Energy Efficient Target (Rhynchophorus Ferrugineus) Tracking in Wireless Sensor Network Using the Cat Swarm Optimization Algorithm and Fuzzy Logic

The Rhynchophorus ferrugineus is a major pest that serves as a carrier for bacterial and fungal diseases, causing significant damage to palm plantations when observed on farms. Nowadays, advancements in wireless communication environments have made it possible to develop low-cost, energy-efficient, multi-functional, and short-range sensor nodes for tracking this pest in palm plantations. In existing target tracking algorithms, the probability of losing the target increases with its speed. Therefore, this paper proposes a new method for target tracking that reduces the likelihood of losing the target. Additionally, considering the energy constraints of battery-powered sensor nodes, we need a scheduling mechanism for their sleep and wake-up cycles to enhance the network's lifespan. To improve energy consumption, this paper utilizes a time scheduling approach to adjust the sleep and wakeup periods of nodes using the Cat Swarm Optimization algorithm and Fuzzy Logic optimization. By simulating the proposed method and comparing it with the Tracking-45-Degree-vectors method in the Opnet simulator, it can be observed that the proposed protocol performs significantly better. Specifically, the end-to-end delay rate improves by 27.02%, the media access delay rate improves by 2.01%, the throughput rate improves by 0.62%, the signal-to-noise ratio improves by 3.28%, and the average battery energy consumption improves by 8.77% compared to the Tracking-45-Degree-vectors protocol. It is worth mentioning that the proposed algorithm has been simulated and tested for a single target scenario.