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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Veröffentlicht in:	IEEE sensors journal Jg. 17; H. 1; S. 139 - 150
Hauptverfasser:	Zimos, Evangelos, Toumpakaris, Dimitris, Munteanu, Adrian, Deligiannis, Nikos
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Deligiannis, Nikos Zimos, Evangelos Toumpakaris, Dimitris Munteanu, Adrian
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SubjectTerms	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)
Title	Multiterminal Source Coding With Copula Regression for Wireless Sensor Networks Gathering Diverse Data
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