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首页> 外文期刊>International journal of ad hoc and ubiquitous computing >Task allocation for crowdsensing based on submodular optimisation
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Task allocation for crowdsensing based on submodular optimisation

机译：基于子模优化的人群感知任务分配

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摘要

Crowdsensing is becoming a hot topic because of its advantages in the field of smart city. In crowdsensing, task allocation is a primary issue which determines the data quality and the cost of sensing tasks. In this paper, on the basis of the sweep covering theory, a novel coverage metric called ' t -sweep k -coverage' is defined, and two symmetric problems are formulated: minimise participant set under fixed coverage rate constraint (MinP) and maximise coverage rate under participant set constraint (MaxC). Then based on their submodular property, two task allocation methods are proposed, namely double greedy (dGreedy) and submodular optimisation (SMO). The two methods are compared with the baseline method linear programming (LP) in experiments. The results show that, regardless of the size of the problems, both two methods can obtain the appropriate participant set, and overcome the shortcomings of linear programming.
机译：人群感知由于其在智慧城市领域的优势而成为热门话题。在人群感知中，任务分配是决定数据质量和感知任务成本的主要问题。本文基于扫描覆盖理论，定义了一种新的覆盖度量，称为“ t-扫描k覆盖”，并提出了两个对称问题：在固定覆盖率约束（MinP）下最小化参与者集并最大化覆盖参与者集约束下的费率（MaxC）。然后根据子模块的性质，提出了两种任务分配方法，即双贪心算法（dGreedy）和子模块优化算法（SMO）。在实验中将这两种方法与基线方法线性规划（LP）进行了比较。结果表明，无论问题多大，两种方法都能获得合适的参与者集，克服了线性规划的缺点。

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    College of Mathematics and Computer Science Fuzhou University|Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education|Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University;

    College of Mathematics and Computer Science Fuzhou University;

    School of Computer Science Northwestern Polytechnical University;

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  • 正文语种 eng
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  • 关键词

    crowdsensing; task allocation; participant selection; submodular optimisation; SMO;

    机译：人群感知任务分配;参加者选择;次模块化优化;SMO;

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