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dc.contributor.authorKonstantinidis, Andreasen
dc.contributor.authorNikolaides, G.en
dc.contributor.authorChatzimilioudis, Georgiosen
dc.contributor.authorEvagorou, G.en
dc.contributor.authorZeinalipour-Yazdi, Constantinos D.en
dc.contributor.authorChrysanthis, Panos K.en
dc.creatorKonstantinidis, Andreasen
dc.creatorNikolaides, G.en
dc.creatorChatzimilioudis, Georgiosen
dc.creatorEvagorou, G.en
dc.creatorZeinalipour-Yazdi, Constantinos D.en
dc.creatorChrysanthis, Panos K.en
dc.description.abstractWi-Fi (or WLAN) based indoor navigation applications for mobiles rely on cloud-based services (s) that take care of a user's (u) localization task using structures called Radio Maps (RMs). It is imperative for u to have a stable WiFi connection in order to either continuously receive location updates from s or to download RMs a priori for offline navigation. Wi-Fi networks however, suffer from intermittent connectivity due to poor network planning that results in sparse deployment of access points and effectively areas where Wi-Fi coverage cannot be guaranteed. This inherently affects the localization accuracy and therefore the navigation experience of users. In this paper, we propose an innovative framework for accurate and fast indoor localization over an intermittently connected WiFi network, coined Prefetching Localization (PreLoc). In Preloc, we prioritize the download of RM records based on knowledge acquired from historic traces of other users inside the same building. Instead of downloading the complete RM from s to u, we propose a Probabilistic Group Selection (PGS) strategy, which identifies RM records that have a higher probability of being necessary to a user moving inside a target area. We have evaluated our framework using a real prototype developed in Android, as well as realistic Wi-Fi traces we collected at the University of Cyprus. Our experimental study reveals that PreLoc using PGS and conventional fingerprint-based indoor positioning algorithms can yield accuracy that is as good as using the same algorithms with a complete RM, even under scenarios of weak Wi-Fi coverage. © 2015 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.sourceProceedings - IEEE International Conference on Mobile Data Managementen
dc.source16th IEEE International Conference on Mobile Data Management, MDM 2015en
dc.subjectLocalization accuracyen
dc.subjectInformation managementen
dc.subjectIndoor positioning systemsen
dc.subjectIn-door navigationsen
dc.subjectIndoor Localizationen
dc.subjectIndoor positioningen
dc.subjectIntermittent connectivityen
dc.subjectRadio navigationen
dc.subjectWi-Fi connectionsen
dc.titleRadio Map Prefetching for Indoor Navigation in Intermittently Connected Wi-Fi Networksen
dc.description.endingpage43 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: Aalborg Universityen
dc.description.noteset al.en
dc.description.notesIEEE Computer Societyen
dc.description.notesIEEE Technical Committee on Data Engineeringen
dc.description.notesThe National Science Foundationen
dc.description.notesUniversity of Pittsburghen
dc.description.notesConference code: 118117en
dc.description.notesCited By :3</p>en
dc.contributor.orcidZeinalipour-Yazdi, Constantinos D. [0000-0002-8388-1549]
dc.contributor.orcidChrysanthis, Panos K. [0000-0001-7189-9816]

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