• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1449-1454
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• 2015

Virtualization lacks capabilities for enabling the application to scale efficiently because of new applications components which are raised to be configured on demand. In this paper, we propose an architecture that affords mobile app based on nomadic smartphone using not only mobile cloud computing-cloudlet architecture but also a dedicated platform that relies on using virtual private mobile networks to provide reliable connectivity through LTE(Long Term Evolution) wireless communication. The design architecture lies with how the cloudlet host discovers service and sends out the cloudlet IP and port while locating the user mobile device. We demonstrate the effectiveness of the proposed architecture by implementing an android application responsible of real time analysis by using a vehicle to applications smartphone interface approach that considers the smartphone to act as a remote users which passes driver inputs and delivers outputs from external applications.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.151-155
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• 2017

With the rapid growth of the up-to-date wireless network and Internet technologies, huge and various types of things around us are connected to the Internet and build the hyper-connected society, and lots of smart applications using these technologies are actively developed recently. IoT connects human, things, space, and data with various types of networks to construct the hyper-connected network that can create, collect, share and appling realtime information. Furthermore, most of the smart applications are concentrated on the service that can collect and store realtime contexts using various sensors and cloud technology, and provide intelligence by making inferences and decisions from them nowadays. In this paper, we design a smart application that can accurately control and process the current state of the specific context in realtime by using the state-of-the-art ICT techniques such as various sensors and cloud technologies on the IoT based mobile computing environment.

• Smart Media Journal
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• v.8 no.3
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• pp.88-94
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• 2019

Livestock disease is a very important issue in the livestock industry because if livestock disease is not responded quickly enough, its damage can be devastating. To solve the issues involving the occurrence of livestock disease, it is necessary to diagnose in advance the status of livestock disease and develop systematic and scientific livestock feeding technologies. However, there is a lack of domestic studies on such technologies in Korea. This paper, therefore, proposes Livestock Disease Forecasting and Livestock Farm Integrated Control System using Cloud Computing to quickly manage livestock disease. The proposed system collects a variety of livestock data from wireless sensor networks and application. Moreover, it saves and manages the data with the use of the column-oriented database Hadoop HBase, a column-oriented database management system. This provides livestock disease forecasting and livestock farm integrated controlling service through MapReduce Model-based parallel data processing. Lastly, it also provides REST-based web service so that users can receive the service on various platforms, such as PCs or mobile devices.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.49-60
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• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.3
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• pp.67-76
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• 2020

With the fast development in wireless communications and vehicular technologies, vehicular ad hoc networks (VANETs) have enabled to deliver data between vehicles. Recently, VANETs introduce a Vehicular Cloud (VC) model for collaborating to share and use resources of vehicles to create value-added services. To construct a VC, a vehicle should search vehicles that intend to provide their own resource. The single-hop search cannot search enough provider vehicles due to a small coverage and non-line-of-sights of communications. On the other hand, the multi-hop search causes very high traffics for large coverage searching and frequent connection breakages. Recently, many Roadside Units (RSUs) have been deployed on roads to collect the information of vehicles in their own coverages and to connect them to Internet. Thus, we propose a RSU-aided vehicular resource search and cloud construction mechanism in VANETS. In the proposed mechanism, a RSU collects the information of location and mobility of vehicles and selects provider vehicles enabled to provide resources needed for constructing a VC of a requester vehicle based on the collected information. In the proposed mechanism, the criteria for determining provider vehicles to provide resources are the connection duration between each candidate vehicle and the requester vehicle, the resource size of each candidate vehicle, and its connection starting time to the requester vehicle. Simulation results verify that the proposed mechanism achieves better performance than the existing mechanism.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.793-794
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• 2009

In recent years a significant increase in computing resources, the Internet, many computing resources associated with multiple users using the service and how it will do for cloud computing or grid computing technology is attracting attention from the networks on physical networks and virtual network links You can configure the virtual network of nodes to configure the virtual network environment, research is being made. Virtualization technology, rather than the actual network operating system is being developed in the field of research for a long time, but this paper is applied to the network field is applied to a mobile network based virtualization technology for virtual mobile network will explore.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.727-742
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• 2020

Point cloud-based 3D maps can obtain many kinds of information for maintenance work on forest road networks. This study was conducted to compare the importance of each factor to select the factors required for the mapping of 3D forest road maps. This can be used as basic data for attribute information required to maintain forest road networks. The results of this study found that out of a total of 30 indexes extracted for mapping 3D forest roads, a total of 21 indexes related to stakeholder groups were significantly different. The importance of the index required by the civil service group was significantly higher than that of the other groups overall. In the case of the academic group, the index importance for cut slope, fill slope, and drainage facility was significantly higher. On the other hand, the index importance for the forestry cooperative and forest professional engineer group was mostly distributed between the civil servants' group and the academic group. In particular, the type of drainage system showed the highest value among the detailed indexes. Overall, drainage related factors in this survey had high coefficient values. The impact of water on forest roads was the most important part in road maintenance. In addition, the soil texture had a high value in relation to slope stability. This is thought to be because the texture of the soil affects the stability of the slope.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.