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    张力, 张书奎, 刘海, 张洋, 陶冶, 龙浩, 于淳清, 祝启鼎. 基于用户关注度以及时间监督的任务分发[J]. 计算机研究与发展, 2022, 59(4): 813-825. DOI: 10.7544/issn1000-1239.20200565
    引用本文: 张力, 张书奎, 刘海, 张洋, 陶冶, 龙浩, 于淳清, 祝启鼎. 基于用户关注度以及时间监督的任务分发[J]. 计算机研究与发展, 2022, 59(4): 813-825. DOI: 10.7544/issn1000-1239.20200565
    Zhang Li, Zhang Shukui, Liu Hai, Zhang Yang, Tao Ye, Long Hao, Yu Chunqing, Zhu Qiding. Task Distribution Based on User Attention and Time Supervision[J]. Journal of Computer Research and Development, 2022, 59(4): 813-825. DOI: 10.7544/issn1000-1239.20200565
    Citation: Zhang Li, Zhang Shukui, Liu Hai, Zhang Yang, Tao Ye, Long Hao, Yu Chunqing, Zhu Qiding. Task Distribution Based on User Attention and Time Supervision[J]. Journal of Computer Research and Development, 2022, 59(4): 813-825. DOI: 10.7544/issn1000-1239.20200565

    基于用户关注度以及时间监督的任务分发

    Task Distribution Based on User Attention and Time Supervision

    • 摘要: 在群智感知器网络中,如何在限定时间内完成发布者指定的感知任务,是移动群智感知任务分发面临的一个重要问题.针对该问题,为了使感知用户间密切协作,并及时将执行感知任务反馈给发送者,提出一种基于用户关注度与时间监督的任务分发(task distribution with user attention and time supervision, TDUATS)算法.该算法首先提出了用户间关注度,执行任务的起始监督、过程监督、完成监督等概念,然后通过分析执行感知任务的用户间关联关系,建立用户间关注度模型,对执行任务的过程进行监督,在此基础上对感知任务进行分发. 实验结果表明,该算法不仅可在限定时间内完成感知任务,而且还可以监督任务执行的过程;有利于发布者及时了解任务的执行情况,对提高任务执行的满意度起到了很好的促进作用; 同时,与对比算法相比较,也有较好的性能表现.

       

      Abstract: In the mobile crowd sensing network, how to complete the sensing task assigned by the publisher within a limited time is an important problem faced by the mobile crowd sensing task distribution. To solve this problem, we propose a task distribution algorithm TDUATS based on user attention and time supervision in order to make the sensing users cooperate closely and feed back the sensing tasks to the sender in time. In the algorithm, the concepts of user attention, initial supervision, process supervision and completion supervision are proposed at first, and then the relationship between users who perform sensing tasks is analyzed. In order to monitor the process of task execution, the attention model among users is established, and the sensing tasks are distributed on this basis.Two real-world mobility datasets experiments demonstrate that the our proposed algorithm can not only complete the sensing task within a limited time, but also supervise the process of task execution, which is conductive to the publisher to timely understand the execution of the task, and plays a good role in improving the satisfaction of task execution. At the same time, compared with the comparison algorithms, sender and publisher can understand the task execution process in time, effectively improving the satisfaction of both parties.

       

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