• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.321-328
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• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.252-258
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• 2007

There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

• Journal of Advanced Navigation Technology
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• v.21 no.4
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• pp.347-352
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• 2017

In this paper, we propose a method that reduces mobile user's handover call dropping probability by using cognitive radio technology(CR) in cellular - based wireless cognitive radio networks. The proposed method predicts a cell to visit by Ziv-Lempel algorithm, and then supports mobile user with prediction of spectrum holes based on CR technology when allocated channels are short in the cell. We make neural network predict spectrum hole resources, and make handover calls use the resources before initial calls. Simulation results show CR technology has the capability to reduce mobile user handover call dropping probability in cellular mobile communication networks.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.1
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• pp.24-32
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• 2006

In this paper, we proposed the multimedia call admission control algorithm with the bandwidth reservation based on the prediction of wireless terminal's location to guarantee quality of service for multimedia applications in cellular networks. This algorithm aims at minimizing possible errors In predicting the moving direction of terminals using a mobility prediction scheme. This prediction reduces the size of bandwidth reserved redundantly. In order to evaluate the performance of the algorithm, the blocking rate of new calls and the forced termination rate of hand-off calls are measured and compared the results with those of existing schemes. The results of the experiment revealed that the algorithm presented in this paper achieved better performance with lower call blocking rates and forced-termination rates than those of other methods.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.135-142
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• 2021

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers' locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasksin shipyards.

• 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 Korean Institute of Communications and Information Sciences
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• v.34 no.7B
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• pp.659-667
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• 2009

Mobile Ad-hoc Network consists of mobile nodes without immobile base station. In mobile ad-hoc network, network cutting has occurred frequently in node because of energy restriction and frequent transfer of node. Therefore, it requires research for certain techniques that react softly in topology alteration in order to improve reliability of transmission path. This paper proposes path selection techniques to consider mobility of node that respond when search path using AOMDV routing protocol. As applying proposed techniques, We can improve reliability and reduce re-searching number of times caused by path cutting.

• Journal of the Korean Home Economics Association
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• v.30 no.4
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• pp.153-165
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• 1992

The objectives of this study were to develop a probabilistic model for both hypotheses testing and mobility prediction. Methodologies being used for the analysis include multivariated analysis for descriptive statistics and logit model for hypotheses testing and prediction. The study used questionaire survey data conducted by Korean Research Institute for Human Settlements (KRIHS) in 1988. There were a total of 1,620 Samples, and both SPSS and Limdep software packages were used for statistical analysis and model testing. The major findings were highlighted as follows; The residential mobility over family life span by the social class were developed with the use of the probability model. Most of households in low class moved downwardly. They had lived the small-owned single detached house in first family life span and moved into the small-rented single detached house in next family life span. Most of households in middle class moved upwardly. They had lived the small-owned apartment in first family life span and moved into the large-owned single detached house in last family life span. Most of households in high class horizontally. They had lived the large-owned single detached house in first family life span and moved into the same one except in last family life span.