The micropulse framework for adaptive waking windows in sensor networks

In this paper we present MicroPulse, a novel framework for adapting the waking window of a sensing device S based on the data workload incurred by a query Q. Assuming a typical tree-based aggregation scenario, the waking window is defined as the time interval ô during which S enables its transceiver in order to collect the results from its children. Minimizing the length of ô enables S to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. Our method is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the Critical Path Method. We show through trace-driven experimentation with a real dataset that MicroPulse can reduce the energy cost of the waking window by three orders of magnitude.