Establishing overlapped multihop clusters in wireless sensor networks

Abstract

The past few years have witnessed increased interest in the potential use of Wireless Sensor Networks (WSNs) in applications such as disaster management, combat field reconnaissance, border protection and security surveillance. Grouping nodes into clusters has been the most popular approach for achieving efficient and scalable performance in WSNs. Most of the published algorithms strive to partition the sensors into disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like intercluster routing, topology discovery and node localisation, recovery from Cluster Head (CH) failure, etc. We formulate the overlapping multihop clustering problem as an extension to the k-Dominating Set (KDS) problem. Then we propose Multihop Overlapping Clustering Algorithm (MOCA); a randomised distributed multihop clustering algorithm for organising the sensors into overlapping clusters. MOCA is validated in a simulated environment. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equal-sized clusters.