Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on n...Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks.As many tasks in this application domain are time critical,offloading to the cloud is prohibitive.Additionally,task deadlines have to be dealt with.This paper addresses two main challenges.First,we present a task migration algorithm supporting deadlines in vehicular edge computing.The algorithm is following the earliest deadline first model but in presence of dynamic processing resources,i.e,vehicles joining and leaving a VMC.This task offloading is very sensitive to the mobility of vehicles in a VMC,i.e,the so-called dwell time a vehicles spends in the VMC.Thus,secondly,we propose a machine learning-based solution for dwell time prediction.Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC.Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya.Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions,advancing the state of the art in vehicular edge computing.展开更多
The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger comfort.However,the substantial and...The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger comfort.However,the substantial and varied data generated by MR-Connected AVs(MR-CAVs),encompassing both highly dynamic and static information,presents formidable challenges for efficient data management and retrieval.In this paper,we formulate our indexing problem as a constrained optimization problem,with the aim of maximizing the utility function that represents the overall performance of our indexing system.This optimization problem encompasses multiple decision variables and constraints,rendering it mathematically infeasible to solve directly.Therefore,we propose a heuristic algorithm to address the combinatorial complexity of the problem.Our heuristic indexing algorithm efficiently divides data into highly dynamic and static categories,distributing the index across Roadside Units(RSUs)and optimizing query processing.Our approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations,thereby shifting the burden away from the vehicles themselves.Our algorithm strategically places data in the cache,optimizing cache hit rate and space utilization while reducing latency.The quantitative evaluation demonstrates the superiority of our proposed scheme,with significant reductions in latency(averaging 27%-49.25%),a 30.75%improvement in throughput,a 22.50%enhancement in cache hit rate,and a 32%-50.75%improvement in space utilization compared to baseline schemes.展开更多
Drivers who are distracted cannot operate their vehicles appropriately,which leads to error-prone behavior on the roads.This behavior increases the risk of collisions for both themselves and surrounding vehicles,makin...Drivers who are distracted cannot operate their vehicles appropriately,which leads to error-prone behavior on the roads.This behavior increases the risk of collisions for both themselves and surrounding vehicles,making it urgent to manage anomalous vehicles with distracted drivers and mitigate their impacts on driving safety.To address this problem,this paper presents an anomaly behavior management system that leverages connected vehicles to improve the safety performance for both individual vehicles and the whole net-work.The proposed system integrates a hierarchical architecture that reduces the risk of collisions caused by anomalous vehicles in large-scale road networks.Connected vehicles monitor anomalous vehicles and estimate speed and lane-changing instructions to avoid dangerous behaviors.The benefits of the proposed system are evaluated using microscopic traffic simulation,which shows a reduction in the risk of collisions and improved mobility for both connected vehicles and the entire network.The paper also conducts a sensitivity analysis of the market penetration rates of connected vehicles and traffic demand levels to understand the system’s reliability at different development stages of connected vehicles and traffic congestion.展开更多
文摘Edge computing is becoming ever more relevant to offload compute-heavy tasks in vehicular networks.In this context,the concept of vehicular micro clouds(VMCs)has been proposed to use compute and storage resources on nearby vehicles to complete computational tasks.As many tasks in this application domain are time critical,offloading to the cloud is prohibitive.Additionally,task deadlines have to be dealt with.This paper addresses two main challenges.First,we present a task migration algorithm supporting deadlines in vehicular edge computing.The algorithm is following the earliest deadline first model but in presence of dynamic processing resources,i.e,vehicles joining and leaving a VMC.This task offloading is very sensitive to the mobility of vehicles in a VMC,i.e,the so-called dwell time a vehicles spends in the VMC.Thus,secondly,we propose a machine learning-based solution for dwell time prediction.Our dwell time prediction model uses a random forest approach to estimate how long a vehicle will stay in a VMC.Our approach is evaluated using mobility traces of an artificial simple intersection scenario as well as of real urban traffic in cities of Luxembourg and Nagoya.Our proposed approach is able to realize low-delay and low-failure task migration in dynamic vehicular conditions,advancing the state of the art in vehicular edge computing.
文摘The integration of Mixed Reality(MR)technology into Autonomous Vehicles(AVs)has ushered in a new era for the automotive industry,offering heightened safety,convenience,and passenger comfort.However,the substantial and varied data generated by MR-Connected AVs(MR-CAVs),encompassing both highly dynamic and static information,presents formidable challenges for efficient data management and retrieval.In this paper,we formulate our indexing problem as a constrained optimization problem,with the aim of maximizing the utility function that represents the overall performance of our indexing system.This optimization problem encompasses multiple decision variables and constraints,rendering it mathematically infeasible to solve directly.Therefore,we propose a heuristic algorithm to address the combinatorial complexity of the problem.Our heuristic indexing algorithm efficiently divides data into highly dynamic and static categories,distributing the index across Roadside Units(RSUs)and optimizing query processing.Our approach takes advantage of the computational capabilities of edge servers or RSUs to perform indexing operations,thereby shifting the burden away from the vehicles themselves.Our algorithm strategically places data in the cache,optimizing cache hit rate and space utilization while reducing latency.The quantitative evaluation demonstrates the superiority of our proposed scheme,with significant reductions in latency(averaging 27%-49.25%),a 30.75%improvement in throughput,a 22.50%enhancement in cache hit rate,and a 32%-50.75%improvement in space utilization compared to baseline schemes.
文摘Drivers who are distracted cannot operate their vehicles appropriately,which leads to error-prone behavior on the roads.This behavior increases the risk of collisions for both themselves and surrounding vehicles,making it urgent to manage anomalous vehicles with distracted drivers and mitigate their impacts on driving safety.To address this problem,this paper presents an anomaly behavior management system that leverages connected vehicles to improve the safety performance for both individual vehicles and the whole net-work.The proposed system integrates a hierarchical architecture that reduces the risk of collisions caused by anomalous vehicles in large-scale road networks.Connected vehicles monitor anomalous vehicles and estimate speed and lane-changing instructions to avoid dangerous behaviors.The benefits of the proposed system are evaluated using microscopic traffic simulation,which shows a reduction in the risk of collisions and improved mobility for both connected vehicles and the entire network.The paper also conducts a sensitivity analysis of the market penetration rates of connected vehicles and traffic demand levels to understand the system’s reliability at different development stages of connected vehicles and traffic congestion.
