A Compressive Sensing (CS) based solution is proposed for centralized and distributed discovery of physical phenomena in large scale Wireless Sensor Networks (WSNs). WSNs monitoring environmental phenomena over large geographic areas collect measurements from a large number of distributed sensors. Compressive Sensing provides an effective means of discovery and reconstruction of functions with only a subset of samples. Traditional CS relies on uniformly distributed samples which limits practicality of CS based recovery. To enhance the flexibility of sampling and implementation, the proposed approach uses random walk based samples. Unlike uniform sampling, random walk based sampling enables individual nodes achieve phenomenon awareness, i.e., the physical distribution of the phenomenon. We also derive a theoretical upper bound for the reconstruction failure probability. Simulation results on the number of samples required and error show that random walk based sampling is comparable to uniform sampling but with superior energy efficiency. More importantly, the proposed scheme provides a practical solution for a range of applications where uniform sampling is less economical or even infeasible.
展开▼
