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Author: search.filters.author.Anuwongpinit, ThanavitSubject: search.filters.subject.Internet of Things

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    INTERNET OF THINGS BASED PRACTICAL SMART ENVIRONMENTAL MONITORING SYSTEM FOR POULTRY FARM
    (2026-01-01)
    Anuwongpinit, Thanavit
    ;
    Manthawornsiri, Chananont
    ;
    Archevapanich, Tuanjai
    ;
    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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    Development of Shuttle-Tram Tracking System using IoT and Comparing of Map localization using Google APIs and Custom Map
    (2025-01-01)
    Thothong, Thirasit
    ;
    Anuwongpinit, Thanavit
    ;
    Sarikprueck, Piampoom
    ;
    Ta, Kuson
    ;
    Wilasuwan, Thanapat
    This paper presents the development of an Internet of Things (IoT)-based Transit Tracking System. At the Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, a public transportation system has been developed to facilitate transit around the campus using shuttle cars. Shuttle stations are placed along the routes, and cars run every 10-15 minutes. However, the problem of users not knowing the precise location and arrival time of each shuttle arose. To solve this, a transit tracking system was developed using IoT technology. IoT devices were installed on each vehicle to monitor their real-time locations. The data is collected via GPS and transmitted over the 4G network to a server, then stored in a database and displayed on a website in real-time using React. To reduce costs, a custom map was developed using the Leaflet library as an alternative to the Google Maps API. Testing within the Faculty of Engineering showed that the designed IoT system could transmit data successfully, with a performance of a custom map close to that of the Google Maps API, while also significantly reducing costs.
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    IoT-based Water Quality Monitoring Station and Forecasting System with Machine Learning
    (2025-01-01)
    Jomjaiekachorn, Thanart
    ;
    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.