Maximal coverage hybrid search algorithm for deployment in wireless sensor networks

A vital design aspect in the setting up of a wireless sensor network is the deployment of sensors. One of the key metrics of the quality of deployment is the coverage as it reflects the monitoring capability of the network. Random deployment is a sub-optimal method as it causes unbalanced deployment and requires sensors in excess of the planned deployment to achieve the same level of coverage. To achieve maximum coverage with a limited number of sensors, planned deployment is a preferred choice. Maximizing the coverage of the region of interest with a given number and type of sensors is an optimization problem. A novel maximal coverage hybrid search algorithm (MCHSA) is proposed in this paper to solve this problem. The MCHSA is a hybrid search algorithm that achieves the balance between exploration and exploitation by applying the particle swarm optimization as a global search technique and using the Hooke–Jeeves pattern search method to improve the local search. The algorithm starts with a good initial population. The proposed MCHSA has low computational complexity and fast convergence. The performance of the MCHSA is analyzed by performing a comparison with the existing algorithms in the literature, in terms of coverage achieved and number of fitness function evaluations. The paper also discusses the tuning of parameters of the proposed algorithm.