Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Wireless sensor networks (WSNs) are adopted in a variety of fields where coverage enhancing is a critical challenge because of the requirements of service quality, cost, and energy consumption. Coverage-enhancing approaches have currently attracted a lot of interest owing to their superior abilities in the deployment of the WSNs, e.g., maximum coverage, minimum sensors, and minimum energy. In this paper, a differential evolution-based regional coverage-enhancing algorithm is proposed for directional 3D WSNs, which is able to maximizing coverage while minimize the number of sensors. First, a directional cone perception model is designed to better display the actual sensing performance of sensor nodes. Subsequently, the coverage region is established to describe the perceptual range of nodes. Thereafter, a three-stage coverage-enhancing method is derived, which includes the pitch angle optimization, the deflection angle optimization and the redundant node sleeping. These strategies are designed to maximize the perception range of a single sensor node, maximize the coverage rate, and minimize the number of nodes, respectively. Finally, simulation results show that our method is able to ensure better performance compared to the state-of-the-art frameworks.