Computer Engineering Commons^™

Distributed Data Aggregation For Sparse Recovery In Wireless Sensor Networks, Shuangjiang Li

Masters Theses

We consider the approximate sparse recovery problem in Wireless Sensor Networks (WSNs) using Compressed Sensing/Compressive Sampling (CS). The goal is to recover the $n \mbox{-}$dimensional data values by querying only $m \ll n$ sensors based on some linear projection of sensor readings. To solve this problem, a two-tiered sampling model is considered and a novel distributed compressive sparse sampling (DCSS) algorithm is proposed based on sparse binary CS measurement matrix. In the two-tiered sampling model, each sensor first samples the environment independently. Then the fusion center (FC), acting as a pseudo-sensor, samples the sensor network to select a subset of …