A Dynamic Cluster Formation Algorithm for Collaborative Information Processing in Wireless Sensor Networks

Abstract

Clustering of sensor nodes has been shown to be an effective approach for distributed collaborative information processing in resource constrained wireless sensor networks to keep network traffic local in order to reduce energy dissipation of long-distance transmissions. Defining the range and topology of clusters to reduce energy consumption and retransmissions due to collisions on shared radio channels is an ongoing research topic. One solution is to minimize overlapping cluster ranges to reduce signal contention, thus reducing the energy dissipation of collisions and retransmissions. In this paper, we propose a dynamic cluster formation (DCF) algorithm that dynamically groups a set of sensor nodes into a logical cluster-based sensing and processing unit, collaborative agent sensor team (CAST), to detect and track localized phenomena. Each cluster head is selected locally such that the total overlap between clusters in a CAST is low and the coverage of each cluster head is high. We compare our approach with optimal solutions, and simulation results show the effectiveness and scalability of our CAST DCF algorithm.