科研成果详情

题名Towards Robust Task Assignment in Mobile Crowdsensing Systems
作者
发表日期2023-07-01
发表期刊IEEE Transactions on Mobile Computing
ISSN/eISSN1536-1233
卷号22期号:7页码:4297-4313
摘要Mobile Crowdsensing (MCS), which assigns outsourced sensing tasks to volunteer workers, has become an appealing paradigm to collaboratively collect data from surrounding environments. However, during actual task implementation, various unpredictable disruptions are usually inevitable, which might cause a task execution failure and thus impair the benefit of MCS systems. Practically, via reactively shifting the pre-determined assignment scheme in real time, it is usually impossible to develop reassignment schemes without a sacrifice of the system performance. Against this background, we turn to an alternative solution, i.e., proactively creating a robust task assignment scheme offline. In this work, we provide the first attempt to investigate an important and realistic RoBust Task Assignment (RBTA) problem in MCS systems, and try to strengthen the assignment scheme’s robustness while minimizing the workers’ traveling detour cost simultaneously. By leveraging the workers’ spatiotemporal mobility, we propose an assignment-graph-based approach. First, an assignment graph is constructed to locally model the assignment relationship between the released MCS tasks and available workers. And then, under the framework of evolutionary multi-tasking, we devise a population-based optimization algorithm, namely EMTRA, to effectively achieve adequate Pareto-optimal schemes. Comprehensive experiments on two real-world datasets clearly validate the effectiveness and applicability of our proposed approach.
关键词evolutionary algorithms Mobile crowdsensing robustness task assignment
DOI10.1109/TMC.2022.3151190
URL查看来源
语种英语English
Scopus入藏号2-s2.0-85124831464
引用统计
被引频次:25[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11551
专题北师香港浸会大学
通讯作者Wang,Liang
作者单位
1.The School of Computer Science,Northwestern Polytechnical University,Xi’an,710060,China
2.The College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,518060,China
3.The State Key Laboratory of Internet of Things for Smart City,Department of Computer and Information Science,University of Macau,Macao,999078,Macao
4.The College of Computer Science and Technology,Jilin University,Changchun,130012,China
5.The BNU-UIC Institute of Artificial Intelligence and Future Networks,Guangdong Key Lab of AI and Multi-Modal Data Processing,BNU-HKBU United International College,Beijing Normal University (BNU Zhuhai),Zhuhai,Guangdong,519088,China
推荐引用方式
GB/T 7714
Wang,Liang,Yu,Zhiwen,Wu,Kaishunet al. Towards Robust Task Assignment in Mobile Crowdsensing Systems[J]. IEEE Transactions on Mobile Computing, 2023, 22(7): 4297-4313.
APA Wang,Liang., Yu,Zhiwen., Wu,Kaishun., Yang,Dingqi., Wang,En., .. & Guo,Bin. (2023). Towards Robust Task Assignment in Mobile Crowdsensing Systems. IEEE Transactions on Mobile Computing, 22(7), 4297-4313.
MLA Wang,Liang,et al."Towards Robust Task Assignment in Mobile Crowdsensing Systems". IEEE Transactions on Mobile Computing 22.7(2023): 4297-4313.
条目包含的文件
条目无相关文件。
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[Wang,Liang]的文章
[Yu,Zhiwen]的文章
[Wu,Kaishun]的文章
百度学术
百度学术中相似的文章
[Wang,Liang]的文章
[Yu,Zhiwen]的文章
[Wu,Kaishun]的文章
必应学术
必应学术中相似的文章
[Wang,Liang]的文章
[Yu,Zhiwen]的文章
[Wu,Kaishun]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。