Budget-Based Clustering with Context-awareness for Sensor Networks

As the scale of modern sensor networks continues to grow, energy consumption, scalability and routing efficiency are becoming key design challenges. Network management plays an important role in achieving these goals. By decomposing a sensor network into smaller groups, clustering and its variants have been presented as efficient ways in network management. In this paper, we propose a dynamic, localized clustering approach derived from generic budget-based clustering techniques. The approach generates dynamic cluster sizes for a hierarchy of cluster heads, with respect to network context such as residual energy and activity rates of sensor nodes. We further refine the local estimated cluster sizes by using additional feedback during clustering process. Simulation results of stochastic deployment are used to demonstrate the performance of our algorithm, as well as the impact of context information as clustering parameters.