• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.97-102
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• 2019

As the number of "Mobility Handicapped" increases, the incidence of "Mobility Handicapped" traffic accidents is also increasing. In order to reduce the incidence of traffic accidents in the "Mobility Handicapped", a service providing system for "Mobility Handicapped" is required. Since these services have different data formats, data heterogeneity occurs. Therefore, the system should resolve the data heterogeneity by mapping the format of the data. In this paper, we design DBaaS as a mobility handicapped system for data interoperability. This system provides a service to extend the flashing time of the traffic lights according to the condition of "Mobility Handicapped" on the occurrence of a fall or a crosswalk in a crosswalk where there is a risk of a traffic accident. These services can reduce the incidence of traffic accidents in "Mobility Handicapped".

• Journal of Communications and Networks
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• v.11 no.4
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• pp.337-349
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• 2009

In this paper, we introduce a novel mobility model for mobile sinks in which the sinks move towards randomly distributed destinations, where each destination is associated with a mission. The novel mobility model is termed the random mobility with destinations. There have been many studies on mobile sinks; however, they merely support two extreme cases of sink mobility. The first case features the most common and general mobility, with the sinks moving randomly, unpredictably, and inartificially. The other case takes into account mobility only along predefined or determined paths such that the sinks can gather data from sensor nodes with minimum overhead. Unfortunately, these studies for the common mobility and predefined path mobility might not suit for supporting the random mobility with destinations. In order to support random mobility with destination, we propose a new protocol, in which the source nodes send their data to the next movement path of a mobile sink. To implement the proposed protocol, we first present a mechanism for predicting the next movement path of a mobile sink based on its previous movement path. With the information about predicted movement path included in a query packet, we further present a mechanism that source nodes send energy-efficiently their data along the next movement path before arriving of the mobile sink. Last, we present mechanisms for compensating the difference between the predicted movement path and the real movement path and for relaying the delayed data after arriving of the mobile sink on the next movement path, respectively. Simulation results show that the proposed protocol achieves better performance than the existing protocols.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.249-255
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• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• Journal of Computing Science and Engineering
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• v.5 no.1
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• pp.71-84
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• 2011

Pervasive applications such as digital memories or patient monitors collect a vast amount of data. One key challenge in these systems is how to extract interesting or unusual information. Because users cannot anticipate their future interests in the data when the data is stored, it is hard to provide appropriate indexes. As location-tracking technologies, such as global positioning system, have become ubiquitous, digital cameras or other pervasive systems record location information along with the data. In this paper, we present an automatic approach to identify unusual data using location information. Given the location information, our system identifies unusual days, that is, days with unusual mobility patterns. We evaluated our detection system using a real wireless trace, collected at wireless access points, and demonstrated its capabilities. Using our system, we were able to identify days when mobility patterns changed and differentiate days when a user followed a regular pattern from the rest. We also discovered general mobility characteristics. For example, most users had one or more repeating mobility patterns, and repeating mobility patterns did not depend on certain days of the week, except that weekends were different from weekdays.

• Journal of IKEEE
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• v.16 no.2
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• pp.81-85
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• 2012

In this paper, we analyze the effect of mobility management method to the data rate of moving users when pico-cell which uses the same frequency bandwidth as that of macro-cell is overlaid over macro-cell. From this analysis, we show that the data rate which is available to the moving user depends on the method of mobility management and relative location of the overlaid pico-cell over macro-cell in the network.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.141-148
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• 2017

The current advancement of mobile devices enables users to collect a sequence of user positions by use of the positioning technology and thus the related research regarding positioning or location information are quite arising. An individual mobility model based on positioning data and time data are already established while group mobility model is not done yet. In this research, group mobility model, an extension of individual mobility model, and the process of establishment of group mobility model will be studied. Based on the previous research of group mobility model from two individual mobility model, a group mobility model with more than two individual model has been established and the transition pattern of the model is represented by Markov chain. In consideration of real application, the computing time to establish group mobility mode from huge positioning data has been drastically improved by use of GPGPU comparing to the use of traditional multicore systems.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.143-149
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• 2012

MANET is a network that is a very useful application to build network environment in difficult situation to build network infrastructure. But, nodes that configures MANET have difficulties in data retrieval owing to resources which aren't enough and mobility. Therefore, caching scheme is required to improve accessibility and availability for frequently accessed data. In this paper, we proposed a technique that utilize mobility prediction of nodes to retrieve quickly desired information and improve data availability. Mobility prediction of modes is performed through distance calculation using location information. We used technique which global cluster table and local member table is managed by cluster head to reduce data consistency and query latency time. We compared COCA and CacheData and experimented to confirm performance of proposed scheme in this paper and efficiency of the proposed technique through experience was confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5805-5825
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• 2019

Smartphones are important platforms because of their sophisticated computation, communication, and sensing capabilities, which enable a variety of applications in the Internet of Things (IoT) systems. Moreover, advancements in hardware have enabled sensors on smartphones such as environmental and chemical sensors that make sensor data collection readily accessible for a wide range of applications. However, dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users that vary throughout the day, which greatly affects the efficacy of sensor data collection. Therefore, it is necessary to consider phone users mobility patterns to design data collection schedules that can reduce the loss of sensor data. In this paper, we propose a mobility-based weighted adaptive opportunistic scheduling framework that can adaptively adjust to the dynamic, opportunistic, and heterogeneous mobility patterns of smartphone users and provide prioritized scheduling based on various application scenarios, such as velocity, region of interest, and sensor type. The performance of the proposed framework is compared with other scheduling frameworks in various heterogeneous smartphone user mobility scenarios. Simulation results show that the proposed scheduling improves the transmission rate by 8 percent and can also improve the collection of higher-priority sensor data compared with other scheduling approaches.

• The Journal of Bigdata
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• v.7 no.2
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• pp.75-90
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• 2022

Recently, complex and sophisticated analysis of transportation is required due to changes in the socioeconomic environment and the development of bigdata technology. Especially, the revision of 3 laws including PERSONAL INFORMATION PROTECTION ACT makes it possible to combine various types of mobility data. But strengthen personal information protection makes inefficiency in utilizing mobility bigdata. In this paper, we proposed the "Virtual fusion methdology via mobility bigdata" which is a methodology for indirect data fusion for various mobility bigdata such as mobile data and transportation card data, in order to resolve legal restrictions and enable various transportation analysis. And we also analyzed regional bus passenger in Seoul capital area and Cheongju city with aforementioned methodology for verification. This methdology could analyze behavioral pattern of passenger with the MCGM(Mobility Comprehensive Genetic Map), graph with position and time, making with mobile data. Consquently, using MCGM, which is a result for indirect data fusion, makes it possible to analyze various transportation problems.