Graph-Based Random Sampling for Massive Access in IoT Networks

Abstract

In this paper the massive access problem in IoT networks is studied from the perspective of graph signal processing (GSP). First, we reveal the connections of massive access in IoT networks and the sampling of a graph signal, and model the massive access problem as a graph-based random sampling problem. Second, inspired by the restricted isometry property (RIP) condition in compressed sensing, we derive the RIP condition for random sampling on band-limited graph signals, showing at the first time that band-limited graph signals can be recovered from randomly-selected noisy samples in a given probability. Based on the proposed RIP condition, the sampling probability of each sensing device is optimized through minimizing the Chebyshev or Gaussian approximations of mean square error between the original and the recovered signals. Experiments on the Bunny and Community graphs verify the stability of random sampling, and show the performance gain of the proposed random sampling solutions.