Abstract
Aiming at the problem of quality assurance of intelligent logistics service in 5G+ edge computing environment, this paper proposes a mechanism based on federated cooperative cache, which aims to utilize the computing and storage resources of edge nodes to realize rapid processing and sharing of logistics data and improve the efficiency and reliability of logistics services. This paper first analyzes the characteristics and challenges of intelligent logistics services under 5G+ edge computing environment, and then introduces the concept and principle of federated cooperative cache, as well as its application scenarios and advantages in intelligent logistics services. Then, this paper designs an intelligent logistics service quality assurance mechanism based on federated cooperative cache, including five modules such as data partitioning, data transmission, data fusion, data access and data update, and gives the corresponding algorithms and processes. Finally, this paper verifies the effectiveness and performance of the proposed mechanism through simulation experiments. Compared with the traditional centralized cache and distributed cache, the proposed mechanism can reduce the data transmission delay, improve the data hit rate and data consistency, so as to ensure the quality of intelligent logistics services. In the future, the federated collaborative cache mechanism can be further optimized to consider the needs of multiple scenarios. And explore the application potential of other areas to drive the continuous development and innovation of intelligent logistics services.
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References
Xiaojuan, L., Yihua, Z., Jijie, W.: Research on security mechanism of edge service based on blockchain. J. Inf. Secur. Res. 8(6), 613–621 (2022)
Fei, T., Qinglin, Q.: New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans. Syst. Man Cybern. Syst. 49(1), 81 (2017)
Chuntao, D., et al.: Edge computing overview: applications, status, and challenges. ZTE Technol. 25(03), 2–7 (2019)
Xiugong, Q., et al.: Research on standard system and application model of edge computing in industrial internet field. Manuf. Autom. 44(02), 183–186 (2022)
Wang, X.F., Wang, C.Y., Li, X.H., et al.: Federated deep reinforcement learning for Internet of things with decentralized cooperative edge caching. IEEE Internet Things J. 7(1), 9441–9455 (2020)
Li, L.X., Xu, Y., Yin, J.Y., et al.: Deep reinforcement learning approaches for content caching in cache-enabled D2D networks. IEEE Internet Things J. 7(1), 544–557 (2020)
Yu, Z.X., Hu, J., Min, G.Y., et al.: Privacy-preserving federated deep learning for cooperative hierarchical caching in fog computing. IEEE Internet Things J. 9(22), 22246–22255 (2022)
Yang, Q., Liu, Y., Chen, T., et al.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Garg, N., Sellathurai, M., Bhatia, V., et al.: Online content popularity prediction and learning in wireless edge caching. IEEE Trans. Commun. 68(2), 1087–1100 (2019)
Fu, Y.R., Salaun, L., Yang, X., et al.: Caching efficiency maximization for device-to-device communication networks: a recommend to cache approach. IEEE Trans. Wirel. Commun. 20(10), 6580–6594 (2021)
Fu, Y.R., Yu, Q., Quek, T.Q., et al.: Revenue maximization for content-oriented wireless caching networks with repair and recommendation considerations. IEEE Trans. Wirel. Commun. 20(1), 284–298 (2021)
Liu, D., Cao, Z., He, Y., Ji, X., Hou, M., Jiang, H.: Exploiting concurrency for opportunistic forwarding in duty-cycled IoT networks. ACM Trans. Sens. Netw. 15(3), 31:1–31:33 (2019)
Agiwal, M., Saxena, N., Roy, A.: Towards connected living: 5G enabled internet of things (IoT). IETE Tech. Rev. 36(2), 190 (2019)
Blanchard, P., El Mhamdi, E.M., Guerraoui, R., et al. Machine learning with adversaries: byzantine tolerant gradient descent. In: Proceedings of the 2017 International Conference on Neural Information Processing Systems, pp. 118–128. PMLR, New York (2017)
Ya-ni, zhang, and ling-yun zhu. Applied in robot path planning of two-way aging A algorithm. Computer Appl. Res. 36(03), 792–795 + 800 (2019). Manuscript east. https://doi.org/10.19734/j.i. SSN. 1001–3695.2017.10.0982
Guo, X., Shen, Y., Cui, Y.: Collaborative filtering recommendation algorithm based on fuzzy clustering and user interest. Softw. Guide1–8 (2023). http://kns.cnki.net/kcms/detail/42.1671.TP.20230728.1110.002.html
Zhu, C, Wang, Z.: Operation optimization of ethylene cracking furnace based on improved deep reinforcement learning. Chin. J. Chem. Eng. 1–19 (2023). http://kns.cnki.net/kcms/detail/11.1946.tq.20230725.1113.002.html
Li, D., Sun, Y.-S., Zhang, Z.-T.: Automatic discovery mechanism and improvement of DDS publishing-subscription communication node. Avionics Technol. 54(01), 20–28 (2023)
Liu, J., Zhang, B., Zhang, Y.: Adaptive federated filter tracking algorithm based on multi-sensor collaboration. J. Beijing Univ. Posts Telecommun. 1–6 (2023). https://doi.org/10.13190/j.jbupt.2022-081
Liu, D., Cao, Z., Jiang, H., Zhou, S., Xiao, Z., Zeng, F.: Concurrent low-power listening: a new design paradigm for duty-cycling communication. ACM Trans. Sens. Netw. 19(1), 4:1–4:24 (2023)
Chen, B., Chen, W., Fan, Z., et al.: 5G+MEC private network intelligent manufacturing factory. Commun. Technol. 54(1), 215–223. 21. (in Chinese)
Xu, J.: Analysis and design of IoT driven granary real-time status monitoring system. Chang’an University, Xi’an (2018)
Jiang, H., Hu, J., Liu, D., Xiong, J., Cai, M.: DriverSonar: Fine-grained dangerous driving detection using active sonar. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5(3), 108:1–108:22 (2021)
Liu, D., Cao, Z., Hou, M., Rong, H., Jiang, H.: Pushing the limits of transmission concurrency for low power wireless networks. ACM Trans. Sens. Netw. 16(4), 40:1–40:29 (2020)
Acknowledgment
This work was supported in part by the Scientific Research Project of Hunan Provincial Department of Education (No. C0497), Aid Program for Science and Technology Inn-ovative Research Team in Higher Educational Institutions of Hunan Province, the Huaihua University Double First-Class initiative Applied Characteristic Discipline of Control Science and Engineering (No. ZNKZN2021-10), and National Training Program Proj-ect of Innovation and Entrepreneurship for Undergraduates (No. S202310548083) and the Teaching Reform Research Project of Hunan Province”POA-based Research on College English Teaching Reform among Local Colleges and Universities of Hunan” (HNJG-2019-825).
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Liu, Y. et al. (2024). Intelligent Logistics Service Quality Assurance Mechanism Based on Federated Collaborative Cache in 5G+ Edge Computing Environment. In: Maglaras, L.A., Douligeris, C. (eds) Wireless Internet. WiCON 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-58053-6_11
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