Cost-efficient sensor deployment in indoor space with obstacles
In this paper, we tackle the problem to achieve k-coverage of a target indoor space with obstacles, that is, any point in the target monitoring area has a line-of-sight to and is located in the sensing range r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> of at least k sensors. We propose heuristic algorithms for computing a deployment pattern achieving the k-coverage in an arbitrary 3D target space with stationary and mobile obstacles, while minimizing the overall deployment cost. For the case with only stationary obstacles, we propose a greedy algorithm that puts sensor nodes one by one on a grid point of the deployable area in the descending order of the cost-performance value (i.e., how many monitoring points are covered by putting a sensor at the deployment point per unit deployment cost). In order to extend the algorithm for the case with a mobile obstacle, we define mobile k-coverage that guarantees k-coverage of a target space for arbitrary position of a mobile obstacle, then provide a sufficient condition for the mobile k-coverage: the half-sphere of radius r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> centered at a monitoring point bounded by the vertical plane containing the point includes at least k sensor nodes. Based on this condition, we propose a heuristic algorithm that puts at least one sensor node in each π over k+1 spherical wedge for each monitoring point. With the proposed algorithms, we have computed the sensor nodes position in an existing indoor environment and confirmed that the obtained WSN deployment in the real space accurately achieves k-coverage.