Balanced spatio-temporal compressive sensing for multi-hop wireless sensor networks

Compressive Sampling (CS) is a powerful sampling technique that allows accurately reconstructing a compressible signal from a few random linear measurements. CS theory has applications in sensory systems where acquiring individual samples is either expensive or infeasible. A Wireless Sensor Network (WSN) is a distributed sensory system comprised of resource-limited sensor nodes. Transferring all the recorded samples in a WSN can easily result in data traffic that can exceed the network capacity. There are ongoing attempts to devise efficient and accurate compression schemes for WSNs and CS has proved to be a key sampling method compared to many other existing techniques. In this paper, specifically targeting the dominant WSN deployments of multi-hop WSNs, we develop a novel CS-based concept of sampling window as an efficient spatio-temporal signal acquisition/compression technique. We show that much higher energy-efficient signal acquisition is possible, if composite temporal and spatial correlations are considered. Our model is also capable of abnormal event detection which is a crucial feature in WSNs. It guarantees balanced energy consumption by the sensor nodes in a multi-hop topology to prevent overloaded nodes and network partitioning.