Match Made in Heaven: Practical Compressed Sensing and Network Coding for Intelligent Distributed Communication Networks

TL;DR

This paper demonstrates that combining compressed sensing and network coding in wireless sensor networks significantly reduces transmissions and improves data reconstruction quality, promising advancements for 5G network efficiency.

Contribution

It introduces a practical scheme integrating compressed sensing and network coding with spatial pre-coding for efficient data transmission in sensor networks.

Findings

Significant reduction in transmitted packets

High reconstruction SNR achieved

Effective in cluster-based wireless sensor networks

Abstract

Based on the impressive features that network coding and compressed sensing paradigms have separately brought, the idea of bringing them together in practice will result in major improvements and influence in the upcoming 5G networks. In this context, this paper aims to evaluate the effectiveness of these key techniques in a cluster-based wireless sensor network, in the presence of temporal and spatial correlations. Our goal is to achieve better compression gains by scaling down the total payload carried by applying temporal compression as well as reducing the total number of transmissions in the network using real field network coding. In order to further reduce the number of transmissions, the cluster-heads perform a low complexity spatial pre-coding consisting of sending the packets with a certain probability. Furthermore, we compare our approach with benchmark schemes. As expected, our numerical results run on NS3 simulator show that on overall our scheme dramatically drops the number of transmitted packets in the considered cluster topology with a very high reconstruction SNR.