Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Duan, Xuting; Zhao, Yunhui; Zheng, Kunxian; Tian, Daxin; Zhou, Jianshan; Gao, Jian

doi:10.32604/cmc.2020.014484

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…However, the centralized structure avoids contention collisions [18][19][20][21] because the central node also allocates an empty slot to each vehicle. In the centralized structure of TDMA, the central node must allocate timeslots efficiently and equitably to each vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…However, safety requires that vehicular communication consider packet delivery and road characteristics. Although studies have been done to reduce the collision rate of packets using TDMA and clustering [16][17][18][19][20][21], the studies could not maximize the use of bandwidth because of inefficient DSRC multichannel usage and not considering the bidirectionality of the road traffic.…”

Section: Related Workmentioning

confidence: 99%

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Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Abbas¹,

Alhwaiti²,

Fatima³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, the importance of vehicle safety supporting system has been highlighted as autonomous driving and platooning has attracted the researchers. To ensure driving safety, each vehicle must broadcast a basic safety message (BSM) every 100 ms. However, stable BSM exchange is difficult because of the changing environment and limited bandwidth of vehicular wireless communication. The increasing number of vehicles on the road increases the competition to access wireless networks for BSM exchange; this increases the packet collision rate. An increased packet collision rate impairs the transmission and reception of BSM information, which can easily cause a traffic accident. We propose a solution, the vehicular safety support system (V3S), which exchanges BSMs reliably even when many vehicles are on the road. The V3S uses a clustering scheme to decrease network traffic by reducing the amount of data exchanged between a vehicle and the roadside unit (RSU). In addition, the V3S reduces the collision rate of wireless network packets by broadcasting the vehicle's BSM in an allocated timeslot using the time division multiple access (TDMA) MAC protocol. The V3S also deals with insufficient bandwidth for dedicated short-range communications (DSRC) by changing DSRC channels according to traffic flow. In evaluating the packet error rate for stable BSM packet delivery, the V3S demonstrates an excellent packet error rate of less than 1%, compared to the 802.11p with its packet error rate of 82%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Abbas¹,

Alhwaiti²,

Fatima³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Recently, studies are being conducted to provide stable vehicular wireless communication [11][12][13][14]. The complexity of the offloading problem can be reduced if stable communication is ensured in task offloading.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

Kwak¹,

Lee²,

Park³

2023

Computer Systems Science and Engineering

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Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively. The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle's processing units. To solve this problem, there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes. However, the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity. They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety. In this paper, we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety. The proposed scheme can handle computation tasks by considering the communication bandwidth, delay, and amount of computation. We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme. The result shows that our proposed scheme improves the utilization of nearby computing nodes, and the offloading tasks can be processed within the time for driving safety.

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“…Security in wireless networks has always been a core challenge, especially ad hoc networks where each time distinct nodes are likely to involve in network formation [7,8]. Similarly, the biggest threat for IoV is a security breach [9,10], which can cause a catastrophic sequence of chain accidents, traffic congestions, and diverting traffic to a specific path. The vehicular network has unique properties, unlike other wireless networks.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Rehman¹,

Hassan²,

Hooi³

et al. 2021

Computers, Materials &Amp; Continua

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Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a vital role in ensuring security, especially during vehicle to vehicle communication. Vehicular networks, having a unique nature among other wireless ad hoc networks, require dedicated efforts to develop trust protocols. Current TM schemes are inflexible and static. Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks. The vehicular network requires agile and adaptive solutions to ensure security, especially when it comes to critical messages. The vehicle network's wireless nature increases its attack surface and exposes the network to numerous security threats. Moreover, internet involvement makes it more vulnerable to cyberattacks. The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics. Machine learning is the best solution to utilize the big data produced by vehicle sensors. To handle the uncertainty Bayesian machine learning statistical model is used. The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available. The results indicated better performance than

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Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Cited by 3 publications

References 12 publications

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Cluster-Based Stable BSM Dissemination System for Safe Autonomous Platooning

Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

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