Authors - Majdi Rawashdeh, Dhia Eddine Salhi, Awny Alnusair, Ali Karime Abstract - Ensuring student safety during school transportation remains a critical challenge, motivating automated, intelligent monitoring solutions. This paper introduces a comprehensive IoT-enabled framework for real-time student identication and attendance management aboard school buses. The proposed architecture combines an ESP32-CAM edge device with a suite of machine learning and deep learning models, evaluated on an augmented facial dataset of 40,000 images derived from the Labeled Faces in the Wild (LFW) benchmark. A comparative study of six pipelinesa basic SVM baseline, SVM with PCA, a distance-based classier, SVM with augmentation and grid search, Random Forest, and a proposed CNN-LSTM hybridis conducted. The CNN-LSTM hybrid achieves the highest accuracy of 99.5%, with precision, recall, and F1score exceeding 99%. The architecture spans four layerssensing, gateway, server, and applicationenabling low-latency communication between the edge device, cloud, and a mobile application serving parents and administrators, with end-to-end inference latency below 200 ms per frame. The results validate the system as a scalable, cost-eective, and highly accurate solution for modernizing student safety and attendance in school transportation.
Open Zoom