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Participatory location fingerprinting through stationary crowd in a public or commercial indoor environment

Participatory location fingerprinting through stationary crowd in a public or commercial indoor environment

Hossain, A. K. M. Mahtab and Loukas, George ORCID: 0000-0003-3559-5182 (2019) Participatory location fingerprinting through stationary crowd in a public or commercial indoor environment. In: MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM, pp. 424-433. ISBN 978-1450372831 (doi:https://doi.org/10.1145/3360774.3360791)

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Abstract

The training phase of indoor location fingerprinting has been traditionally performed by dedicated surveyors in a manner that is time and labour intensive. Crowdsourcing process is more efficient, but is impractical in public or commercial buildings because it requires occasional location fix provided explicitly by the participant, the availability of an indoor map for correlating the traces, and the existence of landmarks throughout the area. Here, we address these issues for the first time in this context by leveraging the existence of stationary crowd that have timetabled roles, such as desk-bound employees, lecturers and students. We propose a scalable and effortless positioning system in the context of a public/commercial building by using Wi-Fi sensor readings from its stationary occupants' smartphones combined with their timetabling information. Most significantly, the entropy concept of information theory is utilised to differentiate between good and spurious measurements in a manner that does not rely on the existence of known trusted users. Our analysis and experimental results show that, regardless of such participants' unpredictable behaviour, including not following their timetabling information, hiding their location or purposefully generating wrong data, our entropy-based filtering approach ensures the creation of a radio-map incrementally from their measurements. Its effectiveness is validated experimentally with two well-known machine learning algorithms.

Item Type: Conference Proceedings
Title of Proceedings: MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Additional Information: MobiQuitous 2019 was held from 12-14 November 2019, Houston, United States.
Uncontrolled Keywords: Indoor Localisation, Location Fingerprinting, Crowdsourcing, Entropy, Wi-Fi, Participatory Sensing.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science > Internet of Things and Security Research Centre (ISEC)
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Faculty of Engineering & Science
Related URLs:
Last Modified: 04 Mar 2022 13:06
URI: http://gala.gre.ac.uk/id/eprint/25076

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