• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.85-87
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• 2018

Motivating by two promising technique of 5G, namely D2D and Edge computing, and the above mentioned problem of the current joint studies, We believe that more study is needed on the benefits of joining these two techniques in a single framework by more precisely taking into account the energy needed to computation, sending data, receiving data and as a result achieving more realistic energy efficiency in 5G cellular networks.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.109-121
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• 2020

As a decrease in labor became a serious issue in the manufacturing industry, smart factory technology, which combines IT and the manufacturing business, began to attract attention as a solution. In this study, we have designed and implemented a real-time remote management system for smart factories, which is connected to an IoT sensor and gateway, for plastic manufacturing plants. By implementing the REST API in which an IoT sensor and smart gateway can communicate, the system enabled the data measured from the IoT sensor and equipment status data to the real-time monitoring system through the gateway. Also, a web-based management dashboard enabled remote monitoring and control of the equipment and raw material processing status. A comparative analysis experiment was conducted on the suggested system for the difference in processing speed based on equipment and measurement data number change. The experiment confirmed that saving equipment measurement data using cache mechanisim offered faster processing speed. Through the result our works can provide the basic framework to factory which need implement remote management system.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.277-278
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• 2014

사물인터넷(IoT)은 '개방형 환경에서 인터넷을 기반으로 사람, 사물, 데이터 및 프로세스를 서로 연결하여 정보를 교류하고 상호 소통하는 지능형 인프라'로서 홈 가전, 교통 물류, 건설, 에너지, 헬스케어, 사회안전 등 여러 분야에서 새로운 상품을 개발하고 공급해 창조경제의 핵심동력 가운데 하나가 될 것으로 기대된다. 그러나 네트워크, 서비스, 플랫폼/디바이스 등 기반 환경에서 다양한 개인정보 침해에 대한 위협이 존재하며 개인정보 보호와 기술 활용이라는 이슈에 관한 논의는 아직 초기 단계에 있다. 따라서 본 연구에서는 IoT 환경에서 정보주체의 민감한 개인정보에 대한 안전한 보호 정책 적용과 효율적 정보기술 활용 및 제공이 가능한 개인정보보호 프레임워크를 제안하고자 한다.

• Journal of the Korean Society of Systems Engineering
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• v.11 no.2
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• pp.85-93
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• 2015

Warehouse Management System (WMS) is a key control for Material Handling System (MHS) and Inventory Control System (ICS). How to design and implement for WMS is crucial factor for achieving the key performance index for Manufacturing Industry. In particular, iron and steel making industry, where the volume and weight is large and hence FIFO (First Input First Out) is not working, how to design WMS is a key factor. In this paper, we systematically define the problem of WMS via developing StR (Stakeholders' Requirements) or ORD (Operational Requirement Documents), SyR (System Requirement), and SA (System Architecture) based on the emerging technologies. In particular, IoT (Internet of Things), CPS (Cyber Physical System) concepts and enabling technologies haves been incorporated in developing Smart WMS. The deliverables of the research can provide a conceptual framework for developing the next generation industrial WMS.

• The Journal of Information Systems
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• v.24 no.4
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• pp.139-152
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• 2015

With the advancement of the Internet/IT technologies and the increased computation power, massive data can be collected, stored, and processed these days. The availability of large databases has brought forth a new era in which companies are hard pressed to find innovative ways to utilize immense amounts of data at their disposal. Indeed, data has opened a new age of business operations and management. There are already many cases of innovative businesses reaping success thanks to scientific decisions based on data analysis and mathematical algorithms. Big Data is a new paradigm in itself. In this article, Big Data is viewed as a new perspective rather than a new technology. This value centric definition of Big Data provides a new insight and opportunities. Moreover, the Business Analytics, which is the framework of creating tangible results in management, is introduced. Then the Internet of Things (IoT), another innovative concept of data collection and networking, is presented and how this new concept can be interpreted with Big Data in terms of the value centric perspective. The challenges and opportunities with these new concepts are also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2396-2413
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• 2018

Trust management is one of the most challenging issues for the highly heterogeneous Internet of Things (IoT). In the context of the IoT, it is difficult to evaluate the node's trustworthiness in the same trust model when a node provides different services. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of IoT environments. With these issues in mind, this paper propose a robust and adaptive trust management system for the IoT that is able to measure the trustworthiness of nodes based on feedbacks collected from participants in a specific context and ensure the availability of trust management services. The main contributions of our system are: 1) Proposing a partly decentralized trust management framework, which improves the resiliency of the trust mechanism; 2) Proposing an adaptive trust evaluation scheme and a three-dimensional context representation makes trust evaluation more accurate and specific; 3) Enhancing the adaptive trust evaluation scheme by incorporating a bad behavior factor in trust estimation, which efficiently distinguishes misleading feedbacks from On-Off attacks. Simulation results show the good performance of the proposed system and especially show effectiveness against On-Off attacks compared to other trust mechanisms.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.157-164
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• 2019

This research examines how IoT makes a significant contribution to the innovation of media firms. The media firms will be able to find new reveue sources and strengthen firms' competence through innovating product, process and business model. While IoT increases the experience of interactivity and immersion for consumption, it improves the way ads are exposed and its impact is measured, leading to revenue increase. For these benefits fulfilled, innovation friendly media eco-system must be established. It is the most critical that media firms should change skeptical attitude toward IoT's potential and actively invest it to employ IoT. The government should create regulatory framework to best utilize the innovative advantages of IoT.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• 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.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.115-129
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• 2022

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.