Aerial edge computing for 6G

doi:10.19682/j.cnki.1005-8885.2022.2006

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (1): 50-63.doi: 10.19682/j.cnki.1005-8885.2022.2006

Aerial edge computing for 6G

Mao Sun, Zhang Yan

1. College of Computer Science, Sichuan Normal University, Chengdu 610101, China 2. Department of Informatics, University of Oslo, Oslo 0316, Norway

收稿日期:2021-12-20 修回日期:2022-01-16 接受日期:2022-01-22 出版日期:2022-02-26 发布日期:2022-02-28
通讯作者: Corresponding author: Zhang Yan

Aerial edge computing for 6G

Mao Sun, Zhang Yan

1. College of Computer Science, Sichuan Normal University, Chengdu 610101, China 2. Department of Informatics, University of Oslo, Oslo 0316, Norway

Received:2021-12-20 Revised:2022-01-16 Accepted:2022-01-22 Online:2022-02-26 Published:2022-02-28
Contact: Corresponding author: Zhang Yan

摘要/Abstract

摘要： In the 6th generation mobile communication system (6G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things (IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle (UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6G are elaborated to provide some guidance for future research.

关键词: mobile edge computing, unmanned aerial vehicle, the 6th generation mobile communication system

Abstract: In the 6th generation mobile communication system (6G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things (IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle (UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6G are elaborated to provide some guidance for future research.

Key words: mobile edge computing, unmanned aerial vehicle, the 6th generation mobile communication system

中图分类号:

TN915.9

Mao Sun, Zhang Yan. Aerial edge computing for 6G[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 50-63.

参考文献

References

[1] CHENG N, XU W C, SHI W S, et al. Air-ground integrated mobile edge networks: Architecture, challenges, and opportunities. IEEE Communications Magazine, 2018, 56(8): 26 - 32.

[2] SAAD W, BENNIS M, CHEN M. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 2020, 34(3): 134 - 142.

[3] LETAIEF K B, CHEN W, SHI Y M, et al. The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 2019, 57(8): 84 - 90.

[4] LU Y L, MAHARJAN S, ZHANG Y. Adaptive edge association for wireless digital twin networks in 6G. IEEE Internet of Things Journal, 2021, 8(22): 16219 - 16230.

[5] RODRIGUES T K, LIU J J, KATO N. Application of cybertwin for offloading in mobile multiaccess edge computing for 6G networks. IEEE Internet of Things Journal, 2021, 8(22): 16231 - 16242.

[6] KHAN A R, OTHMAN M, MADANI S A, et al. A survey of mobile cloud computing application models. IEEE Communications Surveys and Tutorials, 2014, 16(1): 393 - 413.

[7] ZHOU Z Y, FENG J H, TAN L, et al. An air-ground integration approach for mobile edge computing in IoT. IEEE Communications Magazine, 2018, 56(8): 40 - 47.

[8] MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys and Tutorials, 2017, 19(4): 2322 - 2358.

[9] DAO N N, PHAM Q V, TU N H, et al. Survey on aerial radio access networks: Toward a comprehensive 6G access infrastructure. IEEE Communications Surveys and Tutorials, 2021, 23(2): 1193 - 1225.

[10] AGGARWAL S, KUMAR N, TANWAR S. Blockchain-envisioned UAV communication using 6G networks: Open issues, use cases, and future directions. IEEE Internet of Things Journal, 2021, 8(7): 5416 - 5441.

[11] CHIARAVIGLIO L, BLEFARI-MELAZZI N, LIU W, et al. Bringing 5G into rural and low-income areas: Is it feasible? IEEE Communications Standards Magazine, 2017, 1(3): 50 - 57.

[12] ZHOU F H, HU R Q, LI Z, et al. Mobile edge computing in unmanned aerial vehicle networks. IEEE Wireless Communications, 2020, 27(1): 140 - 146.

[13] LI B, FEI Z S, ZHANG Y. UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet of Things Journal, 2019, 6(2): 2241 - 2263.

[14] YANG Z H, PAN C H, WANG K Z, et al. Energy efficient resource allocation in UAV-enabled mobile edge computing networks. IEEE Transactions on Wireless Communications, 2019, 18(9): 4576 - 4589.

[15] MAO S, HE S F, WU J S. Joint UAV position optimization and resource scheduling in space-air-ground integrated networks with mixed cloud-edge computing. IEEE Systems Journal, 2021, 15(3): 3992 - 4002.

[16] JEONG S, SIMEONE O, KANG J. Mobile edge computing via a UAV-mounted cloudlet: Optimization of bit allocation and path planning. IEEE Transactions on Vehicular Technology, 2018, 67(3): 2049 - 2063.

[17] YIN S X, ZHAO S, ZHAO Y F, et al. Intelligent trajectory design in UAV-aided communications with reinforcement learning. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8227 - 8231.

[18] FERDOWSI A, ABD-ELMAGID M A, SAAD W, et al. Neural combinatorial deep reinforcement learning for age-optimal joint trajectory and scheduling design in UAV-assisted networks. IEEE Journal on Selected Areas in Communications, 2021, 39(5): 1250 - 1265.

[19] QU Y B, DAI H P, WANG H C, et al. Service provisioning for UAV-enabled mobile edge computing. IEEE Journal on Selected Areas in Communications, 2021, 39(11): 3287 - 3305.

[20] ANOKYE S, AYEPAH-MENSAH D, SEID A M, et al. Deep reinforcement learning-based mobility-aware UAV content caching and placement in mobile edge networks. IEEE Systems Journal, 2021, Early Access Article.

[21] ZHOU F S, WANG N, LUO G Y, et al. Edge caching in multi-UAV-enabled radio access networks: 3D modeling and spectral efficiency optimization. IEEE Transactions on Signal and Information Processing over Networks, 2020, 6: 329 - 341.

[22] ABBAS N, ZHANG Y, TAHERKORDI A, et al. Mobile edge computing: A survey. IEEE Internet of Things Journal, 2018, 5(1): 450 - 465.

[23] YAO J J, HAN T, ANSARI N. On mobile edge caching. IEEE Communications Surveys and Tutorials, 2019, 21(3): 2525 - 2553.

[24] ZHANG J, ZHOU L, TANG Q, et al. Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing. IEEE Internet of Things Journal, 2019, 6(2): 3688 - 3699.

[25] WU H Q, LYU F, ZHOU C H, et al. Optimal UAV caching and trajectory in aerial-assisted vehicular networks: A learning-based approach. IEEE Journal on Selected Areas in Communications, 2020, 38(12): 2783 - 2797.

[26] MEI H B, WANG K Z, ZHOU D D, et al. Joint trajectory-task-cache optimization in UAV-enabled mobile edge networks for cyber-physical system. IEEE Access, 2019, 7: 156476 - 156488.

[27] GRANT M, BOYD S. CVX: Matlab software for disciplined convex programming, version 2. 1. CVX Research Inc, 2014.

[28] HUA M, WANG Y, LI C G, et al. UAV-aided mobile edge computing systems with one by one access scheme. IEEE Transactions on Green Communications and Networking, 2019, 3(3): 664 - 678.

[29] HU Q Y, CAI Y L, YU G D, et al. Joint offloading and trajectory design for UAV-enabled mobile edge computing systems. IEEE Internet of Things Journal, 2019, 6(2): 1879 - 1892.

[30] WANG Y T, SU Z, ZHANG N, et al. Learning in the air: Secure federated learning for UAV-assisted crowdsensing. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1055 - 1069.

Aerial edge computing for 6G

Aerial edge computing for 6G

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价