Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

Nikos Deligiannis, João F. C. Mota, Evangelos Zimos, Miguel Raul Dias Rodrigues

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
37 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Communications
Early online date29 Aug 2017
Publication statusE-pub ahead of print - 29 Aug 2017


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