Real-Time vehicle tracking architectures using Geolocation, IoT, and cloud-based infrastructure

Abstract

This study adopts an exploratory and applied approach to analyze modern vehicle tracking architectures, emphasizing satellite geolocation, M2M mobile networks, and cloud computing. A structured technical literature review guided the classification of relevant technologies, including hardware, communication protocols, and cloud-based platforms. The proposed system was evaluated in laboratory and field scenarios, demonstrating accurate real-time positioning with an average error of less than five meters using GNSS modules. Data transmission via GSM, GPRS, and 4G networks proved stable, with low latency and minimal packet loss. The packets were sent at 1-minute intervals to optimize data usage from the mobile data plan. Leveraging cloud services like AWS IoT Core and Lambda enabled scalable, serverless processing, while Amazon Timestream ensured efficient time-series data storage. The web dashboard, integrated with Google Maps, provided real-time visualization and historical tracking. Results confirm the system’s robustness, scalability, and applicability to fleet management, logistics, and vehicle safety in urban and semi-urban contexts.