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GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Authors :
Tang Hong
Ruohan Wang
Xiangzheng Ling
Xuefang Nie
Source :
Electronics; Volume 11; Issue 18; Pages: 2873
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

Details

Language :
English
ISSN :
20799292
Database :
OpenAIRE
Journal :
Electronics; Volume 11; Issue 18; Pages: 2873
Accession number :
edsair.doi.dedup.....7f1b8ce9d9e916d5d0d5b2ba4cc20af3
Full Text :
https://doi.org/10.3390/electronics11182873