• Asia pacific journal of information systems
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• v.27 no.4
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• pp.191-215
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• 2017

This study investigated the antecedents and the performance impact of two types of Smartphone functions (communication vs. computing functions) in organizational environment and the moderating impact of Smartphone users' organizational roles. More specifically, identifying two distinct types of Smartphone functions such as communication functions and computing functions (including informational, social network, and resource management functions), we investigated the impact of three antecedents (Smartphone dependency, task mobility, and perceived critical mass) on the use of the two Smartphone functions and how organizational workers' perceived performance gains differ by using these two different Smartphone functions for their workplace activities. We tested our hypotheses with survey data collected from 176 organizational workers. Our findings suggest that Smartphone dependency, task mobility and perceived critical mass of Smartphone use are significantly associated with the use of the two different functions, and that the use of computing functions is more strongly associated with perceived performance gain than the use of communication functions. We also found that managerial roles played by individual workers differently moderate the impact of Smartphone use on perceived performance gain. The present findings enable researchers and practitioners to better understand the impact of Smartphone use in workplaces.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.109-112
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• 2020

Many devices are connected on the internet to give functionalities for interconnected services. In 2020', The number of devices connected to the internet will be reached 5.8 billion. Moreover, many connected service provider such as Google and Amazon, suggests edge computing and mesh networks to cope with this situation which the many devices completely connected on their networks. This paper introduces the current state of the introduction of the wireless mesh network and edge cloud in order to efficiently manage a large number of nodes in the exploding Internet of Things (IoT) network and introduces the existing Network Time Protocol (NTP). On the basis of this, we propose a relatively accurate time synchronization method, especially in heterogeneous mesh networks. Using this NTP, multiple time coordinators can be placed in a mesh network to find the delay error using the average delay time and the delay time of the time coordinator. Therefore, accurate time can be synchronized when implementing IoT, remote metering, and real-time media streaming using IoT mesh network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.484-503
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• 2016

Mobile cloud computing has recently become a new paradigm for the utilization of a variety of shared mobile resources via wireless network environments. However, due to the inherent characteristics of mobile devices, a limited battery life, and a network access requirement, it is necessary for mobile servers to provide a dynamic approach for managing mobile resources efficiently in mobile cloud computing environments. Since on-demand job requests occur frequently and the number of mobile devices is drastically increased in mobile cloud computing environments, a different mobile resource management method is required to maximize the computational power. In this paper, we therefore propose a cooperative, mobile resource sharing method that considers both the inherent properties and the number of mobile devices in mobile cloud environments. The proposed method is composed of four main components: mobile resource monitor, job handler, resource handler, and results consolidator. In contrast with conventional mobile cloud computing, each mobile device under the proposed method can be either a service consumer or a service provider in the cloud. Even though each device is resource-poor when a job is processed independently, the computational power is dramatically increased under the proposed method, as the devices cooperate simultaneously for a job. Therefore, the mobile computing power throughput is dynamically increased, while the computation time for a given job is reduced. We conduct case-based experiments to validate the proposed method, whereby the feasibility of the method for the purpose of cooperative computation is shown.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.55-65
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• 2011

Recently, use of cloud computing service is dramatically increasing due to wired and wireless communications network diffusion in a field of high performance Internet technique. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. In a view of digital forensic investigation, it is difficult to obtain data from cloud computing service environments. therefore, this paper suggests analysis method of AWS(Amazon Web Service) and Rackspace which take most part in cloud computing service where IaaS formats presented for data acquisition in order to get an evidence.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.287-295
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• 2004

Mark Weiser introduced ubiquitous computing in his article titled 'The computer for 21st Century' in 1991. This has been new paradigm after internet. Now, the rapid development of mobile computer, wireless network, and intelligent system has supported ubiquitous computing environment. In the related area of information science, the researchers have studied on ubiquitous computing. But in the field of Korea statistics, this research has not been worked yet. So, we proposed the connection between statistics and ubiquitous computing in this paper. As an example, we showed an efficient cache hoarding for ubiquitous computing using statistical methods. In experimental results, we verified our proposed issue.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.501-505
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• 2007

현재 기술 동향에 따라, 언제 어디서나 통신 및 컴퓨팅이 가능한 유비쿼터스 컴퓨팅 시스템(Ubiquitous Computing System)을 위해 사용자 및 주변 환경의 상황을 인식하는 기술(Context-Aware Computing System)이 필수적인 요소로 부각되고 있다. 하지만, Context-Aware Computing System을 구현하기 위해서 사용자 및 주변 환경으로부터 입력되는 불확실하거나 모호한 상황정보에 대한 표현과 추론에 대한 연구는 부족한 실정이다. 이를 해결하기 위해 Rule-based System을 기반으로 CRS와 DOS의 개념을 도입하여 상황인식 기술을 SoC로 구현할 수 있도록 새로운 Architecture를 제안한다. 마지막으로는 CRS와 DOS가 Network Topolgy에 적용된 RODMRP(Resilient Ontology-based Dynamic Multicast Routing Protocol)의 예를 통해 그 효용성을 입증하고, Ubiquitous Computing System에서 활용 가능한 방법 등을 제시한다.

• Electronics and Telecommunications Trends
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• v.39 no.3
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• pp.69-78
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• 2024

Cloud computing technology based on centralized high-performance computing has brought about major changes across the information technology industry and led to new paradigms. However, with the rapid development of the industry and increasing need for mass generation and real-time processing of data across various fields, centralized cloud computing is lagging behind the demand. This is particularly critical in emerging technologies such as autonomous driving, the metaverse, and augmented/virtual reality that require the provision of services with ultralow latency for real-time performance. To address existing limitations, distributed and edge cloud computing technologies have recently gained attention. These technologies allow for data to be processed and analyzed closer to their point of generation, substantially reducing the response times and optimizing the network bandwidth usage. We describe distributed and edge cloud computing technologies and explore the latest trends in their standardization.

• MALSORI
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• no.48
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• pp.93-105
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• 2003

Since telephone channel has bandlimited frequency characteristics, speech signal over the telephone channel shows degraded speech quality. In this paper, we propose an algorithm using neural network to reconstruct wideband speech from its narrowband version. Although single neural network is a good tool for direct mapping, it has difficulty in training for vast and complicated data. To alleviate this problem, we modularize the neural networks based on appropriate clustering of the acoustic space. We also introduce fuzzy computing to compensate for probable misclassification at the cluster boundaries. According to our simulation, the proposed algorithm showed improved performance over the single neural network and conventional codebook mapping method in both objective and subjective evaluations.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.283-285
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• 2005

If the ubiquitous computing times come before long, Context Awareness will be prominent among improved computing functions which are serviced to user. It is the very sense network, new technology, that makes it possible to recognize circumstances.In a lot of research projects about sensor network, this paper proposes the efficient method of localization for sensor network. I propose the possibility of localization using wireless RSS with no modification of hardware for sensor node and then suggest the limitation factors of method for efficiently improving performance.