Data Aggregation using Compressive Sensing for Energy Efficient Routing Strategy

Abstract

Data gathering in an energy efficient way, is one of the crucial concerns in Wireless Sensor Networks (WSNs). With incorporation of Compressive Sensing (CS), as a data aggregation scheme the information contained in the signal, is safely maintained through its projections, which can be reconstructed later. Inclusion of CS, for an energy efficient routing technique further enhances the lifetime of the network. To improve network lifetime, CS has been employed at level 1 (at the leaf nodes). The dimensionality reduction at the transmitter is done, using a measurement matrix. At the receiver, data is recovered using greedy based methods. Greedy based method offers low complexity, and low implementation cost. The success rate of greedy method, depends on the sparsity of the data. Performance evaluation of greedy based method is analyzed, by considering varying sparsity and plotted against number of measurements, required to reconstruct the signal. At the same time, reconstruction error of every method has been evaluated by varying sparsity of the signal. The same is analyzed by considering real temperature, and humidity data sets. © 2020 The Authors. Published by Elsevier B.V.