A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs

Wang, Yang; Lin, Yuankun; Chen, Lingyu; Shi, Jianghong

doi:10.3390/s20226625

Cited by 5 publications

(2 citation statements)

References 37 publications

(76 reference statements)

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“…In ref. [ 20 ], the authors introduce the VANET environment with a 5G communication mechanism. SDN and 5G are two promising technologies for VANET, which fulfilled the demand for bandwidth-sensitive applications and increased traffic data.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Nazar

Saeed²,

Ali³

et al. 2022

Sensors

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In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content’s early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Nazar

Saeed²,

Ali³

et al. 2022

Sensors

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show abstract

“…Additionally, to enhance the transmission efficiency of VANETs, a new framework of content delivery was suggested, where each moving vehicle can obtain small-volume content files from either the nearest infrastructure (base station) or roadside unit. Next, a stochastic geometry and point process theory was implemented to establish the profit models for both base stations and roadside units [ 20 ]. For improving the quality of service (QoS) across vehicular communications, a model was proposed, based on cooperative communications for a combined vehicle-to-infrastructure (V2I) system in which vehicle-to-vehicle communication was elaborated to increase the system’s reliability and transmission efficiency [ 21 , 22 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Road Traffic Management Strategy for Rapid Clarification of the Emergency Vehicle Route Based on V2V Communications

Osman

Zaki

Abdelsalam

2021

Sensors

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Vehicle-to-vehicle communication is a promising paradigm that enables all vehicles in the traffic road to communicate with each other to enhance traffic performance and increase road safety. Through vehicle-to-vehicle (V2V) communication, vehicles can understand the traffic conditions based on the information sent among vehicles on the road. Due to the potential delay caused by traffic jams, emergency vehicles may not be able to reach their destination in the required time, leading to severe losses. The case is more severe especially in developing countries where no emergency-vehicle-dedicated lanes are allocated. In this study, a new emergency vehicle route-clarifying strategy is proposed. The new clarifying strategy is based on vehicular traffic management in different interference medium scenarios. The proposed model aims, through V2V communication, to find the nearest vehicle with which to communicate. This vehicle plays an important role in reducing the travel time: as the emergency message is received, this vehicle will immediately communicate with all the neighboring vehicles on the road. Based on V2V communications, all the vehicles in the road will clear from the lane in the road for the emergency vehicle can safely reach its destination with the minimum possible travel time. The maximum distance between the emergency vehicle and the nearest vehicle was determined under different channel conditions. The proposed strategy applied an optimization technique to find the varied road traffic parameters. The proposed traffic management strategy was evaluated and examined through different assumptions and several simulation scenarios. The obtained results validated the effectiveness and the accuracy of the proposed model, and also indicated significant improvement in the network’s performance in terms of packet delivery ratio (PDR) and average end-to-end delay (E2E).

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A survey on fuzzy based game theory approaches for supply chain uncertainties in E-Commerce applications

Devi

2022

Materials Today: Proceedings

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A Stackelberg Game-Based Caching Incentive Scheme for Roadside Units in VANETs

Cited by 5 publications

References 37 publications

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

Novel Road Traffic Management Strategy for Rapid Clarification of the Emergency Vehicle Route Based on V2V Communications

A survey on fuzzy based game theory approaches for supply chain uncertainties in E-Commerce applications

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