AR-GAIL: Adaptive routing protocol for FANETs using generative adversarial imitation learning
文献类型：期刊论文
| 作者 | Liu, Jianmin1,2; Wang, Qi2; Xu, Yongjun2 |
| 刊名 | COMPUTER NETWORKS
 |
| 出版日期 | 2022-12-09 |
| 卷号 | 218页码:14 |
| ISSN号 | 1389-1286 |
| 关键词 | Adaptive routing Flying ad-hoc networks (FANETs) Generative adversarial imitation learning (GAIL) |
| DOI | 10.1016/j.comnet.2022.109382 |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000876913600012 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19863]  |
| 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.Univ Chinese Acad Sci, Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, 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. |
入库方式： OAI收割
来源：计算技术研究所
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