Adaptive Compressive Data Gathering for Wireless Sensor Networks

Compressive sensing (CS) based data gathering is a promising approach to reduce data sampling and transmission in wireless sensor networks and thus prolong WSN's lifetime. The physical phenomena are generally nonstationary and thus the sparsity of sensing data varies in temporal and spatial domain. In order to guarantee the reconstruction accuracy with lower energy cost due to the variation of sensing data, this paper proposes an adaptive compressive data gathering scheme containing adaptive measurement and reconstruction. The adaptive measurement is that the number of measurements is tuned adaptively according to the prediction of the change trend of the sensing data. The adaptive reconstruction is based on the Stagewise Orthogonal Matching Pursuit (StOMP) algorithm and using the Proportional Integrative-Derivative (PID) method to adaptively guarantee the reconstruction accuracy. At last, an adaptive compressive data gathering system is built on Crossbow Micaz WSN platform. The experimental results show that the proposed scheme can ensure reconstruction accuracy with low energy cost.