基于车辆载带中继的路边单元突发业务分组调度最优策略

高速公路车联网场景中,路边单元(Roadside units,RSUs)可作为多种周边监测数据的汇入网关,其业务具有突发特性,且可通过移动车辆以"存储—载带—转发"方式传输到与骨干网络互联的RSU.针对RSU间业务传输问题,源RSU可根据实时业务到达率按需匹配资源,以应对业务突发性对分组端到端时延的影响.本文首先针对R...     

基于自适应调制与编码的多跳CDMA Ad hoc网络跨层性能分析与建模

给出了一种新颖的多跳CDMA Ad hoc网络跨层性能分析模型.首先建立了随机网络拓扑的数学模型,进而分析了采用MFR路由策略时源节点到目的节点所需跳数的概率分布情况.然后结合自适应调制编码方案,分析了节点中发送分组队列的平均队列长度、丢包率及平均排队时延等分组级QoS性能.在此基础上,分析了网络的端到端时延和吞吐量性能,并讨论了节点传输范围、发送分组队列长度、扩频增益对网络性能的影响.所提出的分析模型虽然考虑的是特殊的MAC协议和路由策略,然而这种方法可以用来分析采用其它MAC协议和路由协议的系统.     

PFWRR:能实现比例公平的增强型WRR

为了实现比例公平原则,在加权轮循调度(WRR)算法的基础上提出了比例公平WRR调度算法——PFWRR.PFWRR依据各队列的平均分组到达率,调整各队列的调度权值,从而在当队列长度小于等于缓冲长度时,保证各队列的平均分组排队时延符合给定比例;当队列长度大于缓冲时,保证各队列的平均分组丢失率符合给定比例.PFWRR的计算负荷是合理的,因为它仅当系统超载且平均分组到达率发生变化时,才调整各队列的服务率.实测性能显示:当系统超载且不出现分组丢失时,PFWRR实现了比例平均分组排队时延保证,当系统出现分组丢失时,PFWRR实现了比例平均分组丢失率保证.     

基于喷泉码的车联网数据分发方案

数据传播是用于向终端用户提供信息娱乐和安全服务的主要技术之一,在该过程中,位于特定区域的车辆（OBUS）接收由路边单元（RSU）广播的数据。然而,当车辆移动较快,广播内容的数据量较大时,车辆很难在一个RSU的通信范围内收集到完整的广播内容。针对这一问题,论文提出了一种基于喷泉码的多RSU协同数据传输方案,将源数据以喷泉码编码,由多个RSU同时注入到车载自组织网络（VANET）中,RSU根据所处位置以及周边行驶车辆速度、密度使用模糊逻辑决策发送喷泉编码包数量。区域内车辆通过车对车（V2V）通信共享数据包,车辆获取到足够的编码包后重建源数据。实验结果表明,该方案在保证交付率的前提下,有效地降低了交付时间,实现了性能的明显提升。     

VANET中基于车辆分簇的协作缓存策略

随着智能交通系统的发展,移动车辆不仅需要地图信息,还需要更多娱乐内容,而整个网络的容量是有限的,这给车载自组织网络带来很大的压力。边缘缓存是在系统的边缘部署缓存,在车载自组织网络中引入边缘缓存技术,可以有效地缓解网络的压力。对车载自组织网络中的缓存策略进行了研究,提出了一种基于车辆分簇的协作边缘缓存策略,可以充分利用车载自组织网络中有限的缓存资源,内容可以缓存在移动车辆和路边单元中,移动车辆可以从簇头和路边单元获取所需内容,不同的内容获取方式会带来不同的请求时延。由于移动车辆和路边单元的缓存容量是有限的,为了降低内容请求时延和成本,构建了一个时延和成本联合最小化的优化问题,并采用蚁群算法对该问题进行求解,以得到最优的缓存放置方案。仿真结果表明,该算法拥有很好的性能。     

基于SP调度策略的簇树型无线传感网络QoS上界研究

为了改善无线传感网络在最坏条件下的网络服务质量（Quality of Service, QoS）,本文将严格优先级（Strict Priority, SP）队列调度引入簇树型无线传感网络中,分析SP队列调度对网络中数据流获得的保证服务的影响,并利用网络演算推导簇树网络中各深度路由节点的时延上界、缓存上界及网络的端到端时延上界。通过实例分析,表明SP队列调度能够有效减小网络的最大端到端时延。     

基于流量自相似性的网络队列管理算法

网络流量的自相似性会导致数据突发状态持续,传统队列管理算法无法对网络流量突发状态进行预测,从而影响网络端到端时延、丢包率和吞吐性能。针对该问题,提出一种基于网络流量预测的主动队列管理算法P-ARED。基于网络流量的均值和方差给出网络流量等级的概念,讨论网络流量等级转移概率与Hurst参数之间的关系,提出基于贝叶斯估计思想的网络流量等级预测方法。在此基础上,在对自相似网络流量环境下的平均队列长度、缓存队列长度最小阈值等参数优化设置的基础上,基于Hurst参数和自相似流量等级预测结果,重新设计ARED算法中分组丢弃概率的计算方法,以提高缓存队列长度的稳定性。仿真结果表明,P-ARED算法与对比的主动队列管理算法相比,降低了网络端到端时延和丢包率,提高了端到端吞吐性能,其中平均吞吐量最高提升7.63%,平均时延最多降低17.52%。     

基于多跳间时延协作的最久分组优先算法

提出了一种基于多跳间时延协作的Crossbar调度算法。该算法以分组头中记录的时延为权重对分组进行调度，通过控制分组在各跳上的时延来达到调节端到端时延的目的。算法还使路由器避免了维护每个流的状态信息以及对单个流进行的复杂的队列管理和调度。计算机仿真表明，算法具有较高的资源利用率、较低的端到端时延抖动和较低的分组丢弃率等特点。     

基于缓存管理的网络编码中继传输方案

针对中继协作无线网络中节点缓存空间有限以及传输和缓存数据包都存在开销等问题进行了研究,提出了一种基于缓存管理的网络编码中继传输方案。在传输过程中该方案以最小化系统开销为目标,考虑多流综合优化问题,利用马尔可夫链对缓存队列进行建模分析。结合编码流速率增加编码机会,获得中继处不同流的缓存阈值,更合理地实现了编码决策。分析与仿真结果表明该方案能有效权衡编码机会和开销,相比其他方案能有效降低时延且提高网络吞吐量。     

时间和能量敏感的端——边—云车路协同系统资源调度优化方法

随着信息技术的不断发展,智能交通系统逐渐成为未来交通的发展方向.然而,智能交通系统中时间敏感型和计算密集型应用的日益增多,给资源有限的车辆终端带来了严峻挑战.端—边—云层次性计算架构是应对该挑战的有效手段.在基于端—边—云架构的车路协同系统中,车辆用户可以将时间敏感型任务卸载到附近的路边单元执行以保证应用的实时性,而将计算密集型任务卸载到云以满足其算力需求.但是,任务卸载也会导致额外的传输时延和能量开销.此外,任务在传输过程也可能遭受错误而导致可靠性降低.因此,为保障端—边—云车路协同系统中车辆的用户体验,提出一种基于多智能体强化学习的资源调度方案.该方案通过充分利用端—边—云架构的特点并采用集中训练—分散执行的框架来构建深度神经网络,以制定任务卸载和车路计算资源分配的最优决策,最终实现可靠性约束下的系统时延和能耗优化.为验证所提方案的有效性,实验通过效用值来体现算法在时延和能耗2方面的优化.实验结果表明,与现有算法相比,所提方案在满足可靠性约束的前提下,效用值可以提高到221.9%.     

A data distribution scheme for VANET based on fountain code

Data dissemination is one of the applications used to provide infotainment to the end-users in vehicular ad hoc networks (VANET). During this process, the vehicles receive the data broadcast by the RoadSide Unit (RSU). However, it is difficult for vehicles to collect the complete content within the communication range of one RSU when the vehicle moves at a high speed and the amount of broadcast data is large. To solve this problem, a multi-RSU cooperative data distribution scheme based on fountain code (MRFC) is proposed in this paper. The source data are encoded by fountain code and poured into the VANET by multiple cooperative RSUs, then the vehicles in the area share data packets through the V2V resource compensation method, so that all vehicles can obtain enough encoded packets to reconstruct the source data. To improve channel resource utilization and reduce delivery delays, the RSUs use fuzzy logic to determine the number of fountain code packets according to their locations, the speed and density of surrounding vehicles. The experimental results show that on the premise of ensuring the delivery rate, the proposed scheme can reduce the delivery delay by 30–50%, and achieve a significant improvement in performance.

     

基于路径探索的车载自组网贪婪路由算法

为了提高城市中车辆间信息的传输效率，实现车辆间的信息共享，针对目前车载自组网（VANET）中基于地理位置转发的多跳单播路由算法没有考虑城市场景的特殊性，不能很好地适应城市中车辆的高度动态性，使车辆之间的数据包可能在错误的路径上传播，造成丢包率较高、时延较长的问题，提出了一种新的基于路径探索的贪婪路由算法。首先，以数据包传输时延为标准，运用人工蜂群算法对数字地图规划出的多条路由路径进行探索。其次，优化数据包在车辆之间的多跳转发方式。仿真结果表明，与贪婪周边无状态路由（GPSR）协议和最大持续时间最小角的GPSR（MM-GPSR）改进算法比较，在最好情况下，所提算法的数据包到达率分别提高了13.81%和9.64%，而该算法的数据包平均端到端时延分别降低了61.91%和27.28%。     

HMM delay prediction technique for VoIP

This paper proposes a new algorithm for predicting audio packet playout delay for voice conferencing applications that use silence suppression. The proposed algorithm uses a hidden Markov model (HMM) to predict the playout delay. Several existing algorithms are reviewed to show that the HMM technique is based on a combination of various desirable features of other algorithms. Voice over Internet protocol (VoIP) applications produce packets at a deterministic rate but various queuing delays are added to the packets by the network causing packet interarrival jitter. Playout delay prediction techniques schedule audio packets for playout and attempt to make a reasonable compromise between the number of lost packets, the one-way delay and the delay variation since these criteria cannot be optimized simultaneously. In particular, this paper will show that the proposed HMM technique makes a good compromise between the mean end-to-end delay, end-to-end delay standard deviation and average packet loss rate.     

基于BPNN端到端时延预测的多路传输调度

针对移动异构网络环境网络参数动态变化、多路传输过程中数据包乱序引发的吞吐量下降等问题,提出一种基于BP神经网络(back propagation neural network,BPNN)端到端时延预测的多路传输调度方法,通过BP神经网络的构建、训练和学习,实现对端到端传输时延的更准确预测,以此为基础,对子流拥塞状况及网...     

MaxCD: Efficient multi-flow scheduling and cooperative downloading for improved highway drive-thru Internet systems

The limitation of existing wireless wide area networks, coupled with the delay tolerant property of many non-realtime applications (e.g. email, file download) enables drive-thru networking, which depends on roadside units (RSUs) to provide vehicular users with intermittent Internet access service. Focusing on the downlink service, MaxCD – a joint multi-flow scheduling and cooperative downloading protocol is proposed in this paper with the goal of maximizing the amount of data packets that can be downloaded per drive-thru. Based on the macro-level opportunistic scheduling and node cooperation, the best wireless link(s) (with the highest data rate) between the RSU and vehicular users can always be utilized. On the other hand, the advantage of opportunistic overhearing due to the broadcast nature of wireless medium is also exploited to reduce packet retransmission times, so as to further increase the effective data rate. Since the store-carry-forward delivery manner is adopted for the cooperators to avoid introducing interference to the in-range data communication, a multi-channel collision-free relay mechanism is designed to address the reliable and fast data exchange issue when the vehicular users are outside the service area of the RSU. Our theoretical analysis vindicates the performance gain of the cooperation and extensive simulations demonstrate the efficiency of MaxCD.     

On scheduling data access with cooperative load balancing in vehicular ad hoc networks (VANETs)

For overcoming the vehicle to vehicle frequent disconnection problem in VANETs data dissemination, several approaches have been proposed, including the provision of Road Side Units (RSUs). Due to the short wireless transmission range of RSUs and vehicle mobility, a vehicle spends only a short period of time inside the range of an RSU. This limitation, together with possible overload of RSUs sited near busy road junctions, may mean that requests from vehicles are not served within the prescribed deadlines. In this paper, we propose a cooperative load balancing approach among RSUs, in which an RSU can transfer the overload requests to other RSUs. Load transfer is done based on a number of factors: request delay tolerance, current load of the transferee RSU, and the direction in which the vehicle is heading. Using a series of simulation experiments, we demonstrate that the proposed cooperative load balancing approach outperforms the non-cooperative (stand-alone) approaches in a wide range of scenarios based on our performance metrics.     

无线传感网中数据传输延时优化的路由协议

针对无线传感器网络中存在的数据包丢失严重以及端到端延时较高的问题,提出了一种数据传输延时优化的路由协议（RPODTD）。首先,根据数据包传输结果对信道探测情况进行分类,引入有效探测占比与传输效率作为节点的评价指标;然后,通过实际延时以及理论延时的差值估算数据包的排队延时;最后,给定最大和最小排队延时阈值,根据排队延时所属的区间判断是否更改传输路径。在OMNeT++上进行的仿真实验中,与基于链路质量和延时的复合负载均衡路由协议（ComLoB）和拥塞避免多路径路由协议（CA-RPL）相比,RPODTD节点的平均端到端延时分别降低了78.87%和51.81%,节点丢包率分别降低了40.71%和68.43%,节点的死亡率分别降低了25.42%和44.62%。仿真结果表明,RPODTD能有效地减少端到端延时,降低丢包率,延长网络生命周期。     

Enhanced bulk scheduling for supporting delay sensitive streaming applications

Providing end-to-end delay guarantees for delay sensitive applications is an important packet scheduling issue with routers. In this paper, to support end-to-end delay requirements, we propose a novel network scheduling scheme, called the bulk scheduling scheme (BSS), which is built on top of existing schedulers of intermediate nodes without modifying transmission protocols on either the sender or receiver sides. By inserting special control packets, which called TED (Traffic Specification with End-to-end Deadline) packets, into packet flows at the ingress router periodically, the BSS schedulers of the intermediate nodes can dynamically allocate the necessary bandwidth to each flow to enforce the end-to-end delay, according to the information in the TED packets. The introduction of TED packets incurs less overhead than the per-packet marking approaches. Three flow bandwidth estimation methods are presented, and their performance properties are analyzed. BSS also provides a dropping policy for discarding late packets and a feedback mechanism for discovering and resolving bottlenecks. The simulation results show that BSS performs efficiently as expected.