A Gradient-Based Coverage Optimization Strategy for Mobile Sensor Networks

Abstract

A Voronoi-based strategy is proposed to maximize the sensing coverage in a mobile sensor network. Each sensor is moved to a point inside its Voronoi cell using a coverage improvement scheme. To this end, a gradient-based nonlinear optimization approach is utilized to find a target point for each sensor such that the local coverage increases as much as possible, if the sensor moves to this point. The algorithm is implemented in a distributed fashion using local information exchange among sensors. Analytical results are first developed for the single sensor case, and are subsequently extended to a network of mobile sensors, where it is desirable to maximize network-wide coverage with fast convergence. It is shown that under some mild conditions, the positions of the sensors converge to a stationary point of the objective function, which is the overall weighted coverage of the sensors. Simulations demonstrate the effectiveness of the proposed strategy.