DC Field | Value | Language |
---|---|---|
dc.contributor.author | Anagnostopoulos, Theodoros | - |
dc.contributor.author | Ntalianis, Klimis | - |
dc.contributor.author | Fedchenkov, Petr | - |
dc.contributor.author | Sosunova, Inna | - |
dc.contributor.author | Sadov, Oleg | - |
dc.contributor.author | Zaslavsky, Arkady | - |
dc.date.accessioned | 2024-07-04T09:51:10Z | - |
dc.date.available | 2024-07-04T09:51:10Z | - |
dc.date.issued | 2018-01-01 | - |
dc.identifier | scopus-85054847595 | - |
dc.identifier.isbn | 978-3-030-01168-0 | - |
dc.identifier.isbn | 978-3-030-01167-3 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | 85054847595 | - |
dc.identifier.uri | https://uniwacris.uniwa.gr/handle/3000/2665 | - |
dc.description.abstract | Internet of Things (IoT) enables Smart Cities (SC) with novel services for the citizens’ well-being. A Smart Parking (SP) system is an important part of the SC infrastructure, which enables the efficient handling of the demanding SC traffic congestion conditions. Such a system also protects the urban environment towards a green ecosystem. In this paper, we consider Artificial Intelligence (AI) algorithms towards processing of data produced by parking lots and disseminated by IoT technology in the SC of St. Petersburg in Russia. Such algorithms enhance the proposed SP system to predict the number of unoccupied parking lots within the SC parking places. In addition, the SP system uses vehicle navigation to decide the optimal parking place according the current vehicle location and the availability of parking lots in the SC. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Internet of Things, Smart Spaces, and Next Generation Networks and Systems: Proceedings of the 18th International Conference, NEW2AN 2018, and 11th Conference, ruSMART 2018 | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science | en_US |
dc.subject | Artificial Intelligence (AI) | en_US |
dc.subject | Genetic algorithms | en_US |
dc.subject | Internet of Things (IoT) | en_US |
dc.subject | Recurrent Neural Networks (RNN) | en_US |
dc.subject | Smart cities | en_US |
dc.title | An Artificial Intelligence Based Forecasting in Smart Parking with IoT | en_US |
dc.type | Conference Paper | en_US |
dc.relation.conference | 18th International Conference on Next Generation Wired/Wireless Advanced Networks and Systems, NEW2AN 2018, the 11th Conference on Internet of Things and Smart Spaces, ruSMART 2018, 27–29 August 2018, St. Petersburg, Russia | en_US |
dc.identifier.doi | 10.1007/978-3-030-01168-0_4 | en_US |
dc.identifier.scopus | 2-s2.0-85054847595 | - |
dcterms.accessRights | 0 | en_US |
dc.relation.dept | Department of Business Administration | en_US |
dc.relation.faculty | School of Administrative, Economics and Social Sciences | en_US |
dc.identifier.spage | 33 | en_US |
dc.identifier.epage | 40 | en_US |
dc.collaboration | University of West Attica (UNIWA) | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.journals | Open Access | en_US |
dc.publication | Peer Reviewed | en_US |
dc.country | Greece | en_US |
local.metadatastatus | verified | en_US |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | Conference Paper | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | Department of Business Administration | - |
crisitem.author.dept | Department of Business Administration | - |
crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
crisitem.author.orcid | 0000-0002-5587-2848 | - |
crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
Appears in Collections: | Book Chapter / Κεφάλαιο Βιβλίου |
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