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- Publisher Website: 10.1109/TMC.2021.3088268
- Scopus: eid_2-s2.0-85137674659
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Article: Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic Fingerprinting
Title | Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic Fingerprinting |
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Authors | |
Keywords | Fingerprinting geomagnetic field implicit crowdsourcing IMU multimodal signals RF site survey |
Issue Date | 2023 |
Citation | IEEE Transactions on Mobile Computing, 2023, v. 22, n. 2, p. 935-950 How to Cite? |
Abstract | In fingerprint-based indoor localization, fusing radio frequency (RF) and geomagnetic signals has been shown to achieve promising results. To efficiently collect fingerprints, implicit crowdsourcing can be used, where signals sampled by pedestrians are automatically labeled with their locations on a map. Previous work on crowdsourced fingerprinting is often based on a single signal, which is susceptible to signal bias and labeling error. We study, for the first time, implicit multimodal crowdsourcing for joint RF and geomagnetic fingerprinting. The scheme, termed UbiFin, exploits the spatial correlation among RF, geomagnetic, and motion signals to mitigate the impact of sensor noise, leading to highly accurate and robust fingerprinting without the need for any explicit manual intervention. Using clustering and dynamic programming, UbiFin correlates spatially different signals and filters effectively mislabeled signals. We conduct extensive experiments on our campus and a large multi-story shopping mall. Efficient and simple to implement, UbiFin outperforms other state-of-The-Art crowdsourcing schemes to construct RF and geomagnetic fingerprints in terms of accuracy and robustness (cutting fingerprint error by 40 percent in general). |
Persistent Identifier | http://hdl.handle.net/10722/343392 |
ISSN | 2023 Impact Factor: 7.7 2023 SCImago Journal Rankings: 2.755 |
DC Field | Value | Language |
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dc.contributor.author | Tan, Jiajie | - |
dc.contributor.author | Wu, Hang | - |
dc.contributor.author | Chow, Ka Ho | - |
dc.contributor.author | Chan, S. H.Gary | - |
dc.date.accessioned | 2024-05-10T09:07:44Z | - |
dc.date.available | 2024-05-10T09:07:44Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | IEEE Transactions on Mobile Computing, 2023, v. 22, n. 2, p. 935-950 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.uri | http://hdl.handle.net/10722/343392 | - |
dc.description.abstract | In fingerprint-based indoor localization, fusing radio frequency (RF) and geomagnetic signals has been shown to achieve promising results. To efficiently collect fingerprints, implicit crowdsourcing can be used, where signals sampled by pedestrians are automatically labeled with their locations on a map. Previous work on crowdsourced fingerprinting is often based on a single signal, which is susceptible to signal bias and labeling error. We study, for the first time, implicit multimodal crowdsourcing for joint RF and geomagnetic fingerprinting. The scheme, termed UbiFin, exploits the spatial correlation among RF, geomagnetic, and motion signals to mitigate the impact of sensor noise, leading to highly accurate and robust fingerprinting without the need for any explicit manual intervention. Using clustering and dynamic programming, UbiFin correlates spatially different signals and filters effectively mislabeled signals. We conduct extensive experiments on our campus and a large multi-story shopping mall. Efficient and simple to implement, UbiFin outperforms other state-of-The-Art crowdsourcing schemes to construct RF and geomagnetic fingerprints in terms of accuracy and robustness (cutting fingerprint error by 40 percent in general). | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Mobile Computing | - |
dc.subject | Fingerprinting | - |
dc.subject | geomagnetic field | - |
dc.subject | implicit crowdsourcing | - |
dc.subject | IMU | - |
dc.subject | multimodal signals | - |
dc.subject | RF | - |
dc.subject | site survey | - |
dc.title | Implicit Multimodal Crowdsourcing for Joint RF and Geomagnetic Fingerprinting | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TMC.2021.3088268 | - |
dc.identifier.scopus | eid_2-s2.0-85137674659 | - |
dc.identifier.volume | 22 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 935 | - |
dc.identifier.epage | 950 | - |
dc.identifier.eissn | 1558-0660 | - |