Compressive sampling in wireless sensor networks

Communication is emerging as one of the most important topics of today. Data traffic has been increasing incredibly day by day. Wireless communications is convenient to use and has been widely used in daily life with an increasing penetration. Wireless Sensor Networks use sensors to sense the environment and use wireless communications to send and receive the related information. WSN consists of sensor nodes which has limited battery power and a base station to gather the observation data and process it. It is necessary to use energy efficiently in order to increase the lifetime of the network. Furthermore, the energy consumed by the network is directly related to data communication and data processing. Compressive Sampling theory has a solution at this point. If the signal is sparse in a certain transform domain, Compressive Sampling theory states that less number of measurements can be used to reconstruct the function representing the physical parameter of concern. Because of this, CS has become an important technology for WSNs. In this study, the sensor nodes in a WSN are assumed to be distributed over a geographical region using the Gauss distribution, uniform distribution, grid distribution and H-PPP distribution, and the reconstruction performance of the OMP and CoSamp algorithms are assessed over measurements taken from sampling points.