Factor Graphs for Support Identification in Compressive Sensing Aided Wireless Sensor Networks

A new support identification technique based on factor graphs and belief propagation is proposed for compressive sensing (CS) aided wireless sensor networks (WSNs), which reliably estimates the locations of non-zero entries in a sparse signal through an iterative process. Our factor graph based approach achieves a support identification error rate of 10% at an signal to noise ratio (SNR) that is 6 dB lower than that required by the state-of-the-art relative frequency based support identification approach, as well as by the orthogonal matching pursuit (OMP) algorithm. We also demonstrate that our support identification technique is capable of mitigating the signal reconstruction noise by as much as 4 dB upon pruning the sparse sensing matrix. Furthermore, by intrinsically amalgamating the relative frequency based and the proposed factor graph based approach, we conceived a hybrid support identification technique for reducing communication between the sensor nodes and the fusion center (FC), while maintaining high-accuracy support identification and simultaneously mitigating the noise contaminating signal reconstruction.