A4 Conference proceedings

Framework for the Identification of Rare Events via Machine Learning and IoT Networks


Open Access publication

Publication Details
Authors: Nardelli Pedro, Papadias Constantinos, Kalalas Charalamps, Alves Hirley, Christou Ioanns T., Macaluso Irene, Marchetti Nicola, Palacios Raul, Alonso-Zarate Jesus
Publication year: 2019
Language: English
Related Journal or Series Information: International Symposium on Wireless Communication Systems
Title of parent publication: 2019 16th International Symposium on Wireless Communication Systems (ISWCS)
ISBN: 978-1-7281-2528-2
eISBN: 978-1-7281-2527-5
ISSN: 2154-0217
eISSN: 2154-0225
JUFO-Level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019111337968

Abstract

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.


Last updated on 2020-20-03 at 10:03