Recently, the idea of joining compressed sensing and network coding for wireless sensor networks (WSN) has gained a lot of interest, because low latency communication for largescale sensing scenarios can be achieved by reducing the data to transmit significantly. This paper studies the impact of the compressed sensing on network coding in order to enable one step decoding to reconstruct the original data. Therefore, it emphasizes the design of the sensing and coding matrices as well as the reconstruction algorithms that enable accurate reconstructions. Our simulation results performed using the KL1p compressed sensing library added in the NS3 simulator show that the matrices drawn from Gaussian distributions have higher efficiency and scalability especially for multi-hop networks, where the recoding feature of network coding is required. Furthermore, the Subspace Pursuit algorithm outperforms the state-of-the-art reconstruction algorithms with respect to the reconstruction SNR and delay.