Abstract
Nowadays, more and more electric power services are carried on the power information communication network (PICN) including power grid production and scheduling, communication, and environment sensing, in the form of data, voice and video. To improve the resource utilization efficiency, it is necessary to carry out traffic prediction approach in PICN. However, the accessing businesses have diversified characteristics, which are reflected to different types of traffic flow in PICN. Moreover, the traditional PICN is a distributed network and cannot be controlled flexibly, which leads to the poor accuracy of traffic prediction algorithm. To address these problems, we combine the Software Defined Networking (SDN) architecture and Radial Basis Function neural network (RBFNN) for traffic intelligent prediction in PICN. The SDN controller can acquire global knowledge of PICN in each time slot to guide the data sampling process. Further, the complex nonlinear relationships of large-scale network traffics are analyzed by RBFNN model to realize high-precision traffic identification. The proposed scheme is evaluated based on by POX and Mininet platforms. Simulation results show that the proposed SDN-based intelligent prediction scheme can accurately forecast the change trend of each traffic flow and has better performance and lower prediction error than current schemes.
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References
Tang, W., Zhang, K., & Jiang, D. (2018). Physarum-inspired routing protocol for energy harvesting wireless sensor networks. Telecommunication System, 67(4), 745–762.
Jiang, D., Wang, Y., Lv, Z., et al. (2019). Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2930226.
Jiang, T., Wang, H., Daneshmand, M., et al. (2017). Cognitive radio-based smart grid traffic scheduling with binary exponential backoff. IEEE Internet of Things Journal, 4(6), 2038–2046.
Zhu, J., Song, Y., Jiang, D., et al. (2018). A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of things. IEEE Internet of Things Journal, 5(4), 2375–2385.
Jiang, D., Huo, L., Lv, Z., et al. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intelligent Transportation Systems, 19(10), 3305–3319.
Khan, M. W., Zeeshan, M., & Usman, M. (2019). Traffic scheduling optimization in cognitive radio based smart grid network using mini-batch gradient descent method. In Proceedings of CISTI’19 (pp. 1–5).
Wang, K., Li, J., Wu, J., et al. (2018). QoS-predicted energy efficient routing for information-centric smart grid: A network calculus approach. IEEE Access, 6, 52867–52876.
Huo, L., Jiang, D., & Lv, Z. (2018). Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks. Computers & Electrical Engineering, 66(2), 316–331.
Jiang, D., Huo, L., & Song, H. (2018). Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Transactions on Network Science and Engineering, 1(1), 1–12.
Feng, W., Sun, Y., Zhou, Z., et al. (2018). Study on multi-network traffic modeling in distribution communication network access service. In Proceedings of ICACT’18 (pp. 720–723).
Han, X., Li, X., Zhao, H., et al. (2018). Power load forecasting based on improved elman neural network. In Proceedings of ICEI’18 (pp. 152–156).
Huo, L., & Jiang, D. (2019). Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommunication System, 23(4), 1–11.
Jiang, D., Wang, W., Shi, L., et al. (2018). A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering, 5(3), 1–12.
Kato, N., Fadlullah, Z., Mao, B., et al. (2017). The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE Wireless Communications, 24(3), 146–153.
Li, L., Ota, K., Dong, M., et al. (2017). When weather matters: IoT-based electrical load forecasting for smart grid. IEEE Communications Magazine, 55(10), 46–51.
Kong, W., Dong, Z. Y., Jia, Y., et al. (2019). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841–851.
Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE, 13(5), 1–23.
Wang, F., Jiang, D., Wen, H., et al. (2019). Adaboost-based security level classification of mobile intelligent terminals. The Journal of Supercomputing. https://doi.org/10.1007/s11227-019-02954-y.
Subasi, A., Marwani, K., Alghamdi, R., et al. (2018). Intrusion detection in smart grid using data mining techniques. In Proceedings of NCC’18 (pp. 1–6).
Huang, C., Chiang, C., Li, Q., et al. (2017). A study of deep learning networks on mobile traffic forecasting. In Proceedings of PIMRC’17 (pp. 1–6).
Zhang, Y., & Lorenz, P. (2018). AI for network traffic control. IEEE Network, 32(6), 6–7.
Huo, L., Jiang, D., Zhu, X., et al. (2019). An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. International Journal of Communication Systems (pp. 1–12).
Wang, F., Jiang, D., & Qi, S. (2019). An adaptive routing algorithm for integrated information networks. China Communications, 7(1), 196–207.
Lu, Y., Zhang, T., Zeng, Z., et al. (2016). An improved RBF neural network for short-term load forecast in smart grids. In Proceedings of ICCS’16 (pp. 1–6).
Molzahn, D. K., et al. (2017). A survey of distributed optimization and control algorithms for electric power systems. IEEE Transactions on Smart Grid, 8(6), 2941–2962.
Sun, W., Lu, W., Li, Q., et al. (2017). WNN-LQE: Wavelet-neural-network-based link quality estimation for smart grid WSNs. IEEE Access, 5, 12788–12797.
Duan, Z., Yang, Y., Zhang, K., et al. (2018). Improved deep hybrid networks for urban traffic flow prediction using trajectory data. IEEE Access, 6, 31820–31827.
Li, Z., Al Hassan, R., Shahidehpour, M., et al. (2019). A hierarchical framework for intelligent traffic management in smart cities. IEEE Transactions on Smart Grid, 10(1), 691–701.
Jiang, D., Wang, Y., Lv, Z., et al. (2019). Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2930226.
Jiang, D., Zhang, P., Lv, Z., et al. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3(6), 1437–1447.
Jiang, D., Li, W., & Lv, H. (2017). An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing, 2017(220), 160–169.
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2017YFB1010001), the Research on Key Technologies of New Generation Power Data Communication Network Based on SDN/NFV (No. 5700-201952237A-0-0-00). The authors wish to thank the reviewers for their helpful comments.
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Liu, C., Zhang, G., Li, B. et al. A SDN-based intelligent prediction approach to power traffic identification and monitoring for smart network access. Wireless Netw 27, 3665–3676 (2021). https://doi.org/10.1007/s11276-019-02235-9
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DOI: https://doi.org/10.1007/s11276-019-02235-9