End-to-End Network Fault Recovery Mechanism for Power IoT

  • ZanHong WuEmail author
  • Zhan Shi
  • Ying Wang
  • Zhuo Su
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)


With the rapid development of power internet of things (PIoT) and increasing demands of power services, it is difficult for traditional network structure to fully differentiate QoS and reliability requirements of services. Software Defined Network (SDN) is an important virtualization technology. It separates network control plane from data forwarding infrastructure plane, simplifying network management and control. In this paper, a network resource allocation and fault recovery mechanism for PIoT is proposed. Firstly, it builds an SDN-based virtualized system model to abstract the physical network resources. Then, considering the factors such as network operation cost, revenue, service rate, QoS requirements, network load balancing, and network stability, this paper proposes an operating profit maximization model for multimedia services and uses dynamic resource load balancing (DRLB). The simulation results show that proposed mechanism can improve fault recovery ability of network on the basis of ensuring efficiency of resource utilization.


Software Defined Network (SDN) QoS IoT Fault recovery 



This work was supported by the science and technology project of Guangdong power grid (036000KK52160025).


  1. 1.
    Subbiah, S., Perumal, V.: Energy-aware network resource allocation in SDN. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking, Chennai, pp. 2071–2075 (2016)Google Scholar
  2. 2.
    Dutra, D.L.C., Bagaa, M., Taleb, T., Samdanis, K.: Ensuring end-to-end QoS based on multi-paths routing using SDN technology. In: IEEE Global Communications Conference, Singapore, pp. 1–6 (2017)Google Scholar
  3. 3.
    Gomes, R.L., Bittencourt, L.F., Madeira, E.R.M., Cerqueira, E., Gerla, M.: State-aware allocation of reliable virtual software defined networks based on bandwidth and energy. In: 2016 13th IEEE Annual Consumer Communications & Networking Conference, Las Vegas, NV, pp. 411–416 (2016)Google Scholar
  4. 4.
    Cao, B., Lang, W.Q., Chen, Z.: Power allocation in wireless network virtualization with buyer/seller and auction game. In: 2015 IEEE Global Communications Conference, San Diego, CA, USA, pp. 1–6 (2015)Google Scholar
  5. 5.
    Esposito, F., Chiti, F.: Distributed consensus-based auctions for wireless virtual network embedding. In: 2015 IEEE International Conference on Communications, London, UK, pp. 472–477 (2015)Google Scholar
  6. 6.
    Rahman, M.M., Despins, C., Affes, S.: HetNet cloud: leveraging SDN & cloud computing for wireless access virtualization. In: 2015 IEEE International Conference on Ubiquitous Wireless Broadband, Montreal, QC, Canada, pp. 1–5 (2015)Google Scholar
  7. 7.
    Zhou, B.Y., Cao, W., Zhao, S.S., Lu, X, Du, Z.: Virtual network mapping for multi-domain data plane in software-defined networks. In: 2014 4th International Conference on Wireless Communications, Vehicular Technology, Information Theory and Aerospace & Electronic Systems, Aalborg, Denmark, pp. 1–5 (2014)Google Scholar
  8. 8.
    Zubow, A., Doring, M., Chwalisz, M., Wolisz, A.: A SDN approach to spectrum brokerage in infrastructure-based cognitive radio networks. In: 2015 IEEE International Symposium on Dynamic Spectrum Access Networks, Stockholm, Sweden, pp. 375–384 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Electric Power Dispatch & Control CenterGuangdong Power Grid Co., Ltd.GuangzhouChina

Personalised recommendations