Authors :
Jovin P John; Abhijith A; Nadha K N; Denin Antony; Paul Ansel V
Volume/Issue :
Volume 10 - 2025, Issue 8 - August
Google Scholar :
https://tinyurl.com/2j2h8wfa
Scribd :
https://tinyurl.com/yr6v2x6s
DOI :
https://doi.org/10.38124/ijisrt/25aug123
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Abstract :
Accurate real-time location tracking is critical for applications ranging from fleet management to personal
safety. Conventional GPS systems, however, suffer from errors caused by signal multipath effects, atmospheric
interference, and urban canyon distortions. This paper presents a low-cost, cloud- integrated GPS tracking system
augmented with a Kalman filter and dynamic geofencing designed to mitigate these limitations. Leveraging an ESP32
microcontroller and a u-blox NEO-6M module, the proposed architecture achieves position updates at 1 Hz while filtering
sensor noise using a computationally efficient Kalman implementation. Field trials demonstrate a 62% reduction in
median absolute positional error compared to raw GPS data, consistently achieving sub-3-meter accuracy in urban
environments. Processed coordinates are transmitted to a Firebase Realtime Database via Wi-Fi, enabling live visualization
on a web interface with path-history mapping, where previous coordinates are displayed as a trail on the map, and user-
defined geofencing. Circular geofences are dynamically monitored using the Haversine formula, triggering real-time alerts
when boundary breaches occur. The system’s responsiveness is validated through latency measurements (<500 ms end-to-
end delay), with energy consumption optimized to 85 mA during active tracking modes. By integrating low-cost hardware,
adaptive filtering, and cloud analytics, this work provides a scalable solution for IoT applications such as logistics
surveillance and emergency response systems, addressing gaps in both precision and affordability for real-time location-
based services.
Keywords :
Kalman Filter, GPS Tracking, Geofencing, ESP32, Firebase, Haversine Formula, Real-Time Monitoring, IoT, Location-Based Services.
References :
- E. D. Kaplan and C. Hegarty, *Understanding GPS: Principles and Applications*, 3rd ed. Norwood, MA, USA: Artech House, 2017.
- J. J. Ruiz et al., “Performance of GPS Positioning Protocols for Urban Navigation,” *IEEE Trans. Intell. Transp. Syst.*, vol. 23, no. 6, pp. 5525–5537, Jun. 2022, doi: 10.1109/TITS.2021.3069367.
- M. S. Grewal and A. P. Andrews, *Kalman Filtering: Theory and Practice with MATLAB*, 5th ed. Hoboken, NJ, USA: Wiley, 2020.
- Google, *Firebase Realtime Database*, 2023. [Online]. Available: https://firebase.google.com/ docs/database
- C. Veness, *Calculate Distance and Bearing Using Haversine Formula*, 2023. [Online]. Available: https://www.movable- type.co.uk/scripts/latlong.html
- R. Pratama et al., “IoT-Based Vehicle Tracking Using GSM Modules,” *IEEE Sens. J.*, vol. 21, no. 4, pp. 5384–5392, Feb. 2021, doi: 10.1109/JSEN.2020.3033367.
- L. M. González et al., “Wildlife Tracking Using IoT Devices,” *IEEE Internet Things J.*, vol. 10, no. 8, pp. 6794–6805, Apr. 2023, doi: 10.1109/JIOT.2023.3245678.
- F. Almarzouki et al., “Real-Time Asset Tracking Using Cloud Platforms,” *IEEE Access*, vol. 11, pp. 12345–12358, 2023, doi: 10.1109/ACCESS.2023.3245678.
- Espressif Systems, *ESP32 Technical Reference Manual*, 2023. [Online]. Available: https://www.espressif.com/en/products/socs/esp32/resources
- u-blox, *NEO-6M GPS Module Datasheet*, 2023. [Online]. Available: https://www.u-blox.com/en/product/neo-6m
Accurate real-time location tracking is critical for applications ranging from fleet management to personal
safety. Conventional GPS systems, however, suffer from errors caused by signal multipath effects, atmospheric
interference, and urban canyon distortions. This paper presents a low-cost, cloud- integrated GPS tracking system
augmented with a Kalman filter and dynamic geofencing designed to mitigate these limitations. Leveraging an ESP32
microcontroller and a u-blox NEO-6M module, the proposed architecture achieves position updates at 1 Hz while filtering
sensor noise using a computationally efficient Kalman implementation. Field trials demonstrate a 62% reduction in
median absolute positional error compared to raw GPS data, consistently achieving sub-3-meter accuracy in urban
environments. Processed coordinates are transmitted to a Firebase Realtime Database via Wi-Fi, enabling live visualization
on a web interface with path-history mapping, where previous coordinates are displayed as a trail on the map, and user-
defined geofencing. Circular geofences are dynamically monitored using the Haversine formula, triggering real-time alerts
when boundary breaches occur. The system’s responsiveness is validated through latency measurements (<500 ms end-to-
end delay), with energy consumption optimized to 85 mA during active tracking modes. By integrating low-cost hardware,
adaptive filtering, and cloud analytics, this work provides a scalable solution for IoT applications such as logistics
surveillance and emergency response systems, addressing gaps in both precision and affordability for real-time location-
based services.
Keywords :
Kalman Filter, GPS Tracking, Geofencing, ESP32, Firebase, Haversine Formula, Real-Time Monitoring, IoT, Location-Based Services.