Compressive Data Gathering using Sector-based Random Walk in WSNs

This paper addresses the problems of energy consumption, energy consumption balancing, and Compressive Sensing (CS) recovery performance in wireless sensor network. We propose an efficient method named ‘‘Compressive Data Gathering using Sector-based Random Walk (CDG-SRW)’’. The proposed scheme uses the CS theory along with the sector-based random walks to transmit CS measurements from sensors to the sink node. This routing method is presented to decrease the overlap of random walk paths with each other and improve the quality of the measurement matrix Furthermore, we provide a formulation to theoretically study average and minimum number of hops for a random walk path. Numerical results demonstrate that the proposed algorithm reduces energy consumption in comparison with M-CSR by 72 % and also decreases energy consumption variance compared to it by 45 % for an average number of CS measurements. Based on the simulation results, the proposed CDG-SRW algorithm also illustrates a performance improvement in comparison to M-CSR in terms of reconstruction error.