Electronics and Electrical Engineering and Control

Satisfaction-driven services caching and resource allocation for UAV mobile edge computing

  • Wei LI ,
  • Yan GUO ,
  • Ming HE ,
  • Hao YUAN ,
  • Xuebin LAI
Expand
  • College of Communications Engineering,Army Engineering University,Nanjing 210016,China

Received date: 2023-12-25

  Revised date: 2024-03-04

  Accepted date: 2024-05-06

  Online published: 2024-05-27

Supported by

Natural Science Foundation of Jiangsu Province(BK20211227);National Natural Science Foundation of China(61871400);National Talent Project(2022-JCJQ-ZQ-01);Military High-level Personnel Innovation Project(KYZYJQJY2101)

Abstract

With the booming development of the Internet of Things, mobile edge computing of UAVs, as an emerging computing paradigm, offloads intensive tasks to network edge servers, thereby improving user data processing capacity. This paper designs a service caching and resource allocation algorithm that combines the quantum genetic algorithm and the traditional algorithm to address the needs of diversified and different prioritized user application services. Taking into account storage, computation, and energy constraints, the maximum user satisfaction and minimum service placement cost are achieved by jointly optimizing service caching, user offloading policy, time slot allocation, computational resource allocation, and flight trajectory. Specifically, the original problem is decomposed into three subproblems. First, the subproblem of service caching and user offloading is solved based on the quantum genetic algorithm. Second, the closed-form optimal solution for computational resource allocation is obtained based on the Lagrangian duality function. Then, the subproblem of time slot allocation and UAV trajectory optimization is solved using the successive convex approximation technique. Finally, the three subproblems are iterated several times to obtain their optimal solutions. The simulation results show that the algorithm can satisfy the diversified needs of users well, and can also have low services caching.

Cite this article

Wei LI , Yan GUO , Ming HE , Hao YUAN , Xuebin LAI . Satisfaction-driven services caching and resource allocation for UAV mobile edge computing[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(19) : 330017 -330017 . DOI: 10.7527/S1000-6893.2024.30017

References

1 LI B, HE Q, CUI G M, et al. READ: Robustness-oriented edge application deployment in edge computing environment[J]. IEEE Transactions on Services Computing202215(3): 1746-1759.
2 XIA X Y, CHEN F F, HE Q, et al. Cost-effective app data distribution in edge computing[J]. IEEE Transactions on Parallel and Distributed Systems202132(1): 31-44.
3 HU Y C, PATEL M, SABELLA D, et al. Mobile edge computing—A key technology towards 5G[J]. ETSI white paper201511(11): 1-16.
4 SABELLA D, MOUSTAFA H, KUURE P,et al. Toward fully connected vehicles: Edge computing for advanced automotive communications[M]. 5G Automotive Association White Paper2017: 3-6.
5 CHEN Q, ZHU H, YANG L, et al. Edge computing assisted autonomous flight for UAV: Synergies between vision and communications[J]. IEEE Communications Magazine202159(1): 28-33.
6 YANG G, LIANG Y C, ZHANG R, et al. Modulation in the air: Backscatter communication over ambient OFDM carrier[J]. IEEE Transactions on Communications201866(3): 1219-1233.
7 XU X B, ZHAO H, YAO H P, et al. A blockchain-enabled energy-efficient data collection system for UAV-assisted IoT[J]. IEEE Internet of Things Journal20218(4): 2431-2443.
8 MOTLAGH N H, BAGAA M, TALEB T. UAV-based IoT platform: A crowd surveillance use case[J]. IEEE Communications Magazine201755(2): 128-134.
9 TALEB T, DUTTA S, KSENTINI A, et al. Mobile edge computing potential in making cities smarter[J]. IEEE Communications Magazine201755(3): 38-43.
10 AISSIOUI A, KSENTINI A, GUEROUI A M, et al. On enabling 5G automotive systems using follow me edge-cloud concept[J]. IEEE Transactions on Vehicular Technology201867(6): 5302-5316.
11 CHEN S T, JIAO L, LIU F M, et al. EdgeDR: An online mechanism design for demand response in edge clouds[J]. IEEE Transactions on Parallel and Distributed Systems202233(2): 343-358.
12 KOTA N R, NAIDU K. Minimizing energy consumption in H-NOMA based UAV-assisted MEC network[J]. IEEE Communications Letters202327(9): 2536-2540.
13 LIU Y, XIE S L, ZHANG Y. Cooperative offloading and resource management for UAV-enabled mobile edge computing in power IoT system[J]. IEEE Transactions on Vehicular Technology202069(10): 12229-12239.
14 CHEN J X, CAO X B, YANG P, et al. Deep reinforcement learning based resource allocation in multi-UAV-aided MEC networks[J]. IEEE Transactions on Communications202371(1): 296-309.
15 SHNAIWER Y N, KOUZAYHA N, MASOOD M, et al. Multihop task routing in UAV-assisted mobile-edge computing IoT networks with intelligent reflective surfaces[J]. IEEE Internet of Things Journal202310(8): 7174-7188.
16 QIN Z, WEI Z H, QU Y B, et al. AoI-aware scheduling for air-ground collaborative mobile edge computing[J]. IEEE Transactions on Wireless Communications202322(5): 2989-3005.
17 PERVEZ F, SULTANA A, YANG C G, et al. Energy and latency efficient joint communication and computation optimization in a multi-UAV-assisted MEC network[J]. IEEE Transactions on Wireless Communications202423(3): 1728-1741.
18 QU Y B, DAI H P, WANG H C, et al. Service provisioning for UAV-enabled mobile edge computing[J]. IEEE Journal on Selected Areas in Communications202139(11): 3287-3305.
19 HE Y J, GAN Y H, CUI H X, et al. Fairness-based 3-D multi-UAV trajectory optimization in multi-UAV-assisted MEC system[J]. IEEE Internet of Things Journal202310(13): 11383-11395.
20 HAN Z H, ZHOU T, XU T H, et al. Joint user association and deployment optimization for delay-minimized UAV-aided MEC networks[J]. IEEE Wireless Communications Letters202312(10): 1791-1795.
21 SAMIR M, ASSI C, SHARAFEDDINE S, et al. Age of information aware trajectory planning of UAVs in intelligent transportation systems: A deep learning approach[J]. IEEE Transactions on Vehicular Technology202069(11): 12382-12395.
22 FAN Q, ANSARI N. Cost Aware cloudlet Placement for big data processing at the edge[C]∥2017 IEEE International Conference on Communications (ICC). Piscataway: IEEE Press, 2017: 1-6.
23 HAN B, GOPALAKRISHNAN V, KATHIRVEL G, et al. On the resiliency of virtual network functions[J]. IEEE Communications Magazine201755(7): 152-157.
24 POULARAKIS K, LLORCA J, TULINO A M, et al. Joint service placement and request routing in multi-cell mobile edge computing networks[C]∥IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. Piscataway: IEEE Press, 2019: 10-18.
25 HUANG M T, LIANG W F, SHEN X J, et al. Reliability-aware virtualized network function services provisioning in mobile edge computing[J]. IEEE Transactions on Mobile Computing202019(11): 2699-2713.
26 DENG S G, CHEN Y S, CHEN G, et al. Incentive-driven proactive application deployment and pricing on distributed edges[J]. IEEE Transactions on Mobile Computing202322(2): 951-967.
27 BAHREINI T, GROSU D. Efficient algorithms for multi-component application placement in mobile edge computing[J]. IEEE Transactions on Cloud Computing202210(4): 2550-2563.
28 ZHANG Y, JIAO L, YAN J Y, et al. Dynamic service placement for virtual reality group gaming on mobile edge cloudlets[J]. IEEE Journal on Selected Areas in Communications201937(8): 1881-1897.
29 ZHANG T K, XU Y, LOO J, et al. Joint computation and communication design for UAV-assisted mobile edge computing in IoT[J]. IEEE Transactions on Industrial Informatics202016(8): 5505-5516.
30 DI RENZO M, NTONTIN K, SONG J, et al. Reconfigurable intelligent surfaces vs. relaying: Differences, similarities, and performance comparison[J]. IEEE Open Journal of the Communications Society29551: 798-807.
31 KILGOUR M. Book review: Marketing management: An Asian perspective[J]. Australasian Marketing Journal200614(2): 52.
32 杨磊. 服务补救中人格特质对顾客满意度之影响的实证研究[D]. 西安: 陕西师范大学, 2013: 56.
  YANG L. An empirical study on the influence of personality traits on customer satisfaction in service recovery[D]. Xi’an: Shaanxi Normal University, 2013: 56 (in Chinese).
33 ZHAO L, TAN W A, LI B, et al. Joint shareability and interference for multiple edge application deployment in mobile-edge computing environment[J]. IEEE Internet of Things Journal20229(3): 1762-1774.
34 YAN F L. Autonomous vehicle routing problem solution based on artificial potential field with parallel ant colony optimization (ACO) algorithm[J]. Pattern Recognition Letters2018116: 195-199.
35 SAMIR M, SHARAFEDDINE S, ASSI C M, et al. UAV trajectory planning for data collection from time-constrained IoT devices[J]. IEEE Transactions on Wireless Communications202019(1): 34-46.
36 YANG J N, LI B, ZHUANG Z Q. Research of Quantum Genetic Algorith and its application in blind source separation[J]. Journal of Electronics (China)200320(1): 62-68.
37 屈毓锛, 秦蓁, 马靖豪, 等. 面向空地协同移动边缘计算的服务布置策略[J]. 计算机学报202245(4): 781-797.
  QU Y B, QIN Z, MA J H, et al. Service provisioning for air-ground collaborative mobile edge computing[J]. Chinese Journal of Computers202245(4): 781-797 (in Chinese).
38 YANG Z H, XU W, SHI J F, et al. Association and load optimization with user priorities in load-coupled heterogeneous networks[J]. IEEE Transactions on Wireless Communications201817(1): 324-338.
39 LEE H J, PARK J, LEE S H, et al. Joint downlink cell association and bandwidth allocation with user priorities in two-tier HetNets[C]∥2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Piscataway: IEEE Press, 2020: 1-5.
40 WANG M L, ZHANG L, GAO P, et al. Stackelberg-game-based intelligent offloading incentive mechanism for a multi-UAV-assisted mobile-edge computing system[J]. IEEE Internet of Things Journal202310(17): 15679-15689.
41 ZENG Y, ZHANG R. Energy-efficient UAV communication with trajectory optimization[J]. IEEE Transactions on Wireless Communications201716(6): 3747-3760.
42 BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge: Cambridge University Press, 2004.
Outlines

/