摘要
基于车联网(IoV,Internet of vehicles)用户的群智感知网络具有节点覆盖广泛、数据全面及时等优点。该技术实现的一大难点在于,如何通过充分挖掘和利用车联网用户的信息(如用户地理位置等)来选择合适的感知任务参与者,以合理地进行任务分配,进而提高感知任务的完成质量和任务发布者收益。为此提出了一种结合车辆用户轨迹特征与组合多臂赌博机(CMAB,combinatorial multi-armed bandits)算法的群智感知用户任务分配机制。首先,基于用户历史行车轨迹的相似程度,将用户聚类。然后,利用CMAB模型,将轨迹聚类信息作为用户任务分配的依据,求解最佳工作者组合。最后,利用真实出租车轨迹数据集对上述算法进行了验证。实验结果表明,考虑轨迹特征信息的任务分配算法具有更高的准确率,并能使任务发布者获得高收益。同时,所选出的工作者集合有相近的行车轨迹,对于同一地点的任务具有高的完成质量,能有效提高感知数据质量和任务发布者收益,适用于实际应用场景。
The crowdsening network based on Internet of vehicles(IoV)users has the advantages of extensive node coverage,complete and timely data.A major difficulty in the realization of this technology lies in how to fully mine and use the information of connected vehicular users(such as the user's geographic location,etc.)to select appropriate perception task participants,so as to carry out reasonable task assignments,thereby improving the completion quality of perception tasks and task publisher’s benefits.To solve the above problems,a task allocation method combining the trajectory features and the combinatorial multi-armed bandits(CMAB)algorithm was proposed.Firstly,users were clustered based on the similarity of their historical driving trajectories.Then,the CMAB model was adopted so that the trajectory clustering information could be used as the basis for deciding the optimal worker combination.Finally,the proposed algorithm was verified using the real taxi-trajectory dataset.The experimental results show that the task assignment algorithm considering the trajectory feature information has a higher accuracy and higher profit.At the same time,the selected workers have a high completion quality for tasks at the same location,and can effectively improve the quality of perceived data and the benefits of task publishers,which is suitable for practical application scenarios.
作者
冯心欣
郭丹颖
柳泽烽
郑海峰
FENG Xinxin;GUO Danying;LIU Zefeng;ZHENG Haifeng(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China)
出处
《物联网学报》
2021年第3期86-96,共11页
Chinese Journal on Internet of Things
基金
国家自然科学基金资助项目(No.61601126,No.61971139)。
