Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate diff...Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.展开更多
The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which invo...The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which involve on-board units and road side units in the edge network,as well as the concept of cloud computing,which involves the data center that provides service.The security issues are always an important concern in the design of IoV architecture.To achieve a secure IoV architecture,some security measures are necessary for the cloud computing and fog computing associated with the vehicular network.In this paper,we summarize some research works on the security schemes in the vehicular network and cloud-fog-dew computing platforms which the IoV depends on.展开更多
Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing the...Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.展开更多
随着低空经济与智能交通系统的快速发展,以无人机(Unmanned Aerial Vehicle,UAV)为核心的低空通信网络(Low-altitude Communication Networks,LACN)已成为车联网(Internet of Vehicles,IoV)智能化演进的关键支撑技术。通过融合5G-A通感...随着低空经济与智能交通系统的快速发展,以无人机(Unmanned Aerial Vehicle,UAV)为核心的低空通信网络(Low-altitude Communication Networks,LACN)已成为车联网(Internet of Vehicles,IoV)智能化演进的关键支撑技术。通过融合5G-A通感一体化、星地一体化网络及移动边缘计算等前沿技术,低空通信网络为车联网提供了高可靠、低时延、广覆盖的通信与计算能力,有效解决了传统地面网络在复杂交通场景下面临的通信盲区、服务质量瓶颈和算力不足等问题。综述了低空通信网络在车联网中的应用,沿着技术纵向演进与应用横向拓展两条主线,从移动边缘计算、协同定位与感知以及信息娱乐服务3个核心维度深入剖析了现有研究的技术脉络、核心挑战与发展趋势。通过梳理分析发现,当前研究呈现出两大趋势:一是从传统模型驱动优化向数据驱动的智能决策模式转变,深度强化学习等人工智能技术被广泛应用;二是从单点技术优化向“空-地-云”一体化协同架构演进,数字孪生、多无人机协作等成为研究热点。进一步指出,发展无需外部设施支持的自主协同网络、实现空地资源的智能一体化调度,是未来实现全域无缝高精度服务的关键突破口。展开更多
智能交通系统的迅猛发展催生了对实时性与高可靠计算服务的迫切需求,进而推动了车载边缘计算向更具动态性和灵活性的协同计算架构演进。多层空基网络突破了传统地面基础设施在覆盖范围与服务连续性方面的固有局限,正逐步成为支撑车载边...智能交通系统的迅猛发展催生了对实时性与高可靠计算服务的迫切需求,进而推动了车载边缘计算向更具动态性和灵活性的协同计算架构演进。多层空基网络突破了传统地面基础设施在覆盖范围与服务连续性方面的固有局限,正逐步成为支撑车载边缘计算的重要补充与发展方向。为此,构建了一种融合高空平台(High Altitude Platform,HAP)与无人机(Unmanned Aerial Vehicle,UAV)的多层空基边缘计算架构,协同为车联网(Internet of Vehicles,IoV)中的移动车辆提供高效计算支持。针对车辆移动引发的频繁空中小区切换问题,创新性地引入切换感知机制,预测车辆在UAV覆盖下的小区切换时间窗,在车辆与UAV能耗限制下,联合优化系统的带宽分配、计算资源分配与任务卸载决策,以最小化任务总时延,同时规避切换中断风险。为应对混合整数非线性规划(Mixed Integer Nonlinear Programming,MINLP)问题的高计算复杂度,设计了一种3步迭代求解算法,将原问题分解为带宽分配、计算资源分配和卸载决策优化子问题,采用CVX工具、线性松弛与交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)求解。仿真结果表明,相比于基线方案,所提算法在任务大小为5~9 Mb时,任务总时延分别降低了11.9%、23.3%和25.5%。展开更多
文摘Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.
基金supported by National Natural Science Foundation of China under Grant No.61672060.
文摘The Internet of Vehicles(IoV)is becoming an essential factor in the development of smart transportation and smart city projects.The IoV technology consists of the concepts of fog computing and dew computing,which involve on-board units and road side units in the edge network,as well as the concept of cloud computing,which involves the data center that provides service.The security issues are always an important concern in the design of IoV architecture.To achieve a secure IoV architecture,some security measures are necessary for the cloud computing and fog computing associated with the vehicular network.In this paper,we summarize some research works on the security schemes in the vehicular network and cloud-fog-dew computing platforms which the IoV depends on.
基金supported by FCT through the LASIGE Research Unit(UIDB/00408/2020UIDP/00408/2020)+1 种基金the Brazilian National Council for Research and Development(CNPq)(#304315/2017-6#430274/2018-1)。
文摘Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.
文摘随着低空经济与智能交通系统的快速发展,以无人机(Unmanned Aerial Vehicle,UAV)为核心的低空通信网络(Low-altitude Communication Networks,LACN)已成为车联网(Internet of Vehicles,IoV)智能化演进的关键支撑技术。通过融合5G-A通感一体化、星地一体化网络及移动边缘计算等前沿技术,低空通信网络为车联网提供了高可靠、低时延、广覆盖的通信与计算能力,有效解决了传统地面网络在复杂交通场景下面临的通信盲区、服务质量瓶颈和算力不足等问题。综述了低空通信网络在车联网中的应用,沿着技术纵向演进与应用横向拓展两条主线,从移动边缘计算、协同定位与感知以及信息娱乐服务3个核心维度深入剖析了现有研究的技术脉络、核心挑战与发展趋势。通过梳理分析发现,当前研究呈现出两大趋势:一是从传统模型驱动优化向数据驱动的智能决策模式转变,深度强化学习等人工智能技术被广泛应用;二是从单点技术优化向“空-地-云”一体化协同架构演进,数字孪生、多无人机协作等成为研究热点。进一步指出,发展无需外部设施支持的自主协同网络、实现空地资源的智能一体化调度,是未来实现全域无缝高精度服务的关键突破口。
文摘智能交通系统的迅猛发展催生了对实时性与高可靠计算服务的迫切需求,进而推动了车载边缘计算向更具动态性和灵活性的协同计算架构演进。多层空基网络突破了传统地面基础设施在覆盖范围与服务连续性方面的固有局限,正逐步成为支撑车载边缘计算的重要补充与发展方向。为此,构建了一种融合高空平台(High Altitude Platform,HAP)与无人机(Unmanned Aerial Vehicle,UAV)的多层空基边缘计算架构,协同为车联网(Internet of Vehicles,IoV)中的移动车辆提供高效计算支持。针对车辆移动引发的频繁空中小区切换问题,创新性地引入切换感知机制,预测车辆在UAV覆盖下的小区切换时间窗,在车辆与UAV能耗限制下,联合优化系统的带宽分配、计算资源分配与任务卸载决策,以最小化任务总时延,同时规避切换中断风险。为应对混合整数非线性规划(Mixed Integer Nonlinear Programming,MINLP)问题的高计算复杂度,设计了一种3步迭代求解算法,将原问题分解为带宽分配、计算资源分配和卸载决策优化子问题,采用CVX工具、线性松弛与交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)求解。仿真结果表明,相比于基线方案,所提算法在任务大小为5~9 Mb时,任务总时延分别降低了11.9%、23.3%和25.5%。
