• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.162-169
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• 2022

In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A^* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A^* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

• KNOM Review
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• v.24 no.2
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• pp.13-23
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• 2021

Recently, as networks become more complex due to the activation of IoT devices, research on long-term traffic prediction beyond short-term traffic prediction is being activated to predict and prepare for network congestion in advance. The recursive strategy, which reuses short-term traffic prediction results as an input, has been extended to multi-step traffic prediction, but as the steps progress, errors accumulate and cause deterioration in prediction performance. In this paper, an LSTM-based multi-step traffic prediction method using a multi-output strategy is introduced and its performance is evaluated. As a result of experiments based on actual DNS request traffic, it was confirmed that the proposed LSTM-based multiple output strategy technique can reduce MAPE of traffic prediction performance for non-stationary traffic by 6% than the recursive strategy technique.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.159-164
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• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.

• 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.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.1
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• pp.287-295
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• 2017

Recently, MTC (Machine Type Communication) is known as an important part to support IoT (Internet of Things) applications. MTC provides network connectivities between MTC devices without human intervention. In MTC, a large number of devices try to access over communication resource with a short period of time. Due to the limited communication resource, resource contention becomes severe and it brings about access failures of devices. To solve the problem, it needs to regulate device accesses. In this paper, we present an efficient access management scheme. We measure the number of devices which try to access in a certain time period and predict the change of the number of devices in the next time period. Using the predicted change, we control the number of devices which try to access. To verify our scheme, we conduct experiments in terms of success probability, failure probability, collision probability and access delay.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.739-746
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• 2006

The bus probe-based link travel times were more readily available due to bus' fixed route schedule and it was different from that of taxi-based one in its value for the same link. At the same time, the bus-based one showed less accurate information than the taxi-based link travel time, in terms of reliability expressed by 1-RMSE(%) measure. The purpose of this thesis is to develop a heuristic algorithm for mixing both sources-based link travel times. The algorithm used both real-time and historical profile travel times. Real-time source used 4 consecutive periods' average and historical source used average value of link travel time for various congestion levels. The algorithm was evaluated for Seoul urban arterial network 3 corridors and 20 links. The results based on the developed algorithm were superior than the mere fusion based link travel times and the reliability amounted up to 71.45%. Some limitation and future research agenda have also been discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.731-738
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• 2006

Facing congestion, people want to know traffic information about their routes, especially real-time link travel time (LTT). In this paper, as a sequel paper of the previous non-taxi based LTT generating study by Choi et al. (1998), taxi based GPS probes have been tried to produce LTT for urban arterials. Taxis in itself are good deployment mode of GPS probes although it by nature experiences boarding and alighting time noises which should be accounted. A heuristic real-time dynamic outlier filter algorithm for taxi GPS probe has been developed focusing on urban arterials. An actual traffic survey for dynamic link travel times has been conducted using license plate method for the test arterials of Seoul city transportation network. With the algorithm, it is estimated that 70% of outliers have been filtered and the relative error has been improved by 73.7%. The filtering algorithm developed here would be expected to be in use for other spatial sites with some calibration efforts. Some limitations and future research agenda have also been discussed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.6
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• pp.575-582
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• 2016

Seoul city has high population density as well as high traffic congestion, which are vulnerable to exposure of environmental pollutions caused by car traffic. However, recent studies are only on local regions about road traffic and air pollution or health effect of road traffic on residents. Thus, comprehensive study data are needed in terms of overall Seoul regions. In this study utilized the nitrogen dioxide concentration through the national air pollution monitoring network data, 2012 to 2013. It also divided regions into high and low exposure districts via the Origin destination data developed by the Korea transport institute to quantify and evaluate the effect of transport policies and analyzed a correlation of asthma symptoms with high and low exposure districts through raw data of community health survey from the Korea centers for disease control and prevention. Based on the collected data, the pearson's correlation analysis was conducted between air pollution substance concentration and high exposure district and multiple logistic regression analysis was conducted to determine the effect of traffic environment and factors on asthma symptoms of residents. Accordingly, the following results were derived. First, the high exposure district was higher concentrations of nitrogen dioxide ($NO_2$) as per time compared to those of the low exposure district (p<0.01). Second, analysis on correlation between average daily environmental concentration in the air pollution monitoring network and road traffic showed that nitrogen dioxide had a significant positive correlation (p<0.01) with car traffic and total traffic as well as with truck traffic (p<0.05) statistically. Third, an adjusted odds ratio about asthma doctor's diagnosis in the high and low exposure districts was analyzed through the logistic regression analysis. With regard to an adjusted model 2 (adjusted gender, age, health behavior characteristics, and demographic characteristics) odds ratio of asthma doctor's diagnosis in the high exposure district was 1.624 (95% CI: 1.269~2.077) compared to that of the low exposure district, which was significant statistically (p<0.001).

• Journal of Korean Society of Transportation
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• v.17 no.4
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• pp.59-69
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• 1999

A Freeway corridor is a network consisting of a few Primary longitudinal roadways (freeway or major arterial) carrying a major traffic movement with interconnecting roads which offer the motorist alternative paths to his/her destination. Control measures introduced to ameliorate traffic performance in freeway corridors typically include ramp metering at the freeway entrances, and signal control at each intersections. During a severe freeway incident, on-ramp metering usually is not adequate to relieve congestion effectively. Diverting some traffic to the Parallel surface street to make full use of available corridor capacity will be necessary. This is the purpose of the traffic management system. So, an integrated traffic control scheme should include three elements. (a)on-ramp metering, (b)off-ramp diversion and (c)signal timing at surface street intersections. The purpose of this study is to develop an integrated optimal control model in a freeway corridor. By approximating the flow-density relation with a two-segment linear function. the nonlinear optimal control problem can be simplified into a set of Piecewise linear programming models. The formulated optimal-control Problem can be solved in real time using common linear program. In this study, program MPL(ver 4.0) is used to solve the formulated optimal-control problem. Simulation results with TSIS(ver 4.01) for a sample network have demonstrated the merits of the Proposed model and a1gorithm.