DYNAPARC: AI-Driven Predictive Path Failure Management for Industrial IoT-Fog Networks

TY - GEN

T1 - DYNAPARC: AI-Driven Predictive Path Failure Management for Industrial IoT-Fog Networks

AU - Alawadh, Rehab

AU - Ahmadi, Hamed

N1 - © IEEE, 2025. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.

PY - 2025/5/22

Y1 - 2025/5/22

N2 - The increasing adoption of IoT-Fog networks in industrial environments demands resilient systems to meet stringent Quality-of-Service (QoS) requirements. Network failures disrupt critical processes and degrade QoS, necessitating innovative predictive failure management. This paper presents the Dynamic Resilient Path Recovery (DYNAPARC) system, an AI-centric solution leveraging Software-Defined Networking (SDN) to predict and mitigate failures in industrial IoT-Fog networks (IIoT).DYNAPARC integrates AI-based reliability prediction model with SDN's programmable architecture and routing protocols to enhance resilience. A hybrid approach combines proactive and reactive methods: secondary paths are pre-installed (proactively) for immediate failover during primary link failures, while new alternative paths are dynamically calculated in real-time (reactively) for multiple failures, ensuring adaptive routing. To quantify the system’s performance, a novel Network Performance Score (N) measures QoS under failure conditions. Simulations show that DYNAPARC maintains an N score above 0.975135 before and after failures, outperforming traditional reactive and proactive methods. Integrating machine learning in the SDN controller significantly reduces packet loss by selecting the most reliable paths. These results highlight the potential of AI-driven prediction and SDN to achieve predictive reliability, ensuring superior resilience, fast recovery, and efficient traffic management in fog-based IIoT environments.

AB - The increasing adoption of IoT-Fog networks in industrial environments demands resilient systems to meet stringent Quality-of-Service (QoS) requirements. Network failures disrupt critical processes and degrade QoS, necessitating innovative predictive failure management. This paper presents the Dynamic Resilient Path Recovery (DYNAPARC) system, an AI-centric solution leveraging Software-Defined Networking (SDN) to predict and mitigate failures in industrial IoT-Fog networks (IIoT).DYNAPARC integrates AI-based reliability prediction model with SDN's programmable architecture and routing protocols to enhance resilience. A hybrid approach combines proactive and reactive methods: secondary paths are pre-installed (proactively) for immediate failover during primary link failures, while new alternative paths are dynamically calculated in real-time (reactively) for multiple failures, ensuring adaptive routing. To quantify the system’s performance, a novel Network Performance Score (N) measures QoS under failure conditions. Simulations show that DYNAPARC maintains an N score above 0.975135 before and after failures, outperforming traditional reactive and proactive methods. Integrating machine learning in the SDN controller significantly reduces packet loss by selecting the most reliable paths. These results highlight the potential of AI-driven prediction and SDN to achieve predictive reliability, ensuring superior resilience, fast recovery, and efficient traffic management in fog-based IIoT environments.

M3 - Conference contribution

BT - DYNAPARC: AI-Driven Predictive Path Failure Management for Industrial IoT-Fog Networks

PB - IEEE Communications Society

T2 - IEEE International Conference on Computer Communications

Y2 - 19 May 2025 through 22 May 2025

ER -