Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Computational Intelligence for Energy-Efficient Routing in IoT-connected Autonomous Vehicle Networks

PDF
  • Dr. Andrés Ortiz
Dr. Andrés Ortiz
Professor of Industrial Engineering, Universidad Industrial de Santander (UIS), Colombia
Cover

Published 30-12-2022

Creative Commons License

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

How to Cite

[1]
Dr. Andrés Ortiz, “Computational Intelligence for Energy-Efficient Routing in IoT-connected Autonomous Vehicle Networks”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 98–119, Dec. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/74

Abstract

AVs are, by definition, mobile platforms that can sense the environment. These can contribute to the IoT infrastructure by sensing and sharing with other AVs the location of crowded conditions, so that these areas are avoided. Such a benevolent action requires routing in an environment where not only the edge and cloud (wired) core nodes are capable of running the rich transport layer in conjunction with the wireless edge but also mobile nodes quickly switch between these layers and network segments. The ability to switch between these layers and network segments requires cooperation between transport layer users and network layer infrastructure. It is hypothesized that the ease of establishing the cooperation benefits from a cooperative approach, as opposed to the traditional hierarchical approach. A cooperative approach is characterized by the fact that not all functional tasks can be assigned to specialized entities. This feature requires all entities to have some competence in related functions pertinent to the task currently carried out by a different unit.

PDF

Downloads

Download data is not yet available.

References

  1. Z. Li, Q. Sun, H. Zhang, and L. Zhao, "Energy-efficient routing in vehicular ad hoc networks using multi-objective particle swarm optimization," IEEE Access, vol. 7, pp. 102681-102694, 2019.
  2. S. K. Gupta, P. S. Karthik, and M. S. Obaidat, "A novel energy-efficient approach for routing in IoT based autonomous vehicular networks using particle swarm optimization," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 3006-3015, Aug. 2019.
  3. Tatineni, Sumanth. "Exploring the Challenges and Prospects in Data Science and Information Professions." International Journal of Management (IJM) 12.2 (2021): 1009-1014.
  4. Vemori, Vamsi. "Human-in-the-Loop Moral Decision-Making Frameworks for Situationally Aware Multi-Modal Autonomous Vehicle Networks: An Accessibility-Focused Approach." Journal of Computational Intelligence and Robotics 2.1 (2022): 54-87.
  5. Shaik, Mahammad, Srinivasan Venkataramanan, and Ashok Kumar Reddy Sadhu. "Fortifying the Expanding Internet of Things Landscape: A Zero Trust Network Architecture Approach for Enhanced Security and Mitigating Resource Constraints." Journal of Science & Technology 1.1 (2020): 170-192.
  6. Tatineni, Sumanth. "Climate Change Modeling and Analysis: Leveraging Big Data for Environmental Sustainability." International Journal of Computer Engineering and Technology 11.1 (2020).
  7. Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.
  8. H. Zhu, L. Zhang, and Y. Li, "Energy-efficient routing in IoT-enabled vehicular networks using deep reinforcement learning," IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6518-6529, July 2021.
  9. X. Zhang, W. Li, and Q. Tang, "Energy-efficient routing protocol based on ant colony optimization in vehicular ad hoc networks," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2353-2364, June 2020.
  10. J. Tang, G. Li, and C. Chen, "A deep Q-learning approach for energy-efficient routing in IoT networks," IEEE Access, vol. 8, pp. 195854-195864, 2020.
  11. F. Yang, T. Zhang, and W. Zhao, "Energy-efficient routing based on swarm intelligence in IoT networks," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3331-3341, Mar. 2021.
  12. S. K. Shah, A. A. Khan, and F. Akhtar, "Optimization-based energy-efficient routing in vehicular networks using particle swarm optimization," IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1556-1565, Apr. 2020.
  13. L. He, Y. Guo, and M. Wang, "Energy-efficient routing in IoT-enabled vehicular networks using deep learning," IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2332-2343, Mar. 2021.
  14. Y. Liu, X. Liu, and H. Zhu, "Energy-efficient routing protocol for IoT-based vehicular ad hoc networks using fuzzy logic," IEEE Transactions on Green Communications and Networking, vol. 4, no. 3, pp. 982-992, Sep. 2020.
  15. S. Singh, P. Gupta, and M. K. Ghose, "Energy-efficient routing in IoT-enabled vehicular networks using machine learning," IEEE Transactions on Intelligent Vehicles, vol. 6, no. 2, pp. 233-244, June 2021.
  16. T. Zhao, J. Cao, and S. Liu, "A novel energy-efficient routing protocol for IoT networks using artificial neural networks," IEEE Internet of Things Journal, vol. 7, no. 3, pp. 2156-2167, Mar. 2020.
  17. Q. Xu, Y. Zhu, and H. Wang, "Energy-efficient routing in vehicular networks using deep reinforcement learning," IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4364-4374, Apr. 2020.
  18. K. Wang, J. Zhao, and L. Wang, "A bio-inspired energy-efficient routing protocol for IoT-enabled vehicular networks," IEEE Transactions on Green Communications and Networking, vol. 5, no. 1, pp. 123-133, Mar. 2021.
  19. X. Liu, Y. Wang, and Z. Zhou, "Energy-efficient routing in IoT networks using genetic algorithms," IEEE Access, vol. 7, pp. 135778-135790, 2019.
  20. W. Chen, M. Li, and Y. Liu, "Energy-efficient routing protocol for vehicular ad hoc networks using swarm intelligence," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 872-882, Feb. 2021.
  21. Z. Wang, H. Wang, and W. Liu, "Energy-efficient data collection and routing protocol for IoT-based vehicular networks," IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4786-4797, June 2021.