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Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

Deligiannis, N; Mota, JFC; Zimos, E; Rodrigues, MRD; (2017) Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior. IEEE Transactions on Communications , 65 (12) pp. 5333-5347. 10.1109/TCOMM.2017.2746099. Green open access

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Abstract

Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm—built upon belief-propagation principles—that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.

Type: Article
Title: Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCOMM.2017.2746099
Publisher version: http://doi.org/10.1109/TCOMM.2017.2746099
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Wireless sensor networks, Compressed sensing, Pollution measurement, Wireless communication, Correlation, Spatiotemporal phenomena, Sensors, Compressed sensing, side information, copula functions, air-pollution monitoring, wireless sensor networks
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10036002
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