University of Oulu

J. He, L. Kong, T. Frondelius, O. Silvén and M. Juntti, "Decision Triggered Data Transmission and Collection in Industrial Internet of Things," 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020, pp. 1-5, doi: 10.1109/WCNC45663.2020.9120749

Decision triggered data transmission and collection in industrial Internet of Things

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Author: He, Jiguang1; Kong, Long2; Frondelius, Tero3,4;
Organizations: 1Centre for Wireless Communications, FI-90014, University of Oulu, Finland
2Interdisciplinary Centre for Security Reliablility and Trust (SnT), University of Luxembourg, Luxembourg
3R&D and Engineering, W¨artsil¨a, P.O. Box 244, 65101 Vaasa, Finland
4University of Oulu, Erkki Koiso-Kanttilan katu 1, 90014 Oulu, Finland
5Center for Machine Vision and Signal Analysis (CMVS), FI-90014, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-08-12


We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). In the IIoT, the collection, processing, encoding, and transmission of the sensor readings are usually not for the reconstruction of the original data but for decision making at the fusion center. By moving the decision making process to the local end devices, the amount of data transmission can be significantly reduced, especially when normal signals with positive decisions dominate in the whole life cycle and the fusion center is only interested in collecting the abnormal data. The proposed concept combines compressive sensing, machine learning, data transmission, and joint decision making. The sensor readings are encoded and transmitted to the fusion center only when abnormal signals with negative decisions are detected. All the abnormal signals from the end devices are gathered at the fusion center for a joint decision with feedback messages forwarded to the local actuators. The advantage of such an approach lies in that it can significantly reduce the volume of data to be transmitted through wireless links. Moreover, the introduction of compressive sensing can further reduce the dimension of data tremendously. An exemplary case, i.e., diesel engine condition monitoring, is provided to validate the effectiveness and efficiency of the proposed scheme compared to the conventional ones.

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Series: IEEE Wireless Communications and Networking Conference
ISSN: 1525-3511
ISSN-E: 1558-2612
ISSN-L: 1525-3511
ISBN: 978-1-7281-3106-1
ISBN Print: 978-1-7281-3107-8
Pages: 1 - 5
DOI: 10.1109/WCNC45663.2020.9120749
Host publication: 2020 IEEE Wireless Communications and Networking Conference (WCNC)
Conference: IEEE Wireless Communications and Networking Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been performed in the framework of the IIoT Connectivity for Mechanical Systems (ICONICAL), funded by the Academy of Finland. This work is also partially supported by the Academy of Finland 6Genesis Flagship (grant 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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