Mobile Crowdsensing Framework for a Large-Scale Wi-Fi Fingerprinting System
| Autor: | Yungeun Kim, Yohan Chon, Hojung Cha |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
060201 languages & linguistics
Ubiquitous computing business.industry Computer science 06 humanities and the arts 02 engineering and technology Fingerprint recognition computer.software_genre Computer Science Applications Set (abstract data type) Wireless site survey Computational Theory and Mathematics Indoor positioning system 0602 languages and literature Dead reckoning Location-based service Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining business computer Software Computer network |
| Zdroj: | IEEE Pervasive Computing. 15:58-67 |
| ISSN: | 1536-1268 |
| DOI: | 10.1109/mprv.2016.50 |
| Popis: | Although Wi-Fi fingerprinting systems enable location-based services in indoor environments, the service coverage isn't scalable due to costly site surveys. Mobile crowdsensing (MCS), where casual smartphone users conduct the site survey, has recently emerged as a promising solution. Applying MCS to a system introduces new challenges: motivating active participation, inferring location information of unlabeled fingerprints, and managing a large amount of fingerprints. The proposed MCS framework obtains fingerprints from users by exploiting social network service applications and using the pedestrian dead reckoning technique. MCS accurately infers the location information of unlabeled fingerprints using physical-layout and signal-strength measurements. The framework also selects an optimal set of fingerprints, which introduces high accuracy with a slightly increased database size. This case study shows the feasibility of the proposed MCS framework. |
| Databáze: | OpenAIRE |
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