• Journal of Information Processing Systems
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• 제16권4호
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• pp.896-914
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• 2020

With the rapid growth of network traffic, a large number of connected devices, and higher application services, the traditional network is facing several challenges. In addition to improving the current network architecture and hardware specifications, effective resource management means the development trend of 5G. Although many existing potential technologies have been proposed to solve the some of 5G challenges, such as multiple-input multiple-output (MIMO), software-defined networking (SDN), network functions virtualization (NFV), edge computing, millimeter-wave, etc., research studies in 5G continue to enrich its function and move toward B5G mobile networks. In this paper, focusing on the resource allocation issues of 5G core networks and radio access networks, we address the latest technological developments and discuss the current challenges for resource management in 5G.

• 대한임베디드공학회논문지
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• 제18권4호
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• pp.159-164
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• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.

• 사물인터넷융복합논문지
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• 제10권2호
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• pp.77-84
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• 2024

지난 몇 년 동안 5G와 같은 새로운 저 지연 통신 프로토콜을 기반으로 IoT 엣지가 등장하기 시작했다. 그러나, IoT 엣지는 막대한 이점에도 불구하고, 새로운 보완 위협을 초래하여 이를 해결하기 위한 새로운 보안 솔루션이 필요하다. 본 논문에서는 IoT 시스템을 보완하는 클라우드 환경기반의 IoT 엣지 아키텍처 모델을 제안한다. 제안 모델은 IoT 엣지 장치에서 추출한 네트워크 트래픽 데이터를 기계 학습에 작용하여 사전에 보안 위협을 예방한다. 또한, 제안 모델은 로컬 노드에서 보안 데이터 일부를 할당함으로써 액세스 네트워크(엣지)에서의 부하 및 보안을 보장한다. 제안 모델은 IoT 엣지 환경 중 로컬 노드에 데이터 처리 및 관리의 일부 기능을 할당함으로써 액세스 네트워크(엣지)의 부하를 더욱 줄이는 동시에 취약 부분을 안전하게 보호한다. 제안 모델은 다양한 IoT 기능을 네임 서비스로 가상화하고, 필요에 따라 하드웨어 기능과 충분한 계산 리소스를 로컬 노드에 배포한다.

• Journal of Biosystems Engineering
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• 제43권2호
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.974-987
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• 2018

This paper presents a novel and efficient data transmission scheme based on mobile edge computing for the massive IoT environments which should support various type of services and devices. Based on an accurate and precise synchronization process, it maximizes data transmission throughput, and consistently maintains a flow's latency. To this end, the proposed efficient software defined data transmission scheme (ESD-DTS) configures and utilizes synchronization zones in accordance with the 4 usage cases, which are end node-to-end node (EN-EN), end node-to-cloud network (EN-CN), end node-to-Internet node (EN-IN), and edge node-to-core node (EdN-CN); and it transmit the data by the required service attributes, which are divided into 3 groups (low-end group, medium-end group, and high-end group). In addition, the ESD-DTS provides a specific data transmission method, which is operated by a buffer threshold value, for the low-end group, and it effectively accommodates massive IT devices. By doing this, the proposed scheme not only supports a high, medium, and low quality of service, but also is complied with various 5G usage scenarios. The essential difference between the previous and the proposed scheme is that the existing schemes are used to handle each packet only to provide high quality and bandwidth, whereas the proposed scheme introduces synchronization zones for various type of services to manage the efficiency of each service flow. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of throughput, control message overhead, and latency. Therefore, the proposed ESD-DTS is very suitable for upcoming 5G networks in a variety of massive IoT environments with supporting mobile edge computing (MEC).

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1145-1150
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• 2020

다보스 포럼 이후 최근 4차 산업혁명은 전 세계의 국가들의 관심을 갖는 분야가 되고 있다. 4차 산업혁명의 기술 중 유비쿼터스 컴퓨팅 환경은 각종 디바이스, 네트워크 및 소프트웨어 기술의 융합 환경을 필요로 하며, IoT 기술 분야 중 사물을 식별하는 RFID 기술은 산업 전 분야에 응용되고 있으며 경쟁력을 갖추고 있다. RFID 기술을 응용한 시스템은 다양한 산업분야에서 이용되고 있고, 특히! 유통, 물류 분야에서 정확한 재고관리와 SCM 관리에 매우 효율적으로 사용되고 있다. RFID 시스템을 클라우드 기반의 환경으로 구축했을 경우 효과적인 물류관리 시스템과 경제성을 고려하여 유통관리에 신뢰성을 확보할 수 있을 것이다. 본 연구는 클라우드 컴퓨팅 환경에서 RFID 시스템에 관한 연구로 응용 서버를 운영하거나 유지 보수하는 비용을 줄여 경제성과 신뢰성을 향상 시킬 수 있도록 연구한다.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.1-6
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• 2022

최근 IoT 센서를 이용하여 데이터를 로컬에서 생산하고 스트림으로 제공하는 엣지 컴퓨팅(Edge Computing) 응용 분야가 넓어지고 있다. 대량으로 생산된 데이터는 실시간 처리를 위해 모바일 장치의 데이터베이스에 저장했다가 필요한 시점에 서버와 동기화된다. 이러한 응용 분야를 지원하기 위한 다양한 모바일 데이터베이스가 개발되었다. CloudScape, DB2 Everyplace, ASA, PointBase Mobile 등이며 그중 가장 널리 사용되는 대표적 모바일 데이터베이스는 리눅스 기반 SQLite3이다. 이 논문에서는 서버와 동기화 시 필요한 성능에 초점을 맞추었다. SQLite3의 정보 선택 시 필요한 검색 성능을 데이터베이스가 저장된 각 리눅스 파일 시스템의 종류에 따라 비교 분석하였다. 그래서 다양한 검색 쿼리 유형에 따라 파일 시스템별로 성능 차이를 확인하고 인덱스 사용 환경과 테이블 스캔 환경에 따라 더 적합한 리눅스 파일 시스템을 적용하는 기준을 마련하고 제시하였다.

• 한국인터넷방송통신학회논문지
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• 제22권5호
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• pp.17-22
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• 2022

본 논문에서는 지능형 엣지 컴퓨팅을 지원할 수 있는 경량 임베디드 기기를 설계하고, 영상 기기로부터 입력되는 이미지에서 객체를 실시간으로 빠르게 검출할 수 있음을 보인다. 제안하는 시스템은 산업 현장이나 군 지역과 같이 사전에 설치된 인프라가 없는 환경에 적용되는 지능형 영상 관제 시스템이나 드론과 같은 자율이동체에 탑재된 영상 보안 시스템에 적용될 수 있다. 지능형 비전 인지 시스템이 확산 적용되기 위해 온디바이스 AI(On-Device Artificial intelligence) 기술 적용 필요성이 증대되고 있다. 영상 데이터 취득 장치에서 가까운 엣지 기기로의 컴퓨팅 오프 로딩은 클라우드를 중심으로 수행되는 인공지능 서비스 대비 적은 네트워크 및 시스템 자원으로도 빠른 서비스 제공이 가능하다. 또한, 다양한 해킹 공격에 취약한 공격 표면의 감소와 민감한 데이터의 유출을 최소화 할 수 있어 다양한 산업에 안전하게 적용될 수 있을것으로 기대된다.

• 대기
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• 제16권3호
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• pp.225-234
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• 2006

Convective systems propagate eastward with a persistent pattern in the longitude-time space. The characteristic structure and fluctuation of convective system is helpful in determining its predictability. In this study, convective index (CI) was defined as a difference between GOES-9 window and water vapor channel brightness temperatures following Mosher (2001). Then the temporal-spatial scales and variational characteristics of the summer convective systems in the East Asia were analyzed. It is found that the average moving speed of the convective system is about 14 m/s which is much faster than the low pressure system in the summer. Their average duration is about 12 hours and the average length of the cloud streak is about 750km. These characteristics are consistent with results from other studies. Although the convective systems are forced by the synoptic system and are mostly developed in the eastern edge of the Tibetan Plateau, they have a persistent pattern, i.e., appearance of the maximum intensity of convective systems, as they approach the Korean Peninsula. The consistency of the convective systems, i.e., the eastward propagation, suggests that there exists an intrinsic predictability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.281-298
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• 2020

Fog computing has become a popular concept in the application of internet of things (IoT). With the superiority in better service providing, the edge cloud has become an attractive solution to IoT networks. The data outsourcing scheme of IoT devices demands privacy protection as well as computation verification since the lightweight devices not only outsource their data but also their computation. Existing solutions mainly deal with the operations over encrypted data, but cannot support the computation verification in the same time. In this paper, we propose a data outsourcing scheme based on an encrypted database system with linear computation as well as efficient query ability, and enhance the interlayer program in the original system with homomorphic message authenticators so that the system could perform computational verifying. The tools we use to construct our scheme have been proven secure and valid. With our scheme, the system could check if the cloud provides the correct service as the system asks. The experiment also shows that our scheme could be as effective as the original version, and the extra load in time is neglectable.