Compressive Data Persistence in Large-Scale Wireless Sensor Networks

This paper considers a large-scale wireless sensor network where sensor readings are occasionally collected by a mobile sink, and sensor nodes are responsible for temporarily storing their own readings in an energy-efficient and storage-efficient way. Existing data persistence schemes based on erasure codes do not utilize the correlation between sensor data, and their decoding ratio is always larger than one. Motivated by the emerging compressive sensing theory, the authors propose compressive data persistence which simultaneously achieves data compression and data persistence. In the development of compressive data persistence scheme, they design a distributed compressive sensing encoding approach based on Metropolis-Hastings random walk.