Distributed coverage optimization for deployment of directional sensor networks

We propose a new sensor-to-point `coverage distance' to address the anisotropic sensing property of directional sensors. This new coverage distance is then employed to design a distributed coverage optimization algorithm for deployment of directional sensor networks. Different from existing approaches, the newly proposed coverage distance takes advantage of homeomorphism, which could reflect the relationship between the isotropic and the anisotropic spaces. Based on the interpretation of physical sensing models, a homeomorphism is first constructed so that the original anisotropic sensing space is mapped into an isotropic sensing space, in which the sensor-to-point `coverage distance' can be defined through the Euclidean distance. Then, the coverage distance is used to generate an appropriate scene partition, called `Directional Voronoi Partition' (DVP), which warrants the design and implementation of a distributed coverage optimization algorithm for deployment of directional sensor networks. The developed method is tested with an circular sector sensor. Simulation results are provided to verify the effectiveness of the proposed approach.