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    The supply chain information sharing path based on the internet of things
    (2024-07-01)
    Zeng, Xiao
    ;
    Yi, Jing
    In the context of globalization and digitization, supply chain information sharing and circulation have become important links in supply chain management. The current difficulty in sharing supply chain information is significant, and there are issues of information asymmetry and incompleteness among co participants. In order to improve the integrity and efficiency of supply chain information sharing and enhance the competitiveness of the high supply chain, this article conducts in-depth research on the path of supply chain information sharing using Internet of Things technology. This article first analyzes the level of information sharing, influencing factors, and existing problems, then explores the Internet of Things technology, and finally establishes specific methods and means for the supply chain information sharing path through the Internet of Things technology. To verify the effectiveness of the supply chain information sharing path based on the Internet of Things, in this article, a comparison was made between the development effects of enterprise supply chains before and after applying the supply chain information sharing path based on the Internet of Things. The results show that after applying the IoT based supply chain information sharing path, the market price, production capacity, and market forecasting ability of enterprises have all increased by more than 20%. The conclusion indicates that the Internet of Things can help optimize supply chain information sharing and provide a new perspective for enterprise supply chain management and development.
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    Body-part Identification Learning for Preschool Children Using Internet of Things
    (2023-01-01)
    Suwastika, Novian Anggis
    ;
    Sitohang, Paulus Berliz
    ;
    Yasirandi, Rahmat
    ;
    Masrom, Maslin
    ;
    Qonita, Qori
    Children can identify body parts either on dolls or on their bodies. The inability of toddlers (aged 2-3 years) to demonstrate body parts can indicate potential delays in language, cognitive, and body scheme development. The integration of teaching aids for body part identification with the Internet of Things (IoT) offers several advantages, including automation and customization of activities, digital storage of activity results for easy accessibility, real-time and accurate evaluation and feedback, as well as the freedom to repeat activities without limitations. This study focuses on implementing the integration of body part identification trainers with IoT, following the five phases of IoT development: initialization, analysis, design, implementation, and evaluation. The initialization phase involves problem identification, and the position of this research is based on a literature study. The analysis phase establishes parameters for measuring the functionality and performance of the system, as well as requirements for software and hardware components. The design phase produces the system architecture and system flowchart. The implementation phase results in a prototype integrating hardware and software components. Evaluation results based on the predefined functionality and performance parameters set during the analysis phase indicate that all system functionalities have been successfully achieved. The system demonstrates a response time below 1 second for sensor buttons and LED lights, and the feedback system shows 100% accuracy. This prototype highlights the potential and opportunities of implementing IoT to support learning at various levels, specifically at the pre-school level, as shown in this study.
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    Electronic nose ammonia gas monitoring via IoT system for Chlorella sp. cultivation
    (2023-01-01)
    Srinuanjan, Keerayoot
    ;
    Kruakuanphet, Aphichaya
    ;
    Phongwisit, Phachara
    ;
    Yindeesuk, Witoon
    ;
    Kamoldilok, Surachart
    In this paper, we present an electronic nose ammonia gas monitoring via IoT system for Chlorella sp. cultivation. The MQ–137 gas sensor module is selected as the primary sensor for measuring ammonia gas concentration. The pH sensor module is the sensor for measuring the pH levels in Chlorella sp. ponds. The MQ–137 gas sensors are calibrated with a known concentration of ammonia gas in the calibration box. Calibration conditions are set corresponding to low concentration of ammonia gas produced by Chlorella sp. cultivation. The pH sensor module is calibrated against a standard pH buffer. The calibration data obtained from the calibration method is input into computer programming and processed by the ESP8266 microprocessor. After calibration, the MQ–137 gas sensor and pH sensor modules are used to measure the ammonia gas concentration and pH levels in the Chlorella sp. ponds. Chlorella sp. is cultivated under three conditions of light intensity, natural light with an average light intensity of 1521 Lux, and artificial light with a light intensity of 1000 Lux and 2000 Lux, respectively. The ammonia gas concentration and pH levels of Chlorella sp. cultivation are transferred to the cloud system and displayed via the IoT system. And the system can also send a notification to a smartphone when ammonia gas concentration and the pH levels reach the specified value.
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    Vegetation Health Monitoring System for Smart Farm using NDVI Analysis
    (2022-01-01)
    Wisayataksin, Sumek
    ;
    Junta, Nontaput
    ;
    Kuanpreeyawat, Jirat
    We adapt the technique of normalized difference vegetation index (NDVI) to smart farms for monitoring the health of plants remotely over the IoT network. The system consists of a camera with 2 filters attached. The first one is an RGB filter to capture the visible light image, whereas another one is an infrared range filter. Two images are taken from a camera module and analyzed on Raspberry Pi 4 before being transmitted the NDVI image to the webserver for remote controlling and monitoring. The experimental results from actual plants reveal that our methodology can distinguish the healthy and unhealthy plants easily and efficiently.
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    Multi-range Ammonia Gas Sensor Control and Monitor via IoT System
    (2022-01-01)
    Kruakuanphet, Aphichaya
    ;
    Phongwisit, Phachara
    ;
    Yindeesuk, Witoon
    ;
    Kamoldilok, Surachart
    ;
    Srinuanjan, Keerayoot
    In this paper, we present a multi-range of ammonia gas concentration control and monitoring via IoT system. We can select the ammonia gas concentration measurement range by adjusting load resistance within the voltage divider circuit. The appropriate measurement range, which corresponds to the concentration of ammonia gas produced by agricultural and industrial activities, can control and monitor via Blynk application. The MQ-137 gas sensor is selected as the main sensor, and each load resistance condition within the voltage divider circuit is calibrated with known ammonia gas concentration inside a calibration box. The calibration data is input into computer programming and processed by ESP8266 microprocessor. After calibration, we obtained a multi-range ammonia gas sensor suitable for measuring the ammonia gas concentration for each concentration range. The experiment showed that load resistance affects the measurement accuracy of ammonia gas concentration. We can select the load resistance suitable for the ammonia gas concentration and display on a smartphone via IoT system.
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    Water Level Monitoring and Evacuation Guideline Using Ant Colony Optimization on Mobile Application
    (2020-08-01)
    Kimpan, Warangkhana
    ;
    Kasetvetin, Sirawich
    ;
    Kimpan, Chom
    The most natural disasters that have happened in Thailand are storm and flood problems. The people who live near water sources have no warning about the overflowing of water nearby, so they cannot evacuate or get help in time. Thus, there is always a high risk of losing properties or lives. In order to alleviate the losses, this paper proposes water level monitoring on Android application from Internet of Things devices and the guideline for evacuation by applying Ant Colony Optimization which is inspired by the real ant colony. Internet of Things devices are used to monitor the water levels in community for the user who lives near the water sources or near the places which have high risk of flooding. The Hydrostatic level sensors are placed in the water basin near the community to measure the height of the water which can also be observed in real time from mobile application. When the height of the water reaches the critical value that was set in the application, it sends notifications to the user. Moreover, Line bot is used to let the user knows the potential risks from rising water levels. At the critical level, the user needs to evacuate to a safe place located nearby. The application will guide the user to follow the direction to the most safety destination. In case of many people are already evacuated in one place and it reached the maximum amount of limitation, the application will change the recommendation direction to other places nearby using Ant Colony Optimization algorithm for making decisions.