• International journal of advanced smart convergence
• /
• 제5권1호
• /
• pp.1-7
• /
• 2016

Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS(Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS (DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권9호
• /
• pp.4197-4219
• /
• 2017

Cloud computing is a new service to provide dynamic, scalable virtual resource services via the Internet. Cloud market is available to multiple cloud computing resource providers and users communicate with each other and participate in market transactions. However, since cloud computing is facing with more and more security issues, how to complete the allocation process effectively and securely become a problem urgently to be solved. In this paper, we firstly analyze the cloud resource allocation problem and propose a mathematic model based on combinatorial double auction. Secondly, we introduce a trust evaluation mechanism into our model and combine genetic algorithm with simulated annealing algorithm to increase the efficiency and security of cloud service. Finally, by doing the overall simulation, we prove that our model is highly effective in the allocation of cloud resources.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권9호
• /
• pp.4044-4062
• /
• 2016

We investigate the optimization of energy consumption in Mobile Cloud environment in this paper. In order to optimize the energy consumed by the CPUs in mobile devices, we put forward using the asymptotic time complexity (ATC) method to distinguish the computational complexities of the applications when they are executed in mobile devices. We propose a multi-scale scheme to quantize the channel gain and provide an improved dynamic transmission scheduling algorithm when offloading the applications to the cloud center, which has been proved to be helpful for reducing the mobile devices energy consumption. We give the energy estimation methods in both mobile execution model and cloud execution model. The numerical results suggest that energy consumed by the mobile devices can be remarkably saved with our proposed multi-scale scheme. Moreover, the results can be used as a guideline for the mobile devices to choose whether executing the application locally or offloading it to the cloud center.

• 대한원격탐사학회지
• /
• 제38권1호
• /
• pp.103-110
• /
• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• 한국위성정보통신학회논문지
• /
• 제8권3호
• /
• pp.32-40
• /
• 2013

Boltwood 구름센서는 구름으로부터 복사되는 적외선을 감지하여 구름의 유무와 많고 적음을 판별할 수 있는 기상센서의 한 종류이다. 이 구름센서는 한국천문연구원이 진행하고 있는 국가현안과제의 일환인 우주물체 전자광학 감시체계 시스템(OWL, Optical Wide-field patroL)에 사용될 계획이다. 실제 시스템 적용에 앞서, Boltwood 구름센서를 충북대학교 천문대에 설치, 약 2주간 구름센서의 구름감지 성능 시험을 위한 관측을 진행하였다. 구름센서의 성능과 비교할 대상으로 충북대학교 천문대에 현재 설치, 운영 중인 구름량 측정을 위한 CCD 관측시스템을 이용하였다. 성능 테스트 결과, 하늘과 지상의 온도차이와 측광 자료의 별 개수간 명확한 상관관계가 도출되지 못했다. 그 원인으로는 시험 환경상의 문제와 Boltwood 구름센서의 내부 알고리즘 및 하드웨어에 대한 정보공개가 제한 때문인 것으로 판단된다. 이 논문에서는 Boltwood 구름센서와 CCD 관측시스템의 구름지수를 비교, 분석한 과정과 그 상세 결과를 제시하고자 한다.

• 대한원격탐사학회지
• /
• 제39권1호
• /
• pp.111-127
• /
• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2014년도 추계학술대회
• /
• pp.354-356
• /
• 2014

최근 센서 기술이 많은 분야에서 사용되고 있고, 다양한 종류의 센서에서 검출된 데이터는 규격과 단위의 차이에 의해 통합하기가 어렵다. 또한 유사 분야에서 검출된 데이터를 활용하기 위하여 클라우드 상에서 데이터나 프로그램을 서비스로 제공할 경우 이러한 데이터의 통합은 중요하다. 본 논문에서는 이기종 센서에서 발생하는 데이터를 클라우드 환경에서 서비스로 제공될 수 있도록 하기 위한 데이터 통합 방안을 제안한다. 그 방안은 온톨로지를 기반으로 한 표준 메타데이터를 생성하고, 생성된 표준은 센서에 의해 검출된 데이터와 매핑한다. 이에 따라 검출된 데이터는 표준 형식으로 어플리케이션에 전달함으로써, 센서와 어플리케이션 간의 데이터 이동의 효율성을 향상시킬 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권5호
• /
• pp.2394-2406
• /
• 2016

ABE has become an effective tool for data protection in cloud computing. However, since users possessing the same attributes share the same private keys, there exist some malicious users exposing their private keys deliberately for illegal data sharing without being detected, which will threaten the security of the cloud system. Such issues remain in many current ABE schemes since the private keys are rarely associated with any user specific identifiers. In order to achieve user accountability as well as provide key exposure protection, in this paper, we propose a key-insulated ciphertext policy attribute based encryption with key exposure accountability (KI-CPABE-KEA). In our scheme, data receiver can decrypt the ciphertext if the attributes he owns match with the self-centric policy which is set by the data owner. Besides, a unique identifier is embedded into each user's private key. If a malicious user exposes his private key for illegal data sharing, his identity can be exactly pinpointed by system manager. The key-insulation mechanism guarantees forward and backward security when key exposure happens as well as provides efficient key updating for users in the cloud system. The higher efficiency with proved security make our KI-CPABE-KEA more appropriate for secure data sharing in cloud computing.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2021년도 추계학술발표대회
• /
• pp.128-130
• /
• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.

• 전자공학회논문지
• /
• 제51권9호
• /
• pp.93-102
• /
• 2014

센서 네트워크는 다수의 센서들로부터 수집된 각종 데이터를 저장하고, 처리하며, 이를 분석하여 사용자에게 유용한 정보를 제공하는 유용 시스템으로 다양한 분야에서 활용된다. 이러한 센서 네트워크의 구성시 통신 서버와 데이터베이스 서버 모두 클라우드 PaaS를 이용함으로써, 시스템의 비용 절감 및 안정화를 도모할 수 있다. 본 논문에서는 이동체의 위치 정보뿐만 아니라 각종 센서 데이터를 위한 클라우드 서비스로 처리하여 사용자에게 판정 정보를 제공하기 위해서 UDIPSN (센서 네트워크를 제공하는 사용자 결정 정보)을 구현하였다. 마지막으로 제안된 시스템의 동작이 정상적으로 이루어짐을 보였다.