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Multiterminal Source Coding with Copula Regression for Wireless Sensor Networks Gathering Diverse Data This publication appears in: IEEE Sensors Journal Authors: E. Zimos, D. Toumpakaris, A. Munteanu and N. Deligiannis Volume: 17 Issue: 1 Pages: 139-150 Publication Date: Jan. 2017
Abstract: Efficient data compression at a low processing and communication cost is a key challenge in wireless sensor net- works. In this paper, we propose a novel multiterminal source code design, which, contrary to prior work, utilizes both the intra- and the inter-sensor data dependencies. The former is exploited by applying simple DPCM followed by arithmetic entropy coding at each distributed encoder. This approach limits the encoding complexity and provides for a flexible design that adapts to variations in the number of operating sensors. Moreover, we propose a regression method applied at the joint decoder, which aims at leveraging the inter sensor data dependencies. Unlike existing work that focuses on homogeneous data types, the proposed method makes use of copula functions, namely, a statistical model that captures the dependence structure amongst heterogeneous data types. Experimentation using real sensor measurements— taken from the Intel-Berkeley database—shows that the proposed system achieves significant compression improvements compared to state-of-the-art multiterminal and distributed source coding schemes.
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