An Edge-Cloud Smart Classroom System for Energy Efficiency with Intelligent Classroom and Exam Modes

This paper presented a classroom monitoring and automation system developed using ESP32, Firebase and computer vision. The prototype controlled temperature, smart light, door actuation, emergency alerts, student counting and exam monitoring on a single platform. Testing proved that the system responded to sensor events and updated Firebase without noticeable delay. The student counting achieved 92% accuracy and cheating detection achieved 85% accuracy during exam mode trials. The result demonstrates the potential of useful classroom automation using low-cost hardware and common software tools. For future work, we plan to improve the placement of cameras for improved detection accuracy and to run lightweight vision models directly on the embedded hardware, thus removing the dependency on a processing machine external to the hardware. In the future, we will optimise camera placements for better detection accuracy and run lightweight vision models directly on the embedded hardware, without an external processing machine.