Abstract
When a natural disaster occurs, ground base stations (BSs) are destroyed and cannot provide communication services. Rapid restoration of communication is of great significance to the lives of trapped persons. This paper studies the problem of unmanned aerial vehicle (UAV) equipped with mobile edge computing (MEC) servers to provide communication and computing services for ground users in the scenario where the ground infrastructure is destroyed. We designed a UAV-to-Cell offloading system, which provides services in units of cells. By determining the hover locations (HLs) and trajectories, the UAV can handle more tasks with limited battery energy. Since tasks have time limit requirements, the order of processing will affect the task data size of the system. We solve this problem by joint cell clustering and path planning. Among them, elliptic clustering is used to divide the cells, the 3D position of the UAV is determined according to the quality of user service, and the double deep Q-network (DDQN) algorithm is used to determine the trajectory of the UAV. Simulation experiments demonstrate the effectiveness and efficiency of our proposed strategy by comparing it with the baselines.
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References
ITU, DBM.: Measuring digital development - Facts and figures 2021. ITU Publication (2021)
Luo, Q., Hu, S., Li, C., Li, G., Shi, W.: Resource scheduling in edge computing: a survey. IEEE Commun. Surv. Tutor. 23(4), 2131–2165 (2021)
UNDRR: Human cost of disaster - An overview of the last 20 years (2020)
Ning, Z., et al.: 5G-enabled UAV-to-community offloading: joint trajectory design and task scheduling. IEEE J. Sel. Areas Commun. 39(11), 3306–3320 (2021)
Sun, H., et al.: Coverage analysis for cellular-connected random 3D mobile UAVs with directional antennas. IEEE Wirel. Commun. Lett. 12(3), 550–554 (2023)
Al-Hourani, A., Kandeepan, S., Lardner, S.: Optimal LAP altitude for maximum coverage. IEEE Wirel. Commun. Lett. 3(6), 569–572 (2014)
Al-Hourani, A., Kandeepan, S., Jamalipour, A.: Modeling air-to-ground path loss for low altitude platforms in urban environments. In: 2014 IEEE Global Communications Conference (GLOBECOM 2014), pp. 2898–2904. IEEE (2014)
Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Commun. Lett. 20(8), 1647–1650 (2016)
Noh, S.C., Jeon, H.B., Chae, C.B.: Energy-efficient deployment of multiple UAVs using ellipse clustering to establish base stations. IEEE Wirel. Commun. Lett. 9(8), 1155–1159 (2020)
Babu, N., Virgili, M., Papadias, C.B., Popovski, P., Forsyth, A.J.: Cost- and energy-efficient aerial communication networks with interleaved hovering and flying. IEEE Trans. Veh. Technol. 70(9), 9077–9087 (2021)
Wan, Y., Zhong, Y., Ma, A., Zhang, L.: An accurate UAV 3-D path planning method for disaster emergency response based on an improved multiobjective swarm intelligence algorithm. IEEE Trans. Cybern. 53(4), 2658–2671 (2023)
Liu, Y., Li, Y., Niu, Y., Jin, D.: Joint optimization of path planning and resource allocation in mobile edge computing. IEEE Trans. Mob. Comput. 19(9), 2129–2144 (2020)
Wang, D., Tian, J., Zhang, H., Wu, D.: Task offloading and trajectory scheduling for UAV-enabled MEC networks: an optimal transport theory perspective. IEEE Wirel. Commun. Lett. 11(1), 150–154 (2022)
Chang, H., Chen, Y., Zhang, B., Doermann, D.: Multi-UAV mobile edge computing and path planning platform based on reinforcement learning. IEEE Trans. Emerg. Top. Comput. Intell. 6(3), 489–498 (2021)
Wang, L., Wang, K., Pan, C., Xu, W., Aslam, N., Hanzo, L.: Multi-agent deep reinforcement learning-based trajectory planning for multi-UAV assisted mobile edge computing. IEEE Trans. Cogn. Commun. Netw. 7(1), 73–84 (2021)
Zhang, N., Wang, J., Kang, G., Liu, Y.: Uplink nonorthogonal multiple access in 5G systems. IEEE Commun. Lett. 20(3), 458–461 (2016)
Zeng, Y., Zhang, R.: Energy-efficient UAV communication with trajectory optimization. IEEE Trans. Wirel. Commun. 16(6), 3747–3760 (2017)
Hu, Q., Cai, Y., Yu, G., Qin, Z., Zhao, M., Li, G.Y.: Joint offloading and trajectory design for UAV-enabled mobile edge computing systems. IEEE Internet Things J. 6(2), 1879–1892 (2019)
Zhang, T., Xu, Y., Loo, J., Yang, D., Xiao, L.: Joint computation and communication design for UAV-assisted mobile edge computing in IoT. IEEE Trans. Industr. Inf. 16(8), 5505–5516 (2019)
Zhang, K., Gui, X., Ren, D., Li, D.: Energy-latency tradeoff for computation offloading in UAV-assisted multiaccess edge computing system. IEEE Internet Things J. 8(8), 6709–6719 (2020)
Lyu, L., Zeng, F., Xiao, Z., Zhang, C., Jiang, H., Havyarimana, V.: Computation bits maximization in UAV-enabled mobile-edge computing system. IEEE Internet Things J. 9(13), 10640–10651 (2021)
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This paper is supported by the National Nature Science Foundation of China under grant number: T2350710232.
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Li, M., Qi, W., Li, S. (2024). Task Offloading in UAV-to-Cell MEC Networks: Cell Clustering and Path Planning. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_1
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DOI: https://doi.org/10.1007/978-3-031-54528-3_1
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