dc.contributor.author | Liu, Bingchen | |
dc.contributor.author | Zhong, Weiyi | |
dc.contributor.author | Xie, Jushi | |
dc.contributor.author | Kong, Lingzhen | |
dc.contributor.author | Yang, Yihong | |
dc.contributor.author | Lin, Chuang | |
dc.contributor.author | Wang, Hao | |
dc.date.accessioned | 2021-03-22T11:17:42Z | |
dc.date.available | 2021-03-22T11:17:42Z | |
dc.date.created | 2021-03-21T15:18:54Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | International Journal of Mobile Information Systems. 2021, 2021 . | en_US |
dc.identifier.issn | 1574-017X | |
dc.identifier.uri | https://hdl.handle.net/11250/2734790 | |
dc.description.abstract | With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Hindawi | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 11 | en_US |
dc.source.volume | 2021 | en_US |
dc.source.journal | International Journal of Mobile Information Systems | en_US |
dc.identifier.doi | 10.1155/2021/6673094 | |
dc.identifier.cristin | 1899682 | |
dc.description.localcode | Copyright © 2021 Bingchen Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
dc.source.articlenumber | 6673094 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |