Abstract
The study presents a physics-aware simulation framework developed in Python to emulate Internet of Things (IoT)-based monitoring systems for electronic laboratory environments. The framework generates synthetic sensor data for temperature, humidity, and light intensity, representing environmental parameters that often influence the performance, stability, and safety of laboratory electronics and optoelectronic components. By implementing programmable threshold logic and real-time alert dispatch through RESTful APIs, the system enables automated detection of parameter exceedances and notification delivery without the need for physical hardware. The simulation supports both batch and real-time modes, allowing flexible emulation of thermal fluctuations, ambient light shifts, and humidity drift in controlled settings. Application scenarios include overheating prevention in laser-driven devices, monitoring of ambient light in optical benches, and detecting environmental drift in sensitive experiments such as photoemission or cryogenic setups. The results demonstrate reliable threshold crossing detection with response times under 2 seconds and support multivariate data visualisation to identify compound stress conditions. This framework is particularly suited for early-stage prototyping, instructional use, and safety validation in physics-oriented laboratories. By combining domain-specific sensor modelling with IoT-based control logic, the proposed method offers a low-cost and extensible backend for responsive laboratory monitoring and smart alert systems.