Dynamic Routing Path Selection Algorithm Using Reinforcement Learning in Wireless Ad-Hoc Networks 


Vol. 43,  No. 7, pp. 1227-1235, Jul.  2018
10.7840/kics.2018.43.7.1227


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  Abstract

This paper proposes a routing protocol scheme using reinforcement learning which supports dynamic wireless communication conditions in ad-hoc networks. The aim of this scheme is to maximize the utility value of routing path in terms of transmission rate, residual energy and end-to-end delay. Q-learning based routing path selecting algorithm is proposed with consideration of packet successful transmission ratio. Each node represents a state and the next packet transmission path link between nodes is called an action in Q-learning. A reward is given to visited path when a packet reaches to destination based on reliability of packet transmission and energy consumption. The simulation results show that our method can obtain dynamic environmental adaptivity and high utility in various communication situations.

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  Cite this article

[IEEE Style]

Q. Yang and S. Yoo, "Dynamic Routing Path Selection Algorithm Using Reinforcement Learning in Wireless Ad-Hoc Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 7, pp. 1227-1235, 2018. DOI: 10.7840/kics.2018.43.7.1227.

[ACM Style]

Qin Yang and Sang-Jo Yoo. 2018. Dynamic Routing Path Selection Algorithm Using Reinforcement Learning in Wireless Ad-Hoc Networks. The Journal of Korean Institute of Communications and Information Sciences, 43, 7, (2018), 1227-1235. DOI: 10.7840/kics.2018.43.7.1227.

[KICS Style]

Qin Yang and Sang-Jo Yoo, "Dynamic Routing Path Selection Algorithm Using Reinforcement Learning in Wireless Ad-Hoc Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 7, pp. 1227-1235, 7. 2018. (https://doi.org/10.7840/kics.2018.43.7.1227)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880