Cat swarm optimization-based energy efficient target tracking in wireless sensor network

Abstract

Abstract Objective: using the cat swarm optimization algorithm to reduce energy consumption and data transmission delay in WSNs (wireless sensor networks) Materials and Methods: WSNs have emerged as a critical sensing technology with a wide range of military and civilian applications. In these networks, a large number of sensors are distributed throughout an area to track a specific target. Each sensor is regarded as an operating tool that uses limited energy. They can sense and understand events within their sensing domains, as well as communicate with neighbouring sensor nodes. Because the battery determines the lifespan of a sensor, an efficient method of energy consumption must be used. The depletion of batteries' limited energy reserves may result in the network's demise. As a result, devising and developing a time plan for sensors' sleep and wake-up cycles is regarded as a critical goal in line with increasing network lifetime. We proposed a novel and different time planning method for enhancing the lifetimes of WSNs in this paper, using the cat swarm optimization algorithm, in which at least half of the full coverage is provided at all times. The simulation results showed that using a cat swarm optimization algorithm can reduce energy consumption and data transmission delay. It also improves load balance. Opnet simulator version 11.5 was used for the experiments and simulation.s