Unsupervised labelling of sequential data for location identification in indoor environments

P�rez L�pez, Iker; Pinchin, James; Brown, Michael; Blum, Jesse; Sharples, Sarah

Abstract

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.

Citation

Pérez López, I., Pinchin, J., Brown, M., Blum, J., & Sharples, S. (in press). Unsupervised labelling of sequential data for location identification in indoor environments. Expert Systems with Applications, https://doi.org/10.1016/j.eswa.2016.06.003

• Journal Article Type: Article
• Acceptance Date: Jun 2, 2016
• Online Publication Date: Jun 3, 2016
• Deposit Date: Jun 6, 2016
• Publicly Available Date: Jun 6, 2016
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Electronic ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.eswa.2016.06.003
• Keywords: Unsupervised Labelling; Sequential Data; Indoor Positioning; Ubiquitous Computing; Graphical Models
• Public URL: https://nottingham-repository.worktribe.com/output/796821
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0957417416302846
• Contract Date: Jun 6, 2016