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Nowadays, IoT is being used in more and more application areas and the importance of IoT data quality is widely recognized by practitioners and researchers. The requirements for data and its quality vary from application to application or organization in different contexts. Many methodologies and frameworks include techniques for defining, assessing, and improving data quality. However, due to the diversity of requirements, it can be a challenge to choose the appropriate technique for the IoT system. This paper surveys data quality frameworks and methodologies for IoT data, and related international standards, comparing them in terms of data types, data quality definitions, dimensions and metrics, and the choice of assessment dimensions. The survey is intended to help narrow down the possible choices of IoT data quality management technique.
Lina Zhang; Dongwon Jeong; Sukhoon Lee. Data Quality Management in the Internet of Things. Sensors 2021, 21, 5834 .
AMA StyleLina Zhang, Dongwon Jeong, Sukhoon Lee. Data Quality Management in the Internet of Things. Sensors. 2021; 21 (17):5834.
Chicago/Turabian StyleLina Zhang; Dongwon Jeong; Sukhoon Lee. 2021. "Data Quality Management in the Internet of Things." Sensors 21, no. 17: 5834.
쓰레기 문제에 사회적 관심이 고조되면서 이를 해결하기 위한 다양한 연구가 진행되어 왔다. 특히 쓰레기의 현황을 파악하기 위해 드론과 영상 인식 기술이 적용되고 있다. 쓰레기 현황을 정확하게 파악하기 위해서는 정확한 영상 획득이 우선되어야 한다. 기존 연구에서 드론의 고도가 쓰레기 인식률에 미치는 영향에 대한 연구가 진행되었으나, 드론의 속도 또한 매우 중요한 요소이다. 따라서 이 논문에서는 드론의 속도가 영상 인식률에 미치는 영향을 분석하고 고도, 속도와 영상 인식률과의 관계를 정의한다. 실험 결과는 드론의 속도가 느릴수록 정확한 영상을 획득할 것이라는 가설과는 다른 결과를 보였다. 다양한 드론의 속도와 고도에서 촬영된 영상을 실험해 본 결과, 정확한 영상을 획득하기 위한 드론의 최적 속도는 3m/s이며, 최적 고도는 5m로 산출되었다. 이 논문의 연구 결과는 관련 연구의 기초 자료로 활용될 수 있다.
Dongwon Jeong; Yujeong Lee; Sukhoon Lee. A Study on Trash Recognition Rate and Drone Speed. The Journal of Korean Institute of Information Technology 2021, 19, 39 -50.
AMA StyleDongwon Jeong, Yujeong Lee, Sukhoon Lee. A Study on Trash Recognition Rate and Drone Speed. The Journal of Korean Institute of Information Technology. 2021; 19 (5):39-50.
Chicago/Turabian StyleDongwon Jeong; Yujeong Lee; Sukhoon Lee. 2021. "A Study on Trash Recognition Rate and Drone Speed." The Journal of Korean Institute of Information Technology 19, no. 5: 39-50.
Dongwon Jeong; Sukhoon Lee. Statistical Data-based Performance Analysis on Qualitative Evaluation Items of the Blockchain-based Certificate Management System. The Journal of Korean Institute of Information Technology 2020, 18, 115 -124.
AMA StyleDongwon Jeong, Sukhoon Lee. Statistical Data-based Performance Analysis on Qualitative Evaluation Items of the Blockchain-based Certificate Management System. The Journal of Korean Institute of Information Technology. 2020; 18 (9):115-124.
Chicago/Turabian StyleDongwon Jeong; Sukhoon Lee. 2020. "Statistical Data-based Performance Analysis on Qualitative Evaluation Items of the Blockchain-based Certificate Management System." The Journal of Korean Institute of Information Technology 18, no. 9: 115-124.
In general, many IoT devices, including smart phones, use LTE, Wi-Fi, and Bluetooth, and these communication modules generate a lot of energy consumption during periodic data transmission. This paper proposes a method of the data transmission mode change for improving energy efficiency in various communication environments that mobile devices may encounter. We propose an algorithm for setting the mode considering energy efficiency, data transmission performance and cost when the mobile device transmits data, and transmitting the data in an optimized manner according to the state of the mobile device. The proposed algorithm is implemented through experiments on energy efficiency for each communication module, and the scenario is used to verify how efficiently the proposed algorithm uses energy.
Sukhoon Lee; Kwangsu Kim; Dongwon Jeong. A Data Transmission Mode Change Method for Improving Energy Efficiency in IoT Environments. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 2020, 10, 57 -69.
AMA StyleSukhoon Lee, Kwangsu Kim, Dongwon Jeong. A Data Transmission Mode Change Method for Improving Energy Efficiency in IoT Environments. JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE. 2020; 10 (1):57-69.
Chicago/Turabian StyleSukhoon Lee; Kwangsu Kim; Dongwon Jeong. 2020. "A Data Transmission Mode Change Method for Improving Energy Efficiency in IoT Environments." JOURNAL OF ADVANCED INFORMATION TECHNOLOGY AND CONVERGENCE 10, no. 1: 57-69.
이 논문에서는 자격증 위조 방지와 빠른 진위 확인을 위해 블록체인 기술을 이용한 자격증 관리 시스템을 제안한다. 자격증 위조는 사회적으로 심각한 문제를 초래하기 때문에 자격증 위조 방지에 대한 다양한 연구가 진행되어 왔다. 그러나 지금까지 수행된 연구는 위조 문제 및 진위 확인에 많은 시간이 소요된다는 문제점을 지닌다. 따라서 이 논문에서는 기존 연구의 한계점을 분석하고 보안성이 강화된 자격증 위조 방지와 빠른 진위 확인이 가능한 블록체인 기반 자격증 관리 시스템을 제안한다. 제안 시스템은 탈중앙화 형태로 위·변조가 불가능한 블록체인을 이용하여 기존 문제를 해결한다. 실험 및 평가 결과, 위조 방지와 진위 확인 시간 부분에서 기존 시스템보다 나은 성능을 보였다.
Seunghun Bae; Sukhoon Lee; Dongwon Jeong. Design and Implementation of a Blockchain-based Certificate Management System for Counterfeiting Prevention and Quick Authenticity Verification of Certificates. The Journal of Korean Institute of Information Technology 2020, 18, 67 -77.
AMA StyleSeunghun Bae, Sukhoon Lee, Dongwon Jeong. Design and Implementation of a Blockchain-based Certificate Management System for Counterfeiting Prevention and Quick Authenticity Verification of Certificates. The Journal of Korean Institute of Information Technology. 2020; 18 (3):67-77.
Chicago/Turabian StyleSeunghun Bae; Sukhoon Lee; Dongwon Jeong. 2020. "Design and Implementation of a Blockchain-based Certificate Management System for Counterfeiting Prevention and Quick Authenticity Verification of Certificates." The Journal of Korean Institute of Information Technology 18, no. 3: 67-77.
위치 기반 서비스들은 대부분 스마트폰 및 웨어러블 디바이스의 GPS 좌표를 이용한다. 그러나 GPS 좌표들은 센서의 성능 및 오류로 인해 노이즈가 발생하며 이를 제거하기 위하여 다양한 필터들이 연구되었다. 이러한 필터들은 이동 궤적 정제를 위해 사용되지만 여전히 군집된 지역이 존재하는 등 노이즈가 존재한다. 따라서 본 논문에서는 군집 노이즈 필터링에 기반한 이동 궤적 정제 기법을 제안한다. 이를 위하여 사용자의 이동궤적 중 머무르는 지점을 Sequenced DBSCAN을 이용하여 파악하고 이를 제거하는 군집 노이즈 제거 알고리즘을 제안한다. 또한 본 논문은 기존 필터와 제안 알고리즘을 조합하여 최적화된 필터 적용 순서를 찾는다. 실험을 통하여 100개의 이동 궤적을 12개의 필터로 이루어진 11개의 조합으로 정제한다. 그 결과 제안 기법은 효율적이고 정확도가 높은 최적의 조합으로 분석된다.
Sojeong Kim; Sukhoon Lee. Clustered Noise Filtering Based Trajectory Refinement Method. The Journal of Korean Institute of Information Technology 2020, 18, 11 -20.
AMA StyleSojeong Kim, Sukhoon Lee. Clustered Noise Filtering Based Trajectory Refinement Method. The Journal of Korean Institute of Information Technology. 2020; 18 (3):11-20.
Chicago/Turabian StyleSojeong Kim; Sukhoon Lee. 2020. "Clustered Noise Filtering Based Trajectory Refinement Method." The Journal of Korean Institute of Information Technology 18, no. 3: 11-20.
본 논문에서는 노인의 안전을 위해 정확도와 편의성이 향상된 낙상 인지 시스템을 제안한다. 점차 노인의 인구 비율이 증가함에 따라 낙상 사고 발생 빈도가 증가하고 있다. 노인에게 예상치 못한 낙상이 발생할 경우, 스스로 조치하기 어려운 상황이 발생한다. 이를 해결하기 위해 낙상 인지에 관한 많은 연구가 진행되었다. 그러나 기존의 낙상 인지 시스템은 낙상 판별의 정확도가 떨어지고 편의성이 떨어지는 등 다양한 문제점을 보였다. 본 논문에서는 기존 연구의 문제점을 해결하기 위한 새로운 낙상 인지 시스템을 제안하였다. 제안한 시스템은 웨어러블 밴드의 3축 가속도 데이터 및 고도 데이터를 이용하였으며, 실험 및 평가 결과에서 제안한 시스템의 낙상 인지 정확도와 편의성이 향상되었음을 확인하였다.
Seungjae Hong; Sukhoon Lee; Dongwon Jeong. Design and Implementation of a Fall Recognition System Based on 3-Axis Acceleration Data and Altitude Data for Improvement of Fall Recognition Accuracy and Convenience. The Journal of Korean Institute of Information Technology 2020, 18, 115 -125.
AMA StyleSeungjae Hong, Sukhoon Lee, Dongwon Jeong. Design and Implementation of a Fall Recognition System Based on 3-Axis Acceleration Data and Altitude Data for Improvement of Fall Recognition Accuracy and Convenience. The Journal of Korean Institute of Information Technology. 2020; 18 (1):115-125.
Chicago/Turabian StyleSeungjae Hong; Sukhoon Lee; Dongwon Jeong. 2020. "Design and Implementation of a Fall Recognition System Based on 3-Axis Acceleration Data and Altitude Data for Improvement of Fall Recognition Accuracy and Convenience." The Journal of Korean Institute of Information Technology 18, no. 1: 115-125.
라이프로그를 이용한 경로 예측 기법은 정확한 경로 예측을 위하여 많은 양의 학습 데이터를 요구하며, 학습 데이터가 부족할 경우 경로 예측 성능이 저하된다. 학습 데이터 부족은 사용자의 이동 패턴이 유사한 다른 사용자의 데이터를 이용하여 해결이 가능하다. 따라서 이 논문은 사용자 유사도 기반 경로 예측 알고리즘을 제안한다. 이를 위하여 제안 알고리즘은 경로를 3단 그리드 패턴으로 학습하고 코사인 유사도 기법을 이용하여 사용자 간 유사도를 측정한다. 이후, 측정된 유사도를 학습된 모델에 적용하여 경로를 예측한다. 평가를 위하여 기존 경로 예측 기법들과 제안 기법의 경로 예측 정확도를 측정 및 비교한다. 그 결과, 제안 기법의 정확도는 66.6%로 다른 기법들에 비해 평균 1.8% 더 높은 정확도를 가진 것으로 평가된다.
Sumin Nam; Sukhoon Lee. User Similarity-based Path Prediction Method. The Journal of Korean Institute of Information Technology 2019, 17, 29 -38.
AMA StyleSumin Nam, Sukhoon Lee. User Similarity-based Path Prediction Method. The Journal of Korean Institute of Information Technology. 2019; 17 (12):29-38.
Chicago/Turabian StyleSumin Nam; Sukhoon Lee. 2019. "User Similarity-based Path Prediction Method." The Journal of Korean Institute of Information Technology 17, no. 12: 29-38.
Jinu Choi; Sukhoon Lee; Dongwon Jeong. Development of Lifelog Collection Interface and Visualization System for User Location Information Analysis. The Journal of Korean Institute of Information Technology 2019, 17, 1 -11.
AMA StyleJinu Choi, Sukhoon Lee, Dongwon Jeong. Development of Lifelog Collection Interface and Visualization System for User Location Information Analysis. The Journal of Korean Institute of Information Technology. 2019; 17 (7):1-11.
Chicago/Turabian StyleJinu Choi; Sukhoon Lee; Dongwon Jeong. 2019. "Development of Lifelog Collection Interface and Visualization System for User Location Information Analysis." The Journal of Korean Institute of Information Technology 17, no. 7: 1-11.
Jong Hyun Kim; Sukhoon Lee; Dongwon Jeong. Performance Evaluation of App Profile-based Sensor Registry System considering User Mobility and Sensor Density. The Journal of Korean Institute of Information Technology 2019, 17, 87 -97.
AMA StyleJong Hyun Kim, Sukhoon Lee, Dongwon Jeong. Performance Evaluation of App Profile-based Sensor Registry System considering User Mobility and Sensor Density. The Journal of Korean Institute of Information Technology. 2019; 17 (4):87-97.
Chicago/Turabian StyleJong Hyun Kim; Sukhoon Lee; Dongwon Jeong. 2019. "Performance Evaluation of App Profile-based Sensor Registry System considering User Mobility and Sensor Density." The Journal of Korean Institute of Information Technology 17, no. 4: 87-97.
Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.
Dukyong Yoon; Hong Seok Lim; Kyoungwon Jung; Tae Young Kim; Sukhoon Lee. Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research 2019, 25, 201 -211.
AMA StyleDukyong Yoon, Hong Seok Lim, Kyoungwon Jung, Tae Young Kim, Sukhoon Lee. Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research. 2019; 25 (3):201-211.
Chicago/Turabian StyleDukyong Yoon; Hong Seok Lim; Kyoungwon Jung; Tae Young Kim; Sukhoon Lee. 2019. "Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model." Healthcare Informatics Research 25, no. 3: 201-211.
A sensor registry system (SRS) registers sensor metadata and provides them for a seamless semantic process. Recently, network coverage information-based SRS (NC-SRS) was developed to provide sensor information filtering by combining path prediction and network coverage checks. However, the NC-SRS has problems caused by issues such as termination of OpenSignal service and pre-building road segments. Therefore, this paper proposes a sensor registry system-based predictive information service (SRS-PIS) using a grid. SRS-PIS predicts a path based on the grid, checks the network coverage, and filters the sensor. This paper presents a grid-based real-time path prediction algorithm and an algorithm for grouping network service-disabled areas. To obtain network coverage information, we constructed and implemented a grid-based coverage map through experiment to measure the signal strength. As an evaluation, we compared features among SRS-based systems and SRS-PIS, and compared advantages and disadvantages between segment-based and grid-based methods.
Hyunjun Jung; Dongwon Jeong; Sukhoon Lee. Development of Sensor Registry System-Based Predictive Information Service Using a Grid. Sensors 2018, 18, 3620 .
AMA StyleHyunjun Jung, Dongwon Jeong, Sukhoon Lee. Development of Sensor Registry System-Based Predictive Information Service Using a Grid. Sensors. 2018; 18 (11):3620.
Chicago/Turabian StyleHyunjun Jung; Dongwon Jeong; Sukhoon Lee. 2018. "Development of Sensor Registry System-Based Predictive Information Service Using a Grid." Sensors 18, no. 11: 3620.
Seungwon Kwon; Sukhoon Lee. Relational Database Model for Collecting Lifelog from Heterogeneous Smart Watches. The Journal of Korean Institute of Information Technology 2018, 16, 13 -21.
AMA StyleSeungwon Kwon, Sukhoon Lee. Relational Database Model for Collecting Lifelog from Heterogeneous Smart Watches. The Journal of Korean Institute of Information Technology. 2018; 16 (9):13-21.
Chicago/Turabian StyleSeungwon Kwon; Sukhoon Lee. 2018. "Relational Database Model for Collecting Lifelog from Heterogeneous Smart Watches." The Journal of Korean Institute of Information Technology 16, no. 9: 13-21.
Hyunjun Jung; Sukhoon Lee; Dongwon Jeong. Improving Path Prediction Using Grid for Sensor Registry System. The Journal of Korean Institute of Information Technology 2018, 16, 1 -10.
AMA StyleHyunjun Jung, Sukhoon Lee, Dongwon Jeong. Improving Path Prediction Using Grid for Sensor Registry System. The Journal of Korean Institute of Information Technology. 2018; 16 (3):1-10.
Chicago/Turabian StyleHyunjun Jung; Sukhoon Lee; Dongwon Jeong. 2018. "Improving Path Prediction Using Grid for Sensor Registry System." The Journal of Korean Institute of Information Technology 16, no. 3: 1-10.
A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.
Jangwon Gim; Sukhoon Lee; Wonkyun Joo. A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud. Journal of Sensors 2018, 2018, 1 -10.
AMA StyleJangwon Gim, Sukhoon Lee, Wonkyun Joo. A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud. Journal of Sensors. 2018; 2018 ():1-10.
Chicago/Turabian StyleJangwon Gim; Sukhoon Lee; Wonkyun Joo. 2018. "A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud." Journal of Sensors 2018, no. : 1-10.
The Internet of Things (IoT) is expected to provide better services through the interaction of physical objects via the Internet. However, its limitations cause an interoperability problem when the sensed data are exchanged between the sensor nodes in wireless sensor networks (WSNs), which constitute the core infrastructure of the IoT. To address this problem, a Sensor Registry System (SRS) is used. By using a SRS, the information of the heterogeneous sensed data remains pure. If users move along a road, their mobile devices predict their next positions and obtain the sensed data for that position from the SRS. If the WSNs in the location in which the users move are unstable, the sensed data will be lost. Consider a situation where the user passes through dangerous areas. If the user’s mobile device cannot receive information, they cannot be warned about the dangerous situation. To avoid this, two novel SRSs that use network coverage information have been proposed: one uses OpenSignal and the other uses the probabilistic distribution of the users accessing SRS. The empirical study showed that the proposed method can seamlessly provide services related to sensing data under any abnormal circumstance.
Hyunjun Jung; Dongwon Jeong; Sukhoon Lee; Byung-Won On; Doo-Kwon Baik. A Network Coverage Information-Based Sensor Registry System for IoT Environments. Sensors 2016, 16, 1154 .
AMA StyleHyunjun Jung, Dongwon Jeong, Sukhoon Lee, Byung-Won On, Doo-Kwon Baik. A Network Coverage Information-Based Sensor Registry System for IoT Environments. Sensors. 2016; 16 (8):1154.
Chicago/Turabian StyleHyunjun Jung; Dongwon Jeong; Sukhoon Lee; Byung-Won On; Doo-Kwon Baik. 2016. "A Network Coverage Information-Based Sensor Registry System for IoT Environments." Sensors 16, no. 8: 1154.
Sukhoon Lee; Dongwon Jeong; Hyunjun Jung; Doo-Kwon Baik. Design and Implementation of Sensor Registry Data Model for IoT Environment. KIPS Transactions on Software and Data Engineering 2016, 5, 221 -230.
AMA StyleSukhoon Lee, Dongwon Jeong, Hyunjun Jung, Doo-Kwon Baik. Design and Implementation of Sensor Registry Data Model for IoT Environment. KIPS Transactions on Software and Data Engineering. 2016; 5 (5):221-230.
Chicago/Turabian StyleSukhoon Lee; Dongwon Jeong; Hyunjun Jung; Doo-Kwon Baik. 2016. "Design and Implementation of Sensor Registry Data Model for IoT Environment." KIPS Transactions on Software and Data Engineering 5, no. 5: 221-230.
소프트웨어 개발자들은 시스템의 설계를 위해 UML의 클래스 다이어그램과 같은 객체 모델을 이용한다. 객체-관계 변환 방법론은 객체 모델에 표현된 관계성들을 관계형 데이터베이스 테이블로 변환하는 방법론으로, 설계된 시스템의 구현을 위해 적용된다. 기존 객체-관계 변환 방법론의 연구들은 하나의 관계성을 표현하기 위해 여러 변환 기법들을 제안하였다. 하지만 각 변환 기법의 사용기준들이 존재하지 않아 구현에 적용하기 어려운 문제점이 있다. 따라서 이 논문은 각 관계별로 이진 결정 다이어그램 기반의 모델링 규칙을 제안한다. 이를 위해 변환 기법들을 구분하는 조건들을 정의하고, 질의 수행시간을 측정함으로 검증이 요구되는 모델링 규칙들을 평가한다. 평가 후, 이 논문은 명제 논리로 표현된 최종 모델링 규칙을 재정의하고, 사례 연구를 통하여 제안된 모델링 규칙이 설계된 시스템을 구현하는데 유용함을 보인다.
Sooyoung Cha; Sukhoon Lee; Doo-Kwon Baik. A Binary Decision Diagram-based Modeling Rule for Object-Relational Transformation Methodology. Journal of KIISE 2015, 42, 1410 -1422.
AMA StyleSooyoung Cha, Sukhoon Lee, Doo-Kwon Baik. A Binary Decision Diagram-based Modeling Rule for Object-Relational Transformation Methodology. Journal of KIISE. 2015; 42 (11):1410-1422.
Chicago/Turabian StyleSooyoung Cha; Sukhoon Lee; Doo-Kwon Baik. 2015. "A Binary Decision Diagram-based Modeling Rule for Object-Relational Transformation Methodology." Journal of KIISE 42, no. 11: 1410-1422.
With emergence of the Internet of Things (IoT), many sensor network technologies have evolved quickly, and context-aware computing researches have become important to process sensor information. For the context-awareness, sensor filtering technologies have researched, these are yet influenced by capability of mobile resources and mobile network status. To resolve this problem, this paper proposes path prediction-based sensor filtering method. We present our approach, overall concept, and sensor filtering process. Also, this paper shows screenshots of path prediction system as implementation. Our proposed method contributes processing sensor filtering in insufficient mobile resource and unstable mobile network status.
Sukhoon Lee; Dongwon Jeong; Doo-Kwon Baik. Path prediction-based sensor filtering method. 2015 IEEE SENSORS 2015, 1 -4.
AMA StyleSukhoon Lee, Dongwon Jeong, Doo-Kwon Baik. Path prediction-based sensor filtering method. 2015 IEEE SENSORS. 2015; ():1-4.
Chicago/Turabian StyleSukhoon Lee; Dongwon Jeong; Doo-Kwon Baik. 2015. "Path prediction-based sensor filtering method." 2015 IEEE SENSORS , no. : 1-4.
Hyunjun Jung; Dongwon Jeong; Sukhoon Lee; Doo-Kwon Baik. Extending Sensor Registry System Using Network Coverage Information. KIPS Transactions on Software and Data Engineering 2015, 4, 425 -430.
AMA StyleHyunjun Jung, Dongwon Jeong, Sukhoon Lee, Doo-Kwon Baik. Extending Sensor Registry System Using Network Coverage Information. KIPS Transactions on Software and Data Engineering. 2015; 4 (9):425-430.
Chicago/Turabian StyleHyunjun Jung; Dongwon Jeong; Sukhoon Lee; Doo-Kwon Baik. 2015. "Extending Sensor Registry System Using Network Coverage Information." KIPS Transactions on Software and Data Engineering 4, no. 9: 425-430.