Robust self-stabilizing weight-based clustering algorithm

• Authors: Colette Johnen;Le Huy Nguyen
• Venue: Theoretical Computer Science
• Year: 2009

Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such networks cannot rely on centralized and organized network management. The clustering problem consists of partitioning network nodes into non-overlapping groups called clusters. Clusters give a hierarchical organization to the network that facilitates network management and that increases its scalability. In a weight-based clustering algorithm, the clusterheads are selected according to their weight (a node's parameter). The higher the weight of a node, the more suitable this node is for the role of clusterhead. In ad hoc networks, the amount of bandwidth, memory space or battery power of a node could be used to determine weight values. A self-stabilizing algorithm, regardless of the initial system configuration, converges to legitimate configurations without external intervention. Due to this property, self-stabilizing algorithms tolerate transient faults and they are adaptive to any topology change. In this paper, we present a robust self-stabilizing weight-based clustering algorithm for ad hoc networks. The robustness property guarantees that, starting from an arbitrary configuration, after one asynchronous round, the network is partitioned into clusters. After that, the network stays partitioned during the convergence phase toward a legitimate configuration where the clusters verify the ''ad hoc clustering properties''.