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INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
(2026-01-01)
Anuwongpinit, Thanavit
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Manthawornsiri, Chananont
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Archevapanich, Tuanjai
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Purahong, Boonchana
In this article, we propose developing digital innovation of smart monitoring systems in the poultry farm using the Internet of Things (IoT) technology. This work aimed to design and develop a monitoring system based on an IoT system that transforms a traditional farm that uses a manual management system to apply an IoT system for environmental monitoring in a poultry farm. The main components include a hardware component that was designed and implemented to gather data of the poultry houses. Temperature and humidity sensor nodes are applied to monitoring the environment of poultry houses. The LoRa communication module in the sensor node will forward data to the gateway. The second principal component is the cloud server for data acquisition from the gateway. The cloud will be responsible for back-end processing and a web-based dashboard displaying mechanism. This system can work as an alarm notification system using LINE notify API for the LINE application that is the most popular communication application in Thailand. The system was implemented practically in one of the poultry farms in Prachinburi province, Thailand. The results indicate that the proposed system provides significant advantages, including enhanced monitoring accuracy, reduced energy consumption, and improved real-time environmental tracking for poultry farms.
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IoT-based Water Quality Monitoring Station and Forecasting System with Machine Learning
(2025-01-01)
Jomjaiekachorn, Thanart
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Anuwongpinit, Thanavit
;
Purahong, Boonchana
This paper presents an IoT-based water quality monitoring and forecasting system designed for real-time and continuous assessment of water resources. The system integrates Siemens SIMATIC IOT2050 as an Industrial IoT Gateway, which collects data from sensors measuring conductivity, pH, dissolved oxygen, and temperature using RS485 Modbus RTU communication. Data processing occurs at the edge using Node-RED and is transmitted to AWS Cloud via MQTT for storage and visualization on a dashboard. Predictive analysis employs machine learning models, including XGBoost with Optuna parameter tuning and Long Short-Term Memory (LSTM) networks, for water quality forecasting. Results indicate superior performance of LSTM for most parameters, while XGBoost excels in pH prediction. This system demonstrates scalability, reliability, and potential for enhanced water quality management in diverse environments.
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Centralizing Data Warehouse Platform for Mining Management Using AWS Computing Instance
(2024-01-01)
Sathaporn, Posathip
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Chaowalittawin, Vasutorn
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Krungseanmuang, Woranidtha
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Benjangkaprasert, Chawalit
;
Purahong, Boonchana
Mining industry is one of significant industry in the world. In order to increase efficiency, safety, sustainability and environmental impacts in mining process. This article presents a centralized data platform for mining process by design based on microservices architecture which can be supported various of input data source such as manual form, excel file and internet of things (IoT) device and notification when the value is abnormal. Our platform implementation on Amazon Web Service (AWS) cloud. For development of productivity in process and controlling quality the mine environment. The results of the platform operations with various source of data acquisition with response time is less than 1.5 seconds per each request.

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