A Reinforcement Learning-Based Routing Protocol for Clustered EV-VANET

Bi, Xiaohan; Gao, Deyun; Yang, Meiyi

doi:10.1109/itoec49072.2020.9141805

Cited by 15 publications

(19 citation statements)

References 10 publications

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“…Hierarchical landmark routing (H-LANMAR) [111], Hierarchical State Routing (HSR) [85], and Core-Extraction Distributed Ad hoc Routing (CEDAR) [112] are an example of Hierarchical Routing protocols. Moreover, there are a number of Hierarchical routing based on AI and RL, such as Agent Learning-based Clustering VANET routing Algorithm (ALCA) [113], Context-aware Unified Routing for VANETS (CURV) [114], Reinforcement Learning-based Routing protocol for Clustered VANETS (RLRC) [115], RL and Game Theory based VANET Routing (RGVR) [116], and Reinforcement Learning routing in Software Defined Vehicular Routing (RL-SDVN) [117].…”

Section: Hierarchical Routing (Cluster Based)mentioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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Section: Hierarchical Routing (Cluster Based)mentioning

confidence: 99%

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Ameur

Lakas

Bachir

et al. 2022

Vehicular Communications

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“…Due to unstable link connections, reliable communication between vehicular nodes is still not achievable. The work in [138] proposes a reinforcement learning-based routing protocol for clustered electric vehicle (EV)-VANETs, that uses a K-harmonic means algorithm to upgrade the cluster structure's stability. Distributed ITSs require that information be stored independent of fixed infrastructure like…”

Section: A Irregular Vehicle Densitymentioning

confidence: 99%

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Chatterjee¹,

Karmakar

Kaddoum

et al. 2022

IEEE Access

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Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning-and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.INDEX TERMS VANET, V2X, routing protocol, non-learning-based routing, learning-based routing.

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“…Another type of RL used in routing protocols for dynamic WANETs is the SARSA [4] algorithm and its modification, SARSA-λ [7]. SARSA is very similar to the QL algorithm, except that Q-value is updated based on the current state of the agent, the action the agent chooses, the reward the agent gets for choosing this action, the next state that the agent enters after taking that action and, finally, the next action the agent chooses in its new state.…”

Section: Rl-based Routing Protocols For Vanetsmentioning

confidence: 99%

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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A Reinforcement Learning-Based Routing Protocol for Clustered EV-VANET

Cited by 15 publications

References 10 publications

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

Peer-to-peer overlay techniques for vehicular ad hoc networks: Survey and challenges

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

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

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