Reducing Energy Consumption in Tracking the Red Palm Weevil Using Wireless Sensor Networks and Nature-Inspired Evolutionary Algorithms (Fruit Fly Optimization Algorithm and Lion Swarm Optimization Algorithm)
Subject Areas : AI and Robotics
Shayesteh Tabatabaei
1
,
Hassan Nosrati Nahook
2
*
1 - Higher education complex of Saravan
2 - Higher Education Complex of Saravan
Keywords: Clustering, WSN, Rhynchophorus ferrugineus, Target tracking, Fruit Fly algorithm, Lion Optimization algorithm.,
Abstract :
The red palm weevil is one of the most serious pests threatening date palm groves worldwide, causing significant damage and even the destruction of palm trees. Early detection and tracking of this pest are critical to preventing its spread and minimizing the associated damage. Wireless Sensor Networks (WSNs) have emerged as a promising technology for monitoring and identifying this pest in date palm plantations. However, WSNs face various challenges, including limited energy, bandwidth, and computational resources. Therefore, efficient and intelligent methods are required to optimize WSN performance in detecting and tracking this pest. This paper proposes a novel approach that combines two intelligent algorithms—namely, the Fruit Fly Optimization Algorithm (FOA) and the Lion Swarm Optimization (LSO) algorithm—for node clustering in WSNs. The proposed method enhances energy efficiency by reducing the battery consumption of sensor nodes. Simulation results demonstrate that, compared to the LPOBC protocol, the proposed protocol outperforms in terms of energy consumption, end-to-end delay, and throughput. Specifically, end-to-end delay is reduced by 28.286%, throughput is improved by 13.80%, and average battery energy consumption is decreased by 11.86%.
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