计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (4): 298-305.DOI: 10.3778/j.issn.1002-8331.2301-0145

• 大数据与云计算 • 上一篇    下一篇

协作处理任务的多无人机辅助移动边缘计算

曹慧娟,余庚花,陈志刚   

  1. 中南大学  计算机学院,长沙  410083
  • 出版日期:2024-02-15 发布日期:2024-02-15

Cooperative Task Processing in Multi-UAV Assisted Mobile Edge Computing

CAO Huijuan, YU Genghua, CHEN Zhigang   

  1. School of Computer Science, Central South University, Changsha 410083, China
  • Online:2024-02-15 Published:2024-02-15

摘要: 随着通信技术的发展,移动边缘计算(mobile edge computing, MEC)被认为是处理计算密集型和延迟敏感任务的关键技术,然而在灾难响应、紧急救援等情景中,边缘服务器无法快速部署并提供任务处理服务。无人机(unmanned aerial vehicle, UAV)辅助MEC以其部署简便、机动性强的特点受到关注。但是UAV的计算资源和能量有限,如何进行资源分配成为难题。针对该问题,提出了一种高效利用资源的多UAV协作策略(LUAVs-Cor)。该策略通过多UAV协作的方式动态处理任务,为了充分利用UAV空闲的计算资源,通过搜索最优任务组合的方式确定任务传输策略。此外,通过估计UAV处理能力、任务数量及处理情况优化UAV派遣数量,实现了UAV的动态部署并减少了能量消耗。通过大量仿真实验得出,LUAVs-Cor策略的服务容量提升了约6.8%,UAV整体能耗降低了10.3%。提出的LUAVs-Cor策略中无人机的协作代价较小,实现在较低的能耗代价下为更多用户服务。

关键词: 移动边缘计算, 无人机协作, 无人机派遣, 优化算法

Abstract: Mobile edge computing (MEC) is considered as a key technology to deal with computing intensive and delay sensitive tasks. However, in disaster response, emergency rescue and other scenarios, it is impossible to quickly deploy edge servers and provide services. Unmanned aerial vehicle (UAV) assisted MEC has attracted much attention because of its simple deployment and strong mobility. However, the computing resources and energy of UAV are limited, how to allocate resources becomes a difficult problem. To solve this problem, a strategy of efficient utilization resources with multi-UAV cooperation (LUAVs-Cor) is proposed. This strategy dynamically processes tasks through the multi-UAV cooperation. In order to make full use of computing resources of UAVs, the task transmission strategy is determined by searching for the optimal task combination. In addition, the number of UAVs dispatched is optimized according to the UAV processing capacity, the number of tasks and the processing status of tasks, which achieves dynamic deployment of UAVs and reduces energy consumption. By simulation experiments, it is concluded that the service capacity of LUAVs-Cor strategy has been increased by about 6.8%, and the overall energy consumption of UAV has been reduced by 10.3%. LUAVs-Cor strategy has a low collaboration cost and serves more users at a lower energy cost.

Key words: mobile edge computing, UAVs cooperation, UAV dispatching, optimal algorithm