eSENSE: energy efficient stochastic sensing framework for wireless sensor platforms

Abstract

Energy is a precious resource in wireless sensor networks as sensor nodes are typically powered by batteries with high replacement cost. This paper presents eSENSE: an energy-efficient stochastic sensing framework for wireless sensor platforms. eSENSE is a node-level framework that utilizes knowledge of the underlying data streams as well as application data quality requirements to conserve energy on a sensor node. eSENSE employs a stochastic scheduling algorithm to dynamically control the operating modes of the sensor node components. This scheduling algorithm enables an adaptive sampling strategy that aggressively conserves power by adjusting sensing activity to the application requirements. Using experimental results obtained on Power-TOSSIM with a real-world data trace, we demonstrate that our approach reduces energy consumption by 29-36% while providing strong statistical guarantees on data quality.