Sensor Data Reconstruction in Industrial Environments with Cellular Connectivity

Abstract

The reliable acquisition of monitoring information is critical for several industrial use cases relying on wireless sensor network deployments. However, missing sensor measurements are typical in industrial systems empowered by cellular connectivity due to the stochastic nature of the wireless channel. In this paper, we propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. Based on an analytical framework for the network model and a closed-form expression for the outage probability, the impact on the reconstruction error performance is thoroughly explored. Considering a dataset with high spatiotemporal correlation in the sensor observations, our proposed scheme is shown to outperform two baseline data recovery methods in terms of reconstruction error for various network configurations. In addition, despite the presence of imperfect cellular connectivity, our proposed scheme exhibits high event-detection accuracy.

Fault detection and classification in Industrial IoT in case of missing sensor data

“…unbalanced dataset, noise in the measurements, missing data). Similar to the work proposed by the authors of [25], [3], we also investigate the impact of missing data on the detection of rare events in an industrial setting. The authors of [25] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements.…”

“…Similar to the work proposed by the authors of [25], [3], we also investigate the impact of missing data on the detection of rare events in an industrial setting. The authors of [25] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. In [3], the authors focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework, using multiple segmented gap iteration to provide the most appropriate values.…”

Fault Detection and Classification in Industrial IoT in Case of Missing Sensor Data

“…unbalanced dataset, noise in the measurements, missing data). Similar to the work proposed by the authors of [3], [27], we also investigate the impact of missing data on the detection of rare events in an industrial setting. The authors of [27] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements.…”

“…Similar to the work proposed by the authors of [3], [27], we also investigate the impact of missing data on the detection of rare events in an industrial setting. The authors of [27] propose a sensor data reconstruction scheme that exploits the hidden data dynamics to accurately estimate the missing measurements. In [3], the authors focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework, using multiple segmented gap iteration to provide the most appropriate values.…”

Information Processing and Data Visualization in Networked Industrial Systems

“…The installation and maintenance cost associated with fixed-network technologies can thus be significantly reduced. Nevertheless, the integration of advanced wireless connectivity enablers comes inadvertently with challenges which involve distortions and missing data owing to the inherently shared wireless medium [3]. In addition, industrial plants are typically characterized by harsh propagation environments due to the presence of large metallic objects, mobile units and scattering waves which produce rich multi-path components and may cause connectivity links to be in outage [4].…”