Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks
Clustering is a widely used technique to manage the essential operations such as routing and data aggregation in wireless sensor networks (WSNs). We propose two new graph-theoretic distributed clustering algorithms for WSNs that use a weighted matching method for selecting strong links. To the best of our knowledge, our algorithms are the first attempts that use graph matching for clustering. The first algorithm is divided into rounds; extended weighted matching operation is executed by nodes in each round; thus the clusters are constructed synchronously. The second algorithm is the enhanced version of the first algorithm, which provides not only clustering but also backbone formation in an energy-efficient and asynchronous manner. We show the operation of the algorithms, analyze them, provide the simulation results in an ns2 environment. We compare our proposed algorithms with the other graph-theoretic clustering algorithms and show that our algorithms select strong communication links and create a controllable number of balanced clusters while providing low-energy consumptions. We also discuss possible applications that may use the structure provided by these algorithms and the extensions to the algorithms.
graph matching clustering backbone formation sensor networks