Compressive sensing and random walk based data collection in wireless sensor networks

Data collection is one of the most important research topics in wireless sensor networks. Compressive sensing can break through the classical Shannon-Nyquist boundary and reduce the total energy consumption. Random walk technology has a significant advantage in the energy balance. Random walk based compressive sensing data collection mechanism is a combination of these two technologies, which reinforce complementary advantages. However, the traditional scheme faces two challenges, i.e., large transmission energy consumption caused by the overlong multi-hop transmission path and the low recovery accuracy caused by poorly designed compressive sensing measurement. In this paper, we propose a ring topology based compressive sensing data collection scheme (RTCS). The total number of hops is reduced by a ring topology based random walk. The recovery accuracy is improved by the dual compensation based compressive sensing measurement. The theoretical analysis, as well as the experimental evaluation obtained from two different network deployment environments, demonstrates that the proposed scheme can achieve the performance superior to the most closely related work.