Compressive Sensing Based Data Gathering in Clustered Wireless Sensor Networks
In this paper, we study the integration between Compressed Sensing (CS) and clustering methods in Wireless Sensor Networks (WSNs) that significantly reduce power consumption for the networks. In theory, a base station (BS) needs to collect M measurements from the network with N sensors, then applies CS to obtain precisely all N sensor readings. In clustered networks, a cluster-head (CH) collects data from non-CH sensors in its cluster, adds all received and its own data then send the combined measurement to the BS. We further analyze the clustered network with the measurement matrix created by clustering methods, and formulate the total power consumption. Finally, we suggest the optimal number of clusters for the networks consume the least power in practice.