Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal Coverage
Probabilistic Control of Dynamic Crowds Toward Uniform Spatial-Temporal Coverage
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DOI：
10.1109/tmc.2022.3231530
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发表时间：
2024-02
影响因子：
7.9
通讯作者：
Yukio Ogawa;G. Hasegawa;M. Murata
中科院分区：
文献类型：
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作者：
Yukio Ogawa;G. Hasegawa;M. Murata
当车辆人群感测长时间覆盖城市范围的区域时，车辆移动性和连接性在空间和时间上显著变化，但是重要的是实现足够均匀的数据覆盖以满足环境监测场景的要求。因此，我们的目标是确保在整个城市范围内的区域，尽管这样的车辆动态感测数据的均匀时空覆盖。对于一个大的地区，以自愿为基础的办法必须处理大量和各种各样的参与者流动模式。因此，我们提出了一种概率控制机制，自适应地调整每个参与者的激励，而不使用任何先验信息的参与者。我们提供了一个数学分析，以确保稳定的参与者分配的任务（称为工人）的数量，我们评估机制的鲁棒性，通过使用24小时的车辆跟踪数据，从全市范围内的区域。我们的研究结果表明，当参与者的数量是高达1500倍高于所需的工人数量，感测行动的结果在一个分布的平均值约为1和四分位数范围约为4所需的感测间隔;此外，平均值增加了2%时，30%的通信消息随机丢失。
Vehicular mobility and connectivity vary significantly over space and time when vehicular crowd sensing covers a city-wide area for a long time period, but it is important to achieve sufficiently uniform data coverage to satisfy the requirements of an environmental monitoring scenario. Our goal is thus to ensure uniform spatial-temporal coverage of sensed data over a city-wide area despite such vehicle dynamics. For a large area, trajectory-based approaches must deal with a great number and variety of participant mobility patterns. Hence, we propose a probabilistic control mechanism that adaptively adjusts the incentive to each participant, without using any prior information about participants. We provide a mathematical analysis that ensures stability of the number of participants with assigned tasks (called workers), and we evaluate the mechanism's robustness by using 24-hr vehicle trace data from a city-wide area. Our results demonstrate that, when the number of participants is up to 1500 times higher than the required number of workers, sensing actions result in a distribution with a mean of about 1 and an interquartile range of around 4 for a required sensing interval; moreover, the mean increases by 2% when 30% of communication messages are randomly lost.