V-NDN(vehicular named data networking)是一种使用命名数据网络架构的车辆自组织网络(vehicular Ad-hoc network,VANET),主要用来连接移动车辆之间的通信。提高网络中兴趣包的命中率是研究领域急待解决的难题。文章研究了城市道路环境...V-NDN(vehicular named data networking)是一种使用命名数据网络架构的车辆自组织网络(vehicular Ad-hoc network,VANET),主要用来连接移动车辆之间的通信。提高网络中兴趣包的命中率是研究领域急待解决的难题。文章研究了城市道路环境下V-NDN的数据转发策略,考虑到城市道路环境下路侧单元(road side unit,RSU)被均匀广泛部署的特点,提出了一种基于RSU辅助的V-NDN数据转发策略(RSU aided V-NDN)。通过实验仿真与传统的V-NDN和基于蜂窝网络辅助的V-NDN进行对比,结果表明该文提出的数据转发策略有效地提高了网络的服务质量(quality of service,QoS)。展开更多
Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifical...Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.展开更多
The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as info...The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication.Due to the constraints of limited resources,RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method.However,direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue.In this paper,we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks.The renormalization method is compared with two deployment schemes:genetic algorithm(GA)and memetic framework-based optimal RSU deployment(MFRD),to verify the improvement of communication performance.Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.展开更多
为了降低碳排放量和部署成本,利用太阳能给车联网路边设施(Road Side Unit,RSU)供电是一个可行的方法.本文针对太阳能供电的RSU,提出了两个分布式的在线调度策略,旨在最大化服务车辆数.在基于Markov链的调度策略中,采用Markov链表述RSU...为了降低碳排放量和部署成本,利用太阳能给车联网路边设施(Road Side Unit,RSU)供电是一个可行的方法.本文针对太阳能供电的RSU,提出了两个分布式的在线调度策略,旨在最大化服务车辆数.在基于Markov链的调度策略中,采用Markov链表述RSU能量状态,并通过对动作的奖励最大化服务的车辆数;在基于阈值的调度策略中,RSU计算服务车辆时所消耗的能量,并结合自己的能量状态,选择服务的车辆.仿真结果表明,本文提出的在线调度策略增加了服务车辆数.展开更多
文摘V-NDN(vehicular named data networking)是一种使用命名数据网络架构的车辆自组织网络(vehicular Ad-hoc network,VANET),主要用来连接移动车辆之间的通信。提高网络中兴趣包的命中率是研究领域急待解决的难题。文章研究了城市道路环境下V-NDN的数据转发策略,考虑到城市道路环境下路侧单元(road side unit,RSU)被均匀广泛部署的特点,提出了一种基于RSU辅助的V-NDN数据转发策略(RSU aided V-NDN)。通过实验仿真与传统的V-NDN和基于蜂窝网络辅助的V-NDN进行对比,结果表明该文提出的数据转发策略有效地提高了网络的服务质量(quality of service,QoS)。
文摘Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.
文摘The integration of communications,sensing and computing(I-CSC)has significant applications in vehicular ad hoc networks(VANETs).A roadside unit(RSU)plays an important role in I-CSC by performing functions such as information transmission and edge computing in vehicular communication.Due to the constraints of limited resources,RSU cannot achieve full coverage and deploying RSUs at key cluster heads of hierarchical structures of road networks is an effective management method.However,direct extracting the hierarchical structures for the resource allocation in VANETs is an open issue.In this paper,we proposed a network-based renormalization method based on information flow and geographical location to hierarchically deploy the RSU on the road networks.The renormalization method is compared with two deployment schemes:genetic algorithm(GA)and memetic framework-based optimal RSU deployment(MFRD),to verify the improvement of communication performance.Our results show that the renormalization method is superior to other schemes in terms of RSU coverage and information reception rate.
文摘为了降低碳排放量和部署成本,利用太阳能给车联网路边设施(Road Side Unit,RSU)供电是一个可行的方法.本文针对太阳能供电的RSU,提出了两个分布式的在线调度策略,旨在最大化服务车辆数.在基于Markov链的调度策略中,采用Markov链表述RSU能量状态,并通过对动作的奖励最大化服务的车辆数;在基于阈值的调度策略中,RSU计算服务车辆时所消耗的能量,并结合自己的能量状态,选择服务的车辆.仿真结果表明,本文提出的在线调度策略增加了服务车辆数.
