• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.6
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• pp.849-854
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• 2018

Radio base stations, which are widely distributed across large areas, have many difficulties in managing them. Unmanned radio base stations in remote mountains are having a hard time accessing them in case of emergencies. Major telephone service providers only remotely control incoming and outgoing information and local small business partners responsible for maintaining actual facilities do not possess such technologies, so they are each checked during field visits. In this study, in order to process the sensor raw data and smoothing, we apply the particle filters and confirmed that the performance of sensor data accuracy is increased. Integrated system using temperature, humidity, fire condition, and power operation at a wide range of radio base stations under the real-time monitoring status is operated well. It show that all of the status of base station are monitored at the remote office using the cloud server through internet networking.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1203-1206
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• 2017

IoT-Cloud 융합 가상 기계 시스템은 오프로딩 기법을 사용하여 저성능 사물인터넷 장비에서 고성능 클라우드 서버의 연산력을 제공받는다. 이 경우 오프로딩 실행 대상 프로그램은 사물인터넷 장비와 클라우드 서버의 실행환경에서 일관성이 유지되어야하기 때문에 문맥 동기화가 필요하다. 현재 문맥 동기화 방식은 전체 문맥 동기화를 시도하기 때문에 네트워크 오버헤드가 증가하여 비효율적이다. 본 논문은 오프로딩 실행에 필요한 문맥 정보만을 동기화하는 효율적인 문맥 동기화를 위해서 정적 프로파일링을 통해 오프로딩 실행 대상 작업에 동기화가 필요한 문맥 정보들을 사전에 추출하였다. 추출된 문맥 정보를 기반으로 문맥 동기화가 이뤄지면 오프로딩 실행에 필요한 문맥 정보만을 동기화하기 때문에 네트워크 통신 오버헤드 감소를 기대할 수 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.443-452
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• 2021

One of the key technologies in edge computing is that it always provides services close to the user by moving data between edge servers according to the user's movements. As such, the movement of data between edge servers is frequent. As IoT technology advances and usage areas expand, the data generated also increases, requiring technology to accurately track and process each data to properly manage the data present in the edge computing environment. Currently, cloud systems do not have data disposal technology based on tracking technology for data movement and distribution in their environment, so users cannot see where it is now, whether it is properly removed or not left in the cloud system if users request it to be deleted. In this paper, we propose a tracking data server to create and manage the movement and distribution of data for each edge server and data stored in the central cloud in an edge computing environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1688-1696
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• 2020

The paradigm of Internet-of-things(IoT) systems is changing from a cloud-based system to an edge-based system to solve delays caused by network congestion, server overload and security issues due to data transmission. However, edge-based IoT systems have fatal weaknesses such as lack of performance and flexibility due to various limitations. To improve performance, application-specific hardware can be implemented in the edge device, but performance cannot be improved except for specific applications due to a fixed function. This paper introduces a edge-centric metamorphic IoT(mIoT) platform that can use a variety of hardware through on-demand partial reconfiguration despite the limited hardware resources of the edge device, so we can increase the performance and flexibility of the edge device. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to previous studies in which the reconfiguration algorithm was executed on the server.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.37-47
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• 2018

The number of capabilities of Internet of Things (IoT) devices will exponentially grow over the next years. These devices may generate a vast amount of time-constrained data. In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. In addition, most of IoT services will be content-centric rather than host centric to increase the data availability and the efficiency of data delivery. IoT will enable all the communication devices to be interconnected and make the data generated by or associated with devices or objects globally accessible. Also, fog computing keeps data and computation close to end users at the edge of network, and thus provides a new breed of applications and services to end users with low latency, high bandwidth, and geographically distributed. In this paper, we propose Edge-Fog cloud-based Hierarchical Data Delivery ($EFcHD^2$) method that effectively and reliably delivers IoT data to associated with IoT applications with ensuring time sensitivity. The proposed $EFcHD^2$ method stands on basis of fully decentralized hybrid of Edge and Fog compute cloud model, Edge-Fog cloud, and uses information-centric networking and bloom filters. In addition, it stores the replica of IoT data or the pre-processed feature data by edge node in the appropriate locations of Edge-Fog cloud considering the characteristic of IoT data: locality, size, time sensitivity and popularity. Then, the performance of $EFcHD^2$ method is evaluated through an analytical model, and is compared to fog server-based and Content-Centric Networking (CCN)-based data delivery methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1199-1202
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• 2017

IoT-Cloud 융합 가상 기계 시스템은 저성능의 사물인터넷 장비에서 고성능 클라우드 서버의 연산력을 제공받는 오프로딩 기법을 사용한다. 오프로딩 기법을 사용하는 경우 실행 대상 프로그램은 사물인터넷 장비와 클라우드 서버 사이에 일관성이 유지되어야하기 때문에 문맥 동기화가 필요하다. 기존 IoT-Cloud 융합 가상 기계의 문맥 동기화 방식은 전체 문맥 동기화를 시도하기 때문에 네트워크 오버헤드가 증가하여 비효율적이다. 네트워크 오버헤드는 오프로딩 실행 성능을 기존보다 감소시킬 수 있기 때문에 효율적인 오프로딩을 위해서는 오프로딩 실행에 필요한 문맥 정보만을 동기화하여 네트워크 오버헤드를 줄여야 한다. 본 논문에서는 효율적인 오프로딩 실행을 위해 정적 프로파일링을 통해 추출된 문맥 정보를 기반으로 오프로딩 실행에 필요한 문맥 정보만을 동기화하는 문맥 동기화기를 설계 및 구현하였다. 오프로딩 실행에 필요한 문맥 정보만 동기화가 이뤄지면 문맥 동기화 시 발생하는 네트워크 오버헤드의 크기가 줄어들기 때문에 효율적인 오프로딩 실행이 가능하다.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.216-221
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• 2022

The 4th industrial revolution is accelerating the transition to digital innovation in various aspects of our daily lives, and efforts for manufacturing innovation are continuing in the manufacturing industry, such as smart factories. The 4th industrial revolution technology in manufacturing can be used based on AI, big data, IoT, cloud, and robots. Through this, it is required to develop a technology to establish a production facility data collection and analysis system that has evolved from the existing automation and to find the cause of defects and minimize the defect rate. In this paper, we implemented a system that collects power, environment, and status data from production facility sites through IoT devices, quantifies them in real-time in a cloud computing environment, and displays them in the form of MQTT-based real-time infographics using widgets. The real-time sensor data transmitted from the IoT device is stored to the cloud server through a Rest API method. In addition, the administrator could remotely monitor the data on the dashboard and analyze it hourly and daily.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1691-1697
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• 2018

There has been a steady study of unmanned vehicle. So far, continuous research has brought news of the commercialization of unmanned vehicle. In addition, it has been applied in a variety of fields with another industry. A lot of research has been done, too, to apply inert driving indoors. Using LoRa network, which is a network dedicated to IoT, unmanned vehicle control system that is applied to LoRa network from a small space, or from an office hospital to a factory, is installed to increase efficiency when the performs special tasks. This paper presents solutions to a variety of problems by using LoRa network, which is dedicated to IoT, to recognize an unmanned vehicle as a single object, to communicate with surrounding objects, and to receive information necessary for driving indoors from a cloud server.

• 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.14 no.4
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• pp.671-678
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• 2019

The semantic information provided through the semantic-based IoT system will produce new high value-added products that are completely different from what we have known and experienced. From this point of view, the key issue of current IoT technology and applications is the development of an intelligent IoT platform architecture. The proposed system collects the IoT data of the sensors from the cloud computer, converts them into RDF, and annotates them with semantics. The converted semantic data is shared and utilized through the ontology repository. We use KT's IoTMakers as a cloud computing environment, and the ontology repository uses Jena's Fuseki server to express SPARQL query results on the web using Daum Map API and Highcharts API. This gives people the opportunity to access the semantic IoT mash-up service easily and has various application possibilities.