| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2019-07-16 |
| 書誌情報 |
en : IEEE Internet of Things Journal
巻 5,
号 6,
p. 4957-4964,
発行日 2018-11-26
|
| タイトル |
|
|
タイトル |
Human in the Loop: Distributed Deep Model for Mobile Crowdsensing |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Edge computing |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
crowdsensing |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
human-driven |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
big data |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
李, 良知
太田, 香
董, 冕雄
|
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
With the proliferation of mobile devices, crowdsensing has become an appealing technique to collect and process big data. Meanwhile, the rise of fifth generation wireless systems, especially the new cellular base stations with computing ability, brings about the revolutionary edge computing. Although many approaches regarding the mobile crowdsensing have emerged in the last few years, very few of them are focused on the combination of edge computing and crowdsensing. In this paper, we adopt the state-of-the-art edge computing method to solve the crowdsensing problem with the real-time sensing data, and more importantly, make human be in the loop again, in order to respect the users’ willing and privacy. A distributed deep learning model is adopted to extract features from the captured data, which is not only a compression process to reduce the communication cost, but an encryption procedure for safety protection. The proposed model enables the crowdsensing system to fully harness the computing capacity of edge nodes and devices, and obtain a strong data analysis ability to process the captured data. Simulations demonstrate that our approach is robust and efficient, and outperforms other strategies in several related tasks. |
|
言語 |
en |
| 出版者 |
|
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出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
|
|
表示名 |
10.1109/JIOT.2018.2883318 |
|
URL |
https://doi.org/10.1109/JIOT.2018.2883318 |
| DOI |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1109/JIOT.2018.2883318 |
| 日本十進分類法 |
|
|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2327-4662 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
|
内容記述タイプ |
Other |
|
内容記述 |
application/pdf |
IEEEITJ_5_6_4957_4964 (1.3 MB)
