• Journal of Digital Convergence
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• v.15 no.5
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• pp.235-240
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• 2017

Recently, Internet of things have been applied to various fields and are becoming common in our everyday life. Everything in our daily lives is networked, interacting with each other and giving us more useful effects. In this paper, we implemented an algorithm that can illuminate the LED lighting to the authorized person 's place in the network environment connected with the authentication device only once with the user authentication. User authentication in one building User authenticated through a single user search for the location of the user's lab stored in the database using the certified information and LED lights up to the lab where the user works. Through this, unnecessary energy is generated because people do not pass frequently.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.35-57
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• 2021

Fog computing aims to provide the solution of bandwidth, network latency and energy consumption problems of cloud computing. Likewise, management of data generated by healthcare IoT devices is one of the significant applications of fog computing. Huge amount of data is being generated by healthcare IoT devices and such types of data is required to be managed efficiently, with low latency, without failure, and with minimum energy consumption and low cost. Failures of task or node can cause more latency, maximum energy consumption and high cost. Thus, a failure free, cost efficient, and energy aware management and scheduling scheme for data generated by healthcare IoT devices not only improves the performance of the system but also saves the precious lives of patients because of due to minimum latency and provision of fault tolerance. Therefore, to address all such challenges with regard to data management and fault tolerance, we have presented a Fault Tolerant Data management (FTDM) scheme for healthcare IoT in fog computing. In FTDM, the data generated by healthcare IoT devices is efficiently organized and managed through well-defined components and steps. A two way fault-tolerant mechanism i.e., task-based fault-tolerance and node-based fault-tolerance, is provided in FTDM through which failure of tasks and nodes are managed. The paper considers energy consumption, execution cost, network usage, latency, and execution time as performance evaluation parameters. The simulation results show significantly improvements which are performed using iFogSim. Further, the simulation results show that the proposed FTDM strategy reduces energy consumption 3.97%, execution cost 5.09%, network usage 25.88%, latency 44.15% and execution time 48.89% as compared with existing Greedy Knapsack Scheduling (GKS) strategy. Moreover, it is worthwhile to mention that sometimes the patients are required to be treated remotely due to non-availability of facilities or due to some infectious diseases such as COVID-19. Thus, in such circumstances, the proposed strategy is significantly efficient.

• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.208-213
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• 2016

In the fourth industrial revolution, customization is becoming a conversation topic in various domains. Industry 4.0 applies cyber-physical systems (CPS), the Internet of Things (IoT), and cloud computing to manufacturing businesses. One of the main phrases in Industry 4.0 is mass customization. Optimized products or services are developed and provided through customization. Therefore, the competitiveness of a product can be enhanced, and satisfaction is improved. In particular, as IoT technology spreads, customization is an essential aspect of smooth service connections between various devices or things. Customized services in mobile applications are assembled and operate in various mobile devices in the mobile environment. Therefore, this paper proposes a method for improving customized cloud server-based mobile architectures, processes, and metrics, and for measuring the performance improvement of the customized architectures operating in various mobile devices based on the Android or IOS platforms. We reduce the total time required for customization in half as a result of applying the proposed customized architectures, processes, and metrics in various devices.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.75-94
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• 2021

The 4th industrial revolution needs a fusion of artificial intelligence, robotics, the Internet of Things (IoT), edge computing, and other technologies. For the fusion of technologies, cloud computing technology can provide flexible and high-performance computing resources so that cloud computing can be the foundation technology of new emerging services. The emerging services become a global-scale, and require much higher performance, availability, and reliability. Public cloud providers already provide global-scale services. However, their services, costs, performance, and policies are different. Enterprises/ developers to come out with a new inter-operable service are experiencing vendor lock-in problems. Therefore, multi-cloud technology that federatively resolves the limitations of single cloud providers is required. We propose a software platform, denoted as Cloud-Barista. Cloud-Barista is a multi-cloud service common platform for federating multiple clouds. It makes multiple cloud services as a single service. We explain the functional architecture of the proposed platform that consists of several frameworks, and then discuss the main design and implementation issues of each framework. To verify the feasibility of our proposal, we show a demonstration which is to create 18 virtual machines on several cloud providers, combine them as a single resource, and manage it.

• Annual Conference of KIPS
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• 2016.04a
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• pp.949-952
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• 2016

클라우드 기반 IoT 환경에서 광범위하게 설치된 디바이스는 보안성 강화 또는 기능 수정을 위해 소프트웨어를 원격에서 갱신할 필요가 있다. 디바이스는 하드웨어 자원과 네트워크 성능이 한정적이기 때문에 갱신 과정에서 발생하는 네트워크 트래픽을 줄여야하며 서비스가 중지되는 시간을 줄이기 위해 갱신 소요시간을 단축시켜야 한다. 이를 해결하기 위해 본 논문에서는 갱신 과정에서 가상화 기술을 이용하여 이미지를 계층화 하고, 캐싱하는 방식을 이용한 소프트웨어 갱신 프레임워크를 제안한다. 이미지 계층화는 소프트웨어와 종속 파일을 담은 이미지 파일의 수정, 변경, 추가된 부분을 새로운 계층으로 생성하고 관리하는 것을 일컫는다. 캐싱은 갱신 과정에서 서버에서 전송한 이미지를 게이트웨이에 저장하고 다른 디바이스가 갱신을 요청하면 저장된 이미지를 서버를 거치지 않고 전송하는 것을 말한다. 이를 적용하여 새로운 계층만 전송하고, 중복된 데이터의 전송을 줄여 네트워크 트래픽 발생량을 줄이고, 설치 파일의 용량을 줄여 갱신 소요시간을 줄인다. 본 논문에서 제안하는 프레임워크는 트래픽 발생량과 갱신 소요시간이 기존 방식에 비해 감소한다.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.77-86
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• 2019

The advent of Internet of Things (IoT) makes common nowadays computing environments involving programming not a single computer but several heterogeneous distributed computers together. Developing programs separately, one for each computer, increases programmer burden and testing all the programs become more complex. To address the challenge, this paper proposes an RPC-based unified programming language, UniPy, for development of MGC (eMbedded, Gateway, and Cloud) applications in IoT systems configured with popular computers such as Arduino, Raspberry Pi, and Web-based DB server. UniPy offers programmers a view of classes as locations and a very simple form of remote procedure call mechanism. Our UniPy compiler automatically splits a UniPy program into small pieces of the program at different locations supporting the necessary RPC mechanism. An advantage of UniPy programs is to permit programmers to write local codes the same as for a single computer requiring no extra knowledge due to having unified programming models, which is very different from the existing research works such as Fabryq and Ravel. Also, the structure of UniPy programs allows programmers to test them by directly executing them before splitting, which is a feature that has never been emphasized yet.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1036-1054
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• 2019

Recently, concomitant with a surge in numbers of Internet of Things (IoT) devices with various sensors, mobile crowdsensing (MCS) has provided a new business model for IoT. For example, a person can share road traffic pictures taken with their smartphone via a cloud computing system and the MCS data can provide benefits to other consumers. In this service model, to encourage people to actively engage in sensing activities and to voluntarily share their sensing data, providing appropriate incentives is very important. However, the sensing data from personal devices can be sensitive to privacy, and thus the privacy issue can suppress data sharing. Therefore, the development of an appropriate privacy protection system is essential for successful MCS. In this study, we address this problem due to the conflicting objectives of privacy preservation and incentive payment. We propose a privacy-preserving mechanism that protects identity and location privacy of sensing users through an on-demand incentive payment and group signatures methods. Subsequently, we apply the proposed mechanism to one example of MCS-an intelligent parking system-and demonstrate the feasibility and efficiency of our mechanism through emulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.179-185
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• 2019

A core technology of social robot is voice recognition and dialogue engine technology, but too much money is needed for development and an implementation of robot's conversation function is difficult resulting from insufficiency of performance. Dialogue function's implementation between human and robot can be possible due to advance of cloud AI technology and several company's supply of their open API. In this paper, current intelligent social robot technology trend is investigated and effective social robot system architecture is designed. Also an effective analysis and design method of social robot system will be presented by showing user requirement analysis using object-oriented method, flowchart and screen design.