A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks

Energy consumption is one of the major con- straints in wireless sensor networks. A highly feasible strat- egy is to aggressively reduce the spatial sampling rate of sensors (i.e., the density of the measure points in a field). By properly scheduling, we want to retain the high quality of data collection. In this paper, we propose a novel dynamic clustering and scheduling approach. Orthogonal to most ex- isting methods which mainly utilize the overlaps of sensing ranges of sensors to reduce the spatial sampling rate, our method is based on a careful analysis of the surveillance data reported by the sensors. We dynamically partition the sensors into groups so that the sensors in the same group have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. A generic framework is de- veloped to address several important technical challenges, including how to partition the sensors into groups, how to dynamically maintain the groups, and how to schedule sam- pling for the sensors in a group. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.