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

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

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  • Dr. Andrés Ortiz
Dr. Andrés Ortiz
Professor of Industrial Engineering, Universidad Industrial de Santander (UIS), Colombia
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Published 30-12-2022

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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: Sep. 18, 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.

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