Hidden Markov filtering with microscopic traffic modeling for vehicle load estimation in cellular networks

Stamoulakatos, T; Yannopoulos, A; Varvarigou, T; Sykas, E

dc.contributor.author	Stamoulakatos, T	en
dc.contributor.author	Yannopoulos, A	en
dc.contributor.author	Varvarigou, T	en
dc.contributor.author	Sykas, E	en
dc.date.accessioned	2014-03-01T02:49:46Z
dc.date.available	2014-03-01T02:49:46Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34734
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-11144291092&partnerID=40&md5=13e03f20310ff00ee45a56c33a50cae9	en
dc.subject	Hidden Markov Model	en
dc.subject	Location Based Services	en
dc.subject	Microscopic Traffic Model	en
dc.subject	Pattern Recognition	en
dc.subject	Traffic Information	en
dc.subject.other	Markov processes	en
dc.subject.other	Monitoring	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Press load control	en
dc.subject.other	Random processes	en
dc.subject.other	Signal processing	en
dc.subject.other	Vehicles	en
dc.subject.other	Hidden markov model	en
dc.subject.other	Location based services	en
dc.subject.other	Microscopic traffic model	en
dc.subject.other	Traffic information	en
dc.subject.other	Cellular neural networks	en
dc.title	Hidden Markov filtering with microscopic traffic modeling for vehicle load estimation in cellular networks	en
heal.type	conferenceItem	en
heal.identifier.secondary	450-082	en
heal.publicationDate	2004	en
heal.abstract	Location Based Services is a new category of services for mobile phone (MT) users based on MT location. Various techniques can be found in the literature for MT's location estimation. Promising appear to be hybrid techniques which overcome existing limitations of cost, accuracy and network coverage. The technique applied in our study is based on pattern recognition together with Time Advance (TA) measurements. The pattern recognition is performed by Hidden Markov Model (HMM) which is trained with downlink prediction data modeling the strength of the received signals for specific areas. Sets of appropriate HMMs are built with respect to the Timing Advance (TA) which indicates the distance between a base station (BS) and the MT. Being able to identify the TA gives us a first estimation of the location of the MT and which set of HMMs we should use to determine MT's position estimation. This estimation will be based on the comparison between the prediction of the radio level signaling of the area and the RSSI reports of the MT, without the need of MT modifications or cellular network upgrades. After that, Microscopic Traffic modeling can result in vehicle load estimation in main city routes providing in that way Traffic Information Service to MT users.	en
heal.journalName	Proceedings of the IASTED International Conference on Communication Systems and Networks	en
dc.identifier.spage	124	en
dc.identifier.epage	128	en