TY - GEN
T1 - A Fuzzy Logic Reinforcement Learning-Based Routing Algorithm for Flying Ad Hoc Networks
AU - He, Chenguang
AU - Liu, Suning
AU - Han, Shuai
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - With the development of technology, unmanned aerial vehicles (UAV) are getting closer to peoples life. Multiple UAV nodes form the Flying Ad Hoc Network (FANET). Due to the high mobility of the UAV nodes, the topology of the flight ad hoc network also changes rapidly. Aiming at the problem of the high average number of hops and low link connectivity, this paper adopts a fuzzy logic reinforcement learning-based routing algorithm for flying ad hoc networks. The fuzzy logic mainly determines the neighbor nodes of a node in real time. Reinforcement learning reduces the average number of hops of the route determined by fuzzy logic through continuous training. Compared with the ant colony algorithm optimization(ACO), the proposed FANET routing algorithm has significant improvement in both link success rate and average hop count. The situation is more perfect and it can better meet the requirements of the network.
AB - With the development of technology, unmanned aerial vehicles (UAV) are getting closer to peoples life. Multiple UAV nodes form the Flying Ad Hoc Network (FANET). Due to the high mobility of the UAV nodes, the topology of the flight ad hoc network also changes rapidly. Aiming at the problem of the high average number of hops and low link connectivity, this paper adopts a fuzzy logic reinforcement learning-based routing algorithm for flying ad hoc networks. The fuzzy logic mainly determines the neighbor nodes of a node in real time. Reinforcement learning reduces the average number of hops of the route determined by fuzzy logic through continuous training. Compared with the ant colony algorithm optimization(ACO), the proposed FANET routing algorithm has significant improvement in both link success rate and average hop count. The situation is more perfect and it can better meet the requirements of the network.
KW - Flying Ad-Hoc Network
KW - Fuzzy Logic
KW - Reinforcement Learning
KW - Routing.
UR - https://www.scopus.com/pages/publications/85083454050
U2 - 10.1109/ICNC47757.2020.9049705
DO - 10.1109/ICNC47757.2020.9049705
M3 - 会议稿件
AN - SCOPUS:85083454050
T3 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
SP - 987
EP - 991
BT - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Computing, Networking and Communications, ICNC 2020
Y2 - 17 February 2020 through 20 February 2020
ER -