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

ZHONG Weifeng; HUANG Xumin; KANG Jiawen; XIE Shengli

doi:10.11999/JEIT230459

Volume 46 Issue 3

Mar. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 934-943

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

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

PDF( 1567 KB)

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

doi: 10.11999/JEIT230459

School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (62003099, 62001125, 62102099), The Guangzhou Basic and Applied Basic Research Project (2023A04J1704, 2023A04J0340, 2023A04J1699)

Received Date: 2023-05-19
Rev Recd Date: 2023-09-27

Available Online: 2023-10-16

Publish Date: 2024-03-27

Abstract

Abstract

The intelligent transportation system that combines Unmanned Aerial Vehicle (UAV) based traffic monitoring and Mobile Edge Computing (MEC) technologies is considered. In order to ensure the timeliness of data and reduce energy consumption in the system, a UAV computation offloading optimization method considering Age of Information (AoI) is proposed. Firstly, the UAV-assisted MEC system model is established to allow the MEC server to cache commonly used applications and provide UAVs with computation offloading, which supports the UAVs to perform traffic monitoring tasks. By jointly optimizing UAV task offloading decisions, UAV uplink and downlink communication bandwidth allocation, and computing resource allocation of offloaded tasks, the total energy consumption of all UAVs and the MEC server is minimized while satisfying constraints of AoI and resource capacities. Secondly, the system energy consumption minimizing problem is a mixed-integer non-convex optimization problem. Discretization and linearization methods are adopted to quickly obtain an approximately optimal solution to the problem. A discrete point generation algorithm is designed to adjust the approximation error. Finally, simulation results show that even for large non-convex problems, the proposed method can quickly obtain approximately optimal solutions and can satisfy constraints of AoI in different task scenarios, minimizing the system energy consumption as much as possible. The simulation results verify the effectiveness of the proposed method.
- Mobile Edge Computing (MEC),
- Intelligent transportation system,
- Unmanned Aerial Vehicle (UAV),
- Age of Information (AoI),
- Energy consumption optimization

FullText(HTML)

References(25)

References

[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.