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In smart grid operations, Neighborhood Area Networks (NANs) play a crucial role in ensuring low latency communication but face challenges like congestion and cyberattacks. This paper introduces a multi-controller Software-Defined Networking (SDN) framework integrating a proactive prediction model using Graph Neural Networks (GNN) and a reactive Deep Q-Network (DQN) for real-time routing optimization. The framework features a hybrid failover mechanism for seamless transitions across various technologies (Wi-SUN, LoRa, ZigBee, 5G, and wired PLC) and includes an Intrusion Detection System (IDS) for secure path selection. Implementation using Mininet, NS-3, and real-world datasets demonstrates improved failover latency, throughput, packet delivery ratio, and attack detection accuracy compared to existing methods.

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Communication Resilience in Smart Grid Nans: Challenges, Solutions and Future Outlook

  • 会议论文

  • 发表日期：

    2026年

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  • Aditya Sai Srinivas T.,
  • Kushvanth Chowdary Nagabhyru,
  • Ramesh Inala,
  • Shobana Jayakumar,
  • S. R. Arun Raj

摘要

In smart grid operations, Neighborhood Area Networks (NANs) play a crucial role in ensuring low latency communication but face challenges like congestion and cyberattacks. This paper introduces a multi-controller Software-Defined Networking (SDN) framework integrating a proactive prediction model using Graph Neural Networks (GNN) and a reactive Deep Q-Network (DQN) for real-time routing optimization. The framework features a hybrid failover mechanism for seamless transitions across various technologies (Wi-SUN, LoRa, ZigBee, 5G, and wired PLC) and includes an Intrusion Detection System (IDS) for secure path selection. Implementation using Mininet, NS-3, and real-world datasets demonstrates improved failover latency, throughput, packet delivery ratio, and attack detection accuracy compared to existing methods.