Shape Retrieval with Adjustable Precision for Hole Detection in 3D Wireless Sensor Networks

3D Wireless Sensor Networks (WSNs) have attracted considerable attentions due to their geometrical topology characteristics and various application scenarios. Because of the unbalanced energy consumption among the nodes and the obstacles in the sensing space, the holes of the network topology have been one of the main obstacles in the application of the 3D WSNs. The data collection, especially for the data converged at the nodes nearby the holes, depends on the data relaying by the nodes on the boundary of the hole space, which may result in the exhaustive energy consumption of the involved nodes, quickly draining their limited energy and enlarge the hole space further. In this paper, we adopt a 3D clustering approach and propose a Precision-Adjustable Shape Retrieval (PASR) scheme for hole detection in 3D WSNs. In order to provide a fast and efficient hole detection for the network topology, a hierarchical clustering approach is designed by using the adjustable cluster size to conduct data collection for hole detection. Through extensive simulations, our scheme is demonstrated to be able to serve accurate shape retrieval and achieve good balance between the detection precision and the energy consumption.