Optimal Fusion Set based Clustering in WSN for Continuous Objects Monitoring

Based on the application of continuous objects monitoring (COM) for Wireless Sensor Networks (WSNs), the sampling data collected by the sensors have relatively higher correlation and continuity due to the reason that the characteristic parameters of the monitored objects are continuous both in time and in space. In this paper, an Optimal Fusion Set based Clustering (OFSC) algorithm is presented to address the network clustering problem when monitoring the continuous objects. Different from the traditional clustering algorithms which cluster the network only after the cluster heads have been determined, OFSC algorithm, based on the global routing protocol in which the entire network information can be acquired by each node, the cluster head selection is carried out individually by nodes after the clustering results is firstly determined according to the Optimal Fusion Set theory. The performance evaluation results show that our OFSC algorithm can remarkably reduce the data traffic. On the other hand, our OFSC algorithm is a distributed clustering algorithm, which eliminates the computation of the communication cost during the cluster head selection, hence, decreases the computational complexity. Moreover, compared with the traditional LEACH and LEACH-C, our results show that the energy consumption of the entire network can be better balanced when monitoring the continuous objects.