Participatory location fingerprinting through stationary crowd in a public or commercial indoor environment
- Submitting institution
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University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25076
- Type
- E - Conference contribution
- DOI
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10.1145/3360774.3360791
- Title of conference / published proceedings
- MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
- First page
- 424
- Volume
- 0
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Crowdsourced indoor localisation approach relies on continuous/occasional fix of known locations from trusted users. This hinders its successful integration in various public indoor environments. The paper shows that this challenge can be addressed in practice and in scalable way by integrating the concept of a stationary crowd with entropy-based filtering without the need for known trusted users. The potential of this solution’s integration in commercial and complex indoor environments is important where there is still no de-facto indoor localisation standard. This work is applied in H2020 RESCUER project in the form of trustworthy indoor localisation for first responders.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
