9th International Conference on Body Area Networks

Research Article

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

  • @INPROCEEDINGS{10.4108/icst.bodynets.2014.257111,
        author={Nikos Deligiannis and Evangelos Zimos and Dragos Ofrim and Yiannis Andreopoulos and Adrian Munteanu},
        title={Distributed Joint Source-Channel Coding with Raptor Codes for Correlated Data Gathering in Wireless Sensor Networks},
        proceedings={9th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2014},
        month={11},
        keywords={wireless sensor networks (wsns) distributed joint sourcechannel coding (djscc) raptor codes temperature monitoring},
        doi={10.4108/icst.bodynets.2014.257111}
    }
    
  • Nikos Deligiannis
    Evangelos Zimos
    Dragos Ofrim
    Yiannis Andreopoulos
    Adrian Munteanu
    Year: 2014
    Distributed Joint Source-Channel Coding with Raptor Codes for Correlated Data Gathering in Wireless Sensor Networks
    BODYNETS
    ACM
    DOI: 10.4108/icst.bodynets.2014.257111
Nikos Deligiannis,*, Evangelos Zimos1, Dragos Ofrim2, Yiannis Andreopoulos3, Adrian Munteanu1
  • 1: Vrije Universiteit Brussel
  • 2: InterNET SRL, OFRIM Group
  • 3: University College London
*Contact email: n.deligiannis@ucl.ac.uk

Abstract

Correlated data gathering in body area networks calls for systems that perform efficient compression and reliable transmission 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 minimum. 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 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 proposed system leads to energy savings between 12.19% to 16.51% with respect to the baseline system.