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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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Design and Implementation of a Hybrid Real-Time Salinity Intrusion Monitoring and Early Warning System for Bang Kachao, Thailand
(2025-07-01)
Seeboonruang, Uma
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Tanachaichoksirikun, Pinit
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Anuwongpinit, Thanavit
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Sirikaew, Uba
Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions.
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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
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Anuwongpinit, Thanavit
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Sarikprueck, Piampoom
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Ta, Kuson
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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
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Anuwongpinit, Thanavit
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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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Multi-sector Collaboration for Students of different levels to Learn Digital Technology Skills using Competency Assessment from Learning to Create pieces with a 3D printers for Young Innovators Case Study: MogroWittayakom School
(2024-01-01)
Terdyothin, Alongkot
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Archevapanich, Tuanjai
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Chansuthirangkool, Manit
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Inyoo, Pondthip
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Anuwongpinit, Thanavit
This article is the result of collaboration across various associations to create learning spaces for students in compulsory and optional education as well as living skills. This article provides an example of learning digital technology skills through collaboration, starting with the establishment of an innovation room, followed by instructional activities to explore assessment methods for competency in creating works using a 3D printer tailored for young innovators. By employing rubric- based technology tailored to real-world conditions, it aims to fulfill teachers' expectations for student outcomes. This approach helps students understand and apply a rubric to evaluate and improve their own creations. The demographic involves 20 high school students from MogroWittayakhom, selected through purposive sampling. The students participated in a practical performance test, receiving step-by-step training over two days, totaling 12 hours. Upon completing the instruction, students designed and fabricated their projects using Tinkercad software, which were then printed with a 3D printer. The instructor evaluated the students using a rubric-based competency assessment with three scoring levels. The results showed that all 20 participants achieved a high competency level as per the rubric, validating the hypothesis with 100% of the participants meeting the performance criteria.
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Image Enhancement and 27 Pretrained Convolutional Neural Network Models for Diabetic Retinopathy Grading
(2023-01-01)
Kanjanasurat, Isoon
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Anuwongpinit, Thanavit
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Purahong, Boonchana
Diabetic retinopathy (DR) affects the retina's blood vessels and causes vision loss. Fundus images are used to diagnose DR, which is a lengthy process because experienced clinicians must accurately diagnose the disease and identify microlesions early to prevent blindness. Computer vision can be used for retinal image classification. The APTOS dataset contains 5990 normal, moderate, mild, proliferate, and severe retinal images. In this study, we proposed a convolutional neural network (CNN) ensemble for DR fundus grading. Each image channel was enhanced by contrast-limited adaptive histogram equalization (CLAHE) and gamma correction and then fed to 27 pretrained CNN models for one-time training to examine the DR grading. The results showed that MobileNet's green channel with the CLAHE technique is sufficiently fast and accurate for disease classification. The grading retinal images had an accuracy of 96.95%, a precision of 96.17%, a sensitivity of 97.80%, an F1 score of 96.98%, and a specificity of 97.75%. In addition, the proposed method improves the speed and robustness of retinal DR grading.
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Development of IoT portable device for saline water monitoring in Bang Kachao Area of Thailand
(2023-01-01)
Anuwongpinit, Thanavit
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Sutthinoon, Sakditat
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Tanachaichoksirikun, Pinit
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Purahong, Boonchana
This research developed a portable water salinity measurement device using internet of things technology to support farmers in Bang Kachao area in Samut Prakan province, Thailand, to select quality water for cultivation and consumption. The system hardware consists of a microcontroller ESP32 with a GPS module and an electrical conductivity sensor with an analog-to-digital conversion module for saline water measurement. This device can connect to an internet system via a Wi-Fi network and MQTT protocol to transmit the data of saline water value and measuring location to the cloud system. In the cloud system, the node-red system software is installed as a data gateway for device interfacing. In addition, a node-red dashboard is used to collect data from a portable device and previously installed water quality monitoring station for visualizing a portable device location and saline water data. This dashboard is a web page that allows people to monitor the water situation in the area that is suitable to use.
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Medical Drone Managing System for Automated External Defibrillator Delivery Service
(2022-04-01)
Purahong, Boonchana
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Anuwongpinit, Thanavit
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Juhong, Aniwat
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Kanjanasurat, Isoon
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Pintaviooj, Chuchart
One of the common causes of a heart attack is fibrillation, a condition that causes an irregular and often abnormally fast heart rate. There is scientific evidence that the survival rate of sudden cardiac arrest patients who are rescued with cardiopulmonary resuscitation (CPR) and with the use of an automated external defibrillator (AED) is significantly increased. Despite the recommendation that automated external defibrillators should be installed in the workplace, along with a proper management system and training for employees on how to use the device, less than 70% of non-residential areas have an AED installed. The situation is even worse in residential areas, with less than 30% having an AED installed. This research concerns the development of a medical drone managing system that can deliver an AED in case of emergency. An application was developed that can be installed on the mobile phone and/or tablet of the patient or the accompanying person. In the event of a heart attack, the patient or the accompanying person can call a medical drone by sending coordinates to the drone station and a notification to medical staff. The drone station administrator can respond by sending the drone, which automatically lands at the patient’s location. After being tested in a simulation situation, the operational field test yielded satisfactory results. The medical drone can land within 1.5 meters of the destination. The designed AED drone can be used not only to deliver AEDs, but also first aid kits and prescribed drugs suitable for medical care. Such a system is especially useful in the current context of the COVID‐19 pandemic.
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Optimized Gaussian Pulse Design for UWB System Using Particle Swarm Optimization Based-on Generalized Bessel Polynomials
(2022-01-01)
Chutchavong, Vanvisa
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Anuwongpinit, Thanavit
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Purahong, Boonchana
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Archevapanich, Tuanjai
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Janchitrapongvej, Kanok
The ultrawideband system operates a very short pulse with enormous bandwidth to provide high data rates for data transmission. To design the UWB pulse, considering the pulse shape is very necessary, and a spectral emission mask of the designed pulse should meet the FCC spectral mask requirement between frequency range 3.1 GHz to 10.6 GHz. The traditional UWB pulse design is based on the Gaussian derivative. However, the frequency spectrum is not satisfied the FCC spectral mask requirement. In this study, the Gaussian pulse can be designed from the mathematical characteristic of the generalized Bessel polynomial. The spectral efficiency of the proposed pulse can be improved by the combination of the derivative of Gaussian pulse with a weight coefficient optimization with particle swarm optimization (PSO). PSO is a population-based optimization algorithm inspired by animal behavior. PSO is applied with generalized Bessel polynomial transfer function to gain the best weight coefficient, we proposed to optimize its weight vector to design a pulse that exceeds to FCC spectral mask. The results were found in MATLAB software show that generalized Bessel polynomials can approximate the proposed pulse with combination method and PSO. The spectral efficiency is improved to 89.30% and the spectrum is greater close to the FCC spectral mask requirement. To confirm an improved spectral efficiency compared to the previous works.
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Engineering Education Roadmap of the Future Trend of Basic Metaverse based on VR with cooperation between the government and the private sector
(2022-01-01)
Purahong, Boonchana
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Anuwongpinit, Thanavit
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Kanjanasurat, Isoon
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Chansuthirangkool, Manit
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Singto, Kamphon
This article presents an educational roadmap of future trends of Metaverse in VR-based by collaborating between the School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL) with iMAKE company to make a part-time learning plan. The objectives were to measure the achievement and evaluate satisfaction with the development of part-time learning skills in technology on the topic 'Basic Metaverse based on VR'. The sample group was students in a double-degree bachelor's degree program (Dual Degree) between the School of Engineering and the Faculty of Science, KMITL: Bachelor of Engineering (IoT System and Information Engineering) and Bachelor of Science (Industrial Physics) for 16 students by selecting a specific sample group, the engineering education program has a systematic process. The results showed that the achievement of part-time learning skills development in technology on the topic 'Basic Metaverse based on VR' higher than the set criteria 74 %, the overall satisfaction is at a very good level, the mean satisfaction was 4.636 and the sample standard deviation was 0.39.

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