Understanding Compressed Sensing Inspired Approaches for Path Reconstruction in Wireless Sensor Networks

Understanding per-packet routing dynamics in deployed and complex wireless sensor networks (WSNs) has become increasingly important for many essential tasks such as network performance analysis, operation optimization, system maintenance, and network diagnosis. In this paper, we study routing path recovery for data collection in multi-hop WSNs at the sink using a very small and fixed path measurement carried in each packet. We analyze the two recent compressed sensing (CS) inspired approaches called RTR and CSPR. We evaluate RTR versus CSPR as well as other state-of-the-art approaches including MNT and Pathfinder via simulations. Our work provides insights into the better understanding of the profound impacts of different CS-inspired approaches on their respective path reconstruction performance and the resource requirement on sensor nodes. The evaluation results show that the RTR significantly outperforms CSPR, MNT and Pathfinder.