Analysis of Crowdsensed WiFi Fingerprints for Indoor Localization
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 21st Conference of Open Innovations Association FRUCT |
| Place of Publication | Helsinki, Finland |
| Publisher | FRUCT |
| Pages | 268-277 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-952-68653-2-4 |
| Publication status | Published - Nov 2017 |
| Publication type | A4 Article in a conference publication |
| Event | Proceedings of Conference of Open Innovations Association FRUCT - Duration: 1 Jan 2000 → … |
Publication series
| Name | |
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| ISSN (Electronic) | 2305-7254 |
Conference
| Conference | Proceedings of Conference of Open Innovations Association FRUCT |
|---|---|
| Period | 1/01/00 → … |
Abstract
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.
Publication forum classification
Field of science, Statistics Finland
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