Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods.