Edge‑Enabled Sensor Fusion and Hybrid Machine Learning Framework for Real‑Time Smart Parking Detection and Scalable Occupancy Prediction

S. V. Dharani Kumar, Swaraj Satish Kadam, Girija M. S., P. Mathiyalagan, D. B. K. Kamesh, Tamilselvi E.

2025

Abstract

Urban mobility is a real challenge in terms of inefficient parking spaces detection and occupation in densely populated areas. This paper presents an edge-enabled, sensor-integrated, hybrid machine learning framework targeted for real-time smart parking detection and predictive occupancy analytics. Rather than the conventional single-sensor-based and static-learning-dependent models, our system integrates ultrasonic, infrared, magnetic, and vision sensors to achieve the robust detection under various environmental situations. CNN, LSTM and XGBoost are equally combined to accurately make temporal and spatial predictions, and edge processing is energy-efficient to reduce latency and guarantee privacy protection. Learning mechanisms are adaptive and enabling the system to be trained incrementally based on real-time feedback and environment cues, thus scalable for city implementation. This unified method not only enhances the detection performance and energy saving but also is a stepping stone towards intelligent city infrastructure with a balance of responsiveness and sustainability.

Paper Citation

in Harvard Style

Kumar S., Kadam S., S. G., Mathiyalagan P., Kamesh D. and E. T. (2025). Edge‑Enabled Sensor Fusion and Hybrid Machine Learning Framework for Real‑Time Smart Parking Detection and Scalable Occupancy Prediction. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 34-40. DOI: 10.5220/0013857100004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Kumar and Swaraj Kadam and Girija S. and P. Mathiyalagan and D. Kamesh and Tamilselvi E.},
title={Edge‑Enabled Sensor Fusion and Hybrid Machine Learning Framework for Real‑Time Smart Parking Detection and Scalable Occupancy Prediction},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={34-40},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013857100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Edge‑Enabled Sensor Fusion and Hybrid Machine Learning Framework for Real‑Time Smart Parking Detection and Scalable Occupancy Prediction
SN - 978-989-758-777-1
AU - Kumar S.
AU - Kadam S.
AU - S. G.
AU - Mathiyalagan P.
AU - Kamesh D.
AU - E. T.
PY - 2025
SP - 34
EP - 40
DO - 10.5220/0013857100004919
PB - SciTePress