An Energy Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation
Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, i.e., the density of the measure points in a field. By properly scheduling, the authors want to retain the high fidelity of data collection. In this paper, they propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, they dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar.