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Sensor network partitioning based on homogeneity

Discovering communities from sensor networks is an important problem in many real-world applications. The problem desiderata requires to partition the network according to the homogeneity of the sensor measurement and at the same time the partitioning result to be as independent as possible of the typical local change in the network topology. This poses new challenges to the current graph partitioning methodologies. We develop a graph partitioning approach from the perspective of homogeneity. In order to avoid the inherent incompatibility of the current graph partitioning methodologies, an objective functional is designed to be asymptotically close to the Mumford-Shah functional [1]. An variational algorithm ZERO-CUT and a fast approximation algorithm GREEDY-SACK are developed to solve the objective functional. We evaluate the performance of the proposed algorithms on both synthetic and real-world graphs in different applications to demonstrate its advantage and promise in solving the problem of discovering communities from sensor networks.