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Analysis of Crowdsensed WiFi Fingerprints for Indoor Localization

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationProceedings of the 21st Conference of Open Innovations Association FRUCT
Place of PublicationHelsinki, Finland
Number of pages10
ISBN (Electronic)978-952-68653-2-4
Publication statusPublished - Nov 2017
Publication typeA4 Article in a conference publication
EventProceedings of Conference of Open Innovations Association FRUCT -
Duration: 1 Jan 2000 → …

Publication series

ISSN (Electronic)2305-7254


ConferenceProceedings of Conference of Open Innovations Association FRUCT
Period1/01/00 → …


Crowdsensing is more and more used nowadays for indoor localization based on Received Signal Strength (RSS) fingerprinting. It is a fast and efficient solution to maintain fingerprinting databases and to keep them up-to-date. There are however several challenges involved in crowdsensing RSS fingerprinting data, and these have been little investigated so far in the current literature. Our goal is to analyse the impact of various error sources in the crowdsensing process for the purpose of indoor localization. We rely our findings on a heavy measurement campaign involving 21 measurement devices and more than 6800 fingerprints. We show that crowdsensed databases are more robust to erroneous RSS reports than to malicious fingerprint position reports. We also evaluate the positioning accuracy achievable with crowdsensed databases in the absence of any available calibration.

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