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dc.contributor.authorPapachristoforou, L.en
dc.contributor.authorKolios, P.en
dc.contributor.authorPanayiotou, Christosen
dc.contributor.authorEllinas, Georgiosen
dc.creatorPapachristoforou, L.en
dc.creatorKolios, P.en
dc.creatorPanayiotou, Christosen
dc.creatorEllinas, Georgiosen
dc.description.abstractMulti-model event-triggering is a highly promising technique for efficient monitoring of processes where instead of continuous or even periodic triggering of events, communication and control is only applied after some event interrupt. In this work we investigate an adaptive multi-model monitoring technique whereby a local host that switches between the observed models informs remote hosts of these events which in turn adapt their predictions to reduce prediction error and minimize unnecessary triggering events and future model switching, thereby reducing energy consumption and communication bandwidth. The adaptive technique is examined under a real public transport bus service scenario, where local and remote hosts use a set of mobility models to track travel times and update their arrival schedules according to detected deviations, i.e., event interrupts.en
dc.source2019 IEEE 5th World Forum on Internet of Things (WF-IoT)en
dc.titleAdaptive multi-model monitoring of recurrent mobility patternsen
dc.description.endingpage881Πολυτεχνική Σχολή / Faculty of EngineeringΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidPanayiotou, Christos [0000-0002-6476-9025]
dc.contributor.orcidKolios, P. [0000-0003-3981-993X]
dc.contributor.orcidEllinas, Georgios [0000-0002-3319-7677]

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