Delay analysis in practical wireless network coding

Network coding provides a powerful mechanism for improving performance of wireless networks. In this paper, we present an analytical approach for end‐to‐end delay analysis in wireless networks that employs inter‐session network coding. Prior work on performance analysis in wireless network coding mainly focuses on the throughput of the overall network. Our approach aims to analyze the delay of each flow in the network. The theoretical basis of our approach is network calculus. In order to use network calculus to analyze the performance of traffic flows in the network, we have to address three specific problems: identifying traffic flows, characterizing broadcast links, and measuring coding opportunities. We propose solutions for these problems and discuss the practical issues when applying the approach in practice. We make three main contributions. First, we obtain theoretical formulations for computing the queueing delay bounds of traffic flows in wireless networks with network coding. Second, with the formulations, we figure out the factors that affect the queueing delay of a flow and find that first‐in first‐out scheduling cannot fully exploit the benefit of network coding. Third, in order to exploit our findings, we introduce a new scheduling scheme that can improve the performance of current practical wireless network coding. Copyright © 2012 John Wiley & Sons, Ltd.     

A network security situation prediction model based on wavelet neural network with optimized parameters

The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN) with optimized parameters by the Improved Niche Genetic Algorithm (INGA). The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA) so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN), Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and WNN.     

A prediction of dislocation‐free CdTe/CdS photovoltaic multilayers via nano‐patterning and composition grading

Defects in multilayered films have long been a performance‐limiting problem for the semiconductor industry. For instance, CdTe/CdS solar cell efficiencies have had significant improvement in the past 15years or more without addressing the problem of high misfit dislocation densities. Overcoming this stagnation requires a fundamental understanding of interfacial defect formation. Herein, we demonstrate a new first principles‐based CdTe bond‐order approach that enables efficient molecular dynamics to approach the fidelity of density functional theory. Stringent quantum‐mechanical verification and experimental validation tests reveal that our new approach provides an accurate prediction of defects that earlier methods cannot. Using this new capability, we show that misfit dislocations in CdTe/CdS multilayers can be significantly reduced via nano‐patterning and composition grading and more importantly, dislocation‐free multilayers naturally arise when the pattern dimension is reduced below 90nm. Our predictive methods are generally applicable to other materials, highlighting a rational approach towards low‐defect semiconductor films. Copyright © 2015 John Wiley & Sons, Ltd.     

Topology matters in network coding

Seeking to understand the potential of network coding in future internet endeavors, we consider the vital role of network topology with respect to the potential benefits of Random Linear Network Coding (RLNC). First, we propose a set of metrics that capture the essential trade-offs between throughput, confidentiality and decoding delay. Using large network simulation, we are able to evaluate the behavior of RLNC for various topological classes based on random graphs. Our results show significant differences between local dissemination of information (typical of wireless networks) and long range connectivity (typical of peer-to-peer environments). We believe that our results can help pave the way for the creation of better overlay topologies for RLNC protocols in future internet applications.     

Multi-hop Mobility Prediction

With the occurrence of large-scale human trajectories, which imply spatial and temporal patterns, the subject of mobility prediction has been widely studied. A number of approaches are proposed to predict the next location of a user. In this paper, we expect to lengthen the temporal dimension of prediction results beyond one hop. To predict the future locations of a user at every time unit within a specified time, we propose a Markov-based multi-hop mobility prediction (Markov–MHMP) algorithm. It is a hybrid approach that considers multiple factors including personal habit, weekday similarity, and collective behavior. On a GPS dataset, our approach performs prediction better than baseline and state-of-the-art approaches under several evaluation criteria.     

Describing Network Traffic Using the Index of Variability

Commonly used measures of traffic burstiness do not capture the fluctuation of traffic variability over the entire range of time-scales. In this paper, we present a measure of variability, called the Index of Variability (Hv(tau)), that depicts the degree of variability (burstiness) of a typical network traffic process at each time-scale and is analytically tractable for many traffic models. As an illustration, we derive the closed-form expressions of Hv(tau) for two traditional traffic models and generate a variety of two-dimensional (2D) and three-dimensional (3D) Index-of-Variability curves. These curves demonstrate that the Index of Variability can help in determining the complexities of the network traffic variability over the network performance relevant time-scales. We then introduce a practical method for estimating the Index-of-Variability curve from a given traffic trace. Using this method, we estimate the Index-of-Variability curves for 12 long NLANR network traffic traces. The results indicate that the variability of real network traffic varies with time-scales and that the Index of Variability has the ability to discern qualitative differences between traffic traces obtained from different networks. Thus, the Index of Variability offers the potential to gain insights into the dynamics of network traffic that existing tools do not offer.     

Multihop data gathering in wireless sensor networks with a mobile sink

Network performance can be improved by using a mobile sink (MS) to collect sensed data in a wireless sensor network. In this paper, we design an efficient trajectory for MS, collecting data from sensor nodes in a multihop fashion, with the aim of prolonging the network lifetime. Considering event‐driven applications, we present an approach to jointly determine the optimal trajectory for MS and data paths and transmission rates from source nodes to MS, without considering any rendezvous points. In these applications, an MS is supposed to harvest the data from source nodes in a given time‐slot. We first show that this problem is in form of a mixed integer nonlinear programming model, which is NP‐hard. Then, to achieve an approximate solution, we divide the mentioned problem into 2 simple subproblems. In fact, after determining an approximate zone for the trajectory of MS, the optimal data paths and transmission rates from source nodes to the MS are obtained through a mathematical optimization model. Finally, to illustrate the efficiency of the proposed approach, we compare the performance of our algorithm to an rendezvous point–based and also the state‐of‐the‐art approach in different scenarios.     

基于神经网络的网络流量预测算法研究

网络流量预测有助于网络服务质量的提升和网络资源的合理分配,对优化网络管理与运营、保障用户体验质量至关重要。因特网业务的急剧增加和基础网络的快速发展导致网络流量变得更加复杂多样,传统网络流量预测模型难以保证较高的预测精度,而神经网络作为人工智能的重要分支,在预测复杂网络流量时具有显著优势。简述反向传播神经网络、径向基神经网络和长短期记忆神经网络的模型原理,通过分析这些神经网络预测不同时间尺度的网络流量结果,可总结其预测性能与优缺点,为基于神经网络的故障预测和故障定位的学术研究和实际应用提供技术支撑。     

基于集成深度神经网络流量预测的动态网络切片迁移算法

针对5G网络切片(NS)场景下由于缺乏提前对物理网络资源进行感知而导致切片迁移滞后的问题,该文提出一种基于集成深度神经网络流量预测的动态切片调整和迁移算法(DSAM)。首先建立了基于计算、内存、带宽资源配置的网络总惩罚模型;其次,提出基于集成深度神经网络的流量预测算法预测未来网络流量情况,并根据流量类型的不同将其转换成对未来时刻物理网络的资源占用及切片的资源需求感知;最后,根据感知结果,以尽可能大地降低运营商惩罚为目标,通过动态切片调整和迁移策略将虚拟网络功能(VNF)和虚拟链路迁移到满足资源限制的物理节点和链路上。仿真结果表明,所提算法有效提高了切片迁移的效率和网络资源利用率。     

基于多项式卡尔曼滤波的船舶轨迹预测算法

考虑到在船舶航行的实际过程中,船舶自动识别系统（AIS）设备提供的船舶运动点迹往往呈现出信息缺失、非线性、多机动的问题,导致利用AIS设备辅助海上指挥系统难以准确判断船舶位置。针对以上问题,本文在传统卡尔曼滤波理论的基础上构建多项式卡尔曼滤波器拟合非线性系统,补偿航迹定位数据信息缺失、更新较慢等问题,并基于经纬度信息预测船舶运动轨迹。结果表明,该方法实现简单且收敛迅速,能够有效解决实际过程中船舶轨迹的预测问题,满足基本的实效性与准确性,能够为相关海事部门预测船舶目的、行为提供较为可靠的辅助手段。      

Density‐adaptive network reprogramming protocol for wireless sensor networks

Network reprogramming is a process used to update program codes of sensor nodes that are already deployed. To deal with potentially unstable link conditions of wireless sensor networks, the epidemic approach based on 3‐way advertise‐request‐data handshaking is preferred for network reprogramming. Existing epidemic protocols, however, require a long completion period and high traffic overhead in high‐density networks, mainly due to the hidden terminal problem. In this paper, we address this problem by dynamically adjusting the frequency of advertisement messages in terms of the density of sensor nodes, which is the number of sensor nodes in a certain area. We compare the performance of the proposed scheme, called DANP (Density‐Adaptive Network Reprogramming Protocol), with a well‐known epidemic protocol, Deluge. Simulations indicate that, in the grid topologies, DANP outperforms Deluge by about 30% in terms of the completion time and about 50% in terms of the traffic overhead. Significant performance gain is observed in random topologies as well. The performance of DANP is further confirmed via measurements in an experimental test bed. Copyright © 2009 John Wiley & Sons, Ltd.     

Supporting multimedia applications through network redesign

Recent evolutions in high‐performance computing and high speed broadband Internet access have paved a way to enterprise‐wide multimedia applications, which require stern QoS from the underlying networks. In this paper, we have explored threefold studies on existing enterprise network, whereby we proposed an analytical approach to evaluate the performance of the existing network; we have examined the feasibility of existing enterprise networks to accommodate voice over Internet protocol (VoIP) services with acceptable QoS, and we have redesigned the enterprise network to accommodate VoIP services to comply with the user defined QoS. The network performance is evaluated by number of VoIP calls sustained by the network, bandwidth utilization, loss rate and latency through Network Simulation (NS‐2) tool. We have derived a cost model to show the cost‐effectiveness of VoIP services over telephonic network. For a medium‐size enterprise network of 200 clients and 9 servers, our simulation results show that the redesign improves the network performance by increasing the number of VoIP calls by 57% and decreasing bandwidth utilization and packet loss rate by 20% and 7%, respectively. Moreover, the proposed network redesign demonstrates that the network can be scalable and it can handle up to 4% increased voice calls in the future maintaining QoS standards. Copyright © 2012 John Wiley & Sons, Ltd.     

Trust based negotiation bargaining game model for bandwidth management algorithms

Nowadays, Multi-hop Relaying Network (MRN) has gained wide acceptance as a next step towards future radio networks. MRN can extend the service area as well as improve the performance of wireless networks. To exploit the multi-hop relaying operation, an important issue is how to properly control wireless bandwidth. In this paper, a new bandwidth management scheme is proposed for MRNs. By integrating the random arrival rule and Nash bargaining model, the proposed scheme adaptively controls the wireless bandwidth to maximize network efficiency. In our scheme, trust value and bargaining powers are decided according to the Bayesian inference and real-time negotiation process, respectively. This approach can make the network system be close to the optimized network performance. To prove the effectiveness of the proposed scheme, a simulation has carried out. The results demonstrate the effectiveness of the proposed scheme in comparison with other existing schemes.     

Performance Study of a Mobile Multi-hop 802.11a/b Railway Network Using Passive Measurement

In this paper, we study the performance of IEEE 802.11a/b in a large-scale mobile railway networks and introduce our developed passive measurement approach. To provide a comprehensive evaluation, we built an outdoor multi-hop multi-interface railroad testbed (UNL-FRA Testbed), which consists of eight access points deployed along 3.5 mile of railroad track. We propose a novel large-scale passive measurement approach that synchronizes the system clocks of our monitoring systems, merges packet traces collected from multiple wireless channels across a multi-hop network, and enables a global performance view for the entire monitored network and across multiple layers. Based on the testing data collected from 15 field experiments carried out using BNSF locomotives and HyRail vehicles over a period of 18 months we conclude that in typical outdoor 802.11 railway environments the wireless link quality, the channel assignment scheme, and the handoff latency have much more significant impacts on the performance than the velocity. Furthermore, we discuss the implications of our conclusions on guaranteeing the quality of mobile services. We believe this is the first analysis on such a scale for 802.11-family railway networks.     

Performance evaluation of TCP extensions on ATM over high bandwidthdelay product networks

Practical experiments in a satellite network environment assist in the design and understanding of future global networks. This article describes the practical experiences gained from TCP/IP on ATM networks over a high-speed satellite link and presents performance comparison studies of such networks with the same host/traffic configurations over local area and wide area networks. These comparison studies on the LAN, WAN, and satellite environments increase our understanding of the behavior of high-bandwidth networks. NASA's Advanced Communications Technology Satellite (ACTS), with its special characteristics and high data rate satellite channels, and the ACTS ATM Internetwork (AAI) were used in these experiments to deliver broadband traffic. Network performance tests were carried out using application-level software (Netspec) on SONET OC-3 (155.52 Mb/s) satellite links. Finally, we experimentally study the performance, efficiency, fairness, and aggressiveness of TCP Reno, TCP New Reno, and TCP SACK end hosts on ATM networks over high BDP networks     

Rate control for streaming video over wireless

Rate control is an important issue in video streaming applications for both wired and wireless networks. A widely accepted rate control method in wired networks is TCP-friendly rate control (TFRC) (Floyd, 2000). It is equation-based rate control in which the TCP-friendly rate is determined as a function of packet loss rate, round-trip time, and packet size. TFRC assumes that packet loss in wired networks is primarily due to congestion, and as such is not applicable to wireless networks in which the main cause of packet loss is at the physical layer. In this article we review existing approaches to solve this problem. Then we propose multiple TFRC connections as an end-to-end rate control solution for wireless video streaming. We show that this approach not only avoids modifications to the network infrastructure or network protocol, but also results in full utilization of the wireless channel. NS-2 simulations, actual experiments over a 1/spl times/RTT CDMA wireless data network, and video streaming simulations using traces from the actual experiments are carried out to characterize the performance and show the efficiency of our proposed approach.     

A nonstationary traffic train model for fine scale inference from coarse scale counts

The self-similarity of network traffic has been convincingly established based on detailed packet traces. This fundamental result promises the possibility of solving on-line and off-line traffic engineering problems using easily collectible coarse time-scale data, such as simple network management protocol measurements. This paper proposes a statistical model that supports predicting fine time-scale behavior of network traffic from coarse time-scale aggregate measurements. The model generalizes the commonly used fractional Gaussian noise process in two important ways: (1) it accommodates the recurring daily load patterns commonly observed on backbone links and (2) features of long range dependence and self-similarity are modeled only at fine time scales and are progressively damped as the time period increases. Using the data we collected on the Chinese Education and Research Network, we demonstrate that the proposed model fits 5-min data and generates 10-s aggregates that are similar to actual 10-s data.     

VDNet: an infrastructure‐less UAV‐assisted sparse VANET system with vehicle location prediction

Vehicular Ad Hoc Network (VANET) has been a hot topic in the past few years. Compared with vehicular networks where vehicles are densely distributed, sparse VANET have more realistic significance. The first challenge of a sparse VANET system is that the network suffers from frequent disconnections. The second challenge is to adapt the transmission route to the dynamic mobility pattern of the vehicles. Also, some infrastructural requirements are hard to meet when deploying a VANET widely. Facing these challenges, we devise an infrastructure‐less unmanned aerial vehicle (UAV) assisted VANET system called V ehicle‐D rone hybrid vehicular ad hoc Net work (VDNet), which utilizes UAVs, particularly quadrotor drones, to boost vehicle‐to‐vehicle data message transmission under instructions conducted by our distributed vehicle location prediction algorithm. VDNet takes the geographic information into consideration. Vehicles in VDNet observe the location information of other vehicles to construct a transmission route and predict the location of a destination vehicle. Some vehicles in VDNet equips an on‐board UAV, which can deliver data message directly to destination, relay messages in a multi‐hop route, and collect location information while flying above the traffic. The performance evaluation shows that VDNet achieves high efficiency and low end‐to‐end delay with controlled communication overhead. Copyright © 2016 John Wiley & Sons, Ltd.     

并行离散事件网络仿真系统的研究与实现

互联网的不可控、易变和不可预测的特性给网络协议的开发、调试和性能测试带来极大的困难.网络仿真系统构造可控、可重现网络状况的虚拟网络环境,可以在新协议新算法部署之前预测其可用性和性能.文中所述的并行离散事件网络仿真系统具有以下特点:(1)基于并行计算模型BSP的实时事件调度引擎-BREEN;(2)网络应用透明的灵活的交互机制;(3)重用基于流行仿真工具开发的网络模型.最后以网络路径瓶颈带宽测量和分布协作web缓冲系统作为应用实例,分别进行可用性评估和性能预测.     

A service‐agnostic method for predicting service metrics in real time

We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real‐time, client‐side service metrics for video streaming and key‐value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service‐specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real‐time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.