Dynamic Compressive Data Gathering using Angle-based Random Walk in Hybrid WSNs

Abstract

This paper addresses the problems of energy consumption, load-balancing, and Compressive Sensing (CS) recovery performance in hybrid proactive-reactive wireless sensor network, where sensors are categorized into two representative and relay nodes. We jointly consider the dynamic compressive data gathering, angle-based random walk, and spatial correlation to propose an efficient method named “Dynamic Compressive Data Gathering using Angle-based Random Walk (DCDG-ARW)”. The proposed scheme uses the CS theory along with the angle-based random walks to transmit CS measurements from sensors to the sink node. To select a starting node for each random walk path, a heuristic mechanism called Distance-and Correlation-aware Node Selection (DCNS) is presented. Furthermore, a transition probability is defined for the proposed angle-based random walks to jointly investigate dynamic changes of the network, increasing load-balancing, and improving the quality of the measurement matrix. The effectiveness of DCDG-ARW is evaluated in terms of energy consumption, load variance, network’s lifetime, and reconstruction error. Based on the simulation results, the proposed DCDG-ARW algorithm illustrates significant performance improvements in comparison to its counterparts across all the aforementioned aspects.