Reference : Decision Triggered Data Transmission and Collection in Industrial Internet of Things
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
Decision Triggered Data Transmission and Collection in Industrial Internet of Things
He, Jiguang [University of Oulu > Centre for Wireless Communications]
Kong, Long mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Frondelius, Tero []
Silven, Olli []
Juntti, Markku []
2020 IEEE Wireless Communications and Networking Conference (WCNC)
2020 IEEE Wireless Communications and Networking Conference (WCNC)
from 25-05-2020 to 28-05-2020
[en] Industrial Internet of things (IIoT) ; machine learning (ML) ; data transmission ; joint decision making ; condition monitoring ; anomaly detection
[en] 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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