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- Publisher Website: 10.1109/TPDS.2013.250
- Scopus: eid_2-s2.0-84903118017
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Article: Robust trajectory estimation for crowdsourcing-based mobile applications
Title | Robust trajectory estimation for crowdsourcing-based mobile applications |
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Authors | |
Keywords | mobile applications Crowdsourcing motion trajectory robust estimation |
Issue Date | 2014 |
Citation | IEEE Transactions on Parallel and Distributed Systems, 2014, v. 25, n. 7, p. 1876-1885 How to Cite? |
Abstract | Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations. © 2013 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/303425 |
ISSN | 2021 Impact Factor: 3.757 2020 SCImago Journal Rankings: 0.760 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Xinglin | - |
dc.contributor.author | Yang, Zheng | - |
dc.contributor.author | Wu, Chenshu | - |
dc.contributor.author | Sun, Wei | - |
dc.contributor.author | Liu, Yunhao | - |
dc.contributor.author | Xing, Kai | - |
dc.date.accessioned | 2021-09-15T08:25:17Z | - |
dc.date.available | 2021-09-15T08:25:17Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | IEEE Transactions on Parallel and Distributed Systems, 2014, v. 25, n. 7, p. 1876-1885 | - |
dc.identifier.issn | 1045-9219 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303425 | - |
dc.description.abstract | Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations. © 2013 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Parallel and Distributed Systems | - |
dc.subject | mobile applications | - |
dc.subject | Crowdsourcing | - |
dc.subject | motion trajectory | - |
dc.subject | robust estimation | - |
dc.title | Robust trajectory estimation for crowdsourcing-based mobile applications | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPDS.2013.250 | - |
dc.identifier.scopus | eid_2-s2.0-84903118017 | - |
dc.identifier.volume | 25 | - |
dc.identifier.issue | 7 | - |
dc.identifier.spage | 1876 | - |
dc.identifier.epage | 1885 | - |
dc.identifier.isi | WOS:000340282400021 | - |