Passiivsete mobiilipositsiooni sündmuste tõenäosuslik asukoha hinnang
Failid
Kuupäev
2016
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Uurijad, kes on püüdnud mõista inimeste liikumise mustreid, korjavad andmeid mobiilivõrkudelt. Mobiilid teevad sündmuse kirjeid iga kord, kui nendega helistatakse, saadetakse SMSi või kasutatakse Interneti. Sündmuste kirjed sisaldavad informatsiooni sellest, millisesse võrgu transiiversisse mobiiltelefon oli sel hetkel ühendatud. Võrgu ühe transiiveri leviala saab kasutada, et püüda positsioneerida telefoni geograafilist asukohta. Kasutades positsioneerimiseks transiiveri leviala, siis need hinnatavad asukohad pole punktid kaardil, vaid geograafilised alad, kus telefon võib olla kui ta on transiiveriga ühendatud.\n\r Mobiilide ühendamine transiiveritega sõltub mitmest muutujast, mis tähendab, et mobiil ei ole alati ühendatud kõige tugevama signaaliga transiiveriga. See teeb mobiili asukoha hindamise keerulisemaks, sest transiiverite levialad võivad üksteisest üleulatuda.\n\rVõrguplaan kirjeldab võrgus olevate transiiverite levialasid ning seda kasutatakse, et defineerida transiiverite levialasid.\n\rSelles lõputöös hinnatakse mobiilisündmuste positsioneerimise kvaliteeti ruumilise jaotuse tihedusfunktsioonidega. Luuakse erinevad võrguplaani variandid ja erinevate võrguplaanide kvaliteeti hinnatakse Bayesi statistikaga ja kasutatakse reaalsed asukoha andmed. Erinevate võrguplaanide kvaliteeti hinnatakse suurima tõepära meetodiga.\n\rVõrreldi RSSI ja Voronoi põhjal tehtud võrguplaane ja nende modificatsioone ja leiti, et Voronoi võrguplaanide puhul paistis asukoha positsioneerimine paremini kui RSSI võrguplaanide puhul.\n\rLisaks uuriti, kuidas transiiverite levialade üleulatamisel arvestamine Bayesi metoodiga mõjutab asukoha positsioneerimise täpsust. Leiti, et Bayesi levialade üleulatamise metood tegi halvemate võrguplaanide täpsust paremaks, aga paremate võrguplaanide täpsust halvemaks.
Researchers, who are trying to understand human mobility patterns, collect data from cellular telephone networks. Mobiles are creating events every time they are used for calling, SMS, or the Internet. The events contain the information, in which network cell that mobile was at the moment of the event. Cell's coverage can be used for estimating the geographical location of the mobile. The estimated locations are not a point on the map, but the possible area, where the mobile may be when they are connected to that specific cell. \n\rMobiles connecting to cells are depending on multiple variables, meaning, that a mobile may not always connect to the cell with the strongest signal. That makes estimation of the mobile location more difficult, as the coverage areas may overlap with each other. \n\rCell plan is a description of cell coverage areas and there are multiple ways for defining cell coverage areas.\n\rThis thesis is about estimating mobile events positioning quality with spatial probability density functions. Different cell plan variants will be implemented and real ground truth location data is used to find the modification that maximizes the likelihood estimation. \n\rCompared RSSI-based and Voronoi-based cell plans and their modifications and was found that Voronoi-based cell plans are better for location positioning than the RSSI-based cell plans.\n\rFurthermore, Bayesian overlapping method was examined to see does applying it would improve location positioning accuracy. It was found that applying Bayesian overlapping methods improved the accuracy of the worse cell plans, but made accuracy worse for the better cell plans.
Researchers, who are trying to understand human mobility patterns, collect data from cellular telephone networks. Mobiles are creating events every time they are used for calling, SMS, or the Internet. The events contain the information, in which network cell that mobile was at the moment of the event. Cell's coverage can be used for estimating the geographical location of the mobile. The estimated locations are not a point on the map, but the possible area, where the mobile may be when they are connected to that specific cell. \n\rMobiles connecting to cells are depending on multiple variables, meaning, that a mobile may not always connect to the cell with the strongest signal. That makes estimation of the mobile location more difficult, as the coverage areas may overlap with each other. \n\rCell plan is a description of cell coverage areas and there are multiple ways for defining cell coverage areas.\n\rThis thesis is about estimating mobile events positioning quality with spatial probability density functions. Different cell plan variants will be implemented and real ground truth location data is used to find the modification that maximizes the likelihood estimation. \n\rCompared RSSI-based and Voronoi-based cell plans and their modifications and was found that Voronoi-based cell plans are better for location positioning than the RSSI-based cell plans.\n\rFurthermore, Bayesian overlapping method was examined to see does applying it would improve location positioning accuracy. It was found that applying Bayesian overlapping methods improved the accuracy of the worse cell plans, but made accuracy worse for the better cell plans.