QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks
| Autor: | Jianmin Liu, Chentao He, Katia Jaffrès-Runser, Qi Wang, Zhenyu Li, Xu Yida, Yongjun Xu |
|---|---|
| Přispěvatelé: | CAS Institute of Computing Technology (ICT), Chinese Academy of Sciences [Beijing] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), Réseaux, Mobiles, Embarqués, Sans fil, Satellites (IRIT-RMESS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), National Natu-ral Science Foundation of China (NSFC) under Grant No.61602447 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Routing protocol
Computer Networks and Communications Wireless ad hoc network business.industry Computer science ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Q-learning 020206 networking & telecommunications 02 engineering and technology Mobile ad hoc network Energy consumption Multi-objective optimization adaptive parameters [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] Hop (networking) Index Terms-multi-objective routing [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 0202 electrical engineering electronic engineering information engineering Wireless 020201 artificial intelligence & image processing FANETs [INFO.INFO-ES]Computer Science [cs]/Embedded Systems business Computer network |
| Zdroj: | Computer Communications Computer Communications, Elsevier, 2020, 150, pp.304-316. ⟨10.1016/j.comcom.2019.11.011⟩ |
| ISSN: | 0140-3664 1873-703X |
| DOI: | 10.1016/j.comcom.2019.11.011⟩ |
| Popis: | International audience; A network with reliable and rapid communication is critical for Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) consisting of UAVs is a new paradigm of wireless communication. However, the highly dynamic topology of FANETs and limited energy of UAVs have brought great challenges to the routing design of FANETs. It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANET-s) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs. Moreover, few of existing routing protocols simultaneously meet the requirement of low delay and low energy consumption of FANETs. This paper proposes a novel Q-learning based Multi-objective optimization Routing protocol for FANETs to provide low-delay and low-energy service guarantees. Most of existing Q-learning based protocols use a fixed value for the Q-learning parameters. In contrast, Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANETs. In addition, a new exploration and exploitation mechanism is also proposed to explore some undiscovered potential optimal routing path while exploiting the acquired knowledge. Instead of using past neighbor relationships, the proposed method re-estimates neighbor relationships in the routing decision process to select the more reliable next hop. Simulation results show that the proposed method can provide higher packet arrival ratio, lower delay and energy consumption than existing good performing Q-learning based routing method. |
| Databáze: | OpenAIRE |
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