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考虑信息年龄的无人机辅助智能交通系统计算卸载优化

钟伟锋 黄旭民 康嘉文 谢胜利

钟伟锋, 黄旭民, 康嘉文, 谢胜利. 考虑信息年龄的无人机辅助智能交通系统计算卸载优化[J]. 电子与信息学报, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459
引用本文: 钟伟锋, 黄旭民, 康嘉文, 谢胜利. 考虑信息年龄的无人机辅助智能交通系统计算卸载优化[J]. 电子与信息学报, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459
ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli. Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information[J]. Journal of Electronics & Information Technology, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459
Citation: ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli. Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information[J]. Journal of Electronics & Information Technology, 2024, 46(3): 934-943. doi: 10.11999/JEIT230459

考虑信息年龄的无人机辅助智能交通系统计算卸载优化

doi: 10.11999/JEIT230459
基金项目: 国家自然科学基金(62003099, 62001125, 62102099),广州市基础与应用基础研究项目(2023A04J1704, 2023A04J0340, 2023A04J1699)
详细信息
    作者简介:

    钟伟锋:男,副教授,研究方向为移动边缘计算、智能电网

    黄旭民:男,副教授,研究方向为移动边缘计算、车联网

    康嘉文:男,教授,研究方向为无线通信网络

    谢胜利:男,教授,研究方向为自适应信号处理、物联网信息技术

    通讯作者:

    黄旭民 huangxumin@gdut.edu.cn

  • 中图分类号: TN926

Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information

Funds: The National Natural Science Foundation of China (62003099, 62001125, 62102099), The Guangzhou Basic and Applied Basic Research Project (2023A04J1704, 2023A04J0340, 2023A04J1699)
  • 摘要: 该文考虑无人机(UAV)交通监测与移动边缘计算(MEC)技术结合的智能交通系统。为了保障系统中数据时效性并且降低系统能耗,提出计及信息年龄(AoI)的UAV计算卸载优化方法。首先,建立UAV辅助的MEC系统模型,允许MEC服务器缓存常用的应用程序并为UAV提供计算卸载,以支持UAV执行交通监测任务。通过联合优化UAV任务卸载决策、UAV上下行通信带宽分配以及被卸载任务的计算资源分配,最小化所有UAV与MEC服务器的总能耗,同时满足AoI与资源容量等约束条件。其次,系统能耗最小化问题是混合整数非凸优化问题,因此采用离散化和线性化手段,快速获得问题的近似最优解,并设计离散点生成算法来调节近似误差。最后,仿真结果表明,即使对于大型的非凸问题,所提方法也能够快速得到近似最优解,并且可以在不同的任务场景中满足AoI等约束条件,最大限度降低系统能耗。仿真结果验证了所提方法的有效性。
  • 图  1  智能交通系统示意图

    图  2  不同求解方案下的系统能耗

    图  3  不同能耗系数$ {\epsilon}_{{\mathrm{s}}} $与不同信息年龄上限$ {A^{\max }} $下的系统能耗

    图  4  不同能耗系数$ {\epsilon}_{{\mathrm{s}}} $与不同信息年龄上限$ {A^{\max }} $下在MEC服务器上执行的计算任务数量

    表  1  P2与P1的关系

    P2中的新变量和新函数 与原问题P1的关系
    整数变量$ {z_i} $ 用于等价线性化AoI等式(8),见式(18)–式(20)
    连续变量$ {a_i}t_i^L,{a_i}t_i^M,{a_i}e_i^L,{a_i}e_i^M,{a_i}r_i^M $ 用于定义凸包络,精确松弛5个双线性项$ {a_i}t_i^L,{a_i}t_i^M,{a_i}e_i^L,{a_i}e_i^M,{a_i}r_i^M $,见式(21)、式(22)
    连续变量$ r_i^b,r_i^M $ 用于等价替换$ 1/{b_i} $,$ 1/f_i^M $,见式(23)–式(27)
    连续变量$ {m_i} $ 用于等价松弛式(27),见式(28)
    直线函数$ l_{k,k + 1}^1(r_i^M) $和连续变量$ {F_i} $ 用于近似函数$ 1/r_i^M $,见式(33)–式(35)
    直线函数$ l_{k,k + 1}^2(r_i^M) $ 用于近似函数$ 1/{(r_i^M)^2} $,见式(36)
    直线函数$ l_{k,k + 1}^3(r_i^b) $和连续变量$ {B_i} $ 用于近似函数$ 1/r_i^b $,见式(37)、式(38)
    下载: 导出CSV
    算法1 离散点生成算法
     输入:$ \delta $, $ f(r) $, $ r \in [{r_{\min }},{r_{\max }}] $, $ {r_1} = {r_{\min }} $, $ R = \{ {r_1}\} $, $ i = 1 $
     输出:$ R $
     1. Repeat
     2.  令$ {r_L} = {r_i} $, $ {r_R} = {r_{\max }} $, $ E = \{ {r_L},{r_R}\} $
     3.  Repeat
     4.   $ {r^*} = {f'^{ - 1}}\left( {\frac{{f({r_R}) - f({r_L})}}{{{r_R} - {r_L}}}} \right) $
     5.   $ {\varDelta ^*} = {l_{L,R}}({r^*}) - f({r^*}) $
     6.   获得$ \ell $,使得$ E $中第$ \ell $个元素$ {E_\ell } = {r_R} $
     7.   If $ {\varDelta ^*} > \delta $ Then
     8.   $ {r_R} \leftarrow ({E_\ell } + {E_{\ell - 1}})/2 $, $ E \leftarrow E \cup \{ {r_R}\} $
     9.   ElseIf $ {\varDelta ^*} < \delta - {\delta _0} $ Then
     10.   $ {r_R} \leftarrow ({E_\ell } + {E_{\ell + 1}})/2 $, $ E \leftarrow E \cup \{ {r_R}\} $
     11.   EndIf
     12.   对$ E = \{ {E_1},{E_2}, \cdots \} $排序,使得$ {E_1} < {E_2} < \cdots $
     13.  Until $ \delta - {\delta _0} \le {\varDelta ^*} \le \delta $
     14.  $ R \leftarrow R \cup \{ {r_R}\} $, $ i \leftarrow i + 1 $
     15. Until $ {r_R} = {r_{\max }} $
    下载: 导出CSV

    表  2  不同求解方案的运算时间

    UAV 求解器 本方法
    数量 BNB(1 h) δ=0.05 δ=0.1 δ=0.2
    I=10 11 min 9 s 5 s 4 s
    I=20 1 h 28 s 17 s 10 s
    I=30 1 h 78 s 50 s 31 s
    下载: 导出CSV
  • [1] HU Jinna, CHEN Chen, CAI Lin, et al. UAV-assisted vehicular edge computing for the 6G Internet of vehicles: Architecture, intelligence, and challenges[J]. IEEE Communications Standards Magazine, 2021, 5(2): 12–18. doi: 10.1109/MCOMSTD.001.2000017.
    [2] LIU Jianyu, WU Jing, and LIU Mingyu. UAV monitoring and forecasting model in intelligent traffic oriented applications[J]. Computer Communications, 2020, 153: 499–506. doi: 10.1016/j.comcom.2020.02.009.
    [3] 胡硕, 王洁, 孙妍, 等. 无人机视角下的多车辆跟踪算法研究[J]. 智能系统学报, 2022, 17(4): 798–805. doi: 10.11992/tis.202108014.

    HU Shuo, WANG Jie, SUN Yan, et al. Research on multi-vehicle tracking algorithm from the perspective of UAV[J]. CAAI Transactions on Intelligent Systems, 2022, 17(4): 798–805. doi: 10.11992/tis.202108014.
    [4] JIANG Yingying, MIAO Yiming, ALZAHRANI B, et al. Ultra large-scale crowd monitoring system architecture and design issues[J]. IEEE Internet of Things Journal, 2021, 8(13): 10356–10366. doi: 10.1109/JIOT.2021.3076257.
    [5] 李新民, 尹宝林, 魏李莉, 等. 强化学习无人机通信系统中的信息年龄优化[J]. 电子科技大学学报, 2022, 51(2): 213–218. doi: 10.12178/1001-0548.2021128.

    LI Xinmin, YIN Baolin, WEI Lili, et al. Reinforcement learning-based age of information optimization in UAV-enabled communication system[J]. Journal of University of Electronic Science and Technology of China, 2022, 51(2): 213–218. doi: 10.12178/1001-0548.2021128.
    [6] FENG Jialiang and GONG Jie. Joint detection and computation offloading with age of information in mobile edge networks[J]. IEEE Transactions on Network Science and Engineering, 2023, 10(3): 1417–1430. doi: 10.1109/TNSE.2022.3208857.
    [7] 敬乐天, 贾向东, 曹肖攀, 等. 基于DRL的无人机辅助边缘计算服务质量优化[J]. 信号处理, 2022, 38(6): 1316–1324. doi: 10.16798/j.issn.1003-0530.2022.06.018.

    JING Letian, JIA Xiangdong, CAO Xiaopan, et al. Quality of service optimization in UAV-assisted edge computing based on deep reinforcement learning[J]. Journal of Signal Processing, 2022, 38(6): 1316–1324. doi: 10.16798/j.issn.1003-0530.2022.06.018.
    [8] HUANG Jiwei, GAO Han, WAN Shaohua, et al. AoI-aware energy control and computation offloading for industrial IoT[J]. Future Generation Computer Systems, 2023, 139: 29–37. doi: 10.1016/j.future.2022.09.007.
    [9] DIAO Xianbang, GUAN Xinrong, and CAI Yueming. Joint offloading and trajectory optimization for complex status updates in UAV-assisted Internet of things[J]. IEEE Internet of Things Journal, 2022, 9(23): 23881–23896. doi: 10.1109/JIQT.2022.3188608.
    [10] SUN Mengying, XU Xiaodong, QIN Xiaoqi, et al. AoI-energy-aware UAV-assisted data collection for IoT networks: A deep reinforcement learning method[J]. IEEE Internet of Things Journal, 2021, 8(24): 17275–17289. doi: 10.1109/JIQT.2021.3078701.
    [11] 刘玲珊, 熊轲, 张煜, 等. 信息年龄受限下最小化无人机辅助无线供能网络的能耗: 一种基于DQN的方法[J]. 南京大学学报:自然科学, 2021, 57(5): 847–856. doi: 10.13232/j.cnki.jnju.2021.05.015.

    LIU Lingshan, XIONG Ke, ZHANG Yu, et al. Energy minimization in UAV-assisted wireless powered sensor networks with AoI constraints: A DQN-based approach[J]. Journal of Nanjing University:Natural Science, 2021, 57(5): 847–856. doi: 10.13232/j.cnki.jnju.2021.05.015.
    [12] CHEN Xianfu, WU Celimuge, CHEN Tao, et al. Age of information-aware resource management in UAV-assisted mobile-edge computing systems[C]. 2020 IEEE Global Communications Conference, Taipei, China, 2020: 1–6. doi: 10.1109/GLOBECOM42002.2020.9322632.
    [13] ZHENG Guangyuan, XU Chen, WEN Miaowen, et al. Service caching based aerial cooperative computing and resource allocation in multi-UAV enabled MEC systems[J]. IEEE Transactions on Vehicular Technology, 2022, 71(10): 10934–10947. doi: 10.1109/TVT.2022.3183577.
    [14] ZHOU Ruiting, WU Xiaoyi, TAN Haisheng, et al. Two time-scale joint service caching and task offloading for UAV-assisted mobile edge computing[C]. 2022 IEEE Conference on Computer Communications, London, United Kingdom, 2022: 1189–1198. doi: 10.1109/INFOCOM48880.2022.9796714.
    [15] PENG Haixia and SHEN X S. DDPG-based resource management for MEC/UAV-assisted vehicular networks[C]. The 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, Canada, 2020: 1–6. doi: 10.1109/VTC2020-Fall49728.2020.9348633.
    [16] WANG Yuntao, CHEN Weiwei, LUAN T H. , et al. Task offloading for post-disaster rescue in unmanned aerial vehicles networks[J]. IEEE/ACM Transactions on Networking, 2022, 30(4): 1525–1539. doi: 10.1109/TNET.2022.3140796.
    [17] JIANG Xu, SHENG Min, ZHAO Nan, et al. Green UAV communications for 6G: A survey[J]. Chinese Journal of Aeronautics, 2022, 35(9): 19–34. doi: 10.1016/j.cja.2021.04.025.
    [18] 刘漳辉, 郑鸿强, 张建山, 等. 多无人机使能移动边缘计算系统中的计算卸载与部署优化[J]. 计算机科学, 2022, 49(6A): 619–627. doi: 10.11896/jsjkx.210600165.

    LIU Zhanghui, ZHENG Hongqiang, ZHANG Jianshan, et al. Computation offloading and deployment optimization in multi-UAV-enabled mobile edge computing systems[J]. Computer Science, 2022, 49(6A): 619–627. doi: 10.11896/jsjkx.210600165.
    [19] HOANG L T, NGUYEN C T, LI Peng, et al. Joint uplink and downlink resource allocation for UAV-enabled MEC networks under user mobility[C]. 2022 IEEE International Conference on Communications Workshops, Seoul, Korea, 2022: 1059–1064. doi: 10.1109/ICCWorkshops53468.2022.9814687.
    [20] EL HABER E, ALAMEDDINE H A, ASSI C, et al. UAV-aided ultra-reliable low-latency computation offloading in future IoT networks[J]. IEEE Transactions on Communications, 2021, 69(10): 6838–6851. doi: 10.1109/TCOMM.2021.309 6559.
    [21] 卢为党, 詹悦者, 花俏枝, 等. 基于无人机无线能量传输的边缘计算系统能耗优化方法研究[J]. 电子与信息学报, 2022, 44(3): 899–905. doi: 10.11999/JEIT211314.

    LU Weidang, ZHAN Yuezhe, HUA Qiaozhi, et al. Energy consumption optimization in UAV wireless power transfer based mobile edge computing system[J]. Journal of Electronics & Information Technology, 2022, 44(3): 899–905. doi: 10.11999/JEIT211314.
    [22] BOUKOUVALA F, MISENER R, and FLOUDAS C A. Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO[J]. European Journal of Operational Research, 2016, 252(3): 701–727. doi: 10.1016/j.ejor.2015.12.018.
    [23] MCCORMICK G P. Computability of global solutions to factorable nonconvex programs: Part I—Convex underestimating problems[J]. Mathematical Programming, 1976, 10(1): 147–175. doi: 10.1007/BF01580665.
    [24] ZHONG Weifeng, XIE Shengli, XIE Kan, et al. Cooperative P2P energy trading in active distribution networks: An MILP-based Nash bargaining solution[J]. IEEE Transactions on Smart Grid, 2021, 12(2): 1264–1276. doi: 10.1109/TSG.2020. 3031013.
    [25] BOYD S and VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004. doi: 10.1017/cbo9780511804441.
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出版历程
  • 收稿日期:  2023-05-19
  • 修回日期:  2023-09-27
  • 网络出版日期:  2023-10-16
  • 刊出日期:  2024-03-27

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