Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

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.Comment: accepted to IEEE Transactions on Communication

Internet-of-Things data aggregation using compressed sensing with side information

The Internet-of-Things (IoT) is the key enabling technology for transforming current urban environments into so-called Smart Cities. One of the goals behind making cities smarter is to provide a healthy environment that improves the citizens' quality of life and wellbeing. In this work, we introduce a novel data aggregation mechanism tailored to the application of large-scale air pollution monitoring with IoT devices. Our design exploits the intra- and inter-source correlations among air-pollution data using the framework of compressed sensing with side information. The proposed method delivers significant improvements in the data reconstruction quality with respect to the state of the art, even in the presence of noise when measuring and transmitting the data

Bayesian Compressed Sensing with Heterogeneous Side Information

The classical compressed sensing (CS) paradigm can be modified so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages to exploit side information generated from a diverse (or heterogeneous) data source by using the statistical model of copula functions. Through simulations, we demonstrate that the proposed method yields significant reduction in the mean-squared error of the reconstructed signal as compared to state-of-the-art methods in classical compressed sensing and compressed sensing with side information