This document discusses using compressive sensing for efficient data gathering in wireless sensor networks. It proposes using a random walk algorithm to collect random measurements along multiple random walks, allowing for non-uniform sampling unlike traditional compressive sensing theory. The random walk approach can help address constraints like path constraints in wireless sensor networks. It provides the mathematical foundations to reconstruct sparse signals from random measurements collected in a random walk manner using graph theory and l1 minimization. Simulation results show the random walk approach can significantly reduce communication costs and noise compared to other data gathering schemes.
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Efficient Data Gathering with Compressive Sensing in Wireless Sensor Networks