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    Item type:Publication,
    YOLO Based IoT Tracking for Academic Labs on Raspberry Pi
    (2026-06-16)
    Jiamrachada, Tamakorn
    ;
    Nonsiri, Sarayut
    ;
    Kamin, Pichitchai
    ;
    Nanthajirapong, Nathaphon
    Academic IoT laboratories often rely on shared equipment, making efficient borrow-return management essential. Conventional management methods depend on manual recording, which can cause verification delays, data entry errors, and increased staff workload. This study proposes a YOLO based IoT equipment tracking system that uses a camera to detect and count devices inside student equipment boxes for borrow-return recording and inventory monitoring. The system runs on a Raspberry Pi 5 for real-time edge-based processing, while detection results are stored in a database and displayed through a web-based dashboard. Experimental results show that the YOLO12n model achieved an F1-score of 0.996 with a real-time inference speed of 12.01 FPS, demonstrating the system's effectiveness in reducing human error and improving laboratory inventory management efficiency.
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    Industrial IoT Edge Computing Platform for Real-Time Monitoring
    (2022-01-01)
    Glomglome, Sorayut
    ;
    Damrongphokaphan, Watanyou
    ;
    Phormraksa, Kittisak
    ;
    Witchuvanit, Korntawat
    This research's goal is to develop an industrial IoT edge computing platform for Industrial IoT 4.0 that are currently in the process of transitioning from legacy systems to adopting IoT system. Due to connecting and collecting data from each machine part is still a problem of transition from the old system, this is the origin of this research. The system consists of a Remote Terminal Unit (RTU) which collect data from machines with sensors and an Edge Computing Device used to collect data from RTUs via MQTT Protocol. The system also has an IoT platform that processes incoming data for storing in the database and displays on the Web Application in real time and in retrospect and can also alert to Line Notify according to the conditions predefined by the user. Based on the benchmark test, an edge computing device can concurrently handle RTU connections up to 800 connections.