PSS: Predictive Energy-Efficient Sensing Scheduling in Wireless Sensor Networks; Conference paper

摘要

Wireless sensor networks are being widely deployed for providing physical measurements to diverse applications that have wide variety of data quality requirements. Energy is a precious resource in such networks as sensor nodes are typically powered by batteries with limited power and high replacement cost. This paper presents PSS: an energy-efficient stochastic sensing framework for wireless sensor platforms. PSS 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. PSS 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.