Spatial-Temporal Compressive Sensing for Cross-Layer Optimization Data Transmission in Wireless Sensor Networks

Abstract

Inspired by the theory of compressive sensing (CS) and employing cross-layer optimization, we propose a cross-layer optimization data transmission scheme based on spatial-temporal compressive sensing (STCS-DT) in Wireless Sensor Networks (WSNs). The proposed scheme efficiently enhances transmission data efficiency in improving allocation of link capacity, power, transmission rate and channel access via Kronecker sparsifying bases. Specifically, we derive a joint CS reconstruction optimization method which not only consider the spatial and temporal of data streams, but also affiliate with each layer constraints. The optimization results improve the reconstruction accuracy of sensor data streams while reducing the necessary communications, and present the network protocols in physical, MAC, network and transport layers. Numerical results show that our proposed method achieves higher reconstruction accuracy with a smaller number of required transmissions, and with lower decoding delay and complexity as compared to those of the state of the art CS methods.