A Selfish-SCM Based Scheme for Node Clustering in Decentralized Peer-to-Peer Networks

Abstract

Node clustering has wide ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks and so forth. We are interested in the dynamic cluster formation process and effective strategy designs of the nodes in a network. In this work, We propose a completely distributed approach for node clustering that based on the scaled coverage measure (SCM), a measure of the accuracy of the partition of the network into clusters. The novelty of our approach is to allow simultaneous adjustment by each node in joining a new cluster if it sees an immediate benefit from such a change. The acceptance of the new node in the cluster is determined by the improvement of the cluster in its SCM value. Simulation study shows that our approach derives a more accurate partition with less orphan nodes, and converges faster than previously proposed methods, for both random topologies and power law topologies.