Performance analysis of data aggregation for overlapping clusters in heterogeneous sensor network

In sensor network, cluster boundaries often overlap to form a common neighborhood. This overlapped area is composed of sensors, called fuzzy nodes that possess degrees of belongingness towards each of the coinciding clusters. The sensors other than fuzzy nodes are termed as normal or crisp nodes which belong to a distinct cluster with absolute degree of membership. In this paper, we present a novel way of compressing and aggregating data from the fuzzy nodes. We have further performed an analysis of the communication as well as computation latency incurred in the proposed aggregation process. The performance of our method is evaluated in terms of aggregation delay, variation in cluster overlap degree and energy consumption. The simulation results show that our approach is more efficient than some of its referenced counterparts.