Data centric multi-shift sensor scheduling for wireless sensor networks

A multi-shift sensor scheduling method is proposed to extend the operating lifespan of a wireless sensor network. Sensor nodes in the WSN are partitioned into N subnetworks and the operating schedule is partitioned into N shifts of equal duration. Exploiting spatial correlations among sensor nodes, data collected using each subnetwork can well approximate the data collected using original sensor network. Each sub-network also form a connected component to ensure proper data collection. This task is formulated as a NP-hard constrained subset selection problem. A polynomial time heuristic algorithm leveraging breath-first search and subspace approximation is proposed. Simulations using a real world data set demonstrate superior performance and extended lifespan of this proposed method.