多无人机辅助边缘计算下的路径规划研究
Research on Path Planning in Multi-UAV Assisted Edge Computing
DOI: 10.12677/PM.2024.141024, PDF,   
作者: 高煜杰, 韩 韧, 张 生*:上海理工大学光电信息与计算机工程学院,上海
关键词: 边缘计算无人机合作MEC UAV Collaboration
摘要: 无人机(Unmanned Aerial Vehicles, UAVs)越来越多地被用作移动边缘计算(Mobile Edge Computing, MEC)中的移动服务器,并被广泛地集成到现代物联网应用中。本文在现有研究的无人机路径规划中,引入考虑无人机的能量消耗和无人机之间的合作,并用于改善提供给终端用户的服务质量(Quality of Service, QoS)。通过加深考虑无人机在飞行过程中以及处理终端用户的能量消耗以及多无人机的协作能力,提出相应的模型改进,通过观察无人机的飞行路径,验证所提算法的有效性和低耗性,并证明无人机之间团队合作的有效性。实验结果表明,所提模型在合作时更能降低总体能耗,并增强模拟环境的真实性。
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly being utilized as mobile servers in Mobile Edge Computing (MEC) and widely integrated into modern Internet of Things (IoT) applications. In this paper, we introduce a novel approach to UAV path planning that considers both the energy con-sumption of UAVs and their collaboration, aiming to improve the Quality of Service (QoS) provided to end users. By incorporating the energy consumption during UAV flight and the collaborative capabilities of multiple UAVs, we propose model enhancements. The effectiveness and efficiency of the proposed algorithms are validated through the observation of UAV flight paths, demonstrating the effectiveness of team collaboration among UAVs. Experimental results show that the proposed model significantly reduces overall energy consumption when UAVs cooperate, while enhancing the realism of the simulated environment.
文章引用:高煜杰, 韩韧, 张生. 多无人机辅助边缘计算下的路径规划研究[J]. 理论数学, 2024, 14(1): 224-234. https://doi.org/10.12677/PM.2024.141024

参考文献

[1] Dinh, H.T., Lee, C., Niyato, D. and Wang, P. (2013) A Survey of Mobile Cloud Computing: Architecture, Applications, and Approaches. Wireless Communications and Mobile Computing, 13, 1587-1611. [Google Scholar] [CrossRef
[2] Mach, P. and Becvar, Z. (2017) Mobile Edge Computing: A Survey on Ar-chitecture and Computation Offloading. IEEE Communications Surveys & Tutorials, 19, 1628-1656. [Google Scholar] [CrossRef
[3] Abbas, N., Zhang, Y. and Taherkordi, A. (2018) Mobile Edge Computing: A Survey. IEEE Internet of Things Journal, 5, 450-465. [Google Scholar] [CrossRef
[4] Mao, Y., You, C., Zhang, J., Huang, K. and Letaief, K.B. (2017) A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials, 19, 2322-2358. [Google Scholar] [CrossRef
[5] Feng, G., Wang, C. and Li, B. (2019) UAV-Assisted Wire-less Relay Networks for Mobile Offloading and Trajectory Optimization. Peer-to-Peer Networking and Applications, 12, 1820-1834. [Google Scholar] [CrossRef
[6] Hu, Q., Cai, Y., Yu, G., Qin, Z., Zhao, M. and Li, G.Y. (2019) Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems. IEEE Internet of Things Journal, 6, 1879-1892. [Google Scholar] [CrossRef
[7] Diao, X., Zheng, J., Cai, Y., Wu, Y. and Anpalagan, A. (2019) Fair Data Allocation and Trajectory Optimization for UAV-Assisted Mobile Edge Computing. IEEE Communications Letters, 23, 2357-2361. [Google Scholar] [CrossRef
[8] Cheng, N., Lyu, F. and Quan, W. (2019) Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach. IEEE Journal on Se-lected Areas in Communications, 37, 1117-1129. [Google Scholar] [CrossRef
[9] Li, J., Liu, Q., Wu, P., Shu, F. and Jin, S. (2018) Task Offload-ing for UAV-Based Mobile Edge Computing via Deep Reinforcement Learning. IEEE/CIC International Conference on Communications in China (ICCC), Beijing, 16-18 August 2018, 798-802. [Google Scholar] [CrossRef
[10] Xiong, J., Guo, H. and Liu, J. (2019) Task Offloading in UAV-Aided Edge Computing: Bit Allocation and Trajectory Optimization. IEEE Communications Letters, 23, 538-541. [Google Scholar] [CrossRef
[11] Selim, M.M., Rihan, M. and Yang, Y. (2020) Optimal Task Partitioning, Bit Allocation and Trajectory for D2D-Assisted UAV-MEC Systems. Peer-to-Peer Networking and Applications, 14, 215-224. [Google Scholar] [CrossRef
[12] Zhang, J., Zhou, L., Tang, Q., et al. (2019) Stochastic Computa-tion Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing. IEEE Internet of Things Journal, 6, 3688-3699. [Google Scholar] [CrossRef
[13] Xiong, J., Guo, H., Liu, J., et al. (2019) Collabo-rative Computation Offloading at UAV-Enhanced Edge. 2019 IEEE Global Communications Conference, Waikoloa, 9-13 December 2019, 1-6. [Google Scholar] [CrossRef
[14] Chang, H., Chen, Y.C., Zhang, B.C. and Doermann, D. (2021) Multi-UAV Mobile Edge Computing and Path Planning Platform Based on Reinforcement Learning. IEEE Transactions on Emerging Topics in Computational Intelligence, 6, 489-498.
[15] Jeong, S., Simeone, O. and Kang, J. (2018) Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning. IEEE Transactions on Vehicular Technology, 67, 2049-2063. [Google Scholar] [CrossRef
[16] Zhang, J., et al. (2019) Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing. IEEE Internet of Things Journal, 6, 3688-3699. [Google Scholar] [CrossRef
[17] Zeng, Y. and Zhang, R. (2017) Energy-Efficient UAV Commu-nication with Trajectory Optimization. IEEE Transactions on Wireless Communications, 16, 3747-3760. [Google Scholar] [CrossRef
[18] Zhang, B., Liu, W., Mao, Z., Liu, J. and Shen, L. (2014) Coop-erative and Geo-Metric Learning Algorithm (CGLA) for Path Planning of UAVs with Limited Information. Automatica, 50, 809-820. [Google Scholar] [CrossRef
[19] Zhang, B.C., Liu, W.Q., Mao, Z.L., Liu, J.Z. and Shen, L.L. (2014) Cooperative and Geometric Learning Algorithm (CGLA) for Path Planning of UAVs with Limited Information. Automatica, 50, 809-820.