As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the se...As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.展开更多
We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the e...We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.展开更多
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...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。展开更多
基金the National Natural Science Foundation of China(No.71961028)the Key Research and Development Program of Gansu Province,China(No.22YF7GA171)+3 种基金the University Industry Support Program of Gansu Province,China(No.2023QB-115)the Innovation Fund for Science and Technology-Based Small and Medium Enterprises of Gansu Province,China(No.23CXGA0136)the Traditional Chinese Medicine Industry Innovation Consortium Project of Gansu Province,China(No.22ZD6FA021-5)the Scientific Research Project of the Lanzhou Science and Technology Program,China(No.2018-01-58)。
文摘As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of vehicles.However,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy leakage.Therefore,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user privacy.We propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means algorithm.In the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the client.The IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle trajectory.IKV is compared to numerous clustering methods on three datasets.In the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the experiments.Furthermore,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter variations.These results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.
基金Projectsupported by the National Natural Science Foundation of China(Nos.61673397 and 61976225)the Fundamental Research Funds for the Central Universities of Central South University,China(No.2020zztsl29)。
文摘We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.
文摘基于车联网(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。
