NetCodeAIoT: Enhancing Augmented Intelligence of Things for Vehicle Systems in 5G Networks

In recent years, the convergence of Augmented Intelligence with Internet of Things (IoT) technologies has revolutionized numerous domains, from healthcare to transportation. In the area of vehicular and road cooperation applications, the Augmented Intelligence has been studied for addressing problems with data acquisition, processing, and real-time decision-making, particularly in enhancing traffic coordination, vehicle safety, and energy efficiency. Thus, the proposed work explores the integration of Augmented Intelligence with IoT (AIoT) in autonomous vehicles. We present a framework that leverages dynamic network coding and advanced data management strategies, NetCodeAIoT, to enhance the AIoT communication in 5G networks, focusing on applications within the vehicle industry. This pioneering integration of dynamic network coding and deep learning uniquely addresses scalability and security challenges in vehicular AIoT systems. NetCodeAIoT dynamically adjusts the sparsity level of the decoding matrix, implements unequal error protection network coding, and enables instantaneous decoding of data. These techniques optimize transmission efficiency and enhance security in AIoT scenarios, using a deep learning architecture. Experimental evaluations were performed with NetCodeAIoT, demonstrating an improvement in network efficiency and a reduction in average time delay compared to traditional AIoT communication approaches.