Optimal Clustering and Mobile BS Deployment Through Improved PSO

With the rapid advancement of technology, wireless sensor networks (WSNs) have become a prominent focus of research and development, playing a vital role in a wide range of modern sensing applications. However, their performance is often constrained by the limited energy resources of sensor nodes (SNs), significantly affecting network lifetime, particularly when the base station (BS) is located far from the sensing area. This excessive energy demand significantly reduces the overall network lifetime. Unmanned aerial vehicles (UAVs) have emerged as a promising solution to address the limitations of fixed base stations in WSNs by serving as aerial base stations. To tackle the aforementioned challenges, this paper formulates the problem as two sub-problems: clustering optimization and the strategic deployment of UAVs as aerial base stations. These problems are known to be NP-hard and cannot be solved utilizing a deterministic approach. Thus, we propose applying improved particle swarm optimization (IPSO) to tackle clustering and deployment issues, minimizing energy consumption and prolonging the sensor network’s lifespan. The simulation results of optimal clustering show significant improvement in network life time and energy consumption. Results of comparison demonstrate that the proposed algorithm outperforms the PSO, LEACH_GA algorithms in terms of the lifespan of the network and the remaining energy consumption, reaching 33.5% and 17.4% for the first node to die. Moreover, the results of UAV-assisted demonstrate a significant improvement in remaining energy consumption and network lifetime reaching 54.3% and 87.3453%, respectively. A one-way ANOVA test and 95% confidence intervals validate the statistical significance of the proposed approach performance.