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Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing (2404.15292v1)

Published 23 Mar 2024 in eess.SP, cs.IT, and math.IT

Abstract: Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem \textcolor{b2}{aiming} at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. \textcolor{b3}{However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions.} \textcolor{b2}{Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method.} Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods.

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References (44)
  1. F. Liu, G. Tang, Y. Li, Z. Cai, X. Zhang, and T. Zhou, “A survey on edge computing systems and tools,” Proc. IEEE, vol. 107, no. 8, pp. 1537–1562, 2019.
  2. M. Asim, Y. Wang, K. Wang, and P. Huang, “A review on computational intelligence techniques in cloud and edge computing,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 4, no. 6, pp. 742–763, 2020.
  3. L. M. Abualigah, A. Diabat, and M. A. E. Aziz, “Intelligent workflow scheduling for big data applications in IoT cloud computing environments,” Clust. Comput., vol. 24, no. 4, pp. 2957–2976, 2021.
  4. P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Commun. Surv. Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
  5. P. Porambage, J. Okwuibe, M. Liyanage, M. Ylianttila, and T. Taleb, “Survey on multi-access edge computing for internet of things realization,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 2961–2991, 2018.
  6. 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., vol. 23, no. 2, pp. 1655–1673, 2024.
  7. Y. Qu, H. Dai, L. Wang, W. Wang, F. Wu, H. Tan, S. Tang, and C. Dong, “Cotask: Correlation-aware task offloading in edge computing,” World Wide Web, vol. 25, no. 5, pp. 2185–2213, 2022.
  8. W. Khawaja, I. Güvenç, D. W. Matolak, U. Fiebig, and N. Schneckenburger, “A survey of Air-to-Ground propagation channel modeling for unmanned aerial vehicles,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, pp. 2361–2391, 2019.
  9. 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., vol. abs/2308.01511, 2023.
  10. N. N. Ei, M. Alsenwi, Y. K. Tun, Z. Han, and C. S. Hong, “Energy-efficient resource allocation in multi-uav-assisted two-stage edge computing for beyond 5g networks,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 16 421–16 432, 2022.
  11. G. Sun, L. He, Z. Sun, Q. Wu, S. Liang, J. Li, D. Niyato, and V. C. M. Leung, “Joint task offloading and resource allocation in aerial-terrestrial UAV networks with edge and fog computing for post-disaster rescue,” IEEE Trans. Mob. Comput., pp. 1–18, 2024.
  12. 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.
  13. 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.
  14. 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., vol. 62, no. 1, pp. 62–68, 2024.
  15. K. Zhang, Y. Mao, S. Leng, Q. Zhao, L. Li, X. Peng, L. Pan, S. Maharjan, and Y. Zhang, “Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks,” IEEE Access, vol. 4, pp. 5896–5907, 2016.
  16. Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic computation offloading for mobile-edge computing with energy harvesting devices,” IEEE J. Sel. Areas Commun., vol. 34, no. 12, pp. 3590–3605, 2016.
  17. Z. Kuang, L. Li, J. Gao, L. Zhao, and A. Liu, “Partial offloading scheduling and power allocation for mobile edge computing systems,” IEEE Internet Things J., vol. 6, no. 4, pp. 6774–6785, 2019.
  18. M. Li, N. Cheng, J. Gao, Y. Wang, L. Zhao, and X. Shen, “Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization,” IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3424–3438, 2020.
  19. T. Zhang, Y. Xu, J. Loo, D. Yang, and L. Xiao, “Joint computation and communication design for UAV-assisted mobile edge computing in IoT,” IEEE Trans. Ind. Informatics, vol. 16, no. 8, pp. 5505–5516, 2020.
  20. Y. K. Tun, N. D. Tri, K. Kim, M. Alsenwi, W. Saad, and C. S. Hong, “Collaboration in the sky: A distributed framework for task offloading and resource allocation in multi-access edge computing,” IEEE Internet Things J., vol. 9, no. 23, pp. 24 221–24 235, 2022.
  21. 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., vol. 23, no. 1, pp. 347–359, 2024.
  22. 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.
  23. Y. Chen, D. Pi, S. Yang, Y. Xu, J. Chen, and A. W. Mohamed, “HNIO: A hybrid nature-inspired optimization algorithm for energy minimization in UAV-assisted mobile edge computing,” IEEE Trans. Netw. Serv. Manag., vol. 19, no. 3, pp. 3264–3275, 2022.
  24. J. Ji, K. Zhu, D. Niyato, and R. Wang, “Joint cache placement, flight trajectory, and transmission power optimization for multi-UAV assisted wireless networks,” IEEE Trans. Wirel. Commun., vol. 19, no. 8, pp. 5389–5403, 2020.
  25. W. Lee and T. Kim, “Multiagent reinforcement learning in controlling offloading ratio and trajectory for multi-UAV mobile-edge computing,” IEEE Internet Things J., vol. 11, no. 2, pp. 3417–3429, 2024.
  26. Y. M. Park, S. S. Hassan, Y. K. Tun, Z. Han, and C. S. Hong, “Joint trajectory and resource optimization of MEC-assisted UAVs in Sub-THz networks: A resources-based multi-agent proximal policy optimization DRL with attention mechanism,” IEEE Trans. Veh. Technol., vol. 73, no. 2, pp. 2003–2016, 2024.
  27. Q. Wu, J. Chen, Y. Xu, N. Qi, T. Fang, Y. Sun, and L. Jia, “Joint computation offloading, role, and location selection in hierarchical multicoalition UAV MEC networks: A stackelberg game learning approach,” IEEE Internet Things J., vol. 9, no. 19, pp. 18 293–18 304, 2022.
  28. J. Zhang, L. Zhou, F. Zhou, B. Seet, H. Zhang, Z. Cai, and J. Wei, “Computation-efficient offloading and trajectory scheduling for multi-UAV assisted mobile edge computing,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 2114–2125, 2020.
  29. 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.
  30. B. Yang, G. Mao, M. Ding, X. Ge, and X. Tao, “Dense small cell networks: From noise-limited to dense interference-limited,” IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4262–4277, 2018.
  31. A. Boumaalif and O. Zytoune, “Power distribution of device-to-device communications under nakagami fading channel,” IEEE Trans. Mob. Comput., vol. 21, no. 6, pp. 2158–2167, 2022.
  32. 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.
  33. T. D. Burd and R. W. Brodersen, “Processor design for portable systems,” J. VLSI Signal Process., vol. 13, no. 2-3, pp. 203–221, 1996.
  34. Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li, “Mobile-edge computing: Partial computation offloading using dynamic voltage scaling,” IEEE Trans. Commun., vol. 64, no. 10, pp. 4268–4282, 2016.
  35. Y. Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UAV communications for 5G and beyond,” Proc. IEEE, vol. 107, no. 12, pp. 2327–2375, 2019.
  36. 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.
  37. I. Alsyouf and S. Hamdan, “A multi-objective optimization of maintenance policies using weighted comprehensive criterion method (wccm),” pp. 1–4, 2017.
  38. X. Zhang, J. Zhang, J. Xiong, L. Zhou, and J. Wei, “Energy-efficient multi-UAV-enabled multiaccess edge computing incorporating NOMA,” IEEE Internet Things J., vol. 7, no. 6, pp. 5613–5627, 2020.
  39. K. M. Elbassioni and S. Ray, “Threshold rounding for the standard LP relaxation of some geometric stabbing problems,” CoRR, vol. abs/2106.12385, 2021.
  40. U. Feige, M. Feldman, and I. Talgam-Cohen, “Oblivious rounding and the integrality gap,” in Proc. Leibniz Int. Proc. Inform., vol. 60, 2016, pp. 1–23.
  41. J. Zhao, Q. Li, Y. Gong, and K. Zhang, “Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks,” IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 7944–7956, 2019.
  42. M. Grant and S. Boyd, “CVX: MATLAB software for disciplined convex programming, version 2.1,” Online, Mar. 2014.
  43. M. Hong, T. Chang, X. Wang, M. Razaviyayn, S. Ma, and Z. Luo, “A block successive upper-bound minimization method of multipliers for linearly constrained convex optimization,” Math. Oper. Res., vol. 45, no. 3, pp. 833–861, 2020.
  44. Z. Zhao, J. Shi, Z. Li, J. Si, P. Xiao, and R. Tafazolli, “Matching-aided-learning resource allocation for dynamic offloading in mmwave MEC system,” IEEE Trans. Wirel. Commun., vol. 22, no. 11, pp. 7580–7591, 2023.
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