A novel framework for energy-efficient compressive data gathering in heterogeneous wireless sensor network

Wireless sensor network (WSN) suffers from the energy-limited sensor nodes which consume energy heavily depending upon the magnitude of data which is transmitted or received by the nodes in the network. In this paper, our primary aim is to reduce the quantity of data transmitted to the data-collecting sink, which helps in the energy preservation and eventually leads to network longevity. To address this concern, in this paper, we propose a novel framework for energy-efficient compressive data gathering (NFECG) for heterogeneous WSN. NFECG works in four following phases; in the first phase, the cluster head (CH) selection is performed by considering remaining energy, "distance within the nodes and the sink," and node density; in second phase, sleep scheduling is done among the cluster member nodes; further, in third phase, the compression of the aggregated data is performed at the CH level, and equivalent compressed sparse signals are generated which are transmitted to sink. In the last phase, at the sink, decompression is applied to retrieve the original signals. The simulation of NFECG is performed using MATLAB under two cases of different network area and number of nodes. We examine its performance for various performance metrics and also inspect for its scalable characteristics. The results show that for one of the two cases, it improves stability period and network lifetime by 52.59% and 46.09%, respectively, as compared to energy-adjusted high-level data total tree (EHDT) protocol, and also for the other case of network configuration, it acquires supreme performance.