A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks
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2020 21(9):1308-1320
Frontiers of Information Technology & Electronic Engineering www.jzus.zju.edu.cn; engineering.cae.cn; www.springerlink.com ISSN 2095-9184 (print); ISSN 2095-9230 (online) E-mail: [email protected]
A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks∗ Yi-ning CHEN1 , Ni-qi LYU1 , Guang-hua SONG‡1 , Bo-wei YANG1 , Xiao-hong JIANG2 1School 2College
of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
E-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected] Received Aug. 7, 2019; Revision accepted Dec. 8, 2019; Crosschecked Aug. 5, 2020
Abstract: In dense traffic unmanned aerial vehicle (UAV) ad-hoc networks, traffic congestion can cause increased delay and packet loss, which limit the performance of the networks; therefore, a traffic balancing strategy is required to control the traffic. In this study, we propose TQNGPSR, a traffic-aware Q-network enhanced geographic routing protocol based on greedy perimeter stateless routing (GPSR), for UAV ad-hoc networks. The protocol enforces a traffic balancing strategy using the congestion information of neighbors, and evaluates the quality of a wireless link by the Q-network algorithm, which is a reinforcement learning algorithm. Based on the evaluation of each wireless link, the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss. We simulate the performance of TQNGPSR and compare it with AODV, OLSR, GPSR, and QNGPSR. Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR. In high node density scenarios, it also outperforms AODV and OLSR in terms of the packet delivery ratio, end-to-end delay, and throughput. Key words: Traffic balancing; Reinforcement learning; Geographic routing; Q-network https://doi.org/10.1631/FITEE.1900401 CLC number: TP183; TN919.72
1 Introduction In a mobile ad-hoc network for unmanned aerial vehicles (UAVs), point-to-point communication is required for many applications, such as network relay and flight information collection (Bekmezci et al., 2013). Because of the movement of UAVs, the routing problem of finding an efficient path in an unstable situation with frequently changing topology is challenging for the network (Wu et al., 2012). There are many routing protocols for adhoc networks. The optimized link state routing ‡ *
Corresponding author
Project supported by the National Natural Science Foundation of China (No. 61501399) and the National Key R&D Program of China (No. 2018AAA0102302) ORCID: Yi-ning CHEN, https://orcid.org/0000-0002-34352851; Guang-hua SONG, https://orcid.org/0000-0003-3330-4978 c Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2020
(OLSR) protocol, a proactive routing protocol, exchanges network topology information periodically and maintains the forwardi
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