Develop a smart traffic monitoring system that processes live vehicle data at edge nodes to reduce latency and bandwidth consumption. The system will analyze congestion patterns, detect violations, and provide real-time alerts to authorities while minimizing reliance on centralized cloud processing.
Study the fundamentals of edge computing architecture and compare it with traditional cloud-based systems.
Research IoT sensors such as cameras, ultrasonic sensors, and RFID for traffic monitoring.
Design a system architecture where edge devices preprocess traffic video streams locally.
Implement object detection using lightweight machine learning models (e.g., MobileNet, YOLO Tiny) at the edge.
Develop modules to count vehicles, detect signal violations, and estimate traffic density.
Configure edge gateways (e.g., Raspberry Pi or NVIDIA Jetson Nano) for local analytics processing.
Implement data filtering to send only summarized or critical information to the cloud server.
Design a real-time dashboard to display traffic analytics and alerts.
Optimize bandwidth usage by reducing redundant data transmission.
Perform latency analysis comparing edge processing versus cloud processing.
Test the system under simulated high-traffic conditions.
Document system performance metrics including processing time, network usage, and accuracy.
Evaluate scalability for city-wide deployment.
