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A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC (2404.04597v1)

Published 6 Apr 2024 in eess.SY and cs.SY

Abstract: Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear programming (MINLP), we propose a two time-scale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the utility of the system, the QoE of MDs, and the revenue of MEC servers.

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References (43)
  1. B. Hou, S. Yang, F. A. Kuipers, L. Jiao, and X. Fu, “EAVS: Edge-assisted adaptive video streaming with fine-grained serverless pipelines,” in Proc. of IEEE INFOCOM, 2023.
  2. Z. Xu, Z. Yuan, W. Liang, D. Liu, W. Xu, H. Dai, Q. Xia, and P. Zhou, “Learning-driven algorithms for responsive ar offloading with non-deterministic rewards in metaverse-enabled MEC,” IEEE/ACM Trans. Netw., pp. 1–17, 2023.
  3. H. Dai, J. Yu, M. Li, W. Wang, A. X. Liu, J. Ma, L. Qi, and G. Chen, “Bloom filter with noisy coding framework for multi-set membership testing,” IEEE Trans. Knowl. Data Eng., 2022.
  4. Y. Li, T. Zeng, X. Zhang, J. Duan, and C. Wu, “Tapfinger: Task placement and fine-grained resource allocation for edge machine learning,” in Proc. of IEEE INFOCOM, 2023.
  5. Z. Sun, G. Sun, Y. Liu, J. Wang, and D. Cao, “Bargain-match: A game theoretical approach for resource allocation and task offloading in vehicular edge computing networks,” IEEE Trans. Mob. Comput., pp. 1–18, 2023.
  6. Y. Qu, H. Sun, C. Dong, J. Kang, H. Dai, Q. Wu, and S. Guo, “Elastic collaborative edge intelligence for uav swarm: Architecture, challenges, and opportunities,” IEEE Commun. Mag., 2023.
  7. C. Dong, Y. Shen, Y. Qu, K. Wang, J. Zheng, Q. Wu, and F. Wu, “Uavs as an intelligent service: Boosting edge intelligence for air-ground integrated networks,” IEEE Netw., vol. 35, no. 4, pp. 167–175, 2021.
  8. J. Li, G. Sun, L. Duan, and Q. Wu, “Multi-objective optimization for UAV swarm-assisted IoT with virtual antenna arrays,” IEEE Trans. Mob. Comput., 2023.
  9. Y. Qu, H. Dai, H. Wang, C. Dong, F. Wu, S. Guo, and Q. Wu, “Service provisioning for UAV-enabled mobile edge computing,” IEEE J. Sel. Areas Commun., vol. 39, no. 11, pp. 3287–3305, 2021.
  10. Z. Yu, Y. Gong, S. Gong, and Y. Guo, “Joint task offloading and resource allocation in UAV-enabled mobile edge computing,” IEEE Internet Things J., vol. 7, no. 4, pp. 3147–3159, 2020.
  11. Y. Ding, Y. Feng, W. Lu, S. Zheng, N. Zhao, L. Meng, A. Nallanathan, and X. Yang, “Online edge learning offloading and resource management for UAV-assisted MEC secure communications,” IEEE J. Sel. Top. Signal Process., vol. 17, no. 1, pp. 54–65, 2023.
  12. Y. Nie, J. Zhao, F. Gao, and F. R. Yu, “Semi-distributed resource management in UAV-aided MEC systems: A multi-agent federated reinforcement learning approach,” IEEE Trans. Veh. Technol., vol. 70, no. 12, pp. 13 162–13 173, 2021.
  13. H. Guo, Y. Wang, J. Liu, and C. Liu, “Multi-uav cooperative task offloading and resource allocation in 5g advanced and beyond,” IEEE Trans. Wirel. Commun., pp. 1–1, 2023.
  14. S. Goudarzi, S. A. Soleymani, W. Wang, and P. Xiao, “UAV-enabled mobile edge computing for resource allocation using cooperative evolutionary computation,” IEEE Trans. Aerosp. Electron. Syst., pp. 1–14, 2023.
  15. N. Lin, H. Tang, L. Zhao, S. Wan, A. Hawbani, and M. Guizani, “A PDDQNLP algorithm for energy efficient computation offloading in UAV-assisted MEC,” IEEE Trans. Wireless Commun., 2023.
  16. L. Shen, “User experience oriented task computation for UAV-assisted MEC system,” in Proc. of IEEE INFOCOM, 2022, pp. 1549–1558.
  17. P. Chen, X. Luo, D. Guo, Y. Sun, J. Xie, Y. Zhao, and R. Zhou, “Secure task offloading for MEC-aided-UAV system,” IEEE Trans. Intell. Veh., vol. 8, no. 5, pp. 3444–3457, 2023.
  18. Y. Wang, W. Chen, T. H. Luan, Z. Su, Q. Xu, R. Li, and N. Chen, “Task offloading for post-disaster rescue in unmanned aerial vehicles networks,” IEEE/ACM Trans. Netw., vol. 30, no. 4, pp. 1525–1539, 2022.
  19. X. Wang, Z. Ning, S. Guo, and L. Wang, “Imitation learning enabled task scheduling for online vehicular edge computing,” IEEE Trans. Mob. Comput., vol. 21, no. 2, pp. 598–611, 2022.
  20. J. Ji, K. Zhu, and L. Cai, “Trajectory and communication design for cache-enabled UAVs in cellular networks: A deep reinforcement learning approach,” IEEE Trans. Mob. Comput., 2022.
  21. L. Li, D. Shi, R. Hou, X. Li, J. Wang, H. Li, and M. Pan, “Data-driven optimization for cooperative edge service provisioning with demand uncertainty,” vol. 8, no. 6, pp. 4317–4328.
  22. H. Liao, Z. Zhou, W. Kong, Y. Chen, X. Wang, Z. Wang, and S. A. Otaibi, “Learning-based intent-aware task offloading for air-ground integrated vehicular edge computing,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 8, pp. 5127–5139, 2021.
  23. Y. Gou, T. Zhang, J. Liu, T. Yang, S. Song, and J.-H. Cui, “Achieving fair-effective communications and robustness in underwater acoustic sensor networks: A semi-cooperative approach,” IEEE Trans. Mob. Comput., pp. 1–18, 2023.
  24. B. Liang and Z. J. Haas, “Predictive distance-based mobility management for PCS networks,” in Proc. of IEEE INFOCOM, 1999, pp. 1377–1384.
  25. S. Batabyal and P. Bhaumik, “Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey,” IEEE Commun. Surv. Tutorials, vol. 17, no. 3, pp. 1679–1707, 2015.
  26. F. Song, H. Xing, X. Wang, S. Luo, P. Dai, Z. Xiao, and B. Zhao, “Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing,” IEEE Trans. Mob. Comput., pp. 1–18, 2022.
  27. Y. Dai, D. Xu, S. Maharjan, and Y. Zhang, “Joint load balancing and offloading in vehicular edge computing and networks,” IEEE Internet Things J., vol. 6, no. 3, pp. 4377–4387, 2019.
  28. S. Xia, Z. Yao, Y. Li, and S. Mao, “Online distributed offloading and computing resource management with energy harvesting for heterogeneous MEC-enabled IoT,” IEEE Trans. Wirel. Commun., vol. 20, no. 10, pp. 6743–6757, 2021.
  29. V. 3GPP TR 36.873, “Study on 3D channel model for LTE (Release 12),” 2015.
  30. A. Al-Hourani, K. Sithamparanathan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol. 3, no. 6, pp. 569–572, 2014.
  31. G. Sun, X. Zheng, Z. Sun, Q. Wu, J. Li, Y. Liu, and V. C. Leung, “UAV-enabled secure communications via collaborative beamforming with imperfect eavesdropper information,” IEEE Trans. Mob. Comput., 2023.
  32. G. T. . V16.1.0, “Study on channel model for frequencies from 0.5 to 100 GHz (Release 16),” 2020.
  33. L. Cheng, B. E. Henty, D. D. Stancil, F. Bai, and P. Mudalige, “Mobile vehicle-to-vehicle narrow-band channel measurement and characterization of the 5.9 GHz dedicated short range communication (dsrc) frequency band,” IEEE J. Select. Areas Commun., vol. 25, no. 8, pp. 1501–1516, 2007.
  34. Y. Pan, C. Pan, K. Wang, H. Zhu, and J. Wang, “Cost minimization for cooperative computation framework in MEC networks,” IEEE Trans. Wireless Commun., vol. 20, no. 6, pp. 3670–3684, 2021.
  35. Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Trans. Wirel. Commun., vol. 18, no. 4, pp. 2329–2345, 2019.
  36. H. Pan, Y. Liu, G. Sun, J. Fan, S. Liang, and C. Yuen, “Joint power and 3D trajectory optimization for UAV-enabled wireless powered communication networks with obstacles,” IEEE Trans. Commun., vol. 71, no. 4, pp. 2364–2380, 2023.
  37. L. Wang, K. Wang, C. Pan, W. Xu, N. Aslam, and A. Nallanathan, “Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing,” IEEE Trans. Mob. Comput., vol. 21, no. 10, pp. 3536–3550, 2022.
  38. A. Rubinstein, “Perfect equilibrium in a bargaining model,” Econom. J., pp. 97–109, 1982.
  39. K. Binmore, A. Rubinstein, and A. Wolinsky, “The nash bargaining solution in economic modelling,” Rand J Econ, pp. 176–188, 1986.
  40. Z. Yang, S. Bi, and Y. A. Zhang, “Online trajectory and resource optimization for stochastic UAV-enabled MEC systems,” IEEE Trans. Wirel. Commun., vol. 21, no. 7, pp. 5629–5643, 2022.
  41. Z. Zhou, C. Gao, C. Xu, Y. Zhang, S. Mumtaz, and J. Rodriguez, “Social big-data-based content dissemination in internet of vehicles,” IEEE Trans. Ind. Inform., vol. 14, no. 2, pp. 768–777, 2017.
  42. R. Zhou, X. Wu, H. Tan, and R. Zhang, “Two time-scale joint service caching and task offloading for UAV-assisted mobile edge computing,” in Proc. IEEE INFOCOM, 2022, pp. 1189–1198.
  43. Y. Wang, P. Lang, D. Tian, J. Zhou, X. Duan, Y. Cao, and D. Zhao, “A game-based computation offloading method in vehicular multiaccess edge computing networks,” IEEE Internet Things J., vol. 7, no. 6, pp. 4987–4996, 2020.
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