An adaptive intelligent routing algorithm based on deep reinforcement learning

A mobile ad hoc network (MANET) is a self-organizing network composed of multiple mobile nodes. These nodes achieve distributed communication and wireless resource sharing through autonomous collaboration. However, typical routing protocols for MANETs suffer from uneven network load, low network thr...

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Bibliographic Details
Published in:Computer communications Vol. 216; pp. 195 - 208
Main Authors: Bai, Jie, Sun, Jingchuan, Wang, Zhigang, Zhao, Xunwei, Wen, Aijun, Zhang, Chunling, Zhang, Jianguo
Format: Journal Article
Language:English
Published: Elsevier B.V 15.02.2024
Subjects:
Deep reinforcement learning
Mobile ad hoc network
Routing strategy
Mobile ad hoc network
Deep reinforcement learning
Routing strategy
ISSN:0140-3664, 1873-703X
Online Access:Get full text
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Summary:A mobile ad hoc network (MANET) is a self-organizing network composed of multiple mobile nodes. These nodes achieve distributed communication and wireless resource sharing through autonomous collaboration. However, typical routing protocols for MANETs suffer from uneven network load, low network throughput, poor anti-destruction performance, and low transmission reliability. In order to meet the routing protocol characteristics of distributed adaptability, reliability and anti-destruction, balance of load and energy consumption and guarantee of high QoS in MANETs, this paper proposes an adaptive intelligent routing algorithm based on the PER-D3QN model of deep reinforcement learning. To achieve better routing strategy selection, the routing problem is first modeled, a distributed multi-agent reinforcement learning routing framework is designed, and a deep reinforcement learning reward function considering multiple factors is used. Next, the implementation process of the adaptive intelligent routing algorithm using the PER-D3QN model is described in detail. Finally, a mobile self-organizing network environment was constructed based on Networkx, and corresponding data was collected to simulate the algorithm. The simulation results show that the adaptive intelligent routing algorithm proposed in this paper can balance the energy consumption of the network and the load of nodes, its average remaining energy has increased by more than 10% and PL is reduced by at least 10% compared to other algorithms, while guaranteeing high QoS performance of routing, that is, reducing the average end-to-end delay of data packet transmission by over 22% compared to other algorithms and maintaining a high packet delivery ratio.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2023.12.039