• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.27-34
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• 2009

Currently network users, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is designed and implemented based on a model and simulation using data engineering methodology to be avaiable in evaluating large network traffic data. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Environmental Health Sciences
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• v.40 no.3
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• pp.204-214
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• 2014

Objectives: Vehicular emissions are one of the main sources of air pollution in urban areas. Correlation analysis was conducted between air pollutants and traffic volume in order to identify causes of air pollution in Gwangju. Methods: Using traffic volumes and air quality monitoring data from 2002 to 2012 from nine stations (seven urban areas, two roadside areas), especially at three sites where traffic volumes were high, the correlation coefficients were obtained between air pollutants as PM-10 (particulate matter), $NO_2$, $SO_2$, CO and $O_3$ at the stations and traffic volumes near the air monitoring stations. Results: Due to traffic volume and distance between the station and the traffic road, concentrations of pollutants at roadside areas were higher than at urban areas, with the exception of $O_3$. The concentration of $O_3$ showed statistically significance with those of other gas materials as $NO_2$, $SO_2$, and CO in winter (p<0.001) and spring (p<0.05). During the period of October 7 to 20, 2012, excluding periods of yellow dust, smog and rainy season, the ratio of $NO/(NO+NO_2)$ showed the highest value 0.57 and 0.40 at Unam and Chipyeong of two roadside stations, followed by 0.35 at Nongseong with vehicular effects. The correlation coefficient between traffic volume and $O_3$, CO, $NO_2$ became higher when the data on mist and haze days were excluded, than when all hourly data were used in that period, at the three sites of Unam, Chipyeong, and Nongseong. Conclusions: Air quality showed a considerable effect from vehicles at roadside areas compared to in urban areas. Air pollutant diminishment strategies need to be aggressively adopted in order to protect atmospheric environment.

• Journal of Communications and Networks
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• v.12 no.6
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• pp.568-576
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• 2010

The paper presents a control architecture aimed at implementing bandwidth optimization combined with call admission control (CAC) over a digital video broadcasting (DVB) return channel satellite terminal (RCST) under quality of service (QoS) constraints. The approach can be applied in all cases where traffic flows, coming from a terrestrial portion of the network, are merged together within a single DVB flow, which is then forwarded over the satellite channel. The paper introduces the architecture of data and control plane of the RCST at layer 2. The data plane is composed of a set of traffic buffers served with a given bandwidth. The control plane proposed in this paper includes a layer 2 resource manager (L2RM), which is structured into decision makers (DM), one for each traffic buffer of the data plane. Each DM contains a virtual queue, which exactly duplicates the corresponding traffic buffer and performs the actions to compute the minimum bandwidth need to assure the QoS constraints. After computing the minimum bandwidth through a given algorithm (in this view the paper reports some schemes taken in the literature which may be applied), each DM communicates this bandwidth value to the L2RM, which allocates bandwidth to traffic buffers at the data plane. Real bandwidth allocations are driven by the information provided by the DMs. Bandwidth control is linked to a CAC scheme, which uses current bandwidth allocations and peak bandwidth of the call entering the network to decide admission. The performance evaluation is dedicated to show the efficiency of the proposed combined bandwidth allocation and CAC.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.63-72
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• 2004

In conducting traffic safety programs, it is very important to identify hazardous sites in appropriate manner. The rate qualify control method is generally used in identifying hazardous sites since it can interpret the sites in the statistic aspects. The rate qualify control method is based on the assumption that the occurrences of traffic crashes follow the Poisson's distribution in which the expected value of traffic crashes equals the variance of those. However, there is greater variability than expected statistically, we call this phenomenon over dispersion. This study analyzed the problem related to the rate quality control method under the over dispersed data, and established a methodology to solve the problem. As a result of test on the basis of the field data, the new approach produced more reasonable results than those of the Poisson based rate quality control method.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.617-627
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• 2000

ATM networks support diverse traffic types with different service requirement such as data, voice, video and image. This paper analyzes a dynamic priority queue to satisfy Quality of Service (QoS) requirements of traffic. to consider the burstiness of traffic, we assume the arrival to be a Markovian arrival process(MAP) . Performance measures such as loss and delay are derived, Finally, some numerical results show the performance of the system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.129-136
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• 2012

In the US, Japan and Germany the Aerial-Based Vehicle Detection System, which collects real-time traffic data using the Unmanned Aerial Vehicle (UAV), helicopters or fixed-wing aircraft has been developed for the last several years. Therefore, this study was done to find out whether the Aerial-Based Vehicle Detection System could be used for real-time traffic data collection. For this purpose the study was divided into two parts. In the first part the possibility of retrieving real-time traffic data such as travel speed from the aerial photographic image using the image processing technique was examined. In the second part the quality of the retrieved real-time traffic data was examined to find out whether the data are good enough to be used as traffic information source. Based on the results of examinations we could conclude that it would not be easy for the Aerial- Based Vehicle Detection System to replace the present Vehicle Detection System due to technological difficulties and high cost. However, the system could be effectively used to make the emergency traffic management plan in case of incidents such as abrupt heavy rain, heavy snow, multiple pile-up, etc.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.182-191
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• 2012

This paper proposes a high performance QoS (Quality of Service) traffic transmission scheme to provide real-time multimedia services in wireless networks. This scheme is based on both a traffic estimation of the mean rate and a header compression method by dividing this network model into two parts, core RTP/UDP/IP network and wireless access parts, using the IEEE 802.11 WLAN. The improvement achieved by the scheme means that it can be designed to include a means of provisioning the high performance QoS strategy according to the requirements of each particular traffic flow by adapting the header compression for real-time multimedia data. A performance evaluation was carried out to show the effectiveness of the proposed traffic transmission scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6C
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• pp.615-623
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• 2003

This paper presents effective quality-of-service renegotiating schemes for streaming video. The conventional network supporting quality-of-service generally allows a negotiation at call setup. However, it is not efficient for the video application since the compressed video traffic is statistically non-stationary. Thus, we consider the network supporting quality-of-service renegotiations during the data transmission, and study effective quality-of-service renegotiating schemes for streaming video. Simple token bucket model, whose parameters are token filling rate and token bucket size, is adopted for the video traffic model. The renegotiating time instants and the parameters are determined by analyzing the statistical information of compressed video traffic. In this paper, two renegotiating approaches, i.e. fixed renegotiating interval case and variable renegotiating interval case, are examined. Finally, the experimental results are provided to show the performance of the proposed schemes.