K-barrier coverage in wireless sensor networks based on immune particle swarm optimisation

Abstract

Barrier coverage of wireless sensor networks (WSNs) has been an interesting research issue for security applications. In order to increase the robustness of barriers coverage, k-barrier coverage is proposed to address this issue. In this paper, the k-barrier coverage problem is formulated as a global optimisation problem solved by particle swarm optimisation (PSO). However, the performance of PSO greatly depends on its parameters and it often suffers from being trapped in local optima. A novel particle swarm optimisation program named AI-PSO (artificial immune-particle swarm optimisation) is designed and the model of k-barrier coverage problem is proposed to solve this problem. AI-PSO integrates the ability to exploit in PSO with the ability diversity maintenance mechanism of AI (artificial immune) to synthesise both algorithms' strength. Simulation results show that the proposed algorithm is effective for the k-barrier coverage problems.