Hybrid Fault Prediction and Recovery Framework for VANETs using AI and Federated IoT

• Main Authors: Darney, P. Ebby, Vallileka, N., Manoj, Sumitha, Fernando, A. Vegi, Krishnan, R. Santhana, Prasath, S. Ram
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Deep Neural Network; Fault Prediction; Federated Internet of Things; Internet of Things; Network Stability; Prediction algorithms; Predictive models; Recovery Framework; Stability analysis; Support vector machines; Vehicular ad hoc networks; Vehicular Adhoc Networks
• ISSN: 2767-7788
• DOI: 10.1109/ICICT60155.2024.10544657

In contemporary vehicular communication systems, Vehicular Ad Hoc Networks (VANETs) serve a crucial role in enabling seamless data exchange among vehicles and infrastructure components. However, the dynamic and unpredictable nature of vehicular environments renders VANETs susceptible to various faults and disruptions, potentially compromising network performance and jeopardizing safety-critical applications. Therefore, the development of robust fault prediction and recovery mechanisms is imperative to ensure the reliability and resilience of VANETs. This research introduces a novel Hybrid Fault Prediction and Recovery Framework for VANETs utilizing the paradigm of Artificial Intelligence (AI) and Federated Internet of Things (IoT). The proposed algorithm, Deep Neural Network with Federated IoT Model (DNN-FIOT), is presented to enhance fault prediction and recovery efficacy in VANET environments. Extensive simulation analyses comparing DNN-FIOT with existing algorithms are conducted, employing suitable simulation metrics. Results demonstrate the superior performance of DNN-FIOT in terms of fault prediction accuracy, recovery time, and network stability. The proposed framework offers a promising solution for robust fault management in VANETs, ensuring uninterrupted vehicular communication and safety.