Data aggregation using compressive sensing for improved network lifetime in large scale wireless sensor networks

Abstract

In large scale Wireless Sensor Networks(WSN's) the amount of data generated is enormous. The data has to be processed efficiently before it reaches the Base Station (BS) by using an efficient routing algorithm as well as data aggregation methods. The nodes in WSN's are randomly deployed, the data emerging from these nodes are highly correlated either spatially or temporally. The data aggregation scheme should employ simple encoding since the sensor nodes are battery operated. The proposed method discusses about a data aggregation scheme using Compressive Sensing(CS) technique which makes use of correlation among the sensor nodes. Our primary focus is to increase the lifetime of the overall network. The underlying protocols used are Low-energy adaptive clustering hierarchy (LEACH) and Multi-threshold adaptive range clustering (M-TRAC). We have computed several network parameters for different network configuration. The reconstruction algorithm is sufficiently robust against noise. The reconstruction of the data is done using greedy method and L1 norm regularization. The implementation of the algorithm is done using the real data-set from Intel Lab. Simulation results validate the data aggregation scheme guarantees data accuracy and doubles the network lifetime. 2016 International Science Press.