Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29883
DC FieldValueLanguage
dc.contributor.authorEvmides, Nicos-
dc.contributor.authorOdysseos, Lambros-
dc.contributor.authorMichaelides, Michalis P.-
dc.contributor.authorHerodotou, Herodotos-
dc.date.accessioned2023-07-17T07:48:47Z-
dc.date.available2023-07-17T07:48:47Z-
dc.date.issued2022-06-06-
dc.identifier.citationProceedings of 23rd IEEE International Conference on Mobile Data Management, MDM 2022, 6 - 9 June, Paphos, Limassol, pp. 413-418en_US
dc.identifier.isbn9781665451765-
dc.identifier.issn15516245-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/29883-
dc.description.abstractAutomatic identification system (AIS) data provides a wealth of information regarding vessel traffic and is used for a variety of applications such as collision detection and avoidance, route prediction and optimization, search and rescue operations, etc. However, several challenges exist when working with AIS data including huge volume and velocity (as AIS signals are sent by vessels every few seconds), message duplication, various types of data irregularities, as well as the need for real-time processing and analysis. This paper presents a new framework for collecting, processing, storing, and analyzing AIS data in real time plus a set of algorithms for doing so in an efficient and scalable way. At the same time, a set of intelligent services are provided as building blocks for improving and creating new AIS data driven applications. This framework has been operational for the past few years in Cyprus, and has collected and processed around one billion AIS messages from the Eastern Mediterranean Sea.en_US
dc.language.isoenen_US
dc.relation.ispartof23rd IEEE International Conference on Mobile Data Managementen_US
dc.rights© Copyright IEEE - All rights reserved.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectEstimationen_US
dc.subjectData collectionen_US
dc.subjectReal-time systemsen_US
dc.subjectRisk managementen_US
dc.subjectArtificial intelligenceen_US
dc.subjectCollision avoidanceen_US
dc.subjectMonitoringen_US
dc.titleAn Intelligent Framework for Vessel Traffic Monitoring Using AIS Dataen_US
dc.typeConference Papersen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/MDM55031.2022.00091en_US
dc.identifier.scopus2-s2.0-85137594107-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85137594107-
cut.common.academicyear2022-2023en_US
dc.identifier.spage413en_US
dc.identifier.epage418en_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.grantfulltextnone-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-0549-704X-
crisitem.author.orcid0000-0002-8717-1691-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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