• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.31-37
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• 2018

When an IoT system is constructed, a dashboard is also developed to monitor the values of sensors in the IoT System. Much time and money, however, are spent in developing the dashboard software. This paper presents design and implementation details of a dashboard maker called as YellowPeach, which helps to create a dashboard easily and fast without programming. Using YellowPeach, the user can author a dashboard by putting predefined charts to display values of sensors in it, and store the dashboard as a JSON file. Also an web application has been developed to run the dashboard on the web browser. The dashboard viewer web application connects on MQTT-based IoT system, receives values from the sensors, and displays the values on charts in real-time.

• 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 Software and Data Engineering
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• v.5 no.8
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• pp.351-360
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• 2016

Sensors equipped in Internet-of-Thing (IoT) devices are used to measure the surrounding contexts, and IoT applications analyze the contexts to infer situations and provide situation-specific smart services. There are different context acquisition schemes including pulling, pushing, and broadcasting. Most IoT devices support only one of the schemes. Hence, there can be an incompatible issue on data acquisition schemes between applications and devices, and consequently it could result in an increased development cost and inefficiency on application maintenance. This paper presents design patterns which can effectively remedy the incompatibility problem. By applying the patterns, IoT applications with incompatibility can be systematically and effectively developed. And, also its maintainability is expected to increase.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.110-112
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• 2022

본 서비스는 4 차 산업 혁명의 근간에 있는 IoT 서비스가 개인 수준에서 확장되지 않고 있음에 기반하여 여러 IoT 서비스를 통합하여 관리할 수 있는 소프트웨어를 개발, 커뮤니케이션 플랫폼에 삽입하여 원격 제어 및 IoT 서비스를 더욱 편리하게 개인에게 제공하고자 제작된 서비스이다. 플랫폼 사용자가 특정 채팅방에 명령어를 보내면 외부 단말 기기에 대한 원격 제어와 다양한 IoT 서비스가 지원된다. 스마트폰 사용 시간과 가장 많이 사용하는 애플리케이션 통계를 토대로 연동할 커뮤니케이션 플랫폼을 선정하므로 접근성과 서비스 효용성에서 이점을 가지며, 확장성 있는 소프트웨어를 개발하므로 발전 가능성 있는 모델을 제시한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2347-2356
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• 2017

Raspberry Pi has been widely used for a hardware platform to develop the Internet of Things (IoT) applications. The basic task of diverse IoT devices is to obtain their status or environmental information by using various sensors and to send them to a gateway or a remote server. For the purpose, one of application layer protocols for IoT, MQTT is widely used. IoT devices are often powered by batteries and they are required to operate for a long time without replacing or recharging their batteries. Thus, energy efficiency is one of the most critical problems for obtaining and sending sensor data. To develop energy-efficient IoT applications, the information about power consumption characteristics of the applications is necessary. However, there has been little study to analyze power consumption of IoT communication on IoT devices. This paper presents a study to measure and analyze the power consumption of sending/receiving data under various conditions via the MQTT protocol on Raspberry Pi.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.19-24
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• 2018

This paper presents an IoT platform of common interfaces that are flexible IoT applications and Connect the smart devices. The IoT platform includes automatic collaboration discovery the smart Device. It is different things case with connection each device through IoT Platforms are each device and application service. Due to these heterogeneities, it is quite advantages to develop applications working with derived IoT services. This circumstance needs the generic interface and results in a range of IoT architectures by not only the environments settings and discovery resource but also varied uniqe to implementation services working with IoT applications. Therefore, this suggestion of solve the problems and make it possible independent platforms always alive to connection with each devices based on the generic interface. The generic interface is heterogeneity-driven solve the problems and effectively contributions a platform that could be operated in diverse IoT Platforms.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.743-745
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• 2022

본 프로젝트는 환경오염의 원인 중 하나인 음식물 쓰레기에 주목하여, 공유 문화와 음식물 쓰레기 감소의 목적을 가지고 있는 공유냉장고의 홍보와 애플리케이션 개발을 구현하였다. SNS 확산과 모바일 환경 변화를 이용하여 홍보함과 운영주체의 불명확, 음식물 제공과 폐기의 전반적인 데이터 관리에 대한 운영관리체계의 문제점을 해결하기 위해서 기존의 아날로그 방식에서 사물인터넷(IoT)과 애플리케이션 기반의 디지털 방식으로 전환하고자 하였다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.179-186
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• 2024

In this paper, we design and implement an Internet of Things (IoT)-based chick incubation system. The system consists of three key components: the IoT incubator, the IoT server system, and the smartphone application. The IoT incubator is composed of an Arduino board, temperature and humidity sensors, a temperature and humidity controller, a ventilation controller, and an egg turning controller. The temperature and humidity sensors measure the temperature and humidity inside the IoT incubator and send the data to the temperature and humidity controller on the Arduino board. Additionally, it provides the function of transmitting temperature, humidity, and control history data to the IoT server via WiFi. It also offers automatic control of ventilation, egg turning, and temperature and humidity on a daily basis. The IoT server system receives data from the incubator, stores it in a database, and provides query data upon request from the smartphone. The smartphone application retrieves historical data through the server and monitors the temperature and humidity data of the IoT incubator in real-time, controlling the IoT incubator to ensure that the set temperature and humidity ranges are maintained. If the temperature and humidity data deviate from the set ranges, it sends alarms and emergency messages to the user. The IoT-based chick incubation system developed in this paper is a low-cost model due to its reduced manufacturing cost, making it highly beneficial for self-sustaining poultry farms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.664-671
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• 2020

A Smart Building, one that uses automated processes to control its operations, refers in this study to one that uses both Internet of Things (IoT) devices and cloud services software. Cloud service providers (e.g. Amazon, Google, and Microsoft) have recently providedIoT cloud platform application services on IoT devices. According to Postscapes, there are now 152 IoT cloud platforms. Choosing one for a smart building is challenging. We selected Microsoft Azure IoT Hub and Amazon's AWS (Amazon Web Services) IoT. The two platforms were evaluated and selected from a smart building perspective. Each prototype was evaluated on two different IoTplatforms, assuming a typical smart building scenario. The selection was based on information and experience gained from developing the prototype system using the IoT cloud platform. The assessment made in this evaluation may be used to select an IoTcloud platform for smart buildings in the future.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.10
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• pp.797-808
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• 2019

In a closed workplace, the management of the workplace is important because the environmental data at the workplace has a great influence on the safety of workers. Today's industrial sites are transformed into data-based factories that collect and analyze data through sensors in those sites, requiring a management system to ensure safety. In general, a safety management system stores and manages data on a central server associated with a database. Since such management system introduces high possibility of forgery and loss of data, workers often suspect the reliability of the information on the management system. In this paper, we present a worker safety management system based on the EOS blockchain which is considered as third-generation blockchain technology. The developed system consists of a set of smart contracts on the EOS blockchain and 3 decentralized applications associated with the blockchain. According to the roles of users, the worker and manager applications respectively perform the process of initiating or terminating tasks as blockchain transactions. The entire transaction history is distributed and stored in all nodes participating in the blockchain network, so forgery and loss of data is practically impossible. The system administrator application assigns the account rights of workers and managers appropriate for performing the functions, and specifies the safety standards of IoT data for ensuring workplace safety. The IoT data received from sensor platforms in workplaces and the information on initiation, termination or approval of tasks assigned to workers, are explicitly stored and managed in the EOS smart contracts.