Research Article Open Access

An Enhanced Dynamic Source Routing Algorithm for the Mobile Ad-Hoc Network using Reinforcement learning under the COVID-19 Conditions

Ehsan Moqimi1, Amir Najafi1 and Mojtaba Ajami1
  • 1 Zanjan Branch Islamic Azad University, Iran

Abstract

Today, the use of computer networks is evident in all walks of life, including mobile phones, vehicles, personal computers, the workplace and more. Creating and operating networks between each of the above-mentioned devices require good infrastructure to be able to communicate between these devices. To solve this problem, there were networks called ad-hoc networks, which were able to network with each other without the need for specific infrastructure and only through direct and wireless communication between the equipment. But how to make the right connections in these types of networks, as well as high-speed communication for devices, are also challenges for researchers. In this study, we try to implement the communication between these devices by the DSR routing algorithm and, using the Q learning technique of the reinforced learning algorithm, we enhance it to respond to QOS in Ad-Hoc mobile networks compared to previous solutions. In addition to what has been improved in this article. In the following articles, we will try to improve the discussion of communication security using this method and also apply this method to routing VANETs networks.

Journal of Computer Science
Volume 16 No. 10, 2020, 1477-1490

DOI: https://doi.org/10.3844/jcssp.2020.1477.1490

Submitted On: 3 July 2020 Published On: 31 October 2020

How to Cite: Moqimi, E., Najafi, A. & Ajami, M. (2020). An Enhanced Dynamic Source Routing Algorithm for the Mobile Ad-Hoc Network using Reinforcement learning under the COVID-19 Conditions. Journal of Computer Science, 16(10), 1477-1490. https://doi.org/10.3844/jcssp.2020.1477.1490

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Keywords

  • Mobile Ad-Hoc Networks
  • DSR
  • Reinforcement Learning
  • Q learning
  • Routing Algorithm
  • COVID-19