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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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 |
work_keys_str_mv |
AT anismhalla distributedmonitoringbasedonptimepetrinetsandchroniclerecognitionofthetunisianrailwaynetwork AT dimitrilefebvre distributedmonitoringbasedonptimepetrinetsandchroniclerecognitionofthetunisianrailwaynetwork AT mouhanedgaied distributedmonitoringbasedonptimepetrinetsandchroniclerecognitionofthetunisianrailwaynetwork |
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1721331067122614272 |