In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o...In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.展开更多
With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications a...With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.展开更多
The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial veh...The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial vehicles-assisted mobile edge computing(UAV-MEC)has gained attention in providing computing resources to vehicles and optimizing system costs.We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption.We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm(DVCG-MWOA)to address this problem.A novel dynamic clustering algorithm is designed based on vehiclemobility and task offloading efficiency requirements,where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each timeslot in response to vehiclemovement.Within eachUAV-led cluster,cooperative game theory is applied to allocate computing resourceswhile respecting delay constraints,ensuring efficient resource utilization.To enhance offloading efficiency,we improve the multi-objective whale optimization algorithm(MOWOA),resulting in the MWOA.This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device,ultimately achieving a Pareto-optimal solution set for delay and energy consumption.Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches,offering superior performance for vehicular networks.展开更多
With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)of...With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure,thereby reducing the computational burden on connected vehicles.However,this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes.Existing vehicular edge computing platforms have not adequately considered themisbehavior of vehicles.We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment.This approach integrates deep reinforcement learning and reputation management to address task offloading challenges.Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method.展开更多
In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amount...In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.展开更多
Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and th...Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.展开更多
Vehicular Edge Computing(VEC)is a promising technique to accommodate the computation-intensive and delaysensitive tasks through offloading the tasks to the RoadSide-Unit(RSU)equipped with edge computing servers or nei...Vehicular Edge Computing(VEC)is a promising technique to accommodate the computation-intensive and delaysensitive tasks through offloading the tasks to the RoadSide-Unit(RSU)equipped with edge computing servers or neighboring vehicles.Nevertheless,the limited computation resources of edge computing servers and the mobility of vehicles make the offloading policy design very challenging.In this context,through considering the potential transmission gains brought by the mobility of vehicles,we propose an efficient computation offloading and resource allocation scheme in VEC networks with two kinds of offloading modes,i.e.,Vehicle to Vehicle(V2V)and Vehicle to RSU(V2R).We define a new cost function for vehicular users by incorporating the vehicles’offloading delay,energy consumption,and expenses with a differentiated pricing strategy,as well as the transmission gain.An optimization problem is formulated to minimize the average cost of all the task vehicles under the latency and computation capacity constraints.A distributed iterative algorithm is proposed by decoupling the problem into two subproblems for the offloading mode selection and the resource allocation.Matching theorybased and Lagrangian-based algorithms are proposed to solve the two subproblems,respectively.Simulation results show the proposed algorithm achieves low complexity and significantly improves the system performance compared with three benchmark schemes.展开更多
Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging techn...Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.展开更多
Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs anoth...Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs another vehicles as performers for workload processing.An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making,which avoids the information collection from the performers and preserves their privacy.We model the interactions among the requester(leader)and multiple performers(followers)as a Stackelberg game.The requester incentivizes the performers to accept the workloads.We derive the Stackelberg equilibrium under complete information.Furthermore,deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’information private.Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others.Finally,numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.展开更多
Vehicular Edge Computing(VEC)brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles.However,the limited computing capability o...Vehicular Edge Computing(VEC)brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles.However,the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests,thus restricting the performance of VEC system.Besides,a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server.To strengthen the performance of VEC,in this paper we propose to place services beforehand at the edge server,e.g.,by deploying the services/tasks-oriented data(e.g.,related libraries and databases)in advance at the network edge,instead of downloading them from the remote data center or offloading them from vehicles during the runtime.In this paper,we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline.Specifically,the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization.Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution.The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.展开更多
Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so ...Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.展开更多
Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for r...Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.展开更多
To better support the emerging vehicular applications and multimedia services,vehicular edge computing(VEC)provides computing and caching services in proximity to vehicles, by reducing network transmission latency and...To better support the emerging vehicular applications and multimedia services,vehicular edge computing(VEC)provides computing and caching services in proximity to vehicles, by reducing network transmission latency and alleviating network congestion. However, current VEC networks may face some implementation challenges, such as high mobility of vehicles, dynamic vehicular environment,and complex network scheduling. Digital twin, as an emerging technology, can make the virtual representation of physical networks to predict, estimate,and analyze the real-time network state. In this paper, we integrate digital twin into VEC networks to adaptively make network management and policy schedule. We first introduce the framework of VEC networks and present the key problems in a VEC network.Next,we give the concept of digital twin and propose an adaptive digital twin-enabled VEC network. In the proposed network, digital twin can enable adaptive network management via the two-closed loops between physical VEC networks and digital twins. Further,we propose a digital twin empowered VEC offloading problem with vehicle digital models and road side unit (RSU) digital models. A deep reinforcement learning (DRL)-based offloading scheme is designed to minimize the total offloading latency. Numerical results demonstrate the effectiveness of the proposed DRL-based algorithm for VEC offloading.展开更多
The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for computing.In general,the VEC paradigm uses the 5G for wireless communic...The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for computing.In general,the VEC paradigm uses the 5G for wireless communications,where the massive multi-input multi-output(MIMO)technique will be used.However,considering in the VEC environment with many vehicles,the energy consumption of BS may be very large.In this paper,we study the energy optimization problem for the massive MIMO-based VEC network.Aiming at reducing the relevant BS energy consumption,we first propose a joint optimization problem of computation resource allocation,beam allocation and vehicle grouping scheme.Since the original problem is hard to be solved directly,we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them.Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.展开更多
Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and e...Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.展开更多
Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing devices.The continuous emergence of transportation applications has caused a...Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing devices.The continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this problem.However,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized environment.In this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov game.Given that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of tasks.To reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple RSUs.The evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed algorithm.Compared with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.展开更多
基金supported in part by the National Science Foundation of China(Grant No.62172450)the Key R&D Plan of Hunan Province(Grant No.2022GK2008)the Nature Science Foundation of Hunan Province(Grant No.2020JJ4756)。
文摘In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.
基金supported by Future Network Scientific Research Fund Project (FNSRFP-2021-ZD-4)National Natural Science Foundation of China (No.61991404,61902182)+1 种基金National Key Research and Development Program of China under Grant 2020YFB1600104Key Research and Development Plan of Jiangsu Province under Grant BE2020084-2。
文摘With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.
基金funded by Shandong University of Technology Doctoral Program in Science and Technology,grant number 4041422007.
文摘The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial vehicles-assisted mobile edge computing(UAV-MEC)has gained attention in providing computing resources to vehicles and optimizing system costs.We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption.We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm(DVCG-MWOA)to address this problem.A novel dynamic clustering algorithm is designed based on vehiclemobility and task offloading efficiency requirements,where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each timeslot in response to vehiclemovement.Within eachUAV-led cluster,cooperative game theory is applied to allocate computing resourceswhile respecting delay constraints,ensuring efficient resource utilization.To enhance offloading efficiency,we improve the multi-objective whale optimization algorithm(MOWOA),resulting in the MWOA.This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device,ultimately achieving a Pareto-optimal solution set for delay and energy consumption.Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches,offering superior performance for vehicular networks.
基金supported by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)the Science and Technology Research Program of Henan Province of China(232102210134,182102210130)Key Research Projects of Henan Provincial Universities(25B520005).
文摘With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure,thereby reducing the computational burden on connected vehicles.However,this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes.Existing vehicular edge computing platforms have not adequately considered themisbehavior of vehicles.We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment.This approach integrates deep reinforcement learning and reputation management to address task offloading challenges.Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method.
基金supported in part by the National Natural Science Foundation of China(No.61701197)in part by the National Key Research and Development Program of China(No.2021YFA1000500(4))in part by the 111 Project(No.B23008).
文摘In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.
基金supported in part by the National Natural Science Foundation of China under Grant 61971084 and Grant 62001073in part by the National Natural Science Foundation of Chongqing under Grant cstc2019jcyj-msxmX0208in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University,under Grant 2020D05.
文摘Mobile Edge Computing(MEC)is promising to alleviate the computation and storage burdens for terminals in wireless networks.The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries.In addition,Orthogonal Multiple Access(OMA)technique cannot utilize limited spectrum resources fully and efficiently.Therefore,Non-Orthogonal Multiple Access(NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important,especially in highly-dynamic vehicular edge computing networks.The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers.Self-Imitation Learning(SIL)-based Deep Reinforcement Learning(DRL)has emerged as a promising machine learning technique to break through obstacles in various research fields,especially in time-varying networks.In this paper,we first introduce related MEC technologies in vehicular networks.Then,we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL,with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers.Numerical results demonstrate that the proposed algorithm outperforms other methods.
基金The work was supported in part by the National Natural Science Foundation of China(No.62271295,U22A2003,62201329)Shandong Provincial Natural Science Foundation(ZR2020QF002,ZR2022QF002).
文摘Vehicular Edge Computing(VEC)is a promising technique to accommodate the computation-intensive and delaysensitive tasks through offloading the tasks to the RoadSide-Unit(RSU)equipped with edge computing servers or neighboring vehicles.Nevertheless,the limited computation resources of edge computing servers and the mobility of vehicles make the offloading policy design very challenging.In this context,through considering the potential transmission gains brought by the mobility of vehicles,we propose an efficient computation offloading and resource allocation scheme in VEC networks with two kinds of offloading modes,i.e.,Vehicle to Vehicle(V2V)and Vehicle to RSU(V2R).We define a new cost function for vehicular users by incorporating the vehicles’offloading delay,energy consumption,and expenses with a differentiated pricing strategy,as well as the transmission gain.An optimization problem is formulated to minimize the average cost of all the task vehicles under the latency and computation capacity constraints.A distributed iterative algorithm is proposed by decoupling the problem into two subproblems for the offloading mode selection and the resource allocation.Matching theorybased and Lagrangian-based algorithms are proposed to solve the two subproblems,respectively.Simulation results show the proposed algorithm achieves low complexity and significantly improves the system performance compared with three benchmark schemes.
基金supported by the National Key Research and Development Program of China(2020YFB1807500)the National Natural Science Foundation of China(62072360,62001357,62172438,61901367)+4 种基金the key research and development plan of Shaanxi province(2021ZDLGY02-09,2020JQ-844)the Natural Science Foundation of Guangdong Province of China(2022A1515010988)Key Project on Artificial Intelligence of Xi'an Science and Technology Plan(2022JH-RGZN-0003)Xi'an Science and Technology Plan(20RGZN0005)the Xi'an Key Laboratory of Mobile Edge Computing and Security(201805052-ZD3CG36).
文摘Academic and industrial communities have been paying significant attention to the 6th Generation(6G)wireless communication systems after the commercial deployment of 5G cellular communications.Among the emerging technologies,Vehicular Edge Computing(VEC)can provide essential assurance for the robustness of Artificial Intelligence(AI)algorithms to be used in the 6G systems.Therefore,in this paper,a strategy for enhancing the robustness of AI model deployment using 6G-VEC is proposed,taking the object detection task as an example.This strategy includes two stages:model stabilization and model adaptation.In the former,the state-of-the-art methods are appended to the model to improve its robustness.In the latter,two targeted compression methods are implemented,namely model parameter pruning and knowledge distillation,which result in a trade-off between model performance and runtime resources.Numerical results indicate that the proposed strategy can be smoothly deployed in the onboard edge terminals,where the introduced trade-off outperforms the other strategies available.
基金supported in part by National Key R&D Program of China under Grant 2020YFB1807802in part by National Natural Science Foundation of China under Grants 62001125 and 61971148+4 种基金in part by FDCT-MOST Joint Project under Grant 0066/2019/AMJin part by Science and Technology Development Fund of Macao SAR under Grant 0162/2019/A3in part by FDCT SKL-IOTSC(UM)2021-2023in part by Research Grant of University of Macao under Grant MYRG2020-00107-IOTSCin part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011287
文摘Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs another vehicles as performers for workload processing.An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making,which avoids the information collection from the performers and preserves their privacy.We model the interactions among the requester(leader)and multiple performers(followers)as a Stackelberg game.The requester incentivizes the performers to accept the workloads.We derive the Stackelberg equilibrium under complete information.Furthermore,deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’information private.Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others.Finally,numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.
基金supported by National Natural Science Foundation of China(No.62071327)Tianjin Science and Technology Planning Project(No.22ZYYYJC00020)。
文摘Vehicular Edge Computing(VEC)brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles.However,the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests,thus restricting the performance of VEC system.Besides,a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server.To strengthen the performance of VEC,in this paper we propose to place services beforehand at the edge server,e.g.,by deploying the services/tasks-oriented data(e.g.,related libraries and databases)in advance at the network edge,instead of downloading them from the remote data center or offloading them from vehicles during the runtime.In this paper,we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline.Specifically,the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization.Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution.The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.
基金This work was supported in part by the Chung-Ang University Research Scholarship Grants in 2021,and in part by R&D Program for Forest Science Technology(Project No.“2021338B10-2223-CD02)provided by Korea Forest Service(Korea Forestry Promotion Institute).
文摘Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.
基金supported by the National Natural Science Foundation of China(62271096,U20A20157)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)+4 种基金Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)University Innovation Research Group of Chongqing(CXQT20017)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)Chongqing Postdoctoral Science Special Foundation(2021XM3058)Chongqing Postgraduate Research and Innovation Project under grant(CYB22250).
文摘Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting.
文摘To better support the emerging vehicular applications and multimedia services,vehicular edge computing(VEC)provides computing and caching services in proximity to vehicles, by reducing network transmission latency and alleviating network congestion. However, current VEC networks may face some implementation challenges, such as high mobility of vehicles, dynamic vehicular environment,and complex network scheduling. Digital twin, as an emerging technology, can make the virtual representation of physical networks to predict, estimate,and analyze the real-time network state. In this paper, we integrate digital twin into VEC networks to adaptively make network management and policy schedule. We first introduce the framework of VEC networks and present the key problems in a VEC network.Next,we give the concept of digital twin and propose an adaptive digital twin-enabled VEC network. In the proposed network, digital twin can enable adaptive network management via the two-closed loops between physical VEC networks and digital twins. Further,we propose a digital twin empowered VEC offloading problem with vehicle digital models and road side unit (RSU) digital models. A deep reinforcement learning (DRL)-based offloading scheme is designed to minimize the total offloading latency. Numerical results demonstrate the effectiveness of the proposed DRL-based algorithm for VEC offloading.
基金supported by the major science and technology projects in Anhui Province(202003a05020009)the innovation foundation of the city of Bengbu(JZ2022YDZJ0019)the Key Technology Research and Development Project of Hefei(2021GJ029).
文摘The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for computing.In general,the VEC paradigm uses the 5G for wireless communications,where the massive multi-input multi-output(MIMO)technique will be used.However,considering in the VEC environment with many vehicles,the energy consumption of BS may be very large.In this paper,we study the energy optimization problem for the massive MIMO-based VEC network.Aiming at reducing the relevant BS energy consumption,we first propose a joint optimization problem of computation resource allocation,beam allocation and vehicle grouping scheme.Since the original problem is hard to be solved directly,we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them.Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.
基金supported by the National Natural Science Foundation of China (Nos.61901015,62301017).
文摘Federated Learning(FL),a promising deep learning paradigm extensively deployed in Vehicular Edge Computing Networks(VECN),allows a distributed approach to train datasets of nodes locally,e.g.,for mobile vehicles,and exchanges model parameters to obtain an accurate model without raw data transmission.However,the existence of malicious vehicular nodes as well as the inherent heterogeneity of the vehicles hinders the attainment of accurate models.Moreover,the local model training and model parameter transmission during FL exert a notable energy burden on vehicles constrained in resources.In view of this,we investigate FL client selection and resource management problems in FL-enabled UAV-assisted Vehicular Networks(FLVN).We first devise a novel reputation-based client selection mechanism by integrating both data quality and computation capability metrics to enlist reliable high-performance vehicles.Further,to fortify the FL reliability,we adopt the consortium blockchain to oversee the reputation informa-tion,which boasts tamper-proof and interference-resistant qualities.Finally,we formulate the resource scheduling problem by jointly optimizing the computation capability,the transmission power,and the number of local training rounds,aiming to minimize the cost of clients while guaranteeing accuracy.To this end,we propose a reinforcement learning algorithm employing an asynchronous parallel network structure to achieve an optimized scheduling strategy.Simulation results show that our proposed client selection mechanism and scheduling algorithm can realize reliable FL with an accuracy of 0.96 and consistently outperform the baselines in terms of delay and energy consumption.
基金supported by the National Natural Science Foundation of China under Grant Nos.62202140,61832005,62072216,62372214,and 62101463the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20220974+2 种基金the Future Network Scientific Research Foundation Project FNSRFP-2021-ZD-7the Natural Science Foundation of Sichuan Province of China under Grant No.2022NSFSC0863the Sichuan Science and Technology Program under Grant Nos.2023YFH0012 and 2023ZHCG0010.
文摘Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing devices.The continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this problem.However,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized environment.In this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov game.Given that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of tasks.To reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple RSUs.The evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed algorithm.Compared with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.
