Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

TL;DR

This paper introduces a novel data recovery algorithm for heterogeneous sensor data collected via compressive measurements, leveraging copula functions to model dependencies and improve accuracy in resource-constrained environments.

Contribution

It presents a new belief-propagation based recovery method that effectively captures statistical dependencies among diverse signals using copula models.

Findings

Significant performance improvements over existing schemes.

Effective modeling of heterogeneous data correlations.

Enhanced recovery accuracy in environmental monitoring.

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.