A BUS‐aided RSU access scheme based on SDN and evolutionary game in the Internet of Vehicle

Jia, Fang; Chen, Chen; Li, Jiajun; Chen, Lanlan; Li, Na

doi:10.1002/dac.3932

Cited by 17 publications

(11 citation statements)

References 41 publications

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“…The work proposed by Fang Jia et al [24] emphasizes the BUS-aided selection of RSUs, built upon software-defined networking (SDN) and evolutionary games. The authors have concentrated on selecting the best RSU in overlapping areas of RSU.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

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Section: Related Workmentioning

confidence: 99%

“…Some literatures have considered [19,24] either decision delay alone or network selection alone. The proposed work has emphasized both, leading to best network selection and reduced decision delay.…”

mentioning

confidence: 99%

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Hemavathi¹,

Akhila²,

Alotaibi

et al. 2022

Energies

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“…In conclusion, by designing effective schemes of competition, selection, and heredity, the evolutionary game helps non-rational players lack of information to dynamically learn from the environment and make individual optimal decisions. For example, it has been used to remove the untrustworthy vehicles from networks through nature selection [129], cluster formation and optimization [130], optimal access strategy or communication mode selection [131]- [133], [144], and spectrum allocation for social optimum [134]. A matching game aims at constructing stable matches among two sets of elements with ordering preferences.…”

Section: Initial：mentioning

confidence: 99%

Applications of Game Theory in Vehicular Networks: A Survey

Sun

Liu²,

Wang

et al. 2021

IEEE Commun. Surv. Tutorials

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In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming Vehicular Networks (VNs) that provide efficient and secure traffic and ubiquitous access to various applications. However, as the number of nodes in ITS increases, it is challenging to satisfy a varied and large number of service requests with different Quality of Service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve either an individual or a group's objectives. Game Theory (GT), a theoretical framework designed for strategic interactions among rational decision-makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including its basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs integrating with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions for applying GT in VNs are outlined.

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“…Considering the frequent handover when a lot of vehicles are connected to an RSU, Jia et al [107] proposed a BUS-aided RSU connection scheme in SDN-IoV. The scheme employs an evolutionary game where the vehicles in the overlapped regions of the RSUs can adopt the optimal strategies to access to stable RSUs or BUSs.…”

Section: Game Theory In Sdn-based Vnsmentioning

confidence: 99%

“…A. Handover Management in Game GT has shown its potential for effective handover handling in VNs by modeling the network selection or resource management as games [61], [83], [107]. However, the massive handovers in next-generation VNs make the design of game models a challenging task.…”

Section: Challengesmentioning

confidence: 99%

Game Theoretic Approaches in Vehicular Networks: A Survey

Sun,

Liu,

Wang

et al. 2020

Preprint

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In the era of the Internet of Things (IoT), vehicles and other intelligent components in Intelligent Transportation System (ITS) are connected, forming the Vehicular Networks (VNs) that provide efficient and secure traffic, ubiquitous access to information, and various applications. However, as the number of connected nodes keeps increasing, it is challenging to satisfy various and large amounts of service requests with different Quality of Service (QoS ) and security requirements in the highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources so that either an individual or a group objectives can be achieved. Game theory, a theoretical framework designed for strategic interactions among rational decision-makers who faced with scarce resources, can be used to model and analyze individual or group behaviors of communication entities in VNs. This paper primarily surveys the recent advantages of GT used in solving various challenges in VNs. As VNs and GT have been extensively investigate34d, this survey starts with a brief introduction of the basic concept and classification of GT used in VNs. Then, a comprehensive review of applications of GT in VNs is presented, which primarily covers the aspects of QoS and security. Moreover, with the development of fifth-generation (5G) wireless communication, recent contributions of GT to diverse emerging technologies of 5G integrated into VNs are surveyed in this paper. Finally, several key research challenges and possible solutions for applying GT in VNs are outlined.

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A BUS‐aided RSU access scheme based on SDN and evolutionary game in the Internet of Vehicle

Cited by 17 publications

References 41 publications

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

Applications of Game Theory in Vehicular Networks: A Survey

Game Theoretic Approaches in Vehicular Networks: A Survey

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