Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility

Abstract

Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). The maintenance of the cluster structure should be as stable as possible to reduce overhead and make the network topology less dynamic. Hence, stability measures the goodness of clustering. However, for a complex system like MANET, one clustering metric is far from reflecting the network dynamics. Some prior works have considered multiple metrics by combining them into one weighted sum, which suffers from intrinsic drawbacks as a scalar objective function to provide solution for multi-objective optimization. In this paper, we propose a stability-aware multi-metric clustering algorithm, which can (1) achieve stable cluster structure by exploiting group mobility and (2) optimize multiple metrics with the help of a multi-objective evolutionary algorithm (MOEA). Performance evaluation shows that our algorithm can generate a stable clustered topology and also achieve optimal solutions in small-scale networks. For large-scale networks, it outperforms the well-known weighted clustering algorithm (WCA) that uses a weighted sum of multiple metrics.