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.
