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Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature This publication appears in: IEEE Sensors Journal Authors: N. Deligiannis, E. Zimos, D. Mihai Ofrim, Y. Andreopoulos and A. Munteanu Volume: 15 Issue: 8 Pages: 4496-4507 Publication Date: Aug. 2015
Abstract: Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian–Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system.
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