• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.337-350
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• 2014

Internet-of-Things(IoT) is the computing paradigm converged with different technologies, where diverse devices are connected via the wireless network, acquire environmental information from their equipped sensors, and are actuated. IoT applications provide smart services to users by interacting with multiple devices connected to the network. IoT devices provide the simple set of the information and also offer smart services by collaborating with other devices. That is, IoT applications always interact with IoT devices which are becoming very popular at a fast pace. However, due to this fact, developing IoT application results in unconventional technical challenges which have not been observed in typical software applications. Moreover, since IoT computing has its own characteristics which are distinguished from other former paradigms such as embedded computing and mobile computing, IoT applications also reveal their own technical challenges. Therefore, we analyze technical challenges occurring in developing IoT applications and present effective solutions to overcome the challenges. To verify identified issues and presented solutions, we present the result of performing a case study of developing an IoT application. Through the case study, we verify how the unconventional technical issues are raised in a real domain and analyze effectiveness of applying the solutions to the application.

• Smart Media Journal
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• v.13 no.1
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• pp.60-66
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• 2024

The Internet of Things, which began by connecting sensors through a network, is developing into an intelligent IoT by combining it with artificial intelligence technology. Elevators are essential for high-rise buildings in the city, and elevators move from floor to floor and perform the functions of transporting goods and moving users. It is necessary to provide safe and convenient services for elevator users in high-rise buildings or special environments (hospitals, etc.). In an environment where rapid patient transportation is important, such as large hospitals, there is a problem that hospital staff and the general public often use the elevator for patients. In particular, when moving patients where golden time is important, the waiting time to board the elevator is a major hindrance. In order to solve this problem, this study proposes an intelligent IoT system for elevator calling using smartphone Bluetooth. First, we experimented with the elevator calling IoT system using smartphone Bluetooth, and as a result of the experiment, it was confirmed that it can authenticate elevator users and reduce unnecessary waiting time for boarding. In addition, we propose an intelligent IoT system that connects with intelligent IoT.

• Annual Conference of KIPS
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• 2016.04a
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• pp.280-282
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• 2016

오늘날 IoT 사물기기들의 사용 범위가 늘어나면서 해커들은 IoT 기기에서 네트워크상의 서비스 및 정보검색의 기능을 하는 프로토콜인 SSDP의 취약점을 악용하여 SSDP DRDoS(분산 반사 서비스 거부) 공격이라는 새로운 형태의 공격을 만들어 공격을 시도하고 있다. 이는 단순히 우리 생활에서 쓰이는 IoT 기기의 위협뿐만 아니라 사람의 생명이나 주요 기반시설 등에서 사용되어지는 IoT기기들의 공격으로 이어지면 큰 위험을 나타낼 수 있다. 앞으로 IoT기기들이 점차 증가되어 생활에 편리함과 실용성을 가지고 오는 반면에 해커들은 더욱더 교묘하고 복잡하게 공격을 시도를 할 것이다. 특히 본 논문에서는 DRDoS의 몇 가지 공격방법을 알아보고 그중에 IoT 기기에서 사용되어지는 SSDP프로토콜의 관련연구 및 공격방법들을 알아보고 공격시나리오 및 대응방안을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.893-895
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• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.203-210
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• 2015

The ecological environment monitoring system configured with Sensor Node, Gateway, Service Platform, and Web Browser. In this paper, we designed gateway resource tree and service function to do handling in the ecological environment monitoring service. Gateway Service Function based on oneM2M Common Service Function, Gateway Resource Tree configured with Application Part handling Sensor Data and Gateway link handling. lastly Device Registration, Sensing, Control, Profile Management.

• Annual Conference of KIPS
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• 2023.11a
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• pp.162-163
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• 2023

본 논문은 휠체어 사용자들이 직면하는 안전, 이동성 자립성 등의 문제를 해소하는 것을 목적으로 하는 internet of things(IoT) 기반 스마트 휠체어를 연구하였다. 본 연구에서는 휠체어의 중요한 기능인 경사로에 따라 자동으로 기울기가 조정되는 기능을 중점적으로 개발했다. 이 기능은 사용자의 안전을 보장하며, 동시에 이동의 편의성을 증가시키는 역할을 한다.

• Journal of Advanced Navigation Technology
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• v.21 no.1
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• pp.72-77
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• 2017

In recent years, internet of things (IoT) technology has developed rapidly and has been applied in many fields. IoT devices collect and analyze information in various environments and provide useful information to users. Due to the recent increase in IoT devices, many open source platforms based on hardware and software that are cheap and easy to use have been developed and provided. In this paper, we have studied the IoT system based on Arduino which can communicate Bluetooth wireless data with smart phone in real time. And we have developed a wireless system control app based on App Invertor 2 for communication with Android smart phone. Also, we propose an IoT system based on Arduino which can control the devices and implement the function of connectivity between devices.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.71-77
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• 2021

Recently, there is a demand for efficient program development of an IoT application support frameworks considering heterogeneous hardware characteristics. In addition, the scope of hardware support is expanding with the development of neuromorphic architecture that mimics the human brain to learn on their own and enables autonomous computing. However, most existing IoT IDE(Integrated Development Environment), it is difficult to support AI(Artificial Intelligence) or to support services combined with various hardware such as neuromorphic architectures. In this paper, we design an AI component abstract model that supports the second-generation ANN(Artificial Neural Network) and the third-generation SNN(Spiking Neural Network), and implemented an autonomous IoT IDE based on the proposed model. IoT developers can automatically create AI components through the proposed technique without knowledge of AI and SNN. The proposed technique is flexible in code conversion according to runtime, so development productivity is high. Through experimentation of the proposed method, it was confirmed that the conversion delay time due to the VCL(Virtual Component Layer) may occur, but the difference is not significant.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.159-169
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• 2024

This paper proposes a new real-time fire monitoring and evacuation navigation system by integrating Augmented Reality (AR) technology with Internet of Things (IoT) technology. The proposed system collects temperature data through IoT temperature measurement devices installed in buildings and automatically transmits it to a MySQL cloud database via an IoT platform, enabling real-time and accurate data monitoring. Subsequently, the real-time IoT data is visualized on a 3D building model generated through Building Information Modeling (BIM), and the model is represented in the real world using AR technology, allowing intuitive identification of the fire origin. Furthermore, by utilizing Vuforia engine's Device Tracking and Area Targets features, the system tracks the user's real-time location and employs an enhanced A^* algorithm to find the optimal evacuation route among multiple exits. The paper evaluates the proposed system's practicality and demonstrates its effectiveness in rapid and safe evacuation through user experiments based on various virtual fire scenarios.