A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks

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-...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2020-09, Vol.21 (9), p.1308-1320
Main Authors: Chen, Yi-ning, Lyu, Ni-qi, Song, Guang-hua, Yang, Bo-wei, Jiang, Xiao-hong
Format: Article
Language:English
Subjects:
Ad hoc networks
Algorithms
Balancing
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Delay
Electrical Engineering
Electronics and Microelectronics
Instrumentation
Machine learning
Networks
Routing (telecommunications)
Traffic congestion
Traffic control
Unmanned aerial vehicles
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Summary: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.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1900401