Distributed Deployment Strategies for Improved Coverage in a Network of Mobile Sensors With Prioritized Sensing Field

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a given function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results

Deployment Strategies for Target Monitoring and Coverage Improvement in Mobile Sensor Networks

Efficient sensor deployment strategies are developed in this work for target monitoring and coverage improvement in collaborative wireless mobile sensor networks. The objective of the target monitoring problem is to compute the desired sensing and communication radii of sensors as well as their location at every time instant such that a set of prescribed specifications such as connectivity preservation and low energy consumption are satisfied. An energy-efficient strategy is also proposed for tracking a moving target in a sensing field, using a grid of sufficiently small rectangular cells. The grid is converted to a graph with properly weighted edges. A shortest-path algorithm is subsequently used to route information from target to destination by a subset of sensors. In the problem of coverage improvement in mobile sensor networks, on the other hand, the objective is to place each sensor in the field using available local information about its neighbors in such a way that the area covered by sensors is as large as possible, while some important criteria are taken into consideration. Both cases of identical and nonidentical sensors (in terms of sensing radii) are considered, and different iterative algorithms are developed which are shown to be convergent. The relocation algorithms are based on the relative position of each sensor w.r.t. the boundaries of its cell or the corresponding corner point. The algorithms are extended to the case of limited communication range of sensors (leading to inaccurate Voronoi cells), an environment with prioritized sensing (mathematically characterized by a weighting function for different points), and an environment with obstacles (leading to some invisible areas). Simulation results are provided to validate the effectiveness of the proposed algorithms