Distributed Joint Source-Channel Coding with Raptor Codes for Correlated Data Gathering in Wireless Sensor Networks [Invited]

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 com-putational burden at the sensors. Highly-efficient compres-sion mechanisms, e.g., adaptive arithmetic entropy encod-ing, 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) solu-tion for this problem. Following the principles of distributed source coding, our design allows for efficient compression and error-resilient transmission while exploiting the correla-tion 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 WSN deployment for temperature monitoring reveal that, for lossless compres-sion, 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. ∗Corresponding author