Transit search algorithm based on oscillation exploitation factor and Roche limit for wireless sensor network deployment optimization

Abstract

To optimize the deployment of nodes in Wireless Sensor Networks (WSN) and effectively control network node energy consumption, thereby improving the quality of perception services, a Transit search algorithm based on oscillation exploitation factor and Roche limit is proposed. The Roche limit-inspired approach enhances the stellar phase of the algorithm, accelerating the convergence rate in the mid-to-late stages of iteration while ensuring adequate exploration of the solution space. Subsequently, five weakening oscillation development factors are introduced to refine the algorithm’s exploitation phase and improve its fine-tuning accuracy. To validate the effectiveness of these strategies, various approaches are applied to optimize the coverage, waste rate and energy consumption in two models of WSN deployment, with connectivity recorded. The comparison reveals the optimal improved algorithm, SEROTS, which enhances coverage by 1.34% in the obstacle-free model compared to the original TS algorithm, with waste and energy consumption rates reduced by 2.05% and 0.00016%, respectively. In the obstacle model, coverage increases by 1.49%, while waste and energy consumption rates decrease by 6.96% and 0.0004%, respectively. To demonstrate the efficiency of the improved algorithm in optimizing WSN deployment, SEROTS is compared with four optimization algorithms: Egret Swarm Optimization Algorithm (ESOA), Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA) and Differential Evolution (DE). Two models are selected, integrating the three objectives into a single objective function. Simulation results indicate that SEROTS performs best in both models, with an improvement of 0.53% and 0.79% over the second-best algorithm, respectively. Furthermore, the proposed strategies are compared with simulation results from five other studies, achieving higher coverage rates by 1.57%, 3.33%, 0.87%, 3.81% and 0.21%, respectively. Finally, experiments discuss the application in large-scale scenarios, verifying the feasibility and efficiency of the SEROTS algorithm in WSN deployment optimization.