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    王振宁, 曹越, 江恺, 林海, 周欢. 车辆群智感知中激励驱动的车辆选择与调度方法[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202330881
    引用本文: 王振宁, 曹越, 江恺, 林海, 周欢. 车辆群智感知中激励驱动的车辆选择与调度方法[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202330881
    Wang Zhenning, Cao Yue, Jiang Kai, Lin Hai, Zhou Huan. Incentive-Driven Vehicle Selection and Scheduling Method in Vehicular Crowdsensing[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330881
    Citation: Wang Zhenning, Cao Yue, Jiang Kai, Lin Hai, Zhou Huan. Incentive-Driven Vehicle Selection and Scheduling Method in Vehicular Crowdsensing[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202330881

    车辆群智感知中激励驱动的车辆选择与调度方法

    Incentive-Driven Vehicle Selection and Scheduling Method in Vehicular Crowdsensing

    • 摘要: 车辆群智感知旨在利用智能车辆配备的车载传感器和计算资源,收集一系列区域的感知数据. 目前,根据车辆轨迹是否可更改,通常可将感知车辆分为机会型车辆和参与型车辆. 其中,机会型车辆轨迹路线固定,不可随意更改. 而参与型车辆轨迹路线可根据现实需求进行更改. 因此,如何选择合适的机会型车辆完成感知任务,以及如何规划参与型车辆的轨迹是一项挑战性研究问题. 这里,以2种类型感知车辆的不同移动特性为出发点,通过群智感知平台(CSP)管理2种类型的车辆,并分别针对机会型和参与型车辆解决不同的问题. 首先,针对机会型车辆,需选择特定的车辆集合,以完成感知任务并最小化CSP开销. 为解决此问题,提出一项基于反向拍卖的激励机制以选择开销最小的车辆集合完成感知任务,主要包括获胜车辆选择和报酬支付2个阶段,同时验证了所提方法可保证机会型车辆的个体合理性和真实性;其次,针对参与型车辆,需通过CSP调度以规划每个参与型车辆的轨迹,执行感知任务并最小化CSP的开销. 为解决此问题,提出一项基于深度强化学习的方法以调度车辆行驶轨迹,为车辆分配不同的感知任务. 此外,在最小化CSP开销的同时,还考虑感知任务执行的公平性问题,引入感知公平指数以确保不同子区域感知任务完成的均衡性. 最后,基于真实世界数据集的广泛评估表明,所提方法效果良好,并优于其他基准方案.

       

      Abstract: Vehicular crowdsensing aims to utilize a large number of on-board sensors and computing resources in intelligent vehicles to collect sensing data in a series of areas. Currently, sensing vehicles are usually divided into opportunistic vehicles and participatory vehicles, according to whether the vehicle trajectory can be changed. Here, opportunistic vehicles are with fixed trajectories and cannot be changed. The trajectories of participatory vehicles can be changed based on actual needs. Therefore, selecting the appropriate opportunistic vehicle to complete the sensing task and planning the trajectory of the participating vehicle is a challenging research problem. This paper takes the different mobility characteristics of the two types of sensing vehicles as the starting point, and manages the two types of vehicles through the crowd sensing platform (CSP). Then, different problems are solved for opportunistic and participatory vehicles, respectively. Specifically, for opportunistic vehicles, it is necessary to select an appropriate vehicle set to complete the sensing task and minimize the cost of CSP. To solve this problem, this paper proposes a reverse auction-based incentive mechanism to select the minimum cost vehicle set to complete the sensing task. It mainly includes two stages: winning vehicle selection and reward payment. Meanwhile, this paper verifies that the proposed method can ensure individual rationality and authenticity. For participating vehicles, it is necessary to plan the trajectory of each vehicle through CSP scheduling, perform sensing tasks and minimize the cost of CSP. To solve this problem, this paper proposes a deep reinforcement learning-based method to schedule vehicle trajectories and assign different sensing tasks to vehicles. In addition, while minimizing CSP overhead, this paper also considers the fairness issue of sensing task. The sensing fairness index is introduced to ensure the balance of completion of sensing tasks in different sub-regions. Finally, extensive evaluations based on real-world datasets show that the proposed method performs well and outperforms other benchmark schemes.

       

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