University of Oulu

P. Nardelli et al., "Framework for the Identification of Rare Events via Machine Learning and IoT Networks," 2019 16th International Symposium on Wireless Communication Systems (ISWCS), Oulu, Finland, 2019, pp. 656-660. doi: 10.1109/ISWCS.2019.8877287

Framework for the identification of rare events via machine learning and IoT networks

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Author: Nardelli, Pedro1; Papadias, Constantinos2; Kalalas, Charalampos3;
Organizations: 1LUT University, Finland
2Athens Information Technology, Greece
3Centre Tecnològic de Telecomunicacions de Catalunya, CTTC/CERCA, Spain
46G Flagship, University of Oulu, Finland
5Trinity College Dublin, Ireland
6Sociedad Española de Automóviles de Turismo, Spain
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.3 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-01-07


This paper introduces an industrial cyber-physical system (CPS) based on the Internet of Things (IoT) that is designed to detect rare events based on machine learning. The framework follows the following three generic steps: (1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g., aggregator, central controller); (2) Big data fusion: The intelligent node uses machine learning techniques (e.g., data clustering, neural networks) to convert the received (″big″) data to useful information to guide short-term operational decisions related to the physical process; (3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analyzed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. Our proposed general framework, which relies on an IoT network, aims at an ultra-reliable detection/prevention of rare events related to a pre-determined industrial physical process (modelled by a particular signal). The framework will be process- independent, however, our demonstrated solution will be designed case-by-case. This paper is an introduction to the solution to be developed by the FIREMAN consortium.

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Series: International Symposium on Wireless Communication Systems
ISSN: 2154-0217
ISSN-E: 2154-0225
ISSN-L: 2154-0217
ISBN: 978-1-7281-2527-5
ISBN Print: 978-1-7281-2528-2
Pages: 656 - 660
DOI: 10.1109/ISWCS.2019.8877287
Host publication: 2019 16th International Symposium on Wireless Communication Systems (ISWCS) 27-30 August 2019 Oulu, Finland
Conference: International Symposium on Wireless Communication Systems
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by CHIST-ERA (call 2017) via FIREMAN consortium, which is funded by the following national foundations: Academy of Finland (n. 326270, n. 326301), Irish Research Council, Spanish and Catalan Government under grants TEC2017-87456-P and 2017-SGR-891, respectively. This work is partially funded by Academy of Finland 6Genesis Flagship (n. 318927) and ee-IoT (n.319009) and was supported in part by the Research Grant from Science Foundation Ireland and the European Regional Development Fund under Grant 13/RC/2077.
Academy of Finland Grant Number: 326270
Detailed Information: 326270 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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