A deep learning-based smart service model for context-aware intelligent transportation system

Effective means for transportation form a critical city infrastructure, particularly for resource-constrained smart cities. Rapid advancements in information and communication technologies have paved the path for intelligent transportation system (ITS), specifically designed for optimal effectiveness and safety with existing transportation infrastructure. A key function of ITS is its ability to aggregate large volumes of data across various sources for event detection. However, prediction accuracy remains a challenge since ITS event detection is characterized by very stringent latency requirements necessitating the use of lightweight detection schemes, thus seriously compromising the efficiency of ITS. This paper attempts to tackle this problem by introducing an IoT-integrated distributed context-aware fog-cloud ensemble that intelligently manages context instances at fog nodes ensuring availability of context instances for ITS. This system enhances prediction accuracy by utilizing a hybrid convolutional neural network (CNN) where each vehicle within the system retains only local information, while adjacent fog nodes gain access to global events via continual federated learning, updating regularly between fog and cloud models. Experiments presented herein illustrate the superiority of the CNN model, yielding an accuracy of more than 95%, which is an improvement of around 3% compared to the LeNet with same RGB input images.