Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network

This paper falls under the problems of the monitoring of a Discrete Event System (DES) with time constraints. Among the various techniques used for online and distributed monitoring, we are interested in the chronicle recognition. Chronicles are temporal patterns that represent the system’s possible...

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Main Authors: Anis M’halla, Dimitri Lefebvre, Mouhaned Gaied
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2020/6265379
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spelling doaj-1ccc0b33173f41928c8d4d792a8822682021-07-02T11:30:52ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01472090-01552020-01-01202010.1155/2020/62653796265379Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway NetworkAnis M’halla0Dimitri Lefebvre1Mouhaned Gaied2Research Laboratory LARA in Automatic Control, National Engineering School of Tunis (ENIT), University of Tunis el Manar, Tunis, TunisiaGREAH, Université Le Havre Normandie, 75 Rue Bellot, Le Havre 76600, FranceResearch Unit LAS2E, The National Engineering School of Monastir (ENIM), Avenue Ibn El Jazzar 5019 Monastir, TunisiaThis paper falls under the problems of the monitoring of a Discrete Event System (DES) with time constraints. Among the various techniques used for online and distributed monitoring, we are interested in the chronicle recognition. Chronicles are temporal patterns that represent the system’s possible evolutions. The proposed models are based on P-time Petri nets that are suitable to represent with accuracy and modularity the Tunisian railway network. These models are scalable and may be used to represent a large variety of railway networks. Then, monitoring is based on the generation of chronicles that are suitable to detect and isolate traffic incidents in a distributed setting. Consequently, the proposed approach is tractable for large networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.http://dx.doi.org/10.1155/2020/6265379
collection DOAJ
language English
format Article
sources DOAJ
author Anis M’halla
Dimitri Lefebvre
Mouhaned Gaied
spellingShingle Anis M’halla
Dimitri Lefebvre
Mouhaned Gaied
Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
Journal of Electrical and Computer Engineering
author_facet Anis M’halla
Dimitri Lefebvre
Mouhaned Gaied
author_sort Anis M’halla
title Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
title_short Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
title_full Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
title_fullStr Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
title_full_unstemmed Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network
title_sort distributed monitoring based on p-time petri nets and chronicle recognition of the tunisian railway network
publisher Hindawi Limited
series Journal of Electrical and Computer Engineering
issn 2090-0147
2090-0155
publishDate 2020-01-01
description This paper falls under the problems of the monitoring of a Discrete Event System (DES) with time constraints. Among the various techniques used for online and distributed monitoring, we are interested in the chronicle recognition. Chronicles are temporal patterns that represent the system’s possible evolutions. The proposed models are based on P-time Petri nets that are suitable to represent with accuracy and modularity the Tunisian railway network. These models are scalable and may be used to represent a large variety of railway networks. Then, monitoring is based on the generation of chronicles that are suitable to detect and isolate traffic incidents in a distributed setting. Consequently, the proposed approach is tractable for large networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.
url http://dx.doi.org/10.1155/2020/6265379
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AT dimitrilefebvre distributedmonitoringbasedonptimepetrinetsandchroniclerecognitionofthetunisianrailwaynetwork
AT mouhanedgaied distributedmonitoringbasedonptimepetrinetsandchroniclerecognitionofthetunisianrailwaynetwork
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