A Novel Protocol for Routing in Vehicular Ad hoc Network Based on Model-Based Reinforcement Learning and Fuzzy Logicthe Building

Jafarzadeh, Omid; Sargolzaey, Hadi; Dehghan, Mehdi; Esnaashari, Mehdi

doi:10.52547/ijict.12.4.10

Cited by 1 publication

(3 citation statements)

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“…they all belong to the group of model-free algorithms. In [8], the authors proposed a model-based RL (MBRL) algorithm that first needs to create an internal model of the environment, and, based on it, the optimal routing policy will be determined. In this way, the optimal policy is reached faster compared to the QL algorithm.…”

Section: Rl-based Routing Protocols For Vanetsmentioning

confidence: 99%

“…The tenth category of papers is characterised by the application of MBRL and FL in routing protocols, and the appropriate representative is the reinforcement routing protocol for VANETs (RRPV) [8]. RRPV is based on the multi-agent RL (MARL) technique, which means that all nodes in the network represent learning agents that cooperate and at the same time try to find the optimal routing policy.…”

Section: Rl-based Routing Protocols For Vanets and Fanetsmentioning

confidence: 99%

“…For the taken action, the agent receives a reward that depends on the distance and quality of links between nodes (determined in the model learning process). Based on the simulations done in [8], this protocol improves PDR, E2ED and OH.…”

Section: Rl-based Routing Protocols For Vanets and Fanetsmentioning

confidence: 99%

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Reinforcement Learning-Based Routing Protocols in Vehicular and Flying Ad Hoc Networks – A Literature Survey

Bugarcic

Jevtić

Malnar

2022

PROMTT

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Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.

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Section: Rl-based Routing Protocols For Vanetsmentioning

confidence: 99%

Section: Rl-based Routing Protocols For Vanets and Fanetsmentioning

confidence: 99%