无人机任务卸载与充电协同优化  

Joint Optimization of UAV Task Offloading and Charging

作　　者：何涵 刘鹏[1] 赵亮 王青山[3] HE Han;LIU Peng;ZHAO Liang;WANG Qingshan(School of Computer Sci.and Technol.,Hangzhou Dianzi Univ.,Hangzhou 310018,China;School of Computer Sci.,Shenyang Aerospace Univ.,Shenyang 110136,China;School of Mathematics,Hefei Univ.of Technol.,Hefei 230009,China)

机构地区：[1]杭州电子科技大学计算机学院,浙江杭州310018 [2]沈阳航空航天大学计算机学院,辽宁沈阳110136 [3]合肥工业大学数学学院,安徽合肥230009

出　　处：《工程科学与技术》2024年第1期99-109,共11页Advanced Engineering Sciences

基　　金：国家自然科学基金面上项目(62172134)。

摘　　要：在野外恶劣环境应用中,可以使用具有灵活性和便捷性的无人机(UAV),通过无线数据传输辅助携带用户任务到边缘服务器。然而,UAV飞行平台难以提供长时间的任务卸载服务,大大限制了其应用前景。本文研究了在移动边缘计算环境中,如何有效整合UAV的任务卸载和充电调度。首先,构建了一个新的应用模型,该模型协同处理UAV的任务卸载调度和自身充电需求,并在UAV辅助任务卸载应用场景中加入了若干个无线充电平台。其次,考虑了用户任务的价值和UAV的充电需求,以在时延敏感和能量约束的条件下优化UAV辅助用户设备进行任务卸载的收益。最后,采用深度强化学习算法,对深度Q网络(DQN)进行调优后形成Fixed DQN算法,以有效处理模型中的大规模状态动作搜索空间问题。本文以UAV仅作为任务载体并考虑其自主充电需求为前提,通过在一个半径为3000 m、含有11个节点的区域验证Fixed DQN算法的可行性;并在不同用户节点数量、充电节点数量及服务时间条件下,通过与蚁群算法、遗传算法和DQN算法的对比实验评估其性能。实验结果表明:本文提出的Fixed DQN算法在所有测试条件下均显著优于蚁群算法、遗传算法和DQN算法,特别是在节点数量增加和服务时间延长的情景中;此外,Fixed DQN算法相对于DQN算法的性能提升突显了深度强化学习在参数调优方面的有效性。研究结果证实了Fixed DQN算法在解决UAV任务卸载和充电调度问题中的高效性和调参策略的重要性。In applications of harsh outdoor environments,unmanned aerial vehicles(UAVs),known for their flexibility and convenience,were utilized to assist in carrying user tasks to edge servers through wireless data transmission.However,it was found that UAV flight platforms struggled to provide long-duration task offloading services,significantly limiting their application prospects.This study investigated how to effectively integrate UAV task offloading and charging scheduling in a mobile edge computing environment.Firstly,a new application model was constructed,which cohesively managed UAV task offloading scheduling and its own charging needs,incorporating several wireless charging platforms into the UAV-assisted task offloading application scenario.These platforms enabled UAVs to autonomously recharge during task execution,providing automated charging services without the need for human intervention.UAVs independently decided whether to proceed to the nearest charging node for power replenishment based on their current power level and upcoming task offloading plans.However,opting to recharge at a charging station not only incurred additional time and energy consumption from cruising altitude to the charging station but also required consideration of the time cost during the charging process and its impact on overall task scheduling.When UAVs decided to recharge,additional time and effort were needed to descend from cruising altitude to the charging node.Secondly,the value of user tasks and UAV charging needs were considered to optimize the benefits of UAV-assisted user device task offloading under conditions sensitive to delay and energy constraints.This involved not only optimizing the UAV’s flight path and task allocation but also its charging schedule,ensuring sufficient charging and efficient operation while executing tasks.Such a cooperative scheduling strategy enabled UAVs to maximize the processing of user tasks while maintaining necessary operational energy,thereby enhancing the performance of the entire mobi

关 键 词：边缘计算 无人机 任务卸载 强化学习 充电调度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]