Practical Compressed Sensing and Network Coding for Intelligent Distributed Communication 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.

On the joint design of compressed sensing and network coding for wireless communications

“…Consequently, in the future, we plan the in‐depth study of this algorithm and will propose modifications that would suit both techniques to meet with the current 5G network requirements, mainly, the lower delays. On the other hand, we showed that joining compressed sensing and network coding for cluster‐based WSNs could result in significant losses due to the recombinations at cluster heads when one increases the number of hops 37 . We expect that these losses could be rectified if we operate both network coding and compressed sensing in the finite fields, especially in scenarios where the scalability of the framework is indispensable.…”

“…In addition, Compressive Network Coding is a joint source and network coding method for approximate data gathering 36 . Finally, the JOint COMpressed sensing and network COding ( JoComCo ) for cluster‐based topologies in WSNs was proposed in 2018 37 . It exploits the temporal and spatial correlations as well as the intercorrelation and intracorrelation among the sensors belonging to different clusters.…”

“…This could be a baseline for future research in the same area, perhaps tackling a wider range of topologies, such as mesh‐based WSNs. We also stress that a majority of current research directions, which take advantage of the spatial and temporal correlations, focus on cluster‐based WSNs topologies, such as industrial applications 37,38 …”

“…μ guarantees that at least l data vectors, where l is the trace of B , denoted by tr ( B ) ( ), at the cluster head are received with a high probability. Note that the full details related to the design and choice of μ are found in the work of Taghouti et al 37 Consequently, the resulting spatial compressed sensing matrix is defined as .…”

Correlation of Multiple Strategic Sources Decreases Their Age of Information Anarchy

“…Also, the idea of AoI stems from time redundancy of data, implying that multiple close updates congest the processing at the end server without significantly enhancing the information freshness, and it may be convenient to evenly spread them over time. We are extending this concept to spatial redundancy, where some updates can be avoided if another source has already sent an update about a process that correlates with the one of interest or is even the same [22].…”