Unsupervised labelling of sequential data for location identification in indoor environments
In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we for...
| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/33787/ |
| _version_ | 1848794704795140096 |
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| author | Pérez López, Iker Pinchin, James Brown, Michael Blum, Jesse Sharples, Sarah |
| author_facet | Pérez López, Iker Pinchin, James Brown, Michael Blum, Jesse Sharples, Sarah |
| author_sort | Pérez López, Iker |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape. |
| first_indexed | 2025-11-14T19:20:26Z |
| format | Article |
| id | nottingham-33787 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:20:26Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-337872020-05-04T17:57:54Z https://eprints.nottingham.ac.uk/33787/ Unsupervised labelling of sequential data for location identification in indoor environments Pérez López, Iker Pinchin, James Brown, Michael Blum, Jesse Sharples, Sarah In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets. Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape. Elsevier 2016-06-03 Article PeerReviewed Pérez López, Iker, Pinchin, James, Brown, Michael, Blum, Jesse and Sharples, Sarah (2016) Unsupervised labelling of sequential data for location identification in indoor environments. Expert Systems with Applications . ISSN 0957-4174 Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models http://www.sciencedirect.com/science/article/pii/S0957417416302846 doi:10.1016/j.eswa.2016.06.003 doi:10.1016/j.eswa.2016.06.003 |
| spellingShingle | Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models Pérez López, Iker Pinchin, James Brown, Michael Blum, Jesse Sharples, Sarah Unsupervised labelling of sequential data for location identification in indoor environments |
| title | Unsupervised labelling of sequential data for
location identification in indoor environments |
| title_full | Unsupervised labelling of sequential data for
location identification in indoor environments |
| title_fullStr | Unsupervised labelling of sequential data for
location identification in indoor environments |
| title_full_unstemmed | Unsupervised labelling of sequential data for
location identification in indoor environments |
| title_short | Unsupervised labelling of sequential data for
location identification in indoor environments |
| title_sort | unsupervised labelling of sequential data for
location identification in indoor environments |
| topic | Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models |
| url | https://eprints.nottingham.ac.uk/33787/ https://eprints.nottingham.ac.uk/33787/ https://eprints.nottingham.ac.uk/33787/ |