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 spatial compression and real field network coding. Furthermore, we compare our approach with benchmark schemes. As expected, our numerical results run on NS3 simulator show that overall our scheme dramatically drops the number of transmitted packets in the considered cluster topology by almost 94 % with a very high reconstruction SNR.