Real Time Device Tracking System

Abstract

Conventional approaches for tracking devices, including manual logs, periodic inspections, and RFID-based monitoring, are still widely used in many organizational environments. However, these methods suffer from several limitations such as dependency on human effort, risk of data inaccuracies, and lack of effective mechanisms to prevent misuse or unauthorized handling. In large-scale deployments, these systems fail to provide continuous visibility of device location and operational status. To overcome these challenges, this work presents a real-time device tracking framework that continuously monitors device movement using sensor-driven data acquisition. The system utilizes technologies such as GPS modules, IoT-based sensing, and wireless communication to capture and process tracking data without requiring active user involvement. This enables accurate, real-time monitoring and improves overall asset visibility and control. The proposed solution is implemented entirely in Python and integrates a lightweight database with an interactive web-based dashboard for visualization and management. The system also incorporates anomaly detection to identify irregular device behavior and enhance security. Experimental evaluation demonstrates high tracking accuracy, low latency, and a significant reduction in administrative workload. The framework is scalable, cost-effective, and suitable for deployment across academic, industrial, and enterprise environments.

Introduction

This work presents a Python-based real-time device tracking system designed to overcome the limitations of traditional methods such as manual logging, RFID, and barcode systems, which lack real-time visibility and scalability. By leveraging IoT, GPS, and wireless communication, the system enables continuous, automated monitoring of devices with improved accuracy and reduced human intervention.

The system is built using a modular architecture consisting of device registration, data acquisition, tracking, anomaly detection, logging, and a Streamlit-based dashboard. It uses SQLite for structured storage, asynchronous processing for real-time performance, and thread-safe mechanisms to ensure reliable data handling. An anomaly detection module identifies unusual device behavior using sliding window analysis, enhancing security and misuse detection.

A web dashboard provides real-time visualization, filtering, and reporting, enabling efficient monitoring and management of devices. While the system performs well, it faces challenges such as signal variability in indoor environments and scalability limits for very large deployments.

Experimental results show strong performance with 95.8% overall tracking accuracy, low false detection (<0.4%), and fast response times (as low as 68 ms after optimization). The system also significantly reduces administrative workload by automating tracking, logging, and reporting processes, making it an efficient and scalable solution for modern asset management.

Conclusion

This work introducesa Python-based real-time device tracking system that integrates continuous monitoring, anomaly detection, structured data logging, and an interactive web dashboard within a unified framework. The system demonstrates reliable performance in tracking device activity with high accuracy and low latency while ensuring efficient data management. By automating monitoring and reporting processes, the proposed solution significantly reduces manual effort and enhances operational efficiency. The use of a browser-based dashboard enables administrators to access tracking information easily without requiring technical expertise. Features such as real-time updates, data visualization, and automated report generation provide practical advantages for managing devices in academic, industrial, and enterprise environments. The system is lightweight, cost-effective, and can be deployed using commonly available hardware, making it suitable for real-world applications. Future work will focus on improving scalability and performance through advanced techniques. This includes the integration of optimized indexing methods for faster data retrieval in large-scale deployments, enhancement of anomaly detection using machine learning models, and support for edge deployment on low-power devices. Additionally, incorporating multi-user access control and secure communication mechanisms will further strengthen the system’s usability and reliability. These enhancements will enable the system to scale effectively while maintaining efficiency and security.

References

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Copyright

Copyright © 2026 Ranvir Kumar Priyanshu, Rajan Yadav, Suresh Kumar Tiwari, Dr. Sanjay Pachauri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.