摘要
近年来,随着自主无人飞行器技术和框架的成熟,无人机的研究和应用日益广泛。城市将部署大量的无人飞行器(UAV),用于承担通讯协助、环境监测和道路安全巡检等任务。对于那些计算密集型的任务,由于无人飞行器的电池寿命和计算资源有限,无人飞行器执行时会导致效率降低和不可持续。为此,论文采用协同移动边缘计算,可以降低能量消耗和任务执行延迟。首先,将无人飞行器的计算卸载策略建模为能耗和延迟的优化问题;其次,通过凸优化得到最节能的卸载数据速率;最后,通过基于模拟退火的粒子群优化(SAPSO)得到满足延迟约束的最优数据分配方案。仿真结果验证了所提出的无人飞行器计算卸载策略的有效性。
In recent years,UAV research and applications have become increasingly widespread as autonomous UAV technol-ogy and frameworks have matured.Cities will deploy a large number of unmanned aerial vehicles(UAV)to undertake tasks such as communication assistance,environmental monitoring and road safety patrols.For those tasks that are computationally intensive,UAV execution can lead to reduced efficiency and unsustainability due to the limited battery life and computational resources of UAVs.For this reason,this paper uses collaborative mobile edge computing,which can reduce energy consumption and task execu-tion latency.Firstly,the computational offloading strategy of the UAV is modelled as an optimization problem in terms of energy con-sumption and latency.Secondly,the most energy-efficient offloading data rate is obtained by convex optimization.Finally,the opti-mal data allocation scheme satisfying the latency constraint is obtained by simulated annealing-based particle swarm optimization(SAPSO).The simulation results validate the effectiveness of the proposed computational offloading strategy for unmanned aerial ve-hicles.
作者
李立
王司令
周洋
王恒
LI Li;WANG Siling;ZHOU Yang;WANG Heng(School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710000;School of Defence Science&Technology,Xi'an Technological University,Xi'an 710000)
出处
《计算机与数字工程》
2023年第6期1270-1275,共6页
Computer & Digital Engineering
基金
国家级重点研发计划(编号:2022YFE0123400)
陕西省技术创新引导专项(基金)项目(编号:2022QFY01-16)资助。
关键词
边缘计算
无人机
任务卸载
模型
edge computing
unmanned aerial vehicle
task offloading
model
