  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.
