AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning

doi:10.1016/j.comnet.2022.109382

	AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning
	Liu, Jianmin 1,2; Wang, Qi 1; Xu, Yongjun 1
	2022-12-09
发表期刊	COMPUTER NETWORKS
ISSN	1389-1286
卷号	218 页码:14
摘要	Flying ad hoc networks (FANETs), as the emerging communication paradigm, have been widely used in civil and military fields. Packet routing in FANETs is challenging due to dynamic network conditions. Traditional topology-based routing protocols are unsuitable for FANETs with dynamic network topologies. Routing protocols based on reinforcement learning (RL) may be the first choice for FANETs because of their good learning ability. However, existing RL-based routing protocols for FANETs have limited adaptability to network dynamics due to ignoring neighborhood environment states, and are prone to get stuck in suboptimal routing policies owing to inappropriate reward design and delayed reward issues. We propose AR-GAIL, an adaptive routing protocol based on Generative Adversarial Imitation Learning (GAIL), which aims to select the minimal end-to-end delay route according to ongoing network conditions for FANETs. We formulate the routing decision process as a Markov decision process (MDP) and design a novel MDP state which consists of the current node state and the neighborhood environment state. Moreover, we develop an efficient value function-based GAIL learning framework to learn the routing policy from expert routes instead of a predefined reward function. The simulation shows that AR-GAIL can adapt well to network dynamics. Compared with state-of-the-art routing protocols, AR-GAIL shows outstanding performance in terms of the end-to-end delay and packet delivery ratio.
关键词	Adaptive routing Flying ad-hoc networks (FANETs) Generative adversarial imitation learning (GAIL)
DOI	10.1016/j.comnet.2022.109382
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000876913600012
出版者	ELSEVIER
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19864
专题	中国科学院计算技术研究所期刊论文
通讯作者	Wang, Qi
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jianmin,Wang, Qi,Xu, Yongjun. AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning[J]. COMPUTER NETWORKS,2022,218:14.
APA	Liu, Jianmin,Wang, Qi,&Xu, Yongjun.(2022).AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning.COMPUTER NETWORKS,218,14.
MLA	Liu, Jianmin,et al."AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning".COMPUTER NETWORKS 218(2022):14.