• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1510-1532
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• 2017

In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.

• KNOM Review
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• v.22 no.3
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• pp.1-12
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• 2019

Recently, researching using big data and AI has emerged as a major issue in the ICT field. But, the size of big data for research is growing exponentially. In addition, users of data transmission of existing network method suggest that the problem the time taken to send and receive big data is slower than the time to copy and send the hard disk. Accordingly, researchers require dynamic and flexible network technology that can transmit data at high speed and accommodate various network structures. SDN/NFV technologies can be programming a network to provide a network suitable for the needs of users. It can easily solve the network's flexibility and security problems. Also, the problem with performing AI is that centralized data processing cannot guarantee real-time, and network delay occur when traffic increases. In order to solve this problem, the edge-computing technology, should be used which has moved away from the centralized method. In this paper, we investigate the concept and research trend of SDN, NFV, and edge-computing technologies, and analyze the trends of data central network technologies used by combining these three technologies.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• International Journal of High-Rise Buildings
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• v.10 no.1
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• pp.1-8
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• 2021

Currently, big data and open data, together with traditional measured data, have come to constitute a new data environment, expanding new technical paths for quantitative analysis of the street environment. Streets provide precious linear public space in high-density residential areas. Pedestrian activities are the main body of street vitality. In this paper, 441 street segments were selected from 21 residential districts in high-density downtown area of Shanghai as cases, to quantitatively evaluate the influencing factors of pedestrian activities. Bivariate analysis was performed, and the results showed that street vitality was not only correlated with a highly populated environment, but also with other factors. In particular, the density of entrances and exits of residential properties, the proportion of walkable areas, and the density of retail and service facilities, were correlated with the vitality of street segments. The magnitudes of correlation between the street environmental factors and the pedestrian traffic differed across various trip purposes. Segment connectivity factors were more correlated with walking for leisure than for transportation. While public transportation factors were mainly correlated with walking for transportation, vehicular traffic factors were negatively correlated with walking for leisure.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.9-20
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• 2016

Ozone has been a problem in big cities. That is secondary air pollutant produced by nitrogen oxide and VOCs in the atmosphere. In order to solve this, the first to be the analysis of the $NO_x$ and VOCs. The main source of nitrogen oxide is the road mobile. Industrial sources in Seoul are particularly low, and mobile traffics on roads are large, so 45% of total $NO_x$ are estimated that road mobile emissions in Seoul. Thus, it is necessary to clarify the relation with the activity of road mobile source and $NO_x$ concentration. In this study, we analyzed the 4 locations with roadside automatic monitoring systems in their center. The V.K.T. calculating areas are set in circles with 50 meter spacing, 50 meter to 500 meter from their center. We assumed the total V.K.T. in the set radius affect the $NO_x$ concentration in the center. We used the hourly $NO_x$ concentrations data for the 4 observation points in July for the interference of the other sources are minimized. We used the intersection traffic survey data of all direction for construction of the V.K.T. data, the mobile activities on the roads. ArcGIS application was used for calculating the length of roads in the set radius. The V.K.T. data are multiplied by segment traffic volume and length of roads. As a result, the $NO_x$ concentration can be expressed as linear function formula for V.K.T. with high predictive power. Moreover we separated background concentration and concentrations due to road mobile source. These results can be used for forecasting the effect of traffic demand management plan.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.68-71
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• 2009

All-IP network requires change of the existing IP network engineering methods as the convergence service market between communication and broadcasting industries using IP network is growing rapidly. Especially the video services like IPTV require more strict transmission quality and higher bandwidth than the existing data services. So it is difficult to design All-IP network by the over-provisioning method which used to be used for the existing IP network design. It also requires a heavy investment which becomes one of big obstacles to the IPTV service expansion. In order to reduce the investment costs, it is required to design an optimized network by maximizing the utilization of the network resources and at the same time maintaining the customer satisfaction in terms of service quality. In this paper, we first analyze the effects of IPTV traffic on the existing internet. Then we compare two traffic engineering technologies, which are dimensioning without admission control and dimensioning with admission control, on the All-IP network design by simulation. Finally, we suggest cost effectiveness of traffic engineering technologies for designing the All-IP network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.111-121
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• 2004

With rapid growth of the Internet, network intrusions have greatly increased and damage of attacks has become more serious. Recently some kinds of Internet attacks cause significant damage to overall network performance. Current Intrusion Detection Systems are not capable of performing the real-time detection on the backbone network In this paper, we propose the broadband network intrusion detection system using the exponential smoothing method. We made an experiment with real backbone traffic data for 8 days. The results show that our proposed system detects big jumps of traffic volume well.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.45-52
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• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.