Cost-Efficient Deployment for Full-Coverage and Connectivity in Indoor 3D WSNs

In WSNs, coverage of the target field and wireless connectivity among sensor nodes are important criteria in terms of the performance required for WSN. In indoor environments, the target field is in general 3D space, thus WSNs deployed for such environments are called 3D WSNs. The sensor node deployment problem for 3D coverage and connectivity is NP-hard even without obstacles in the target field. Furthermore, no study has systematically examined the optimal 3D WSN deployment considering both obstacles and deployment cost. In this paper, we propose a new heuristic algorithm for computing a near optimal sensor node deployment that minimizes the cost for achieving the full coverage and node connectivity of a 3D target space with obstacles. First, we represent the monitoring area as well as the sensor node deployable area in the target 3D space, by a set of grid points. Our algorithm puts sensor nodes one by one on a grid point of the deployable area in the descendant order of the cost-performance value (i.e., how many monitoring space points are covered by the deployable area point per unit deployment cost) of the deployable area points. Then, the algorithm adds extra nodes to cover the shadow area of each node’s sensing region cut off by the obstacles. Moreover, to ensure the connectivity among all WSN nodes, the algorithm adds extra nodes and/or moves each unconnected sensor node one by one towards the closest connected sensor node in order to reduce the number of extra nodes. We implemented our proposed method in the UbiREAL simulator and evaluated the performance through simulations. As a result, we have confirmed that our proposed method can provide reliable and cost-efficient solutions for WSN deployment in indoor environments.