Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks

We discuss the problem of maximizing the sensor field coverage for a specific number of sensors while minimizing the distance traveled by the sensor nodes. Thus, we define the movement task as an optimization problem that involves the adjustment of sensor node positions in a coverage optimization mission. We propose a coverage optimization algorithm based on sampling to enhance the coverage of 3D underwater sensor networks. The proposed coverage optimization algorithm is inspired by the simple random sampling in probability theory. The main objective of this study is to lessen computation complexity by dimension reduction, which is composed of two detailed steps. First, the coverage problem in 3D space is converted into a 2D plane for heterogeneous networks via sampling plane in the target 3D space. Second, the optimization in the 2D plane is converted into an optimization in a line segment by using the line sampling method in the sample plane. We establish a quadratic programming mathematical model to optimize the line segment coverage according to the intersection between sensing circles and line segments while minimizing the moving distance of the nodes. The intersection among sensors is decreased to increase the coverage rate, while the effective sensor positions are identified. Simulation results show the effectiveness of the proposed approach.