Implementation and Performance Evaluation of ESP32-Based Multi-Sensor IoT System for Real-Time Environmental Monitoring and Early Warning

Abstract

Real-time environmental monitoring has become increasingly important due to growing urban and industrial activities that affect air quality, noise levels, and physical environmental stability. However, many existing monitoring systems remain relatively expensive, lack portability, and are limited to passive monitoring functions without clear performance evaluation. This study aims to implement and evaluate the performance of an Internet of Things (IoT)-based multi-sensor environmental monitoring system integrated with a mobile application and real-time early warning features. The system is developed using an ESP32 microcontroller connected to DHT22, MQ135, SW-420, and KY-037 sensors to monitor temperature, humidity, air quality, vibration, and noise levels. Sensor data are transmitted to a server via a RESTful API, stored in a MySQL database, and visualized in real time through a Flutter-based mobile application. The research adopts a Research and Development (R&D) approach, encompassing requirement analysis, system design, implementation, integration, and functional testing. The experimental results indicate that the system can transmit multi-sensor data reliably with low response time, present environmental information in real time, and consistently deliver early warning notifications when environmental parameters exceed the defined threshold values. This study contributes by providing a practical and replicable performance evaluation of an IoT-based multi-sensor system suitable for small-scale environmental monitoring.