddesha K, "LS-FCS-DV Hop Least Square Fuzzy Chicken Swarm Optimization-based approach for WSN Localization ," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp. .120-129, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P112

Abstract
Wireless Sensor Network (WSN) has drawn plenty of interest from the general public and experts lately. Utilization of it crosses traditional bounds in various scientific applications such as military observation, regulating temperatures, humidity tracking, and observing the weather. WSNs comprise several nodes, all of which serve as sensors and are primarily liable for data collection. Energy, electricity, performance, and deployment challenges are some of the limitations that these nodes must work within. The strategic placement of nodes significantly impacts the effectiveness of data transmission. Furthermore, due to the absence of location information, the information becomes worthless. Therefore, localization plays a crucial role in WSN applications. Several methods have been introduced for localization; however, localization error impacts the performance of these methods. To overcome the drawbacks of existing methods, this article introduces a novel hybrid approach where least square and DV hop localization schemes are used as base localization models. Further, combined chicken swarm optimization and the fuzzy logic-based model were also incorporated to improve the overall performance. The experimental analysis demonstrates that the proposed has reported the average localization error as 0.0644, 0.085 and 0.125 for varied transmission range, anchor node and the ratio of anchor nodes, respectively, showing a significant improvement in localization accuracy.

Keywords
Chicken swarm optimization, DV-hop, Fuzzy logic, Localization, Wireless sensor networks.

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