A Modified Fault Detection Bayesian Learning Model For Inter Connected Vehicle Networks

Authors

  • Syed Umar  Professor, Department of Computer Science, Wollega University, Nekemte, Ethiopia
  • Tadele Debisa Deressa  Lecture, Department of Computer Science, Wollega University, Nekemte, Ethiopia
  • Bodena Terfa  Lecturer & HOD, Department of Informatics, Wollega University, Nekemte, Ethiopia

DOI:

https://doi.org/10.32628/IJSRSET218242

Keywords:

Battery Storage, Super capacitors, Renewable Resources, Wind Power, Supervisory Controller, Battery Lifetime

Abstract

Currently an important worldwide web, The IoT represents the biggest connected vehicle network of all, but will evolve into a much larger network of connected vehicles. Though a concept promising, the combination of different enabling frequencies does pose various intrinsic and defining challenges in the form of communication systems like privacy and protection. It is also important to establish an effective and dependable strategy to access information for solutions which emerge from increasingly complex vehicle and data systems because of the proliferation of wireless medium. In this article, we provide and improve a new algorithm known as Advanced Fault Detection and Management with Bayesian Network techniques, in which we intend to locate and adjust spatial vehicle faults in real time. Often, we apply measurement method to discover the most effective fault detection methodology, which is the turning point. A sequence of recent studies illustrated findings shows that the suggested approaches include fault detection and correction utilizing tools accessible previously.

References

  1. D. J. Hill, B. S. Minsker, and E. Amir, “Real-Time Bayesian Anomaly Detection for Environmental Sensor Data”, Water Resources Research, vol. 45, no. 4, pp. 450-455, 2007.
  2. Wen Sun, Jiajia Liu, and Haibin Zhang, “When Smart Wearables Meet Intelligent Vehicles: Challenges and Future Directions”, 1536-1284/17/$25.00 © 2017 IEEE.
  3. Ford's New Wearables Lab Wants to Make Driving Your Car Safer with Sensors,” 2016;: http://www.wareable.com/ cars/fords-new-wearableslab-driving-safer-sensors-2016,accessed Jan. 9, 2017.
  4. H. Minh, A. Benslimane, and D. Deng, “Reliable Broadcasting Using Polling Scheme Based Receiver for Safety Applications in Vehicular Networks,” IEEE Internet of Things J., vol.1, no. 4, 2014, pp. 289–99.
  5. S. Woo, H. J. Jo, and D. H. Lee, “A Practical Wireless Attack on the Connected Car and Security Protocol for In-Vehicle CAN,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 5, 2015, pp. 993–1006
  6. S. Kaplan et al., “Driver Behavior Analysis for Safe Driving: A Survey,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 6, 2015, pp. 3017–32.
  7. K. Perera and D. Dias, “An Intelligent Driver Guidance Tool Using Location Based Services,” Proc. 2011 IEEE Int'l. Conf. Spatial Data Mining and Geographical Knowledge Services, 2011.
  8. A. Bujari, C. E. Palazzi, and D. Ronzani, “Multimedia Transmissions over Vehicular Networks,” Proc. IEEE 27th Annual Int'l. Symp. Personal, Indoor, and Mobile Radio Commun., 2016.
  9. Haibin Zhang, Qian Zhang, Jiajia Liu,"Fault Detection and Repairing for Intelligent Connected Vehicles Based on Dynamic Bayesian Network Model", 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
  10. A. Benslimane, “Localization in Vehicular Ad Hoc Networks”, in Proceeding of Systems Communications, pp. 19-25, 2005.
  11. F. Yang, S. Wang, J. Li, Z. Liu, and Q. Sun, “An overview of internet of vehicles”, China Communications, vol. 11, no. 10, pp. 1-15, 2014.
  12. W. Sun, J. Liu, and H. Zhang, “When smart wearable's meet intelligent vehicles: challenges and future directions”, IEEE Wireless Communications Magazine, vol. 24, no. 3, pp. 58-65, 2017.
  13. F. Zhou, and A. Benslimane, “Reliable safety message dissemination with minimum energy in VANETs”, in Proceeding of Global Communications Conference, pp. 587-592, 2014.
  14. H. Zhang, J. Li, B. Wen, and J. Liu, “Connecting intelligent things in smart hospitals using NB-IoT”, IEEE Internet of Things Journal, 2018.DOI: 10.1109/JIOT.2018.2792423.
  15. S. Yang, and Z. H. Liu, “Anomaly Detection for Internet Of Vehicles: A Trust Management Scheme With Affinity Propagation”, in Proceeding of Mobile Information Systems, pp. 1-10, 2016.
  16. Y. Lou, and Y. Dong, “Structure Learning Algorithm of DBN Based on Particle Swarm Optimization”, in Proceeding of IEEE International Symposium on Distributed Computing and Applications for Business Engineering and Science, pp. 102-105, 2015.
  17. G. Trabelsi, and P. Leray, “Benchmarking dynamic Bayesian network structure learning algorithms”, in Proceeding of IEEE International Conference on Modeling, Simulation and Applied Optimization, pp. 1-6, 2013.
  18. Kamakshaiah Kolli etc… A Traditional Analysis for Efficient Data Mining with Integrated Association Mining into Regression Techniques, 3rd International Conference on Communications and Cyber Physical Engineering, LNEE, volume 698,pp 1393-1404, ISBN: 978-981-15-7961-5.
  19. Syed Umar et el., “Securities and threats of cloud computing and solutions” 2018 2nd International Conference on Inventive Systems and Control (ICISC), IEEE Explorer, Pages: 1162-1166.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2021-04-30

Issue

Volume 8, Issue 2, November-December-2021

Section

Research Articles

License

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Creative Commons License

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

How to Cite

[1]
Syed Umar, Tadele Debisa Deressa, Bodena Terfa "A Modified Fault Detection Bayesian Learning Model For Inter Connected Vehicle Networks" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 2, pp.182-189, November-December-2021. Available at doi : https://doi.org/10.32628/IJSRSET218242