• International journal of advanced smart convergence
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• v.5 no.1
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• pp.1-7
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• 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)
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• v.11 no.9
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• pp.4197-4219
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• 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)
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• v.10 no.9
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• pp.4044-4062
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• 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.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 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.

• Journal of Satellite, Information and Communications
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• v.8 no.3
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• pp.32-40
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• 2013

The Boltwood Cloud Sensor is meteorological sensor that is used to estimate an amount of clouds in the sky. This sensor will be installed for OWL(Optical Wide-field patroL) telescope and observatory system of Korea Astronomy and Space Science. Before applying this sensor to an observatory system, we performed test observations at Chungbuk University Observatory at Jincheon, Chungbuk. During the test run, a significant correlation between air temperature difference and the number of visible stars recorded in the CCD frames has not been found. This preliminary result can be attributed to test environment of the observation and our lack of knowledge on calculation algorithm as well as the hardware system of the Boltwood Cloud Sensor.In this paper, we present the procedure and the result of the performance test employing the cloud sensor.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.111-127
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• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.354-356
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• 2014

Recently, The sensor technology Has been used in many fields, it is detected data of various types of sensors, is very difficult to integrate due to differences in the standards and each other unit. Also, when we providing a service or program on a data cloud, it is important that integrates of such data in order to take advantage of the detected data in the similar field. In this paper, we propose a approaches to integrating data to be provided as a service in the cloud of data arising from a heterogeneous sensors. The approaches are generating a standard meta-data based on ontology, it is mapping with detected data by the sensor data. Accordingly, the detected data is possible to improve the efficiency of data transfer between the sensor and the application by sending an application in a standard format.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2394-2406
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• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.128-130
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• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.93-102
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• 2014

Sensor networks are utilized in various fields as the useful system that provides the useful information to the user, by storing, processing and analyzing the various data collected from sensors. In construction of such sensor networks, by utilizing cloud PaaS for both communication server and the database server, the cost reduction and stabilization of the system can be achieved. In this paper, UDIPSN (User Decision Information Providing Sensor Network) was implemented to provide the decision information to the users by being treated as a cloud service for the location information of a moving object as well as various sensor data. Finally, we showed that the operation of the proposed system was performed properly.