• International journal of advanced smart convergence
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• v.4 no.1
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• pp.31-34
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• 2015

Recently, the various types of data are began to increase. In order to manage the data efficiently, a variety of cloud services are being made. However, while providing a cloud service, the problem is the cost and waste a lot of human power to manage the data that is generated and managed by the server. To solve this problem, it is build the cloud environment using a single board computer with Raspberry pi. In this paper, we used Raspberry pi as a cloud server to provide services for the users. And we construct a Total Server to manage the generated data. It can separate the processing of data and the provision of services. We ensure the efficient operation by building a cloud environment with Raspberry pi and by managing the data which generated in cloud environment with the total server.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1598-1607
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• 2016

Safe transmitting monitoring data is essential for supporting IoT-Cloud services efficiently. In this paper, we find ways to configure data path flexibly in SDN based for IoT-Cloud services utilizing SmartX-mini Playground. To do this, we use ONOS(Open Network Operating System) SDN Controller, ONOS NBI Applications made from us to check flexible and safe data path configuration for IoT-Cloud monitoring data transmitting in real IoT-SDN-Cloud environments.

• Journal of Computing Science and Engineering
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• v.10 no.1
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• pp.21-31
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• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4522-4536
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• 2020

Nowadays, cloud is the fastest emerging technology in the IT industry. We can store and retrieve data from the cloud. The most frequently occurring problems in the cloud are security and privacy preservation of data. For improving its security, secret information must be protected from various illegal accesses. Numerous traditional cryptography algorithms have been used to increase the privacy in preserving cloud data. Still, there are some problems in privacy protection because of its reduced security. Thus, this article proposes an ElGamal Elliptic Curve (EGEC) Homomorphic encryption scheme for safeguarding the confidentiality of data stored in a cloud. The Users who hold a data can encipher the input data using the proposed EGEC encryption scheme. The homomorphic operations are computed on encrypted data. Whenever user sends data access permission requests to the cloud data storage. The Cloud Service Provider (CSP) validates the user access policy and provides the encrypted data to the user. ElGamal Elliptic Curve (EGEC) decryption was used to generate an original input data. The proposed EGEC homomorphic encryption scheme can be tested using different performance metrics such as execution time, encryption time, decryption time, memory usage, encryption throughput, and decryption throughput. However, efficacy of the ElGamal Elliptic Curve (EGEC) Homomorphic Encryption approach is explained by the comparison study of conventional approaches.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.124-128
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• 2024

Cloud computing is a technology for delivering information in which resources are retrieved from the internet through a web-based tools and applications, rather than a direct connection with the server. It is a new emerging computing based technology in which any individual or organization can remotely store or access the information. The structure of cloud computing allows to store and access various information as long as an electronic device has access to the web. Even though various merits are provided by the cloud from the cloud provides to cloud users, it suffers from various flaws in security. Due to these flaws, data integrity and confidentiality has become a challenging task for both the storage and retrieval process. This paper proposes a novel approach for data protection by an improved auditing based methodology in cloud computing especially in the process of cloud storage. The proposed methodology is proved to be more efficient in auditing the threats while storing data in the cloud computing architecture.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.592-599
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• 2014

Cloud computing enables users to easily store their data and simply share data with others. Due to the security threats in an untrusted cloud, users are recommended to compute verification metadata, such as signatures, on their data to protect the integrity. Many mechanisms have been proposed to allow a public verifier to efficiently audit cloud data integrity without receiving the entire data from the cloud. However, to the best of our knowledge, none of them has considered about the efficiency of public verification on multi-owner data, where each block in data is signed by multiple owners. In this paper, we propose a novel public verification mechanism to audit the integrity of multi-owner data in an untrusted cloud by taking the advantage of multisignatures. With our mechanism, the verification time and storage overhead of signatures on multi-owner data in the cloud are independent with the number of owners. In addition, we demonstrate the security of our scheme with rigorous proofs. Compared to the straightforward extension of previous mechanisms, our mechanism shows a better performance in experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1002-1015
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• 2018

The advent of Internet of Things (IoT) and the evident inadequacy of Cloud networks concerning management of numerous end nodes have brought about a shift of paradigm giving birth to Fog computing. Fog computing is an extension of Cloud computing that extends Cloud resources at the edge of the network, closer to the user. Cloud computing has become one of the essential needs of people over the Internet but with the emerging concept of IoT, traditional Clouds seem inadequate. IoT entails extremely low latency and for that, the Cloud servers that are distant and unknown to the user appear to be unsuitable. With the help of Fog computing, the Fog devices installed would be closer to the user that will provide an immediate storage for the frequently needed data. This paper discusses data migration between different storage types especially between Cloud devices and then presents a mechanism to migrate data between Cloud and Fog Layer. We call this mechanism Adaptive Deadline-Aware Scheme (ADAS) for Data migration between Cloud and Fog. We will demonstrate that we can access and process latency sensitive "hot" data through the proposed ADAS more efficiently than with a traditional Cloud setup.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.303-312
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• 2017

This paper indirectly created high density point cloud data using unmanned aerial vehicle image. Then, we tried to suggest new concept of classification technique where particular objects from point cloud data can be selectively classified. For this, we established the classification technique that can be used as search factor in classifying color information in point cloud data. Then, using suggested classification technique, we implemented object classification and analyzed classification accuracy by relative comparison with self-created proof resource. As a result, the possibility of point cloud data classification was observable using the image's information. Furthermore, it was possible to classify particular object's point cloud data in high classification accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.83-90
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• 2022

Traditional Cloud Computing would be unable to safely host IoT data due to its high latency as the number of IoT sensors and physical devices accommodated on the Internet grows by the day. Because of the difficulty of processing all IoT large data on Cloud facilities, there hasn't been enough research done on automating the security of all components in the IoT-Cloud ecosystem that deal with big data and real-time jobs. It's difficult, for example, to build an automatic, secure data transfer from the IoT layer to the cloud layer, which incorporates a large number of scattered devices. Addressing this issue this article presents an intelligent algorithm that deals with enhancing security aspects in IoT cloud ecosystem using butterfly optimization algorithm.