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dc.contributor.authorRauniyar, Ashish
dc.contributor.authorBerge, Truls Svenn
dc.contributor.authorHåkegård, Jan Erik
dc.date.accessioned2022-10-05T12:29:24Z
dc.date.available2022-10-05T12:29:24Z
dc.date.created2022-07-29T13:44:10Z
dc.date.issued2022
dc.identifier.citationProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. 2022, 206-210.en_US
dc.identifier.issn1551-2282
dc.identifier.urihttps://hdl.handle.net/11250/3024071
dc.description.abstractWith the advent of ubiquitous sensors and Internet of Things (IoT) applications, research and development initiatives on smart cities are ramping up worldwide. It enables remote monitoring, management, and control of devices and the generation of fresh and actionable insight from huge quantities of real-time data. Real-time noise and emissions monitoring of vehicles remain indispensable in a smart city context. Effective management and control of noise and emissions of vehicles on the road are necessary and possible through analyzing lots of sensor data in real-time to take an actionable insight. To contribute to this, as part of an ongoing effort of the European Union project called ''NEMO: Noise and Emissions Monitoring and Radical Mitigation'', in this paper, we present the design and development of an IoT-based real-time noise and emissions monitoring system for vehicles in a smart city context. Real-world sensor data of the vehicles in some European cities are collected during the pilot tests. We have developed a complete application for infrastructure managers and analysts to monitor the sensor data related to noise and emissions of vehicles in real-time. The data of the individual road vehicles and trains in selected EU cities and from trains on a track in the Netherlands are collected in the cloud and analyzed with artificial intelligence (AI) algorithms for classification such as high emitter, medium emitter, and normal emitters. We present the development of a complete software solution that can be integrated with existing intelligent transportation systems in smart cities. Finally, we report the initial vehicle classification results from the Rotterdam (Netherlands) pilot test as a representative example for the NEMO monitoring system.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectMaskinlæringen_US
dc.subjectMachine learningen_US
dc.subjectStøyen_US
dc.subjectNoiseen_US
dc.subjectEmisjonen_US
dc.subjectEmissionen_US
dc.subjectIntelligente Transportsystemeren_US
dc.subjectIntelligent Transportation Systemsen_US
dc.titleNEMO: Internet of Things based Real-time Noise and Emissions MOnitoring System for Smart Citiesen_US
dc.title.alternativeNEMO: Internet of Things based Real-time Noise and Emissions MOnitoring System for Smart Citiesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.subject.nsiVDP::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.subject.nsiVDP::Information and communication science: 420en_US
dc.subject.nsiVDP::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.subject.nsiVDP::Information and communication science: 420en_US
dc.source.pagenumber206-210en_US
dc.source.journalProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshopen_US
dc.identifier.doi10.1109/SAM53842.2022.9827835
dc.identifier.cristin2040080
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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