dSENSE: Data-driven Stochastic Energy Management for Wireless Sensor Platforms

Abstract

Wireless sensor networks are being widely deployed for providing physical measurements to diverse applications. Energy is a precious resource in such networks as nodes in wireless sensor platforms are typically powered by batteries with limited power and high replacement cost. This paper presents dSENSE: a data-driven approach for energy management in sensor platforms. dSENSE is a node-level power management approach that utilizes knowledge of the underlying data streams as well as application data quality requirements to conserve energy on a sensor node. dSENSE employs sense-on-change---a sampling strategy that aggressively conserves power by reducing sensing activity on the sensor node. Unlike most existing energy management techniques, this strategy enables explicit control of the sensor along with the CPU and the radio. Our approach uses an efficient statistical data stream model to predict future sensor readings. These predictions are coupled with a stochastic scheduling algorithm to dynamically control the operating modes of the sensor node components. Using experimental results obtained on PowerTOSSIM 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.