• The Journal of Information Systems
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• v.31 no.2
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• pp.63-88
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• 2022

Purpose The purpose of this study is to empirically analyze the factors affecting the intention to use cloud computing in the smart work environment. This study is meaningful in that it examines the characteristics of smart work and the fit of cloud computing characteristics, and it is a study that reveals the factors affecting the intention to use cloud computing by applying the integrated model of TTF and TAM. Design/methodology/approach In order to understand the factors affecting the intention to use cloud computing in the smart work environment, a research model that integrates TTF and TAM with the hypothesis was proposed. In order to verify the research hypothesis, this study conducted a survey on individuals with experience in smart work and cloud computing. And with the data 280 collected in the survey, path analysis was performed using the PLS structural equation. Findings As a result, it was found that task characteristics and technology characteristics had a positive (+) effect on task-skill fit, and task-skill fit had a positive (+) effect on perceived ease of use and usefulness. Also, task-skill fit, perceived ease of use, and perceived usefulness were found to have a positive (+) effect on intention to use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.952-973
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• 2021

In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.140-146
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• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.21-30
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• 2017

Physical computing is performed through interaction with the real world making it suitable for cultivating student abilities in computing knowledge and thought processes. Furthermore, if users can develop programs under block-coding environment, it will be more easy and more intuitive. However, the existing block coding environment has a problem that the physical devices must be continuously connected to the computer. Blockly is an open source library that adds a visual code editor linked with graphic blocks to demonstrate coding concepts through web and mobile apps. Using Blockly, we describe a development environment for physical computing on mobile platform, which combines physical computing with an established block-coding environment, and activates it through wireless communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.505-511
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• 2013

Recently, cloud computing is being emerged paradigm that a support computing resource flexible and scalable to users as the want in distributed computing environment. Actually, cloud computing can be implemented and provided by virtualization technology. In this paper, we studied open source based cloud computing and built a educational practice system through cloud computing. Virtualization-based cloud computing provides optimized computing resources, as well as easy to manage practical resource and result. Therefore, we can save the time for configuration of practice environment. In the view of faculty, they can easily handle the practice result. Also, those practice condition reuse comfortably and apply to various configuration simply. And then we can increase capabilities and availabilities of limited resources.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.769-772
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• 2005

This paper describes a low-voltage and low-power channel selection analog front end with continuous-time low pass filters and highly linear programmable-gain amplifier(PGA). The filters were realized as balanced Gm-C biquadratic filters to achieve a low current consumption. High linearity and a constant wide bandwidth are achieved by using a new transconductance(Gm) cell. The PGA has a voltage gain varying from 0 to 65dB, while maintaining a constant bandwidth. A filter tuning circuit that requires an accurate time base but no external components is presented. With a 1-Vrms differential input and output, the filter achieves -85dB THD and a 78dB signal-to-noise ratio. Both the filter and PGA were implemented in a 0.18um 1P6M n-well CMOS process. They consume 3.2mW from a 1.8V power supply and occupy an area of $0.19mm^2$.

• The Journal of Society for e-Business Studies
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• v.16 no.1
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• pp.71-100
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• 2011

There are expectations about future internet technology with IT development by end-users. Cloud computing is attracted to satisfy those demands. However, adoption of cloud computing is not active that much. Therefore, this study verified how cloud computing environment affects performance of team project. We conducted empirical study on performance of team project with cloud computing as technology tool focusing on Task-Technology Fit Model. We collected samples that were undergraduate and graduate school students and had experience on initial cloud computing such as Google-Docs and Webhard when they conducted team project for assignment. We focused on accessibility and reliability as task-technology fit and those variables treated as first order factor. Result showed that cloud computing is suitable technology tool for team project. This study suggests positive effects of cloud computing for collaboration by proving perceived fit and performance in initial cloud computing.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.7-14
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• 2016

Recently, as cloud computing technologies are developed, it enable to work anytime and anywhere by smart phone and computer. Also, cloud computing technologies are suited to reduce costs of maintaining IT infrastructure and initial investment, so cloud computing has been developed. As demand about cloud computing has risen sharply, problems of power consumption are occurred to maintain the environment of data center. To solve the problem, first of all, power consumption has been measured. Although using power meter to measure power consumption obtain accurate power consumption, extra cost is incurred. Thus, we propose prediction method about power consumption without power meter. To proving accuracy about proposed method, we perform CPU and Hard disk test on cloud computing environment. During the tests, we obtain both predictive value by proposed method and actual value by power meter, and we calculate error rate. As a result, error rate of predictive value and actual value shows about 4.22% in CPU test and about 8.51% in Hard disk test.