Segmental dynamic duty cycle control for sampling scheduling in wireless sensor networks

Wireless sensor networks for environment monitoring are usually deployed in the fields where electric or manual intervention cannot be accessed easily. Therefore, we hope to minimize the times of sampling to reduce energy consuming. Energy-efficient sampling scheduling can be realized using compressive sensing theory on the basis of temporal correlation of the physical process. However, the degree of correlation of neighboring data varies over time, which may lead to different reconstructive quality for different parts of data if constant duty cycle is used. We proposed SDDC, a segmental dynamic duty cycle control method, for sampling scheduling in wireless sensor networks based on compressive sensing. Using a priori knowledge obtained by means of analysis on earlier sensing data, dynamic duty cycle is determined according to the linear degree of data in each segment. The experimental results using data from soil respiration monitoring sensor networks show that the proposed SDDC method can lead to better reconstructive quality compared to constant duty cycle of the same average sampling rate. That is to say, the SDDC method needs smaller sampling rate if the reconstructive error threshold is given and consequently saves more energy.