QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks-Reference-Cited by-同舟云学术

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QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks

Published:2020-01 Issue: Volume:150 Page:304-316
ISSN:0140-3664
Container-title:Computer Communications
language:en
Short-container-title:Computer Communications

Author:

Liu Jianmin,Wang Qi,He ChenTao,Jaffrès-Runser Katia,Xu Yida,Li Zhenyu,Xu YongJun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference27 articles.

1. Multi-uav agent-based coordination for persistent surveillance with dynamic priorities;Batista Da Silva,2017

2. Wireless sensor networks and multi-uav systems for natural disaster management;Erdelj;Comput. Netw.,2017

3. Gpsr:greedy perimeter stateless routing for wireless networks;Karp,2000

4. Qgrid: Q-learning based routing protocol for vehicular ad hoc networks;Li,2014

5. Qlar: A q-learning based adaptive routing for manets;Serhani,2017

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1. Obtaining the optimal shortest path between two points on a quasi-developable Bézier-type surface using the Geodesic-based Q-learning algorithm;Engineering Applications of Artificial Intelligence;2024-10

2. A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks;Vehicular Communications;2024-10

3. Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters;Mathematics;2024-08-28

4. Reliable air quality assessment in dynamic environments using reward-learning routing with flying manet technology;GLOBAL NEST J;2024

5. Distributed Routing Optimization Algorithm for FANET Based on Multiagent Reinforcement Learning;IEEE Sensors Journal;2024-08-01

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