Topology-based Clusterhead Candidate Selection in Wireless Ad-hoc and Sensor Networks

Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. Neighboring devices elect one appropriate device as clusterhead. Due to the dynamic environment, clusterhead selection becomes an important issue. We consider the problem of appropriate clusterhead selection in wireless ad-hoc networks and sensor networks. This work presents topological criteria for robust clusterhead candidate selection, resilient to sporadic node mobility and failure as well as for efficient information dissemination. One of the main ideas of our approach is to avoid selecting nodes located close to the network partition border as such nodes are more likely to move out of the partition, thus causing a clusterhead re-election. We conducted experiments both for static topologies as well as for cases in the presence of node mobility. Our results showed that the frequency of clusterhead re-election and average shortest path length from the clusterhead decrease when considering topological criteria. Additionally, the clusters tend to be robust to clusterhead failure. The presented mechanisms rely on local topological information only and do not require geographical data.