dc.contributor.author | Nikitin, A. | en |
dc.contributor.author | Laoudias, Christos | en |
dc.contributor.author | Chatzimilioudis, Georgios | en |
dc.contributor.author | Karras, P. | en |
dc.contributor.author | Zeinalipour-Yazdi, Constantinos D. | en |
dc.creator | Nikitin, A. | en |
dc.creator | Laoudias, Christos | en |
dc.creator | Chatzimilioudis, Georgios | en |
dc.creator | Karras, P. | en |
dc.creator | Zeinalipour-Yazdi, Constantinos D. | en |
dc.date.accessioned | 2019-11-13T10:41:32Z | |
dc.date.available | 2019-11-13T10:41:32Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 978-1-5386-3932-0 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54642 | |
dc.description.abstract | In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fingerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone, and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap. © 2017 IEEE. | en |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.source | Proceedings - 18th IEEE International Conference on Mobile Data Management, MDM 2017 | en |
dc.source | 18th IEEE International Conference on Mobile Data Management, MDM 2017 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026732851&doi=10.1109%2fMDM.2017.61&partnerID=40&md5=6f8bac8b6f7cbb51961850b39c445d8f | |
dc.subject | Estimation | en |
dc.subject | Offline | en |
dc.subject | Localization | en |
dc.subject | Open systems | en |
dc.subject | Accuracy | en |
dc.subject | Localization accuracy | en |
dc.subject | Information management | en |
dc.subject | Open source software | en |
dc.subject | Indoor positioning systems | en |
dc.subject | Indoor | en |
dc.subject | In-door navigations | en |
dc.subject | Cramer-Rao bounds | en |
dc.subject | Cramer-rao lower bound | en |
dc.subject | Gaussian process regression | en |
dc.title | ACCES: Offline accuracy estimation for fingerprint-based localization | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/MDM.2017.61 | |
dc.description.startingpage | 358 | |
dc.description.endingpage | 359 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: Daejeon International Marketing Enterprise | en |
dc.description.notes | Daejeon Metropolitan City | en |
dc.description.notes | IEEE | en |
dc.description.notes | IEEE Technical Committee on Data Engineering (TCDE) | en |
dc.description.notes | Korea Advanced Institute of Science and Technology (KAIST) School of Computing | en |
dc.description.notes | Conference code: 128910</p> | en |
dc.contributor.orcid | Zeinalipour-Yazdi, Constantinos D. [0000-0002-8388-1549] | |
dc.contributor.orcid | Laoudias, Christos [0000-0002-2907-7488] | |
dc.gnosis.orcid | 0000-0002-8388-1549 | |
dc.gnosis.orcid | 0000-0002-2907-7488 | |