Work

Intelligent routing agent based on Q-learning and Markov decision processes for routing optimization in DTN networks

Year: 2025

Type: article

Abstract: : DTN networks present significant routing challenges due to their intermittent connectivity and node mobility. Traditional routing protocols, often pre-established, prove inadequate in adapting to the changing dynamics of DTN networks. To address these challenges, the integration of advanced artificial intelligence techniques, notably intelligent agents, is emerging as a promising solution. This article proposes an approach based on an intelligent agent optimized by Q-learning, capable of perceiving, analyzing, and acting autonomously to optimize routing in these dynamic environments. The intelligent agent integrates three main modules: a perception module to collect data in real time, a decision module based on Q-Learning to autonomously adapt its decisions and routing strategies, and an action module to execute the decisions made. The modeling of DTN networks as Markov Decision Processes (MDP) enables the agent to dynamically learn and select optimal routing strategies, without prior knowledge of network transitions. The simulations show that this approach improves routing performance compared to traditional protocols, with gains in terms of delivery rate, latency reduction, energy efficiency, and resource optimization. These results highlight the potential of intelligent agents to address the complex challenges of DTN networks.

Source: International Journal of Intelligent Networks

DOI: 10.1016/j.ijin.2025.07.001

Author: El Mastapha Sammou

Institution: Cadi Ayyad University

Language: English

Topic: Opportunistic and Delay-Tolerant Networks

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $700