DESIGN AND IMPLEMENTATION OF LOW COST AIR QUALITY AIR QUALITY MONITORING SYSTEM USING CLOUD SERVER AND GATEWAY

• Main Author: Izzatul Fauzan H, Muhammad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/73885
• Institution: Institut Teknologi Bandung

The air quality data of Bandung City is not yet obtainable comprehensively to represent the entire city of Bandung. Representing the entirety of Bandung requires the use of 10 air monitoring devices, but the Bandung City Environmental and Sanitation Agency (DLHK) only has one monitoring device. The acquisition of air monitoring devices also incurs a significant cost, approximately 400 million Indonesian Rupiah, making it difficult for DLHK to meet the ideal conditions. Furthermore, air quality monitoring devices monitor PM_2.5, PM_10, CO, temperature, and humidity. All these issues can be addressed with a Low-Cost Air Quality Monitoring System. This Low-Cost Air Quality Monitoring System is designed to facilitate DLHK in making informed decisions to reduce high air pollution. The system being developed consists of three main subsystems: the Cloud Database Subsystem, Cloud Processing Subsystem, and Access Point Subsystem. The Cloud Database Subsystem is used to store and process data received from the Air Monitoring Devices, while the Cloud Processing Subsystem calculates the Air Pollution Standard Index (ISPU) and processes average air pollution data. Real- time data storage is performed through the Buffer Management Module, while the Communication to Server Module is responsible for transmitting data from the Gateway to the Server. The evaluation results indicate that the proposed system meets the specifications for real-time data transmission. The data transmission latency values from the Cloud Server to the Webserver and from the Gateway to the Cloud Server are below the specified threshold. The successful implementation of the Cloud Server and Gateway blocks also confirms the system's capability. Further development of the system involves implementing Machine Learning techniques to predict future air pollution levels and expanding the range of pollutants that can be processed, such as SO2, NO2, HC, and O3. These improvements will provide more comprehensive air quality data and enable proactive action to be taken.