Publication Details
Nikos Deligiannis, Evangelos Zimos, Dragos Mihai Ofrim, Yiannis Andreopoulos, Adrian Munteanu

Proceedings of the 9th International Conference on Body Area Networks

Contribution To Book Anthology


Correlated data gathering in body area networks calls for systems that perform efficient compression and reliable trans- mission of the measurements, while imposing a small computational burden at the sensors. Highly-efficient compression mechanisms, e.g., adaptive arithmetic entropy encoding, do not address the problem adequately, as they have high computational demands. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Following the principles of distributed source coding, our design allows for efficient compression and error-resilient transmission while exploiting the correlation amongst sensors' readings at energy-robust sink nodes. In this way, the computational complexity and in turn, the energy consumption at the sensor node is kept to a mini- mum. Our DJSCC design is based on a new non-systematic Slepian-Wolf Raptor code construction that achieves good performance at short code lengths, which are appropriate for low-rate data gathering within local or body area sensor networks. Experimental results using a Wireless Sensor Network (WSN) deployment for temperature monitoring reveal that, for lossless compression, the proposed system leads to a 30.08% rate reduction against a baseline system that performs adaptive arithmetic entropy encoding of the temperature readings. Moreover, under AWGN and Rayleigh fading channel losses, the pro- posed system leads to energy savings between 12.19% to 16.51% with respect to the baseline system.