• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.98-105
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• 2022

It is essential for vertical smart farms that artificially grow crops in an enclosed space to properly utilize air environment facilities to create an appropriate growth environment. However, domestic vertical smart farm companies are creating a growing environment by relying on empirical data rather than systematic methods. Using IoT to create a growing environment based on systematic and precise monitoring can increase crop production yield and maximize profitability. This study aims to construct a monitoring system using IoT and to analyze the cause by demonstrating the imbalance of temperature environment, which is a significant factor in crop cultivation. 1) The horizontal temperature distribution of the multi-layer shelf was measured with different operating methods of LED and air conditioner. As a result, there was a temperature difference of "up to 1.7℃" between the sensors. 2) As a result of measuring the vertical temperature distribution, the temperature difference was "up to 6.3℃". In order to reduce this temperature gap, a strategy for proper arrangement and operation of air conditioning equipment is required.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.407-408
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• 2022

본 논문에서는 AIoT를 위한 비주얼 블록 코딩 도구를 설계하였다. AI 블록 코딩이 가능한 EduB 플랫폼에 피지컬 컴퓨팅을 가능하게 하는 모듈을 추가함으로써 블록을 사용한 쉬운 피지컬컴퓨팅 코딩과 AIoT 코딩이 가능하다. 도구는 WebSocket과 Wifi를 사용해 EduB와 타겟보드인 RaspberryPi의 무선 통신을 하며, 블록으로 생성된 코드를 RaspberryPi 내부에서 실행하여 GPIO와 SenseHAT을 제어할 수 있게 하였다. 따라서, 코딩 결과를 콘솔 출력이나 그래프로만 확인할 수 있어 정적이던 AI 교육을 LED나 모터를 제어해 동적으로 결과를 확인할 수 있게 하여 흥미와 관심을 유발할 수 있도록 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.718-723
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• 2018

In a positive Internet of Medical Things (IoMT) environment, by combining the latest computer network technology with IoT technology, remote health care such as health care and monitoring is improved through the provision of quality medical information services. In this paper, we identified and compared the platforms applied with blockchain and presented the results of developing the product distribution de-centralized DApp. In the process, we developed a distribution platform that can use blockchain technology to identify product fraud, manage data, manage customers' information, prevent forgery, track transaction history, and facilitate product transactions.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.9-17
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• 2023

Deep learning with large amount of computations is difficult to implement on micro-sized IoT devices or moblie devices. Recently, lightweight deep learning technologies have been introduced to make sure that deep learning can be implemented even on small devices by reducing the amount of computation of the model. Quantization is one of lightweight techniques that can be efficiently used to reduce the memory and size of the model by expressing parameter values with continuous distribution as discrete values of fixed bits. However, the accuracy of the model is reduced due to discrete value representation in quantization. In this paper, we introduce various quantization techniques to correct the accuracy. We selected APoT and EWGS from existing quantization techniques, and comparatively analyzed the results through experimentations The selected techniques were trained and tested with CIFAR-10 or CIFAR-100 datasets in the ResNet model. We found out problems with them through experimental results analysis and presented directions for future research.

• Informatization Policy
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• v.31 no.2
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• pp.82-104
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• 2024

Smart home services are growing rapidly as the development of the Internet of Things (IoT) opens the era of the so-called "Connected Living." Although personal information leaks through smart home cameras are increasing, however, users-while concerned-tend to take passive measures to protect their personal information. This study theoretically explained and verified how to design effective software update notification messages for smart home cameras to ensure that users comply with the recommended security behavior (i.e., update installation). In a survey experiment participated in by 120 actual users, the effectiveness of both emotional appeals (i.e., security breach warning images for fear appeals) and rational appeals (i.e., loss-framed messages emphasizing the negative consequences of not installing the updates) were confirmed. The results of this study provide theoretical interpretations and practical guidelines on the message design features that are effective for threat appraisals (i.e., severity, vulnerability) of smart home camera users and their protection motivation.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.27-35
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• 2017

In near future, IoT data is expected to be a major portion of Big Data. Moreover, sensor data is expected to be major portion of IoT data, and its' research is actively carried out currently. However, processed results may not be trusted and used if outlier data is included in the processing of sensor data. Therefore, method for detection and deletion of those outlier data before processing is studied in this paper. Moreover, we used Spark which is memory based distributed processing environment for fast processing of big sensor data. The detection and deletion of outlier data consist of four stages, and each stage is implemented with Mapper and Reducer operation. The proposed method is compared in three different processing environments, and it is expected that the outlier detection and deletion performance is best in the distributed Spark environment as data volume is increasing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.465-466
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• 2015

Recently Internet of things(IoT) industry has grown, Giant company of the world, have developed a variety of equipment using this. Several things are to be able to enjoy the game of devices have been developed game that play with the device has been released. This paper aims to derive the necessary element for the game development around IoT equipment. In this study, we investigated several cases using IoT device that have been released and have been developed abroad, literature related to game development reviewed. As a result, majority case using in game field, it appeared by using virtual reality that supported the 3D screen using a head-mounted display, in addition, have been released motion-based game.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.15-16
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• 2018

본 논문에서는 다수의 센서 노드로 구성된 Internet of Things (IoT) 환경에서 새로운 환경에 대해 적응하는데 걸리는 시간을 줄이기 위한 새로운 스케줄링 기법을 제안한다. IoT 환경에서는 데이터 수집 및 전송 패턴이 사전에 정의되어 있지 않기 때문에 기존 정적인 Packet scheduling 기법으로는 한계가 있다. Q-learning은 네트워크 환경에 대한 사전지식 없이도 반복적 학습을 통해 Scheduling policy를 확립할 수 있다. 본 논문에서는 기존 Q-learning 스케줄링 기법을 기반으로 각 큐의 패킷 도착률에 대한 bound 값을 이용해 Q-table과 Reward table을 초기화 하는 새로운 Q-learning 스케줄링 기법을 제안한다. 시뮬레이션 결과 기존 기법에 비해 변화하는 패킷 도착률 및 서비스 요구조건에 적응하는데 걸리는 시간이 감소하였다.

• Annual Conference of KIPS
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• 2014.04a
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• pp.788-791
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• 2014

IoT (Internet of Things)는 사물들 (Things)이 각자의 판단에 의해 협업을 하는 시스템이다. 미래의 IoT 기술은 우리의 일상생활뿐만 아니라 전반적으로 영향력이 강해질 것 이다. 하지만 아직 우리가 추구하는 IoT시대에 접어들기 위해서는 사물간 통신을 하는데 있어 발생되는 여러 문제들이 해결되지 않았다. 예를 들면 통신 프로토콜은 어떠한 것을 사용해야 하는지와 같은 것들이다. IoT에서 센서 (Sensor)가 센싱 (Sensing)을 하는 것, 통신을 하는 것, 일을 처리하는 것은 모두 이벤트에 해당된다. 이벤트는 "어떤 상태의 변화에 의해 상황이 변하는 것"[2]을 말한다. 이러한 이벤트는 순서에 따라 시간관계가 생기게 된다. 이러한 이벤트의 시간관계와 기존에 시간 간격에 대한 논문을 접목하여 본 논문에서 새로운 이벤트 시간 관계를 정의하고 그에 대한 성질을 식별했다. 그 결과 7가지의 시간 성질 (AFTER, MEETS, EQUALS, BEFORE, OVERLAPS, STARTS, FINISHES)[3]을 가지고 상호 적용하여 새로운 성질들을 식별했다. 이 성질들을 이용하면 향후 시간과 관련된 이벤트를 식별하고 활용이 가능하다.