Comprehensive Analysis of Caching Performance under Probabilistic Traffic Patterns for Content Centric Networking

The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

Factors and Pattern of Injuries Associated with Road Traffic Accidents in Hilly District of Nepal

Introduction: In 21st century, road traffic accidents (RTA) are considered as increasing epidemic of non-communicable disease which is abandoned and needs special attention to prevent them. The aim of this study was to assess the factors and pattern of injuries associated with road traffic accidents. Methods: A cross sectional study was conducted among 112 RTA victims and 56 drivers in Palpa District of Nepal. The association of factors and pattern of injuries with exposure to accidents was assessed using Fisher’s exact test. Bivariate logistic regression examined the association between driving and socio-demographics factors and exposure to road accidents. Results: Of 112 RTA victims, 50% were in the age group of 21 to 40 years and 71.4% were male. Drivers who were in the age less than or equal to 30 years were more likely (OR: 3.6;95% CI: 1.0, 14.3) to expose to an accident than those who were above 30 years. Similarly, those having driving speed less than 40 km/hr were less likely to expose to an accident than those with speed 40 - 60 km/hr (OR: 6.0;95% CI: 0.8, 73.5) and those with speed more than 60 km/hr (OR 7.8;95% CI: 1.0, 100.1). Moreover, the driving experience was also found positively associated (OR: 5.6;95% CI: 1.1, 35.5) with the exposure to an accident. Conclusion: Being in younger age group, male gender, morning time, the driving speed, driving experiences, and driving hours on the road were positively associated with RTA. The efforts should be made to enforce laws in control of speed targeting experienced drivers and those with younger age groups.

Safer Design and Less Cost Operation for Low-Traffic Long-Road Illumination Using Control System Based on Pattern Recognition Technique

The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.

基于运动学片段的纯电动出租车行驶特征模式挖掘

在逐步推行出租车全面电动化的背景下,针对目前对纯电动出租车行驶状态评估的不足,建立一种基于运动学片段的纯电动出租车行驶特征模式挖掘方法,研究纯电动出租车行驶状态特征.首先,基于行驶轨迹GPS数据,从速度特征、加减速和行驶状态3个方面,确定超速比例、加减速频率、行驶速度、怠速时间占比等13个特征指标刻画运动学片段,建立纯电动出租车运动学片段提取方法,研究纯电动出租车行驶状态特征.然后,根据行驶特征指标主成分的特征值大小及累积贡献率,确定关键特征指标,结合K-均值聚类算法,生成多时空场景下的纯电动出租车行驶特征模式,综合评价车辆行驶状态.最后,以深圳市共计9天采样间隔为1 s的700万条纯电动出租车GPS行驶轨迹数据为驱动,提取了1 757条纯电动出租车运动学片段.根据安全性、效率性和舒适性8个关键特征指标进行聚类分析,生成包含主干路、次干路和支路在早高峰、平峰和晚高峰9种时空场景下27类纯电动汽车行驶状态的特征模式库.研究结果表明:综合安全性、效率性、舒适性3方面,早高峰期间的纯电动出租车综合行驶状态优于平峰和晚高峰时段;基于运动学片段、主成分分析及多时空场景聚类分析的纯电动出租车行驶特征模式挖掘方法,能够有效反映并评估纯电动出租车行驶状态,并向驾驶员提供合理的驾驶建议.

基于心率变异性的飞行员脑力负荷特征

为了探索五边飞行不同阶段飞行员脑力负荷水平,寻找表征脑力负荷的心电指标,对飞行学员展开五边飞行任务的六自由度模拟飞行实验,测量飞行过程中的脑力负荷水平,采集不同飞行阶段飞行员心电各指标数据,利用因子分析构建飞行员脑力负荷模型,分析和验证不同飞行阶段飞行员脑力负荷的变化特征。结果表明:相比心率指标,心率变异性指标在测量飞行员脑力负荷时具有更高的信度和效度;起飞和上升阶段的脑力负荷处于较低水平并呈下降趋势;巡航阶段的脑力负荷水平较低且变化趋势平滑;进近着陆阶段的脑力负荷水平最高。此外,突发事件会显著影响脑力负荷,引起飞行员脑力负荷的显著增加。

大型站房临近既有线路施工智慧监测监控技术

新建厦门北站项目在5、6号新建地铁出入口施工的同时,需确保既有地铁1号线及4号线结构安全和正常运营,对施工技术和管理层面提出了更高的要求。为解决上述问题,采用智慧监测监控技术,能够实现既有线路的结构位移、隧道结构收敛等自动化监测,从而实现现场监测数据的实时传输和处理。经过项目的实践验证,该技术能够为相关方及时提供准确可靠的监测数据和预警信息,对于保障既有线路安全性和稳定性具有重要意义。

A Fast Multi-Pattern Matching Algorithm for Mining Big Network Data

The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.

Predicting the Geographic Traffic Distribution in Cellular Networks

Early detection and rapid resolution network congestion can considerably improve network capacity. Consequently, much research has been carried out on predicting traff ic patterns in 3G networks. This paper introduces an access point centric approach that is implemented by two prediction models, the traffic abstraction model and the order-k Markov model. Traffi c predictions are carried out to support the congestion control in the semi-smart antenna systems. The simulation result shows that the cumulative error rate is below 25% even carrying out multi-step-ahead predictions.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

PerfMon: Measuring Application-Level Performance in a Large-Scale Campus Wireless Network

WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.

A Statistical Framework for Real-Time Traffic Accident Recognition

Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.

PROBE:NOISE-AND-ROTATION RESISTANCE OF HOPFIELD NEURAL NETWORK IN IMAGED TRAFFIC SIGN RECALL

This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.

Traffic flow sensitivity to visco-elasticity

This letter reports traffic flow sensitivity to visco-elasticity, with the traffic flow modeling briefly described at first and then used to do traffic flow simulations whose results can reflect the properties of spatial-temporal evolution of ring traffic flow. It reveals that visco-elasticity plays crucial role in formation of traffic flow patterns, implying that self-organization of traffic flow is crucial in determining traffic flow status.

面向骨干网流量的非线性组合预测方法

为了提高骨干网流量多变复杂的情况下网络流量预测的准确度,提出基于灰狼优化支持向量机和双向长短期记忆循环神经网络的网络流量非线性组合预测方法.该组合方法利用灰狼算法优化的支持向量机泛化能力强、双向长短期记忆神经网络善于处理非线性问题且容错能力强的优势,以残差平方和最小为目标,根据两种方法的预测误差调节各自方法权重,动态加权得到组合方法的预测结果.仿真结果表明,本文所提预测方法在小时间尺度流量预测中发挥了组合模型的优势,可以更加准确的进行网络流量预测.

南京市交通网络发展格局变化监测分析

利用南京市监测区原始数据集,构建交通网络空间格局发展的指标体系,监测分析交通网络空间密度、空间便捷度、空间通达性及其变化情况,并进行交通网络空间格局分析和监测评价。监测结果表明,2015-2016年南京市交通网络通达性与交通网络密度、交通网络便捷度整体有所增强,但区域差异更为明显。南京市交通网络空间格局的发展导向,中心城区应以调整路网结构、交通方式为主,外围地区以增强交通网络密度、交通通达性为主。

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.