Spatial correlated data collection in wireless sensor networks with multiple sinks

Due to the high density of node deployment in wireless sensor network, the sensing data of nodes in spatially proximate locations are highly correlated. By effectively exploiting this spatial correlation in the data collection process, unnecessary energy costs for redundant data transmission can be largely reduced. In this paper, we focus on collecting spatial correlated data in multi-sink scenario. The main challenge in this scenario is that data collection process should consider how to exploit the spatial correlation and decide which sink the data are transmitted to at the same time. To address this challenge, we propose an algorithm to select a subset of sensor nodes to represent the whole multi-sink sensor network based on the spatial correlated sensing readings. In this algorithm, only these representatives named sources need to upload their data to the chosen sinks. The problem is firstly formulated as a Binary Integer Linear Programming (BILP). Since the problem is proved to be NP-Complete, two heuristic algorithms are designed for approximation. The simulation results show that the proposed algorithms can largely reduce the number of the sources and then significantly improve energy efficiency.