• 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 the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.133-141
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• 2005

We need to study on simulation method of user's mobility Patterns for the performance analysis of the location management in wireless mobile networks. For this purpose ,this paper presents a user mobility model of wireless mobile networks with regular Patterns Sometimes mobile users randomly move , but they show the movement characteristics that regularly change their locations in some patterns in given time slots. So, we suggest the mobility model that can describe user's time related movement patterns. This model consists of some elements-positions, transitions , transition Probabilities which are variable, and some geographical paths for each transitions. We describe the simulation method for users' mobilities with random movements and regular movements , too. The simulation results by the model show that the suggested model can generate movement scenarios having regular patterns related with location and time.

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.29-41
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• 2008

Currently, IETF MANEMO(Mobile Adhoc for NEMO) working group is working on standardization supporting internal routing in nested NEMO networks. Nested NEMO has a independent topology feature that Mobile IP and basic NEMO protocol did not caused. This is the reason that causes exceptional mobility pattern. Such mobility patterns also trigger each other reconfiguration requirements. This paper classified and analyzed probable new mobility patterns in nested NEMO network. In concludion, we derived configuration problem from the new mobility patterns and suggested differential reconfiguration requirements through analytical approach.

• 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 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.

• Journal of the Korean Geographical Society
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• v.30 no.2
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• pp.120-131
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• 1995

To analyze mobility patterns, this study used three Constraint (Capability Constraint, Coupling Constraint, Authority Constraint) models which were proposed in Dr. Hagerstrand's Time-space theory. This paper shows that three constraint models have some effects upon mobility by age. In this study, Capability Constraint means a certain special constraint that is what we can't do during proceeding basic natural urges like sleep, fare, etc. Coupling constraint is a physical one. Each person limits the action range for staying on a special place in special time. For instance, students have to stay in school so that they have mobility constraints. Authority Constraint is a social one. When we use urban facilities or traffic, we may be controlled by mobility sphere by an agreement or a social position. It is social agreement that the opening hour of a store, the time table of mass-transportation and a social positional control that the personal income, the standard of education. In this study it has been in a process of determination of the cluster number that degree of influences a social constraint to mobility. Considering the mobility constraint of characteristics of space divides urban and rural, people in urban area have higher mobility rate than in rural area. Resuets of determination of the cluster, show similar mobility pattern. People in urban area are connected verity of mobility which related to urban space structures with determination of cluste-number. That is to say, mobility patterns can be changed by space charactcristics. Constraints by sex and age are also social constraints and they are influenced by mobility patterns. For instance, females at the age of twenties have similar mobility pattern to the same age male but they have sudden changes after thirty's age. Male entertains a similar pattern without restriction of age. That is to say, management by sex as a social constraint affects mobility. To establish more realistic traffie policy, mobility formation should be reflected to the space in a view of social-behavioral science. To embody this, some problems should be investigated as follows. 1. As a problem of methodology, if sufficient samples ensured, we could subdivide clusters and could open up a new method of analyzing the mobility clusters by using the neuro-network. 2. Extracting actions connected with mobility and finding life cycle which is classified by daily cluste-characteristics, suitable counterproposal could be presented to the traific policy.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1061-1069
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• 2015

Recently, transportation big data generated in the transportation sector has been widely used in the transportation policies making and efficient system management. Bus passengers' mobility patterns are useful insight for transportation policy maker to optimize bus lines and time intervals in a city. We propose a new methodology to discover mobility patterns by using transportation card data. We first estimate the bus stations where the passengers get-off because the transportation card data don't have the get-off information in most cities. We then applies LDA (Latent Dirichlet Allocation), the most representative topic modeling technique, to discover mobility patterns of bus passengers in Cheong-Ju city. To understand discovered patterns, we construct a data warehouse and perform multi-dimensional analysis by bus-route, region, time-period, and the mobility patterns (get-on/get-off station). In the case of Cheong Ju, we discovered mobility pattern 1 from suburban area to Cheong-Ju terminal, mobility pattern 2 from residential area to commercial area, mobility pattern 3 from school areas to commercial area.

• Journal of Preventive Medicine and Public Health
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• v.55 no.2
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• pp.134-143
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• 2022

Objectives: Many governments have imposed-and are still imposing-mobility restrictions to contain the coronavirus disease 2019 (COVID-19) pandemic. However, there is no consensus on whether policy-induced reductions of human mobility effectively reduce the effective reproduction number (Rt) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several studies based on country-restricted data reported conflicting trends in the change of the SARS-CoV-2 Rt following mobility restrictions. The objective of this study was to examine, at the global scale, the existence of regional specificities in the correlations between Rt and human mobility. Methods: We computed the Rt of SARS-CoV-2 using data on worldwide infection cases reported by the Johns Hopkins University, and analyzed the correlation between Rt and mobility indicators from the Google COVID-19 Community Mobility Reports in 125 countries, as well as states/regions within the United States, using the Pearson correlation test, linear modeling, and quadratic modeling. Results: The correlation analysis identified countries where Rt negatively correlated with residential mobility, as expected by policymakers, but also countries where Rt positively correlated with residential mobility and countries with more complex correlation patterns. The correlations between Rt and residential mobility were non-linear in many countries, indicating an optimal level above which increasing residential mobility is counterproductive. Conclusions: Our results indicate that, in order to effectively reduce viral circulation, mobility restriction measures must be tailored by region, considering local cultural determinants and social behaviors. We believe that our results have the potential to guide differential refinement of mobility restriction policies at a country/regional resolution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.388-394
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• 2007

In wireless network, we propose a predictive location update scheme which considers mobile user's(MU's) mobility patterns. MU's mobility patterns can be found from a movement history data. The prediction accuracy and model complexity depend on the degree of application of history data. The more data we use, the more accurate the prediction is. As a result, the location management cost is reduced, but complexity of the model increases. In this paper, we classify MU's mobility patterns into four types. For each type, we find the respective optimal number of application of history data, and predictive location area by using the simulation. The optimal numbers of four types are shown to be different. When we use more than three application of history data, the simulation time and data storage are shown to increase very steeply.

• Journal of the Korean Society of Physical Medicine
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• v.13 no.3
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• pp.81-89
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• 2018

PURPOSE: This study was conducted to monitor the performance of breathing exercises by patients with lumbar instability who had altered breathing patterns. METHODS: To investigate the effects of breathing exercises on spinal posture, mobility, and stabilization in patients with lumbar instability with altered breathing patterns, 30 adult participants were enrolled on the basis of the selection criteria and randomly assigned to the breathing exercise group (BEG) or trunk stabilization exercise group (SEG). A pre-test was performed prior to the intervention exercise program. The intervention exercise program consisted of 15 sessions (three sessions per week for 5 weeks) between August and September of 2016. The post-test was performed on the 6th week of intervention. RESULTS: Pre- and post-test comparisons of BEG and SEG revealed significant improvements in all tested items in the SEG, except for spinal mobility, while significant improvements in spinal postures 1 and 2, spinal mobility, and stabilization were found in the BEG. Between-group comparisons revealed that there were no significant differences in spinal posture 1, spinal posture 2, spinal mobility, or stabilization, whereas significant differences were found in spinal posture 2 and spinal mobility, with the BEG showing greater improvements than the SEG. CONCLUSION: Based on the findings in the present study, it is believed that breathing exercises have important effects on spinal posture, mobility, and stabilization in patients with lumbar instability who have altered breathing patterns.