DEVELOPMENT OF IOT NETWORK FOR TRAFFIC PATTERN IN EDUCATIONAL SYSTEMS
Abstract
This project focuses on the development of an Internet of Things (IoT) network for monitoring traffic patterns within educational systems using a smart attendance system. The study addresses the growing challenges of congestion, inefficient movement, and safety concerns in school environments. The proposed system integrates facial recognition technology with IoT-enabled devices to automate attendance tracking while simultaneously collecting data on student movement across campus.
The system utilises ESP32-CAM for image capture, TensorFlow Lite for machine learning inference, and Edge Impulse for model training. Attendance data is processed in real time and stored on a cloud-based platform using Google Sheets. The collected data is analysed to identify traffic patterns, peak periods, and congestion points.
Results from the implementation show improved efficiency in attendance tracking and significant potential in optimising campus movement. The system demonstrates high accuracy and low latency, making it suitable for real-time applications. This research contributes to the development of smart campus solutions and provides a scalable model for broader smart city applications.
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