Multiterminal Source Coding With Copula Regression for Wireless Sensor Networks Gathering Diverse Data

Efficient data compression at a low processing and communication cost is a key challenge in wireless sensor networks. 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 dependences. The former is explo...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 17; no. 1; pp. 139 - 150
Main Authors: Zimos, Evangelos, Toumpakaris, Dimitris, Munteanu, Adrian, Deligiannis, Nikos
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Arithmetic coding
Coders
Copula regression
Data compression
Decoding
differential pulse-code modulation (DPCM)
distributed source coding (DSC)
Experimentation
Mathematical analysis
multiterminal (MT) source coding
Sensors
Source code
Source coding
Statistical analysis
Statistical models
Temperature measurement
Temperature sensors
Wireless networks
Wireless sensor networks
Wireless sensor networks (WSNs)
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Efficient data compression at a low processing and communication cost is a key challenge in wireless sensor networks. 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 dependences. The former is exploited by applying simple differential pulse-code modulation followed by arithmetic entropy coding at each distributed encoder. This approach limits the encoding complexity and provides for a flexible design that adapts to the 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 dependences. 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 with the state-of-the-art multiterminal and distributed source coding schemes.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2016.2585042