Abstract
To deal with the diverse computing tasks generated by Internet of Things devices (IoTDs), unmanned aerial vehicles (UAVs)-assisted edge offloading technology has emerged. However, there are many challenges in edge offloading, such as the dynamic change of channel state and the limited computing resources. Therefore, it is crucial to plan the trajectories of UAVs intelligently to improve the offloading efficiency. In this paper, we propose an individual-inference-based distributed deep deterministic policy gradient (IID-DDPG) algorithm for multi-UAV trajectory planning. The proposed IID-DDPG algorithm adopts a distributed training method, which only involves the interactions of neighbor agents. Consequently, the IID-DDPG algorithm has strong scalability and small communication burden. In addition, the causal inference method is first used to measure the importance of neighbor information. Secondly, an information aggregation network is designed to assist UAV agents to infer global knowledge, so as to enhance the stability of distributed training. Finally, the effectiveness and superiority of the proposed IID-DDPG algorithm are verified by extensive simulations.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Figa_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs42486-024-00159-8/MediaObjects/42486_2024_159_Fig9_HTML.png)
Similar content being viewed by others
Data availability
No data was used for the research described in the article.
References
Akhlaqi, M.Y., Hanapi, Z.B.M.: Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions. J. Netw. Comput. Appl. 212, 103568 (2023)
Cai, T., Yang, Z., Chen, Y., Chen, W., Zheng, Z., Yu, Y., Dai, H.-N.: Cooperative data sensing and computation offloading in uav-assisted crowdsensing with multi-agent deep reinforcement learning. IEEE Trans. Netw. Sci. Eng. 9(5), 3197–3211 (2021)
Cheng, Z., Gao, Z., Liwang, M., Huang, L., Du, X., Guizani, M.: Intelligent task offloading and energy allocation in the uav-aided mobile edge-cloud continuum. IEEE Netw. 35(5), 42–49 (2021)
Gao, A., Wang, Q., Liang, W., Ding, Z.: Game combined multi-agent reinforcement learning approach for uav assisted offloading. IEEE Trans. Veh. Technol. 70(12), 12888–12901 (2021)
Han, H., Zhan, C., Lv, J., Xu, C.: Energy minimization for cellular connected aerial edge computing system with binary offloading. IEEE Internet Things J. (2023). https://doi.org/10.1109/JIOT.2023.3323289
Hoang, L.T., Nguyen, C.T., Pham, A.T.: Deep reinforcement learning-based online resource management for uav-assisted edge computing with dual connectivity. IEEE/ACM Trans. Networking. 31(6), 2761–2776 (2023)
Jaques, N., Lazaridou, A., Hughes, E., Gulcehre, C., Ortega, P., Strouse, D., Leibo, J.Z., De Freitas, N.: Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In: International Conference on Machine Learning, 3040–3049 (2019). PMLR
Lai, C.-C., Tsai, A.-H., Wang, L.-C., et al.: Adaptive and fair deployment approach to balance offload traffic in multi-uav cellular networks. IEEE Trans. Vehicular Technol. 72(3), 3724–3738 (2022)
Li, X., Teng, M., Wu, J., Qin, X.: Dual-label aware service replacement for interaction quality improvement in heterogeneous mec system. CCF Trans. Pervasive Comput. Interact. 3, 129–146 (2021)
Li, B., Yang, R., Liu, L., Wang, J., Zhang, N., Dong, M.: Robust computation offloading and trajectory optimization for multi-uav-assisted mec: a multi-agent drl approach. IEEE Internet Things J. 11(3), 4775–4786 (2024)
Li, S., Zhang, S., Wang, Z., Zhou, Z., Wang, X., Mumtaz, S., Guizani, M., Frascolla, V.: Asynchronous fdrl-based low-latency computation offloading for integrated terrestrial and non-terrestrial power iot. IEEE Netw. (2023b). https://doi.org/10.1109/MNET.2023.3320894
Lin, N., Tang, H., Zhao, L., Wan, S., Hawbani, A., Guizani, M.: A pddqnlp algorithm for energy efficient computation offloading in uav-assisted mec. IEEE Trans. Wirel. Commun. 22(12), 8876–8890 (2023). https://doi.org/10.1109/TWC.2023.3266497
Miao, Y., Hwang, K., Wu, D., Hao, Y., Chen, M.: Drone swarm path planning for mobile edge computing in industrial internet of things. IEEE Trans. Ind. Inf. 19(5), 6836–6848 (2022)
Ning, Z., Hu, H., Wang, X., Guo, L., Guo, S., Wang, G., Gao, X.: Mobile edge computing and machine learning in the internet of unmanned aerial vehicles: a survey. ACM Comput. Surveys. 56(1), 1–31 (2023)
Peng, H., Shen, X.: Multi-agent reinforcement learning based resource management in mec-and uav-assisted vehicular networks. IEEE J. Select. Areas Commun. 39(1), 131–141 (2020)
Shah, Z., Javed, U., Naeem, M., Zeadally, S., Ejaz, W.: Mobile edge computing (mec)-enabled uav placement and computation efficiency maximization in disaster scenario. IEEE Trans. Veh. Technol. 72(10), 13406–13416 (2023)
Shen, M., Gu, A., Kang, J., Tang, X., Lin, X., Zhu, L., Niyato, D.: Blockchains for artificial intelligence of things: a comprehensive survey. IEEE Internet Things J. 10(16), 14483–14506 (2023)
Shi, H., Tian, Y., Li, H., Huang, J., Shi, L., Zhou, Y.: Task offloading and trajectory scheduling for uav-enabled mec networks: an madrl algorithm with prioritized experience replay. Ad Hoc Netw. 154, 103371 (2024)
Song, Z., Qin, X., Hao, Y., Hou, T., Wang, J., Sun, X.: A comprehensive survey on aerial mobile edge computing: challenges, state-of-the-art, and future directions. Comput. Commun. 191, 233–256 (2022)
Tang, Q., Chang, L., Yang, K., Wang, K., Wang, J., Sharma, P.K.: Task number maximization offloading strategy seamlessly adapted to uav scenario. Comput. Commun. 151, 19–30 (2020)
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. Cognit. Commun. Netw 7(1), 73–84 (2020)
Wang, H., Yu, Y., Jiang, Y.: Fully decentralized multiagent communication via causal inference. IEEE Trans. Neural Netw. Learn. Syst. 34(12), 10193–10202 (2022a)
Wang, C., Deng, D., Xu, L., Wang, W.: Resource scheduling based on deep reinforcement learning in uav assisted emergency communication networks. IEEE Trans. Commun. 70(6), 3834–3848 (2022b)
Wang, L., Zhao, X., Lu, Z., Wang, L., Zhang, S.: Enhancing privacy preservation and trustworthiness for decentralized federated learning. Inf. Sci. 628, 449–468 (2023). https://doi.org/10.1016/j.ins.2023.01.130
Wei, D., Ma, J., Luo, L., Wang, Y., He, L., Li, X.: Computation offloading over multi-uav mec network: a distributed deep reinforcement learning approach. Comput. Netw. 199, 108439 (2021)
Wu, S., Xu, W., Wang, F., Li, G., Pan, M.: Distributed federated deep reinforcement learning based trajectory optimization for air-ground cooperative emergency networks. IEEE Trans. Veh. Technol. 71(8), 9107–9112 (2022)
Xu, J., Li, D., Gu, W., Chen, Y.: Uav-assisted task offloading for iot in smart buildings and environment via deep reinforcement learning. Build. Environ. 222, 109218 (2022a)
Xu, Y., Zhang, T., Zou, Y., Liu, Y.: Reconfigurable intelligence surface aided uav-mec systems with noma. IEEE Commun. Lett. 26(9), 2121–2125 (2022b)
Ye, Y., Wei, W., Geng, D., He, X.: Dynamic coordination in uav swarm assisted mec via decentralized deep reinforcement learning. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 1064–1069 (2020). IEEE
Zhang, X., Debroy, S.: Resource management in mobile edge computing: a comprehensive survey. ACM Comput. Surveys. 55(13s), 1–37 (2023)
Zhang, T., Chen, C., Xu, Y., Loo, J., Xu, W.: Joint task scheduling and multi-uav deployment for aerial computing in emergency communication networks. Sci. China Inf. Sci. 66(9), 192303 (2023)
Zhao, N., Cheng, Y., Pei, Y., Liang, Y.-C., Niyato, D.: Deep reinforcement learning for trajectory design and power allocation in uav networks. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), 1–6 (2020). IEEE
Zhao, N., Ye, Z., Pei, Y., Liang, Y.-C., Niyato, D.: Multi-agent deep reinforcement learning for task offloading in uav-assisted mobile edge computing. IEEE Trans. Wirel. Commun. 21(9), 6949–6960 (2022)
Zhou, H., Wu, T., Chen, X., He, S., Guo, D., Wu, J.: Reverse auction-based computation offloading and resource allocation in mobile cloud-edge computing. IEEE Trans. Mobile Comput. 22(10), 6144–6159 (2023a). https://doi.org/10.1109/TMC.2022.3189050
Zhou, H., Li, M., Wang, N., Min, G., Wu, J.: Accelerating deep learning inference via model parallelism and partial computation offloading. IEEE Trans. Distrib. Syst. 34(2), 475–488 (2023b)
Zhou, H., Jiang, K., He, S., Min, G., Wu, J.: Distributed deep multi-agent reinforcement learning for cooperative edge caching in internet-of-vehicles. IEEE Trans. Wirel. Commun. 22(12), 9595–9609 (2023c). https://doi.org/10.1109/TWC.2023.3272348
Funding
This work was supported in part by the National Natural Science Foundation of China (No. 62373102), the Jiangsu Natural Science Foundation (No. BK20221455), and the Anhui Provincial Key Research and Development Project (No. 2022i01020013).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no Conflict of interest.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fan, C., Wang, Q. & Wang, X. Distributed multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted edge offloading. CCF Trans. Pervasive Comp. Interact. (2024). https://doi.org/10.1007/s42486-024-00159-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42486-024-00159-8