Compressed sensing and network coding techniques have seen a widespread interest in many disciplines during the last decade. Recently, a novel idea emerged for the combination of these areas in wireless communications to leverage the benefits from network coding while taking advantage of the correlations in the (sensory) data. The potential gains, such as lower latency for large‐scale sensing scenarios, reduced energy consumption, and a decrease in the amount of data during transmissions, are alluring to many use‐cases. However, a common issue one faces when joining both techniques is encountered in the fact that network coding is designed to operate over finite fields, whereas compressed sensing is mainly concerned with real numbers. This paper studies the impact of compressed sensing on network coding to enable one step decoding for the reconstruction of compressed data. We emphasize and discuss the design of the sensing and coding matrices, as well as the algorithms that enable accurate reconstructions. We employ the KL1p compressed sensing library and the NS3 simulator to evaluate this joint design. Our simulations show that using normalized coefficient matrices drawn from Gaussian distributions has higher efficiency and scalability including, but not limited to, multi‐hop networks, where the recoding feature of network coding can be exploited. Furthermore, the Subspace Pursuit algorithm outperforms the state‐of‐the‐art reconstruction algorithms, with respect to the reconstruction signal‐to‐noise ratio, by more than two folds compared to the other benchmark algorithms in cluster‐based Wireless Sensor Networks, where recoding using real network codes are involved.

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.