V2X多节点协同分布式卸载策略

为了应对车联网中计算资源密集、可分离型任务的卸载环境动态变化和不同协同节点通信、计算资源存在差异的问题,提出了一种在V2X下多协同节点串行卸载、并行计算的分布式卸载策略。该策略利用车辆可预测的行驶轨迹,对任务进行不等拆分,分布式计算于本地、MEC及协同车辆,建立系统时延最小化的优化问题。为求解该优化问题,设计了博弈论的卸载机制,以实现协同节点串行卸载的执行顺序;鉴于车联网的动态时变特性,利用序列二次规划算法,给出了最优的任务不等拆分。仿真结果表明,所提策略能够有效减少计算任务系统时延,且当多协同节点分布式卸载服务时,所提策略在不同的参数条件下仍然能够保持稳定的系统性能。     

车联网云边协同计算场景下的多目标优化卸载决策

车联网场景下的计算任务对时延非常敏感,需要云边协同计算来满足这类需求。针对车联网云边协同计算场景下如何高效地进行服务卸载并同时考虑服务的卸载决策以及边缘服务器和云服务器的协同资源分配问题,设计了基于云边协同的车辆计算网络架构,在该架构下,车载终端、云服务器和边缘服务器都可以提供计算服务;通过对缓存任务进行分类并将缓存策略引入车联网场景,依次设计了缓存模型、时延模型、能耗模型、服务质量模型以及多目标优化问题模型;给出了一种基于改进的多目标优化免疫算法的卸载决策方案。最后,通过对比实验验证了所提卸载决策方案的有效性。     

基于移动云服务的车联网任务卸载策略

针对车联网数据卸载策略在选择边缘服务器时忽略负载均衡的问题,提出一种基于移动云服务的车联网任务卸载策略。该策略基于一种新型网络架构,采用强化学习实现卸载任务指派,将任务卸载的问题转化为车联网服务收益的问题,以通信资源和计算资源构建约束条件,结合整个计算资源系统任务处理时延最低和系统可靠性的要求,寻找可用的服务器节点,实现对卸载任务的最优指派。实验仿真表明,所提出的方法能够减少系统的负载率和任务完成时间,降低了最大链路带宽占用率,从而提升了任务卸载的效率。     

一种基于车边云协同的车联网计算卸载策略

随着智能交通的快速发展和车联网中数据流量爆炸式的增长,汽车终端请求卸载的任务对时延和带宽有了更加严苛的要求。在现有的云计算服务模式中,车辆可以访问云服务器来获得强大的计算、存储和网络资源,但缺点是通信传输时延较大,仅依靠云计算可能会导致过度的延迟。为了更加合理利用资源、减小时延、优化卸载策略,提出了一种基于粒子群优化算法的“车-边-云”协同卸载方案。首先通过接入点附近的软件定义网络（Software Define Network,SDN)控制器根据终端用户附近边缘节点、本地终端和云计算节点的计算资源和容量情况得出最优的卸载策略,充分利用本地、移动边缘计算（Mobile Edge Computing,MEC）设备、云端的计算资源,然后通过粒子群优化算法得出“车-边-云”各计算节点的卸载系数,即最优卸载策略。实验结果表明,相比于其他卸载策略,所提的卸载机制对时延优化效果明显,提高了计算资源的利用率。     

车联网中基于MEC和任务优先级的智能卸载策略研究

在移动边缘计算和云计算共同组成的车联网中,为了保障驾驶员和路人的安全,需要满足车辆任务的高可靠低时延要求.一种基于MEC和任务优先级的智能卸载策略以降低由时延所组成的系统总成本,该策略使用KNN算法,根据任务优先级对任务卸载位置进行选择.仿真结果表明,该资源分配策略能有效减少系统总成本.     

车联网中基于NOMA-MEC的卸载策略研究

随着车联网(IoV)的迅猛发展,请求进行任务卸载的汽车终端用户也逐渐增长,而基于移动边缘计算(MEC)的通信网络能够有效地解决任务卸载在上行传输时延较高的挑战,但是该网络模型同时也面临着信道资源不足的问题。该文引入的非正交多址(NOMA)技术相较于正交多址(OMA)能够在相同的信道资源条件下为更多的用户提供任务卸载,同时考虑到任务卸载过程中多方面的影响因子,提出了混合NOMA-MEC卸载策略。该文设计了一种基于深度学习网络(DQN)的博弈算法,帮助车辆用户进行信道选择,并通过神经网络多次迭代学习,为用户提供最优的功率分配策略。仿真结果表明,该文所提出的混合NOMA-MEC卸载策略能够有效地优化多用户卸载的时延以及能耗,最大限度保证用户效益。     

车联网中基于NOMA-MEC的卸载策略研究

随着车联网(IoV)的迅猛发展,请求进行任务卸载的汽车终端用户也逐渐增长,而基于移动边缘计算(MEC)的通信网络能够有效地解决任务卸载在上行传输时延较高的挑战,但是该网络模型同时也面临着信道资源不足的问题。该文引入的非正交多址(NOMA)技术相较于正交多址(OMA)能够在相同的信道资源条件下为更多的用户提供任务卸载,同时考虑到任务卸载过程中多方面的影响因子,提出了混合NOMA-MEC卸载策略。该文设计了一种基于深度学习网络(DQN)的博弈算法,帮助车辆用户进行信道选择,并通过神经网络多次迭代学习,为用户提供最优的功率分配策略。仿真结果表明,该文所提出的混合NOMA-MEC卸载策略能够有效地优化多用户卸载的时延以及能耗,最大限度保证用户效益。     

无人机辅助车联网中基于车辆动态聚类的协作卸载策略全文替换

在智能交通系统中,利用无人机基站可为车辆提供接入服务及广域覆盖。然而针对分布密集且动态性强的网联自动驾驶场景,动态节点智能交互频繁且须低时延以保证驾驶安全,如何动态部署无人机基站以满足网络的性能指标是一个关键问题。针对此问题,提出了基于车辆动态聚类的协作卸载策略,以降低车辆计算卸载时延,提升服务质量。首先,考虑车辆卸载请求及无人机基站的服务能力进行车簇的划分和匹配,并根据车辆位置的改变对车簇进行实时动态调整;然后,多无人机基站针对全域车辆的卸载请求基于遗传算法协作进行最优卸载决策及资源分配。仿真结果表明,与已有策略相比,所提策略可以有效降低计算卸载时延、提升服务质量。     

工业物联网中数字孪生辅助任务卸载算法

针对工业物联网(IIoT)设备资源有限和边缘服务器资源动态变化导致的任务协同计算效率低等问题,该文提出一种工业物联网中数字孪生(DT)辅助任务卸载算法。首先,该算法构建了云-边-端3层数字孪生辅助任务卸载框架,在所创建的数字孪生层中生成近似最佳的任务卸载策略。其次,在任务计算时间和能量的约束下,从时延的角度研究了计算卸载过程中用户关联和任务划分的联合优化问题,建立了最小化任务卸载时间和服务失败惩罚的优化模型。最后,提出一种基于深度多智能体参数化Q网络(DMAPQN)的用户关联和任务划分算法,通过每个智能体不断地探索和学习,以获取近似最佳的用户关联和任务划分策略,并将该策略下发至物理实体网络中执行。仿真结果表明,所提任务卸载算法有效降低了任务协同计算时间,同时为每个计算任务提供近似最佳的卸载策略。     

车载边缘计算中多任务部分卸载方案研究

现有车载应用设备对时延有更严苛的要求,车载边缘计算(VEC)能够充分利用网络边缘设备,如路边单元(RSU)进行协作处理,可有效地降低时延。现有研究多假设RSU计算资源充足,可提供无限的服务,但实际其计算资源会随着所需处理任务数量的增加而受限,对时延敏感的车载应用造成限制。该文针对此问题,提出一种车载边缘计算中多任务部分卸载方案,该方案在充分利用RSU的计算资源条件下,考虑邻近车辆的剩余可用计算资源,以最小化总任务处理时延。首先在时延限制和资源约束下分配各任务在本地、RSU和邻近车辆的最优卸载决策变量比例,其次以最小处理时延为目的在一跳通信范围内选择合适的空闲车辆作为处理部分任务的邻近车辆。仿真结果表明所提车载边缘计算中多任务部分卸载方案相较现有方案能较好地降低时延。     

An efficient task offloading strategy based on Aquila Student Psychology Optimization Algorithm in internet of vehicles-fog computing systems

Internet of vehicles (IoV) comprises connected vehicles and connected autonomous vehicles and offers numerous benefits for ensuring traffic and safety competence. Several IoV applications are delay-sensitive and need resources for computation and data storage that are not provided by vehicles. Therefore, these tasks are always offloaded to highly powerful nodes, namely, fog, which can bring resources nearer to the networking edges, reducing both traffic congestion and load. Besides, the mechanism of offloading the tasks to the fog nodes in terms of delay, computing power, and completion time remains still as an open concern. Hence, an efficient task offloading strategy, named Aquila Student Psychology Optimization Algorithm (ASPOA), is developed for offloading the IoV tasks in a fog setting in terms of the objectives, such as delay, computing power, and completion time. The devised optimization algorithm, known as ASPOA, is the incorporation of Aquila Optimizer (AO) and Student Psychology Based Optimization (SPBO). Task offloading in the IoV-fog system selects suitable resources for executing the tasks of the vehicles by considering several constraints and parameters to satisfy the user requirements. The simulation outcomes have shown that the devised ASPOA-based task offloading method has achieved better performance by achieving a minimum delay of 0.0009 s, minimum computing power of 8.884 W, and minimum completion time of 0.441 s.     

基于深度强化学习的云边协同计算迁移研究

基于单一边缘节点计算、存储资源的有限性及大数据场景对高效计算服务的需求,本文提出了一种基于深度强化学习的云边协同计算迁移机制.具体地,基于计算资源、带宽和迁移决策的综合性考量,构建了一个最小化所有用户任务执行延迟与能耗权重和的优化问题.基于该优化问题提出了一个异步云边协同的深度强化学习算法,该算法充分利用了云边双方的计...     

NOMA物联网下多UAV辅助MEC系统的资源分配

为解决偏远地区或突发灾害等场景中的物联网(Internet of Things, IoT)设备的任务计算问题,构建了一个非正交多址接入(Non-orthogonal Multiple Access, NOMA)-IoT(NOMA-IoT)下多无人机(Unmanned Aerial Vehicle, UAV)辅助的NOMA多址边缘计算(Multiple Access Edge Computing, MEC)系统。该系统中设备的计算能耗、卸载能耗和MEC服务器计算能耗直接受同信道干扰、计算资源和发射功率的影响,可通过联合优化卸载策略、计算资源和发射功率最小化系统加权总能耗。根据优化问题的非凸性和复杂性,提出了一种有效的迭代算法解决：首先,对固定卸载策略,计算资源和发射功率分配问题可通过连续凸逼近转化为可解的凸问题;其次,对固定计算资源和发射功率,利用联盟形成博弈解决卸载策略问题,以最小化IoT设备之间的同信道干扰。仿真结果表明,较OMA接入方式,NOMA接入方式减少本地计算能耗、卸载能耗及计算能耗约20%;较无卸载策略方法,包含卸载策略方法在减少系统加权总能耗方面效果较为明显。     

面向自动驾驶的边缘计算技术研究综述

边缘计算在自动驾驶的环境感知和数据处理方面有着极其重要的应用.自动驾驶汽车可以通过从边缘节点获得环境信息来扩大自身的感知范围,也可以向边缘节点卸载计算任务以解决计算资源不足的问题.相比于云计算,边缘计算避免了长距离数据传输所导致的高时延,能给自动驾驶车辆提供更快速的响应,并且降低了主干网络的负载.基于此,首先介绍了基于...     

Research on adaptive dual task offloading decision algorithm for parking space recommendation service

In order to improve the efficiency of tasks processing and reduce the energy consumption of new energy vehicle (NEV), an adaptive dual task offloading decision-making scheme for Internet of vehicles is proposed based on information-assisted service of road side units (RSUs) and task offloading theory. Taking the roadside parking space recommendation service as the specific application Scenario, the task offloading model is built and a hierarchical self-organizing network model is constructed, which utilizes the computing power sharing among nodes, RSUs and mobile edge computing (MEC) servers. The task scheduling is performed through the adaptive task offloading decision algorithm, which helps to realize the available parking space recommendation service which is energy-saving and environmental-friendly. Compared with these traditional task offloading decisions, the proposed scheme takes less time and less energy in the whole process of tasks. Simulation results testified the effectiveness of the proposed scheme.     

Q-learning-based task offloading strategy for satellite edge computing

In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three-tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q-learning-based reinforcement learning offloading strategy in which both the time-sensitive constraints and data requirements of the computation-intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency.     

Multiuser computation offloading for edge-cloud collaboration using submodular optimization

A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation,a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally,when computing tasks are offloaded to edge and cloud from devices,the proposed scheme still maintains stable system utilities under ultra-limited resources.     

Stackelberg game-based task offloading in mobile edge computing-enabled hierarchical multi-coalition unmanned aerial vehicle networks

The unmanned aerial vehicle (UAV) coalition networks have been widely used in emergency mission scenarios. The introduction of the mobile edge computing (MEC) paradigm into multi-coalition UAV networks further improves the mission processing performance of UAV coalitions. In this paper, we investigate the problem of minimizing total task processing delay of UAV members in MEC-enabled coalition-based UAV networks. First, we propose a hierarchical offloading model in which multiple UAV heads decide its position selection strategy and multiple UAV members decide its offloading strategy when offloading tasks to UAV heads. Considering data arrival from multiple UAV member nodes at each UAV head, the first come first served (FCFS) queuing model is introduced when the UAV head processes tasks from members. Second, the hierarchical offloading delay minimization problem is formulated as a multi-leader multi-follower Stackelberg game. The existence of a Stackelberg equilibrium (SE) is proved by showing that multi-leader subgame and multi-follower subgame are exact potential games (EPGs) with Nash equilibrium (NE). We design a best response-based hierarchical iterative offloading algorithm to solve SE. Finally, the simulation results show that the performance of the proposed scheme is better than that of other benchmark methods and the proposed scheme can effectively reduce the total delay for all UAV members.