Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

A Mobile Crowdsourcing Approach for Smart Edge-Based Driver Drowsiness Detection

DOWNLOAD PDF
  • K Pavan,
  • K Venkata Sandeep,
  • M Keerthana,
  • P Hari Kumar,
  • P Chaitanya
K Pavan
Department of CSE, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Venkata Sandeep
Department of CSE, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
M Keerthana
Department of CSE, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Hari Kumar
Department of CSE, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Chaitanya
Department of CSE, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15262840

Published 2025-04-22

Keywords

  • – Driver Drowsiness,
  • Deep learning,
  • Smart Edge,
  • Convolutional Neural Network (CNN),
  • Long Short-Term Memory (LSTM)

How to Cite

K Pavan, K Venkata Sandeep, M Keerthana, P Hari Kumar, & P Chaitanya. (2025). A Mobile Crowdsourcing Approach for Smart Edge-Based Driver Drowsiness Detection. International Journal of Computational Learning & Intelligence, 4(4), 781–791. https://doi.org/10.5281/zenodo.15262840

Copyright (c) 2025 K Pavan, K Venkata Sandeep, M Keerthana, P Hari Kumar, P Chaitanya

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Drowsy driving is a major contributor to road accidents, accounting for approximately 15.5% of fatal crashes. With the increasing prevalence of mobile devices and roadside infrastructure, implementing an effective drowsiness detection system can significantly enhance road safety. While numerous approaches have been proposed, most existing solutions lack a distributed framework that ensures efficiency while safeguarding driver privacy. This paper introduces a two-stage Smart Edge-based Driver Drowsiness Detection System that leverages edge computing for real-time analysis. The system utilizes mobile devices within vehicles to monitor driver behavior without transmitting sensitive data. The decision-making process is carried out at the edge, where drowsiness is confirmed by correlating driver condition data from mobile clients with vehicle movement patterns. Our method incorporates: (1) a distributed edge framework with hierarchical nodes—Main Edge Node (MEN) and Local Edge Node (LEN)—for improved data processing, and (2) an optimized data fusion strategy, integrating (i) local detection of drowsiness through facial analysis using a Convolutional Neural Network (CNN), (ii) global movement tracking via acceleration data processed by the YoLov5 algorithm, and (iii) a two-layer Long Short-Term Memory (LSTM) model for final drowsiness assessment. The proposed approach achieves an average detection accuracy of 97.7%, demonstrating its effectiveness in preventing drowsy driving incidents

DOWNLOAD PDF

References

  1. Periasamy, K., Periasamy, S., Velayutham, S., Zhang, Z., Ahmed, S. T., & Jayapalan, A. (2022). A proactive model to predict osteoporosis: An artificial immune system approach. Expert Systems, 39(4), e12708.
  2. Ahmed, S. T., Basha, S. M., Ramachandran, M., Daneshmand, M., & Gandomi, A. H. (2023). An edge-AI-enabled autonomous connected ambulance-route resource recommendation protocol (ACA-R3) for eHealth in smart cities. IEEE Internet of Things Journal, 10(13), 11497-11506.
  3. Kumar, S. S., Ahmed, S. T., Sandeep, S., Madheswaran, M., & Basha, S. M. (2022). Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques. Computers, Materials & Continua, 72(1).
  4. Pasha, A., Ahmed, S. T., Painam, R. K., Mathivanan, S. K., Mallik, S., & Qin, H. (2024). Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease. Heliyon, 10(9).
  5. Sreedhar, K. S., Ahmed, S. T., & Sreejesh, G. (2022, June). An Improved Technique to Identify Fake News on Social Media Network using Supervised Machine Learning Concepts. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC) (pp. 652-658). IEEE.
  6. Ahmed, S. T., Fathima, A. S., Nishabai, M., & Sophia, S. (2024). Medical ChatBot assistance for primary clinical guidance using machine learning techniques. Procedia Computer Science, 233, 279-287.
  7. National Safety Council. (n.d.). Fatigued driver. https://www.nsc.org/road/safety-topics/fatigued-driver
  8. AAA Foundation for Traffic Safety. (2010). The prevalence and impact of drowsy driving. https://aaafoundation.org/prevalence-impact-drowsy-driving/
  9. Hussein, M. K., Abbas, A. T., & Ahmad, A. (2021). Driver drowsiness detection techniques: A survey. BICITS, 45–51.
  10. Lawoyin, S., Kitchin, B., Fante, R., & Alam, M. (2015). Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229(2), 163–173.
  11. Iwamoto, H., Matsumoto, S., & Takahashi, T. (2021). Real-driving-implementable drowsy driving detection using heart rate variability. Japanese Journal of Applied Physics, 54(15), 526–531.
  12. Khare, S., & Bajaj, V. (2020). Real-time driver drowsiness detection using deep learning. CCIS, 86–97.
  13. Park, S., Kim, H., & Park, J. (2017). Feature representation learning for drowsiness detection. In ACCV (pp. 154–164).
  14. Mandal, B., Li, L., Wang, G., & Lin, J. (2017). Bus driver fatigue detection based on visual analysis. IEEE Transactions on Intelligent Transportation Systems, 18(3), 545–557.
  15. Lyu, J., Liu, Y., & Yang, F. (2018). Multi-granularity deep framework for driver drowsiness detection. IEEE Transactions on Cybernetics, [details incomplete—please provide full citation if needed].
  16. Hashemi, M., Etemad, A., & Bahrami, A. (2020). A deep learning-based multimodal fatigue monitoring system. In Proceedings of the International Conference on Omni-Layer Intelligent Systems (COINS) (pp. 1–8).
  17. Lamaazi, H., Belqasmi, F., & Glitho, R. (2020). A mobile edge-based crowdsensing framework for heterogeneous IoT. IEEE Access, 8, 207524–207536.
  18. Lamaazi, H., Belqasmi, F., & Glitho, R. (2021). Smart-3DM: Data-driven decision-making using smart edge computing. Journal of Network and Computer Applications, 131, 151–165.
  19. Baek, J. W., Kim, M. H., & Cho, Y. H. (2018). Real-time drowsiness detection algorithm for driver state monitoring. In ICUFN (pp. 73–75).
  20. Reddy, B., Tapp, K., & Roy-Chowdhury, A. (2017). Deep learning model compression for embedded drowsiness detection. In CVPR Workshops (pp. 438–445).
  21. Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., & Hariri, B. (2020). YawDD: Yawning detection dataset. [Dataset].
  22. Weng, C.-H., Lai, Y.-H., & Su, M.-C. (2017). Hierarchical deep belief networks for drowsiness detection. In ACCV (pp. 117–133).
  23. Massoz, Q., Lefever, L., & François, M. (2016). The ULG DROZY multimodality drowsiness database. In WACV (pp. 1–7).
  24. Ghoddoosian, R., & Soleymani, M. (2019). A dataset for early drowsiness detection. In CVPR Workshops (pp. 178–187).
  25. Carlos, M. R., Gonçalves, J. D., & Ferreira, J. C. (2020). Smartphone accelerometers and aggressive driving detection. IEEE Transactions on Intelligent Transportation Systems, 21(8), 3377–3387.
  26. Zylius, G. (2017). Route-independent aggressive and safe driving features from accelerometers. Sensors, 9(2), 103–113.