With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information b...With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information being attacked and tampered with,it is difficult to ensure information security.This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time,calculate the appropriate speed,and plan a reasonable driving route for the driver.To solve these problems,this paper proposes a trusted edge resource allocation framework for assisted driving service,which includes two stages:the blockchain generation stage(the first stage)and assisted driving service stage(the second stage).Furthermore,in the first stage,a delay-and-throughput-oriented block generation model for the mobile terminal is designed.In the second stage,a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing(MEC)servers and low resource utilization of the system.And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning(DRL)algorithm.Finally,compared with joint computation and communication resources’allocation(JCCR)and resource allocation method based on binary offloading(RAB),our proposed scheme can optimize the delay by 7.4%and 26.7%,and support various application services of the vehicular networks more effectively.展开更多
With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive servi...With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.展开更多
基金supported by State Grid Corporation of China Science and Technology Project“Key Technology and Application of New Multi-Mode Intelligent Network for State Grid”(5700-202024176A-0-0-00).
文摘With the continuous progress of information technique,assisted driving technology has become an effective technique to avoid traffic accidents.Due to the complex road conditions and the threat of vehicle information being attacked and tampered with,it is difficult to ensure information security.This paper uses blockchain to ensure the safety of driving information and introduces mobile edge computing technology to monitor vehicle information and road condition information in real time,calculate the appropriate speed,and plan a reasonable driving route for the driver.To solve these problems,this paper proposes a trusted edge resource allocation framework for assisted driving service,which includes two stages:the blockchain generation stage(the first stage)and assisted driving service stage(the second stage).Furthermore,in the first stage,a delay-and-throughput-oriented block generation model for the mobile terminal is designed.In the second stage,a balanced offloading algorithm for assisted driving service based on edge collaboration is proposed to solve the problems of unbalanced load of cluster mobile edge computing(MEC)servers and low resource utilization of the system.And this paper optimizes the throughput of blockchain and delay of the transportation network through deep reinforcement learning(DRL)algorithm.Finally,compared with joint computation and communication resources’allocation(JCCR)and resource allocation method based on binary offloading(RAB),our proposed scheme can optimize the delay by 7.4%and 26.7%,and support various application services of the vehicular networks more effectively.
基金supported by the Science and Technology Project of State Grid Corporation of China:Research and Application of Key Technologies in Virtual Operation of Information and Communication Resources.
文摘With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.
