Paper Info
Title
Unsupervised labelling of sequential data for location identification in indoor environments.
Abstract
Presents indoor positioning as an unsupervised labelling task on sequential data.Forms a spatial classifier without resorting to pre-determined maps.Differentiates location between unknown closely spaced zones indoors.Presents a valuable working framework for real-world positioning problems.Extends literature studying applications of graphical models. 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.
Year
DOI
Venue
2016
10.1016/j.eswa.2016.06.003
Expert Syst. Appl.
Keywords
Field
DocType
Unsupervised labelling,Sequential data,Indoor positioning,Ubiquitous computing,Graphical models
Wireless network,Data mining,Sequential data,Contextual information,Computer science,Labelling,Artificial intelligence,Ubiquitous computing,Graphical model,Classifier (linguistics),Machine learning,Probabilistic framework
Journal
Volume
Issue
ISSN
61
C
0957-4174
Citations 
PageRank 
References 
2
0.38
24
Authors
5
Name
Order
Citations
PageRank
Iker Perez131.76
James Pinchin2155.55
Michael A. Brown341.80
Jesse Michael Blum4102.05
Sarah C. Sharples523620.39