Wireless channels are prone to many impairments, such as noise and fading. Weak channels between the nodes in the wireless sensor network (WSN) can cause reception of erroneous packets. Retransmission mechanisms are mainly used to tackle the problem of erroneous reception in WSN communication protocols. Weak channels can cause high number of retransmissions in order to deliver a packet correctly, which will consume high energy of both the transmitting and the receiving nodes. Error correcting codes (ECCs) can be used to reduce number of retransmissions, but most ECCs have complex decoding algorithms, which leads to high processing energy consumption at the receiving nodes in the WSN. In this paper, we present a low power consumption decode-and-forward approach for the multi-hop WSNs; a serial concatenation convolutional codes (SCCC) encoder is implemented at the source node while the complex iterative decoding algorithm is shifted to the sink (base station). The intermediate nodes run a Viterbi decoding algorithm to decode only the inner code of the SCCC encoder. We investigate the effect of changing constraint length of both the inner and the outer codes and the effect of changing encoding block size. We show that most packets can be decoded at the base station at low signal-to-noise ratio (SNR) channels with the penalty of small energy loss in decoding the packet at the nodes in the network.

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Wireless Sensor Networks (WSNs) are composed of small wireless nodes equipped with sensors, a processor, and a radio communication unit, all normally powered by batteries. For most WSN applications, the network is expected to function for several months or years. In the common monitoring application scenario, adjacent nodes in a WSN often sense spatially correlated data. Suppressing this correlation can significantly improve the lifetime of the network. The maximum possible network data compression can be achieved using distributed source coding (DSC) techniques when nodes encode at Slepian-Wolf rates. This paper presents contributions to the lifetime optimization problem of WSNs in the form of two algorithms: the Updated-CMAX (UCMAX) power-aware routing algorithm to optimize the routing tree and the Rate Optimization (RO) algorithm to optimize the encoding rates of the nodes. The two algorithms combined offer a solution that maximizes the lifetime of a WSN measuring spatially correlated data. Simulations show that our proposed approach may significantly extend the lifetime of multihop WSNs with nodes that are observing correlated data.