A Cross-Layer Approach to Data-aided Sensing using Compressive Random Access

Abstract: In this paper, data-aided sensing as a cross-layer approach in Internet-of-Things (IoT) applications is studied, where multiple IoT nodes collect measurements and transmit them to an Access Point (AP). It is assumed that measurements have a sparse representation (due to spatial correlation) and the notion of Compressive Sensing (CS) can be exploited for efficient data collection. For data-aided sensing, a node selection criterion is proposed to efficiently reconstruct a target signal through iterations with a small number of measurements from selected nodes. Together with Compressive Random Access (CRA) to collect measurements from nodes, compressive transmission request is proposed to efficiently send a request signal to a group of selected nodes. Error analysis on compressive transmission request is carried out and the impact of errors on the performance of data-aided sensing is studied. Simulation results show that data-aided sensing allows to reconstruct the target information with a small number of active nodes and is robust to nodes' decision errors on compressive transmission request.