A sparsity feedback-based data gathering algorithm for Wireless Sensor Networks

As a means of detecting abnormal events in Wireless Sensor Networks (WSNs), this paper presents a Compressive Sensing (CS)-based algorithm, called Minimum Spanning Tree and Mobile Agent-based Greedy Shortest Path (MST-MA-GSP). The algorithm first of all uses a sparsity feedback mechanism to accurately estimate the sparsity k of the sensor measurements. It then uses Monte Carlo experiments to determine the minimum number of required measurements M_min. According to the value of M_min, the algorithm adaptively adjusts the number of measurements M in order to maximize its recovery performance. The experiments show that the proposed algorithm is superior to other compressive data gathering (CDG) algorithms in terms of energy balance, whilst the adaptive M_min mechanism guarantees a reconstruction accuracy of at least 99%. Additionally, the sparse binary matrix used in the MST-MA-GSP algorithm offers better recovery of sparse zero-one data than other CDG-based measurement matrices.