A Novel Load Balancing Strategy of Software-Defined Cloud/Fog Networking in the Internet of Vehicles

The Internet of Vehicles(IoV)has been widely researched in recent years,and cloud computing has been one of the key technologies in the IoV.Although cloud computing provides high performance compute,storage and networking services,the IoV still suffers with high processing latency,less mobility support and location awareness.In this paper,we integrate fog computing and software defined networking(SDN) to address those problems.Fog computing extends computing and storing to the edge of the network,which could decrease latency remarkably in addition to enable mobility support and location awareness.Meanwhile,SDN provides flexible centralized control and global knowledge to the network.In order to apply the software defined cloud/fog networking(SDCFN) architecture in the IoV effectively,we propose a novel SDN-based modified constrained optimization particle swarm optimization(MPSO-CO) algorithm which uses the reverse of the flight of mutation particles and linear decrease inertia weight to enhance the performance of constrained optimization particle swarm optimization(PSO-CO).The simulation results indicate that the SDN-based MPSO-CO algorithm could effectively decrease the latency and improve the quality of service(QoS) in the SDCFN architecture.

Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN

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.

The Development of Self-Balancing Controller for One-Wheeled Vehicles

The purpose of this study is to develop a self-balancing controller (SBC) for one-wheeled vehicles (OWVs). The composition of the OWV system includes: a DSP motion card, a wheel motor, and its driver. In addition, a tilt and a gyro, for sensing the angle and angular velocity of the body slope, are used to realize self-balancing controls. OWV, a kind of unicycle robot, can be dealt with as a mobile-inverted-pendulum system for its instability. However, for its possible applications in mobile carriers or robots, it is worth being further developed. In this study, first, the OWV system model will be derived. Next, through the simulations based on the mathematical model, the analysis of system stability and controllability can be evaluated. Last, a concise and realizable method, through system pole-placement and linear quadratic regulator (LQR), will be proposed to design the SBC. The effectiveness, reliability, and feasibility of the proposal will be con- firmed through simulation studies and experimenting on a physical OWV.

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Development of City Transport System and Two-wheel Vehicles

Based on the study on the city transport systems of some typical cities worldwide,this paper put forward that each city transport system has its own development mode,which is influenced by the city development plan,economic development level,traveling vehicle composition etc..When some problems occur,such as the congestions caused by contradiction between the road capacity and vehicle composition,the city transport system may come into temporary maturity period.If the improvement for road system is limited meanwhile,optimized structure of vehicle composition should be an effective solution in this case.With the development of economy-internationalization,the development speed of city transport modernization is rapid.When traveling easiness is conflicting with efficiency,the advantages of public transport system become more obvious.Correspondingly,the superiority of two-wheel vehicles will reappear.Though the important function of two-wheel vehicles for alleviating city traffic problems is obvious,however,their development strategy must be reasonably proposed,and operation regulations must be performed accordingly.

Temperature Research of Permanent Magnet Brushless DC Motor for Electric Balanced Vehicle

A power system structure composed of a brushless DC motor and a cycloidal reducer for electric balanced vehicle has been proposed, and the temperature of important components in this structure would be discussed. The loss generated by the cycloid reducer is negligible, it’s only need to analyze the thermal field of motor. Since the temperature change will affect the material properties of the target motor, the electromagnetic and temperature fields, involved in the motor, are selected for coupling calculation to determine whether the final temperature distribution can meet the requirements of vehicle for use.

Battery Management System Used in Electric Vehicles

A battery management system(BMS) which contains state of charge(SOC) determination,battery charging control and its optimization,and cell balancing is proposed.Thevenin's model having a RC network characterizing the battery polarization is used for SOC determination.The polarization voltage of the battery model is identified using the nonlinear least square technique.The paper presents a new SOC definition method considering the SOC limit of each cell for battery pack in series.The relationship between the battery current and polarization voltage is analyzed.And then the battery charging approach that the charging current can be adaptively adjusted by the polarization voltage control system is investigated based on fuzzy logic control theory.Cell balancing control strategy is also proposed based on the principle of the maximum use for the capacity and energy of the battery pack.

Control System of Two-Wheel Self-Balancing Vehicle

This study mainly concerns a motion model and the main control algorithm of two-wheel self-balancing vehicle models.Details of the critical parameters fetching and output value of two-wheel self-balancing vehicle models are introduced,including those concerning balance control,speed control and direction control.An improved cascade coupling control scheme is proposed for two-wheel vehicles,based on a proportional-integral-derivative(PID)control algorithm.Moreover,a thorough comparison between a classic control system and the improved system is provided,and all aspects thereof are analyzed.It is determined that the control performance of the two-wheel self-balancing vehicle system based on the PID control algorithm is reliable,enabling the vehicle body to maintain balance while moving smoothly along a road at a fast average speed with better practical per-formance.

多方向跳跃翻筋斗鼠群算法的UAV三维路径规划

针对鼠群搜索算法(Rat Swarm Optimizer,RSO)在求解无人机(Unmanned Aerial Vehicle,UAV)三维路径规划问题时的收敛精度低,易早熟等问题,提出了一种基于多方向跳跃翻筋斗鼠群算法(Multi-Direction Jump and Somersault RSO,MJSRSO)的UAV三维路径规划方法。首先,建立了UAV三维路径规划优化模型;其次,在MJSRSO算法中,引入It⁃erative混沌自适应小孔成像学习策略初始化种群以增强种群多样性;同时融入了多方向跳跃型捕食和莱维翻筋斗追逐策略,并利用双平衡自适应对称锥形因子更好地控制算法局部开发和全局探索,提高算法的寻优能力。最后,利用MJSRSO算法求解了不同的UAV三维路径规划问题。仿真结果表明,这里提出的MJSRSO算法能够规划出代价最小的安全飞行路径,其寻优性能优于其他算法,证明了所提出的无人机三维路径规划方法的有效性和优越性。

多舱共配绿色车辆路径问题的改进变邻域搜索算法

针对社区团购前置仓配送场景中“多中心、高时效、多品类、高排放”难题,本文提出多车场带时间窗的绿色多舱车车辆路径问题(MDMCG-VRPTW),构建混合整数线性规划模型,并设计改进的变邻域搜索算法(IVNS)实现求解.采用两阶段混合算法构造高质量初始解.提出均衡抖动策略以充分探索解空间,引入粒度机制以提升局部搜索阶段的寻优效率.标准算例测试结果验证了两阶段初始解构造算法和IVNS算法的有效性.仿真实验结果表明,模型与算法能够有效求解MDMCGVRPTW,且改进策略提高了算法的求解效率和全局搜索能力.最后,基于对配送策略和时效性的敏感性分析,为相关配送企业降本增效提供更多决策依据.

双重信息引导的蚁群算法求解绿色多舱车辆路径问题

针对当前实际运输中广泛存在的绿色多舱车辆路径问题(GMCVRP),文章提出一种双重信息引导的蚁群优化算法(DIACO)进行求解.首先,在DIACO的全局搜索阶段,重新构建传统蚁群优化算法(TACO)中的信息素浓度矩阵(PCM),使其同时包含客户块信息和客户序列信息,即建立具有双重信息的PCM(DIPCM),从而更全面学习和累积优质解的信息;采用3种启发式方法生成较高质量个体,用于初始化DIPCM,可快速引导算法朝向解空间中优质区域进行搜索.其次,在DIACO的局部搜索阶段,设计结合自适应策略的多种变邻域操作,用于对解空间的优质区域执行深入搜索.再次,提出信息素浓度平衡机制,以防止搜索陷入停滞.最后,使用不同规模的算例进行仿真测试和算法对比,结果验证了DIACO是求解GMCVRP的有效算法.

基于LQR控制的两轮自平衡小车设计

采用LQR控制算法,实现一种两轮自平衡小车的系统设计.搭建了以STM32F103RCT6为主控制器、MPU6050模块为姿态数据采集、N10激光雷达模块为测距的硬件系统.根据两轮自平衡小车的数学模型,利用LQR控制算法实现小车系统的平衡控制,使用Matlab/Simulink进行系统仿真及分析,并在硬件平台上进行实际测试.实验结果表明:小车功能多样、系统运行平稳、抗干扰能力强、调节速度快,具有应用推广价值.

考虑配送紧迫度与公平的应急车辆调度模型

突发事件下的应急车辆调度,既要优先考虑需求紧迫度高的关键节点,又要避免其他节点配送延时过大产生次生影响。基于此,该文建立了考虑需求紧迫度与配送延时公平的应急车辆调度模型。首先,采用熵权-TOPSIS法计算节点需求紧迫度,并引入配送延时均衡指标避免节点之间的配送延时差异过大;其次,以配送的总时间、总成本和配送延时均衡度为多目标函数,配送中心库存量、路径连续性等为约束条件,建立优化模型;最后,通过K-Means算法和CW节约算法划分子路网,结合可变邻域局部搜索算法改进蚁群算法。结果表明:相比于基本模型,所提模型下,需求紧迫度高的节点的平均配送延时和最大配送延时分别降低25.26%、22.98%,配送延时方差增加34.30%;相比于仅考虑需求紧迫度的模型,所提模型下,需求紧迫度高节点的平均配送延时、最大配送延时和配送延时方差分别降低10.79%、20.26%和40.57%。这表明所提模型能更好地兼顾关键节点的优先和普通节点的公平。

变输出无模型自适应无人艇艏向控制方法分析

[目的]针对无人艇(USV)艏向控制存在模型摄动、外部干扰等不确定性问题,提出一种适用于USV的改进无模型自适应控制(MFAC)方法。[方法]基于USV艏向控制子系统存在非自衡特性,直接使用紧格式MFAC方法讨论USV的艏向控制子系统在不确定影响下的控制问题。在标准控制准则中引入历史输入项,提出一种变输出紧格式MFAC方法,削弱USV艏向控制子系统中存在的积分作用。最后,开展艏向控制仿真实验以及“海豚IB”小型USV的外场试验验证所提方法的有效性。[结果]结果显示,相比标准紧格式MFAC方法和PID方法,变输出紧格式MFAC方法的USV艏向控制更加稳定。[结论]所做研究可为USV提供一种对环境扰动、模型摄动等影响不敏感、具有较好自适应性及鲁棒性的艏向控制算法。

液压平衡回路在冶金特种车辆上的应用

基于某系列的SUN经典平衡阀典型结构及性能特点,给出该系列平衡阀开启的平衡条件及其回路的一般性能指标。在此基础上针对冶金特种车辆常见的平衡回路,从负载特性、保压能力及工作周期占比3个方面,重点分析了3种典型平衡回路的工况特点。进一步采用框图分析、对比计算、AMESim仿真等方法指出各自平衡回路在变量系统中应用时的可靠性、调速特性、节能特性等方面的指标差异与局限,为特定工况下平衡阀选择及其平衡回路的设计及优化提供有益参考。

基于特性的商用车整体式车桥及耦合悬架动力学模型研究

车桥及悬架是汽车的重要部件,为了实时模拟商用车的整体式车桥及多种结构类型的悬架,本文采用基于总成特性的建模技术路线,将车桥的运动解耦为Motion运动学和Ride动力学,将悬架特性划分为耦合承载特性、RC/PC导向特性和耦合K&C运动学特性,创新性地考虑了车桥的俯仰动态效应、桥间承载非线性动态耦合关系、桥间K&C运动学耦合关系,建立了能够激发出商用车制动抖振负面现象的动力学模型,并提出了针对耦合悬架的K&C试验方案和模型参数辨识方法。最后,通过对比K&C试验数据,并与TruckSim模型作对比,系统层级上验证了本模型的精准性;通过将车辆参数填入UniTruck软件,对某款商用车进行了仿真,并与TruckSim仿真结果作对比,整车层级上验证了本模型的有效性。

基于安全裕度的网联自主汽车换道行为风险量化及动态平衡模型

伴随车联网技术的发展,道路交通流呈现智能网联自动驾驶汽车与传统人工驾驶车辆混合共存发展态势,研究网联新型混合车流换道驾驶行为的风险特性极其重要。基于安全裕度理论,建立了换道行为风险量化模型,采用故障树分析法,推导换道的时间和空间风险,进行时空融合的风险评定量化,以判断车辆是否处于安全变道状态,并动态平衡车辆换道行为可能存在的风险。运用SUMO软件对建立的量化模型进行仿真验证分析,碰撞时间倒数与瞬时风险系数均值分别下降0.1与0.05,同时变化趋势趋于稳定。安全裕度风险量化模型使换道风险得到了有效控制的同时,交通流的稳定性得到了较大提高,可保障未来网联环境中自主驾驶车辆队列的稳态运行,从而提高交通容量和交通效率。

考虑时滞的自平衡车控制系统稳定性分析

研究了自平衡车控制系统的时滞相关稳定性问题。首先,应用牛顿经典力学,对自平衡车进行数学建模,给出了自平衡车瞬时力学平衡方程,并对控制电机模型进行线性化处理。在此基础上,建立了基于PID控制器并考虑传输时滞的自平衡车系统的整体状态空间模型。为了评估系统的时滞稳定裕度,基于Lyapunov-Krasovskii(L-K)泛函方法,并应用广义自由矩阵积分不等式技术,建立了系统的时滞相关稳定性判据。然后基于给出的稳定性判据,借助Matlab中的线性矩阵不等式(linear matrix inequality,LMI)工具箱,研究了速度环PID参数对自平衡车控制系统时滞稳定裕度的影响。最后,通过仿真实验验证了所提出系统稳定性判据的有效性。

城市轨道交通车辆均衡修检修制度和计划性检修制度对比分析

[目的]为了尽可能降低城市轨道交通车辆购置费和运维成本,我国部分城市目前已开展均衡修检修制度,因此有必要将车辆均衡修检修制度和原有计划性检修制度进行对比分析。[方法]以重庆轨道交通为例,分别介绍了两种检修制度下的检修修程和检修周期,并对两种检修制度下的车辆上线率和检修列位进行了对比分析。[结果及结论]相较传统计划性检修制度,均衡修检修制度下每列列车每年扣车时间减少了19 d,且所需检修列位数增加了18.5%。结果表明,城市轨道交通车辆均衡修检修制度更为合理。

组合式自平衡抱胎举升机结构设计与功能分析

为应对大型车辆维修和保养需求,针对大型车辆特点结合模块化设计理念,设计了一款组合式自平衡抱胎举升机。该文介绍了我国汽车维修使用的举升机设备发展情况,并总结了举升机现阶段的技术不足,结合大型车辆维修保养实际情况,设计了组合式自平衡抱胎举升机。经试验证实,该机具有灵活可靠、电气化程度高、升降控制精确、平衡性好等多重技术优势,适宜应用于货车、客车等大型车辆维修保养。