Telemetry website for vehicle-to-vehicle and vehicle-to-infrastructure communication
Intelligent Transportation Systems (ITS) highlights the need for efficient Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This research establishes a real-time telemetry framework based on Message Queuing Telemetry Transport (MQTT) operating through WebSocket to enable r...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
UiTM Press
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/126338/1/126338.pdf https://ir.uitm.edu.my/id/eprint/126338/ https://jeesr.uitm.edu.my |
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| Summary: | Intelligent Transportation Systems (ITS) highlights the need for efficient Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. This research establishes a real-time telemetry framework based on Message Queuing Telemetry Transport (MQTT) operating through WebSocket to enable rapid data transmission between vehicle units, traffic light units, and a web-based monitoring system. The system combines GPS data processing, real-time vehicle tracking, and traffic light status updates to display information through a web-based monitoring system that integrates Google Maps API for map interface. The backend of the web development utilizes GPS coordinates to determine vehicle speed before storing structured telemetry data in an online database by using InfluxDB, a timeseries database for historical analysis. Performance evaluation of the telemetry system focuses on MQTT protocol efficiency, WebSocket latency, and GPS integrity to ensure a robust and reliable V2I telemetry system. Existent literature indicates that MQTT communication protocol, particularly with QoS 1, ensures efficient and reliable telemetry with minimal delay, it is expected to deliver the benefit to this telemetry system. Additionally, the study highlights that increased vehicle speeds correlate with higher GPS positioning errors, necessitating improved filtering techniques for enhanced positional accuracy. Given these findings, this research explores the requirement for robust V2I communication frameworks in ITS, paving the way for future advancements through machine learning-based predictive modeling, 5G connectivity, and edge computing for enhanced processing efficiency. By addressing critical gaps in real-time telemetry, this study offers a roadmap to the development of scalable and efficient ITS solutions that support urban traffic optimization. |
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