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Reseach Article

Optimal Assistive Drive System using Mobile Cloud Computing

by Sameh A. Salem
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 46
Year of Publication: 2019
Authors: Sameh A. Salem
10.5120/ijca2019918624

Sameh A. Salem . Optimal Assistive Drive System using Mobile Cloud Computing. International Journal of Computer Applications. 182, 46 ( Mar 2019), 45-51. DOI=10.5120/ijca2019918624

@article{ 10.5120/ijca2019918624,
author = { Sameh A. Salem },
title = { Optimal Assistive Drive System using Mobile Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 182 },
number = { 46 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 45-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number46/30465-2019918624/ },
doi = { 10.5120/ijca2019918624 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:14:28.994351+05:30
%A Sameh A. Salem
%T Optimal Assistive Drive System using Mobile Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 46
%P 45-51
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

No one can deny that mobile devices are increasingly becoming an essential part of our lives, and being used for information delivery, access and communication. In this paper, a novel assistive drive system with mobile offloading is proposed. Three effective measures are integrated for reliable and early drowsiness detection, namely behavioral, vehicle, and physiological measures. These measures give higher quality and relevant information. Additionally, the proposed system uses mobile devices to process readings. However, with huge amount of data and intensive computations, mobiles cannot deliver results in reasonable times. A possible approach is to offload computations onto the cloud.

References
  1. “World Health Organization”. Global status report on road safety 2017. World Health Organization, 2017.
  2. “Regulatory impact and small business analysis for hours of service options,” Technical report, Federal Motor Carrier Safety Administration, February 2011.
  3. H. Abbood, W. Al-Nuaimy, A. Al-Ataby, S. A. Salem, and H. S. Alzubi, “Prediction of driver fatigue: Approaches and open challenges,” 2014 14th UK Workshop on Computational Intelligence (UKCI), 2014.
  4. R. Buyya, J. Broberg, and Gościński Andrzej, Cloud computing: principles and paradigms. Hoboken, NJ: Wiley, 2011.
  5. R. Sosan and C. F. Azim, “RETRACTED ARTICLE: Mobile Cloud Computing: The Taxonomy and Comparison of Mobile Cloud Computing Application Models,” Wireless Personal Communications, vol. 89, no. 4, pp. 1435–1435, 2016.
  6. A. U. R. Khan, M. Othman, S. A. Madani, and S. U. Khan, “A Survey of Mobile Cloud Computing Application Models,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 393–413, 2014.
  7. A. Priyanka, "Mobile cloud computing." International Journal of Engineering and Advanced Technology (IJEAT) vol. 2, pp. 606-609, 2013.
  8. S. Abolfazli, Z. Sanaei, M. Alizadeh, A. Gani, and F. Xia, “An experimental analysis on cloud-based mobile augmentation in mobile cloud computing,” IEEE Transactions on Consumer Electronics, vol. 60, no. 1, pp. 146–154, 2014
  9. W. Zhang,  Y. Wen, “Energy-Efficient Task Execution for Application as a General Topology in Mobile Cloud Computing,”  IEEE Transactions on Cloud Computing, vol. 6, issue 3, pp. 708-719, 2018.
  10. B.-G. Chun, S. Ihm, P. Maniatis, M. Naik, and A. Patti, “Clonecloud: Elastic execution between mobile device and cloud” Proceedings of the sixth conference on Computer systems - EuroSys 11, 2011.
  11. M. V. Barbera, S. Kosta, A. Mei, and J. Stefa, “To offload or not to offload? The bandwidth and energy costs of mobile cloud computing,” 2013 Proceedings IEEE INFOCOM, 2013.
  12. S. A. Said, S. A. Salem, S. G. Sayed, “Energy Aware Mobile Cloud Computing Algorithm for Android Smartphones,” In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017, AISI2017, Advances in Intelligent Systems and Computing, vol 639. Springer, Cham, 2018.
  13. E. R. Davies, Machine vision theory, algorithms, practicalities. San Francisco, CA, USA: Elsevier, 2012
  14. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
  15. J. Zhu, Z. Chen, “Real Time Face Detection System Using Adaboost and Haar-like Features,” In 2nd IEEE International conference on Information Science and Control Engineering, pp. 404-407, 2015.
  16. Juseong Lee,  Hoyoung Tang , Jongsun Park, “Energy Efficient Canny Edge Detector for Advanced Mobile Vision Applications”,  IEEE Transactions on Circuits and Systems for Video Technology, vol, 28, issue 4, pp. 1037-1048, 2018.
  17. Anamika Singh ; Manminder Singh ; Birmohan Singh, " Face detection and eyes extraction using sobel edge detection and morphological operations" IEEE International conference on Advances in Signal Processing (CASP), pp. 295-300, 2016.
  18. R. C. Gonzalez and R. E. Woods, Digital image processing. Upper Saddle River, NJ, USA: Prentice-Hall, Inc. 2006.
  19. R. Laganiere, OpenCV 3 Computer Vision Application Programming Cookbook. Birmingham: Packt Pub., 2017.
  20. ParallaxInc.[Online].Available:https://www.parallax.com/product/28015. [Accessed: 15-Oct-2018]
  21. S. Otmani, T. Pebayle, J. Roge, and A. Muzet, “Effect of driving duration and partial sleep deprivation on subsequent alertness and performance of car drivers,” Physiology & Behavior, vol. 84, no. 5, pp. 715–724, 2005.
  22. B.-G. Lee, S.-J. Jung, and W.-Y. Chung, “Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection,” IET Communications, vol. 5, no. 17, pp. 2461–2469, 2011.
  23. V. P. Nambiar, M. Khalil-Hani, C. Sia, and M. N. Marsono, “Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability,” 2012 IEEE International Conference on Circuits and Systems (ICCAS), 2012.
  24. G. D. Furman, A. Baharav, C. Cahan, and S. Akselrod, “Early detection of falling asleep at the wheel: A Heart Rate Variability approach,” 2008 Computers in Cardiology, 2008.
  25. B.-S. Lin, W. Chou, H.-Y. Wang, Y.-J. Huang, and J.-S. Pan, “Development of Novel Non-Contact Electrodes for Mobile Electrocardiogram Monitoring System,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 1, pp. 1–8, 2013.
  26. I.-J. Wang, L.-D. Liao, Y.-T. Wang, C.-Y. Chen, B.-S. Lin, S.-W. Lu, and C.-T. Lin, “A Wearable Mobile Electrocardiogram measurement device with novel dry polymer-based electrodes,” TENCON 2010 - 2010 IEEE Region 10 Conference, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Mobile Cloud Computing Computational Offloading Energy Preserving Fatigue detection Computer vision.