Associativity-based adaptive weighted clustering for large-scale mobile ad hoc networks

We propose and analyze a distributed adaptive clustering algorithm for large-scale ad hoc networks. The algorithm calculates a stability weight for each node based on its power and spatial and temporal stability. The nodes having the highest stability weight get elected as clusterheads. Frequent clusterhead change is minimized by cautious invocation of re-clustering. Frequency of control messages is adapted to the mobility pattern of clustermembers. The algorithm balances load across clusterheads and adapts hop-distance to the network density by keeping the cluster size around an optimum value. It reduces the overall communication complexity by minimizing the control traffic overhead and by eliminating the ripple effect of re-clustering. We analyze effectiveness of the algorithm through simulations.