CLC number: TP183; TN919.72
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-08-05
Cited: 0
Clicked: 6371
Citations: Bibtex RefMan EndNote GB/T7714
Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang. A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1308-1320.
@article{title="A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks",
author="Yi-ning Chen, Ni-qi Lyu, Guang-hua Song, Bo-wei Yang, Xiao-hong Jiang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="9",
pages="1308-1320",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900401"
}
%0 Journal Article
%T A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks
%A Yi-ning Chen
%A Ni-qi Lyu
%A Guang-hua Song
%A Bo-wei Yang
%A Xiao-hong Jiang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 9
%P 1308-1320
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900401
TY - JOUR
T1 - A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks
A1 - Yi-ning Chen
A1 - Ni-qi Lyu
A1 - Guang-hua Song
A1 - Bo-wei Yang
A1 - Xiao-hong Jiang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 9
SP - 1308
EP - 1320
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900401
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.
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