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首页> 外文期刊>IEEE systems journal >VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing
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VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

机译：VTRACER：在线车辆轨迹压缩符合移动边缘计算时

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摘要

Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. V Tracer is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle's GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online mapmatcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. V Tracer meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented V Tracer on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate thatV Tracer achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.
机译：由于车载全球定位系统（GPS）器件的增殖，可以容易地跟踪车辆。 v示踪剂是用于在线轨迹压缩和追踪车辆的经济高效的移动系统，将流GPS数据作为输入。在线轨迹压缩，在揭示下一个车辆的GPS位置之前寻求简洁和（接近）空间无损数据表示，逐渐成为缓解通信带宽，存储和云计算等负担的有希望的方法。通常，准确的在线MapMatcher是一个先决条件。这种两相方法是非虚拟性的，因为我们需要克服由资源受限的GPS设备和繁重计算任务引起的基本矛盾。 V示踪剂通过利用移动边缘计算的想法来满足挑战。更具体地说，我们将沉重的计算任务卸载到司机的附近智能手机（即，智能手机播放Cloudlet的角色），在驾驶期间几乎空闲。更重要的是，它们具有相对更强大的计算能力。我们在Android平台上实现了V示踪剂，并根据中国重庆市生成的真正驾驶跟踪数据集进行评估。实验结果表明，该示踪剂在匹配的精度，压缩比率方面实现了出色的性能，并且还造成了可接受的记忆，能量和应用尺寸。

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  • 来源
    《IEEE systems journal》 |2020年第2期|1635-1646|共12页
  • 作者

    Chen Chao; Ding Yan; Wang Zhu; Zhao Junfeng; Guo Bin; Zhang Daqing;

  • 作者单位

    Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China|Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China;

    Chongqing Univ Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China|Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China;

    Northwestern Polytech Univ Dept Comp Sci Xian 710072 Peoples R China;

    Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China;

    Northwestern Polytech Univ Dept Comp Sci Xian 710072 Peoples R China;

    Inst Mines TELECOM TELECOM SudParis F-91000 Evry France;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Global positioning system (GPS) devices; mobile edge computing; resource-constrained; trajectory mapping; trajectory compression;

    机译：全球定位系统（GPS）设备;移动边缘计算;资源约束;轨迹映射;轨迹压缩;

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