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首页> 中文期刊> 《计算机学报》 >群智感知中基于公交系统的任务分发机制研究

群智感知中基于公交系统的任务分发机制研究

         
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摘要

任务分发作为实现群智感知的重要环节,为了激励更多用户参与数据的采集和共享,已有研究通常利用机会网络进行任务扩散并降低参与者的执行成本,但在节点选择过程中并未充分考虑感知任务的多样性将对节点接触时间、参与数量、感知区域类型等带来的挑战.基于此,该文利用城市中公交载体的轨迹可预测、活动覆盖范围大、乘客节点自主聚集且交互时间有保证等优势,提出了一种基于公交系统的任务差异化分发方法.首先,利用泰森多边形的划分思想,实现感知任务与目标区域的合理覆盖.其次,对感知区域内一定预算约束下的任务差异化分发问题进行分析,并分别提出了两种分发算法:基于覆盖差异的分发算法(COV-DA)和基于扩散差异的分发算法(SPR-DA).最后,利用真实数据集,通过仿真实验从分发准确性和平均移动距离对两种算法的性能进行比较分析.实验结果表明,SPR-DA算法具有更好的分发准确性,而使用COV-DA算法时,移动节点在完成任务时则需要更短的移动距离.%As mobile smart devices become increasingly popular and are equipped with increasingly powerful sensors, they have been pervasively applied in crowdsensing as effective tools to solve large-scale sensing tasks in urban areas.Task requesters can allocate sensing tasks to mobile nodes through a crowdsensing platform, eliminating the cost of deploying and maintaining large numbers of fixed sensors.However, some crowdsourcing tasks (e.g.audio and video transmission) that generate a large amount of sensed data may bring higher network traffic costs to participants using a 3 G/4 G network, which may affect their enthusiasm and cause enormous transmission pressure on the network.To reduce communication costs, if sensed data does not require realtime uploading, researchers have applied opportunistic network and "store-carry-forwarding"totransmit data.But they have ignored a significant problem that it is impossible for a large amount of data to be transferred in such a short momentary encounter.With the increasing sensing capabilities of smart devices, the types and sizes of sensed data have changed greatly.The data transmission paradigm in an opportunistic network is based on the assumption that data exchange can be completed as long as the mobile nodes can connect with each other.It implies that the data size can be ignored or that the data can be exchanged in a transient encounter between mobile nodes.In this paper, we built a data diffusing and transmission paradigm in crowdsensing based on a City Public Traffic System (PTS), and emphatically thoroughly discuss a paradigm for Multi-Task diffusion and transmission within budget constraints.This paradigm makes full use of the advantages of a bus to realize the rapid transmission of large-scale sensed data:predictable trajectory, wide coverage area, fast moving speed and long contact duration among passengers.First, we use the division of voronoi diagram to divide the target area and achieve the reasonable coverage of sensing tasks and target area.Secondly, we analyze the problem of task differential distribution under certain budget constraints, and propose two distribution algorithms:distribution algorithm based on coverage difference (COV-DA) and distribution algorithm based on diffusion difference (SPR-DA).Both transmission schemes are under budget constraint that aims to maximize the overall transmission utility of all data brought into the PTS.By diffusing and scheduling all data in the PTS, the data can be uploaded to the crowdsensing platform by proper mobile passengers.Finally, to effectively evaluate the performance of our algorithm, the COV-DA and SPR-DA algorithms are compared with the classical Greedy and Epidemic algorithms in detail from the accuracy of task distribution, the average moving distance and the time of task distribution.The experimental results show that the SPR-DA algorithm has better distribution accuracy, while using the COV-DA algorithm, the mobile node needs a shorter moving distance when the task is completed.Compared with Greedy and Epidemic algorithm, COV-DA and SPR-DA have better performance in distributing accuracy and average mobile distance, and can achieve a tradeoff between overall transmission utility and transmission redundancy, and save more network traffic costs and other resources for mobile nodes.

著录项

  • 来源
    《计算机学报》 |2019年第2期|295-308|共14页
  • 作者

    安健; 彭振龙; 桂小林; 向乐乐; 梁丹薇;

  • 作者单位

    西安交通大学电子与信息工程学院;

    西安 710049;

    西安交通大学深圳研究院;

    广东深圳 518057;

    西安交通大学电子与信息工程学院;

    西安 710049;

    西安交通大学陕西省计算机网络重点实验室;

    福建 泉州 362000;

    西安交通大学电子与信息工程学院;

    西安 710049;

    西安交通大学深圳研究院;

    广东深圳 518057;

    云计算物联网电子商务智能福建省高校工程研究中心;

    西安 710049;

    西安交通大学电子与信息工程学院;

    西安 710049;

    西安交通大学电子与信息工程学院;

    西安 710049;

    云计算物联网电子商务智能福建省高校工程研究中心;

    西安 710049;

  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 计算机网络;
  • 关键词

    群智感知; 任务分发; 公交系统; 预算约束;

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