Distributed compressive data gathering in wireless sensor networks

Efficient data aggregation in sensor networks is becoming important with the increase of the number of nodes in the networks. In this paper, we propose a distributed compressed sensing-based data gathering scheme in wireless sensor networks in which the sensor readings possess both inter- (spatial) and intra- (temporal) signal correlations that may take the form of a jointly sparse structure. Simulations are performed using real data sets to evaluate the recovery performance in our proposed framework. Results show that the use of this scheme permits almost perfect signal reconstruction with significantly reduced number of measurements which in turn reduces the communication cost, saves energy consumption by the sensor nodes and prolongs the lifetime of the network.