Sensing Driven Clustering for Monitoring and Control Applications
Yen-Ting Lin, Seapahn Megerian · 2007
Clustering the nodes in a wireless sensor network is a fundamental step in many distributed self organization, re- source management, communication, and processing algorithms. Although in many system management tasks a purely network topology-based clustering may be sufficient, a more sensor-centric approach to clustering is required for many distributed sensing, control, and actuation applications. In this paper we investigate the sensing-driven node clustering problem by first formulating it as an instance of weighted bi-partite matching between sensors and phenomena of interest. In order to make the clustering algorithm implementation practical in a larger networks, we then present two distributed approaches: (i) a simple, low- cost, deterministic approach and (ii) a probabilistic balanced clustering approach. Depending on the number of sensors in the system and cluster capacities, our simulation studies indicate that switching from one approach to the other, under specific conditions, can achieve near optimal results while keeping the communication costs low.