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

This paper presented a classroom monitoring and automa-
tion system developed using ESP32, Firebase and computer

vision. The prototype controlled temperature, smart light, door

actuation, emergency alerts, student counting and exam mon-
itoring 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 light-
weight 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.