Sensor Data Reconstruction in Industrial Environments with Cellular Connectivity
| Main Authors: | Kalalas, Charalampos, Alonso-Zárate, Jesús |
|---|---|
| Format: | Proceeding eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4114559 |
Daftar Isi:
- 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.
- Grant numbers : SPOT5G - Single Point of attachment communications heterogeneous mobile data networks ( TEC2017-87456-P) and Framework for the Identification of Rare Events via MAchine learning and IoT Networks (FIREMAN - Framework for the Identification of Rare Events via MAchine learning and IoT Networks).@ 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.