Optimally Mapping an Iterative Channel Decoding Algorithm to a Wireless Sensor Network

Retransmission based schemes are not suitable for energy constrained wireless sensor networks. Hence, there is an interest in including parity bits in each packet for error control. From an information-theoretic perspective the most efficient usage of network capacity can be achieved by performing full encoding/decoding at each node and using a variable rate in accordance with the link-quality. However, such an approach represents a major burden on power-constrained sensors. In this paper, we propose a more practical approach that is based on optimally distributing iterative channel decoding over sensor networks. In such a paradigm, the guarantee with which the base station, or
collector
, gets the data from a sensor is a function of the processing within the intermediate nodes between source and destination (in-network processing). There are two extreme cases: a) Complete channel decoding at each hop and b) decoding only at the final destination. In this paper, we present a novel scheme in which intermediate nodes conduct partial decoding of LDPC coded packets. In this scheme each node is assigned some number of decoding iterations. The relay node conducts LPDC decoding for that number of iterations and forwards the packet, without ensuring a complete error correction. We show that such partial processing is sufficient to improve the end-to-end reliability significantly. Additionally, we show that it is feasible to tradeoff complexity/energy usage with distortion/reliability by varying the assignment of number of iterations. Finally, we present a low-complexity dynamic programming algorithm that optimally assigns iterations within the network to facilitate operation along an optimal
energy-distortion
curve.