Localisation based on Wi-Fi Fingerprints: A Crowdsensing Approach with a Device-to-Device Aim

Abstract : Crowdsensing is for a few years a new way to gather information. Most smartphones and mobile operating systems provide applications which are able to sense and gather several data from the environment of the device. Thanks to this collected data, it is possible to combine information from several probes. A very common use case is the collection of network scans with location to help the localisation feature of these devices. Nevertheless, most users are not aware of this spying. The collected data might represent infringements of privacy. One possible solution to keep gathering these data while maintaining privacy would consist in device-to-device communications in order to break the links between data and users. In this article we propose an approach to test the feasibility of such a system. We collected data from mobile users to combine location and network scans data. With this data, we test the accuracy level we can reach while using Wi-Fi localisation. We analyse how a new measure should be pushed and how many scans should be realised to provide location-based Wi-Fi. We analyse the minimal dataset to cover the set of locations covered by users and prove that a multiuser gathering system can benefit the users.
Type de document :
Communication dans un congrès
DAMN! 2017 - 1st Workshop on Data Analytics for Mobile Networking, Mar 2017, Kauna, Big Island, Hawaii, United States. 2017, 〈http://damn2017.conf.citi-lab.fr/〉
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Contributeur : Hervé Rivano <>
Soumis le : lundi 6 mars 2017 - 12:16:27
Dernière modification le : vendredi 29 septembre 2017 - 16:32:03
Document(s) archivé(s) le : mercredi 7 juin 2017 - 13:15:35

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  • HAL Id : hal-01483696, version 1

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Patrice Raveneau, Stéphane D 'Alu, Hervé Rivano. Localisation based on Wi-Fi Fingerprints: A Crowdsensing Approach with a Device-to-Device Aim. DAMN! 2017 - 1st Workshop on Data Analytics for Mobile Networking, Mar 2017, Kauna, Big Island, Hawaii, United States. 2017, 〈http://damn2017.conf.citi-lab.fr/〉. 〈hal-01483696〉

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