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Seagull optimization algorithm for node localization in wireless sensor networks
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AI
DOI：10.1007/s11042-024-18331-8.png)
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Abstract
En 中文
Numerous applications of wireless sensor networks (WSNs) highly depend on the node location, such as maritime rescue, agriculture, and hazardous environments. GPS-enabled sensors are not cost-effective or energy efficient. Henceforth, the precise location of sensor nodes significantly impacts the performance of WSNs. Finding the position of a target node (an unknown node) is known as node localization. Gaussian noise and RSSI are cost-effective approaches for estimating the location of a target node. In this paper, the Seagull optimization algorithm and its enhanced versions are applied to increase the NL accuracy of localized nodes. To enhance the accuracy and improve the randomness of the seagull optimization algorithm (SOA)., levy flight and a chaotic map are employed in this work to enhance the seagull optimization algorithm Furthermore, the chaotic map-based SOA (C-SOA) and the Levy flight-based SOA (LF-SOA) are used for node location in WSNs. The performance evaluation and result comparison of SOA, C-SOA, and LF-SOA show that LF-SOA is better than C-SOA and SOA.
Keywords:
Node localization (NL)
Seagull optimization algorithm (SOA)
Chaotic map
Levy flight
Journal
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3
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1.9W
Citations:
3.2W