A Framework for Indoor Positioning Including Building Topology
In many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9933451/ |
| _version_ | 1798022607311732736 |
|---|---|
| author | Andrea Brunello Angelo Montanari Nicola Saccomanno |
| author_facet | Andrea Brunello Angelo Montanari Nicola Saccomanno |
| author_sort | Andrea Brunello |
| collection | DOAJ |
| description | In many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting. Research in this area has been faced with a number of challenges, leading to the proposal of a number of localisation algorithms, sampling strategies, benchmark datasets, and representations of building information. This proliferation made the modeling of the indoor positioning domain quite hard from both a theoretical and a practical point of view. In this paper, we propose a general and extensible framework, based on a relational database, that pairs fingerprints with building information. We show how the proposed system successfully deals with a number of problems that affect indoor positioning, supporting a large set of relevant tasks. The source code of the framework is available online, as well as an implementation of it, that provides an interactive open repository of indoor positioning data. |
| first_indexed | 2024-04-11T17:32:41Z |
| format | Article |
| id | doaj.art-cf8855de40494692b1aa4c4ce654282c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T17:32:41Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cf8855de40494692b1aa4c4ce654282c2022-12-22T04:11:58ZengIEEEIEEE Access2169-35362022-01-011011495911497410.1109/ACCESS.2022.32183019933451A Framework for Indoor Positioning Including Building TopologyAndrea Brunello0https://orcid.org/0000-0003-2063-218XAngelo Montanari1https://orcid.org/0000-0002-4322-769XNicola Saccomanno2https://orcid.org/0000-0001-5916-3195Department of Mathematics, Computer Science and Physics, University of Udine, Udine, ItalyDepartment of Mathematics, Computer Science and Physics, University of Udine, Udine, ItalyDepartment of Mathematics, Computer Science and Physics, University of Udine, Udine, ItalyIn many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting. Research in this area has been faced with a number of challenges, leading to the proposal of a number of localisation algorithms, sampling strategies, benchmark datasets, and representations of building information. This proliferation made the modeling of the indoor positioning domain quite hard from both a theoretical and a practical point of view. In this paper, we propose a general and extensible framework, based on a relational database, that pairs fingerprints with building information. We show how the proposed system successfully deals with a number of problems that affect indoor positioning, supporting a large set of relevant tasks. The source code of the framework is available online, as well as an implementation of it, that provides an interactive open repository of indoor positioning data.https://ieeexplore.ieee.org/document/9933451/Building topologyfingerprintingindoor positioningmulti-sensor datarelational databases |
| spellingShingle | Andrea Brunello Angelo Montanari Nicola Saccomanno A Framework for Indoor Positioning Including Building Topology IEEE Access Building topology fingerprinting indoor positioning multi-sensor data relational databases |
| title | A Framework for Indoor Positioning Including Building Topology |
| title_full | A Framework for Indoor Positioning Including Building Topology |
| title_fullStr | A Framework for Indoor Positioning Including Building Topology |
| title_full_unstemmed | A Framework for Indoor Positioning Including Building Topology |
| title_short | A Framework for Indoor Positioning Including Building Topology |
| title_sort | framework for indoor positioning including building topology |
| topic | Building topology fingerprinting indoor positioning multi-sensor data relational databases |
| url | https://ieeexplore.ieee.org/document/9933451/ |
| work_keys_str_mv | AT andreabrunello aframeworkforindoorpositioningincludingbuildingtopology AT angelomontanari aframeworkforindoorpositioningincludingbuildingtopology AT nicolasaccomanno aframeworkforindoorpositioningincludingbuildingtopology AT andreabrunello frameworkforindoorpositioningincludingbuildingtopology AT angelomontanari frameworkforindoorpositioningincludingbuildingtopology AT nicolasaccomanno frameworkforindoorpositioningincludingbuildingtopology |