Integrated communication and location monitoring system for vehicle monitoring via smartphone calls

• Published in: Alexandria engineering journal Vol. 128; pp. 1159 - 1167
• Main Authors: Al-Wesabi, Fahd N., Alshahrani, Amnah, Alanazi, Rakan, Alshahrani, Mohammed Mujib, Sorour, Shaymaa, Alghamdi, Asmaa Mansour, Alamri, Malak Zayed, Alanazi, Sultan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2025; Elsevier
• Subjects: Embedded Systems; Fleet Management; GPS/GSM communication; Real-time monitoring; Smartphone Integration; Transportation Technology; Vehicle tracking; Vehicle tracking; Fleet Management; Embedded Systems; GPS/GSM communication; Transportation Technology; Real-time monitoring; Smartphone Integration
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2025.08.035

Real-time tracking and monitoring of vehicles has become increasingly important for ensuring security, optimizing logistics, and enhancing personal convenience. Current vehicle tracking systems often rely on expensive, complex hardware or continuous data transmission, creating barriers to widespread adoption. A streamlined and cost-effective model is proposed to address this gap, combining a smartphone application with a microcontroller to simplify vehicle monitoring. Prior research has established the reliability of Global Positioning System (GPS) /Global System for Mobile Communication (GSM) modules for location tracking, but has underutilized the potential of combining embedded sensors with machine learning analytics. When the user initiates a missed call or SMS to the embedded device, the system retrieves the vehicle’s geographic coordinates and transmits a Google Maps link to the user's smartphone. Previous tracking solutions typically required constant connectivity or specialized tracking platforms; the proposed approach offers an on-demand, low-maintenance alternative. The proposed work advances the field by introducing a novel architecture where an OBD-II connected hardware module collaborates with smartphone and cloud components through optimized data protocols. The system was evaluated through controlled tests and real-world deployments with 25 vehicles over six months. Prototype testing demonstrated < 5 m GPS accuracy and < 3-second response time across various network conditions, with field trials showing 90 %+ user satisfaction in fleet management scenarios. This work contributes to transportation technology by proving the feasibility of hybrid tracking architectures. Future work will explore blockchain applications for data security and expanded IoT sensor integration, potentially transforming how vehicle-monitoring systems balance cost, accuracy, and functionality.