• Journal of KIISE
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• v.45 no.2
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• pp.141-149
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• 2018

Nowadays, mobile computing has become a common computing paradigm that provides convenience to people's daily life. More and more useful mobile applications' appearance makes it possible for a user to manage personal schedule, enjoy entertainment, and do many useful activities. However, there are some inherent defects in a mobile device that battery constraints and bandwidth limitations. These drawbacks get a user into troubles when to run computationally intensive applications. As a remedy scheme, component offloading makes room for handling mentioned issues via migrating computationally intensive component to the cloud server. In this paper, we will present the predictive offloading method for efficient mobile cloud computing. At last, we will present experiment result for validating applicability and practicability of our proposal.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.85-95
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• 2011

Recently, sudden interests of mobile cloud computing as well as conventional internet environment are rapidly increased as cloud computing spread out in our web society. Mobile devices including smart phone are rapidly changing in a wholesale way that covers hardwares, applications, and services. However, the Internet access using mobile device is not quite smooth in this local mobile internet environment which suffers from lack of understanding and observance of Web Standards. Although most of domestic web sites are developed focusing on various functions and eye-catching designs, this should became one of the main factors that make the usability and accessability decrease when accessing web with smart phones or table PCs. Therefore, this paper suggested a web interface that considered usability and accessability under mobile cloud environment and we tried to prove it via usability test. It could be found that there was an improvement of usability of interface of the main page that has been optimized to the mobile device environment suggested from the previous research we present, but this paper aimed to prove a usability improvement of total website as a whole by performing the usability test on the entire website. Selecting a special website optimized for mobile cloud computing, we prove an improvement of usability and accessibility. Therefore, we offer a guideline about user interface design applications to developers or companies who want to construct mobile website.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2671-2676
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• 2015

Nowadays, Mobile devices are now capable of supporting a wide range of applications. Unfortunately, some of applications demand an ever increasing computational power and mobile devices have limited resources due to their constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To deal with mobile devices' constraints, researchers envision extending cloud computing services to mobile devices using virtualization techniques to shift the workload from mobile devices to a powerful computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile devices), its functioning and in our future work, analyze effects of distance between cloudlet and mobile devices.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.377-386
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• 2020

Mobile cloud computing involves mobile or embedded devices as clients, and features small devices with constrained resource and low availability. Due to the fast expansion of smart phones and smart peripheral devices, researches on mobile cloud computing attract academia's interest more than ever. Computation offloading, or code offloading, enhances the performance of computation by migrating a part of computation of a mobile system to nearby cloud servers with more computational resources through wired or wireless networks. Code offloading is considered as one of the best approaches overcoming the limited resources of mobile systems. In this paper, we analyze the factors and the performance of code offloading, especially focusing on static program partitioning and data protection. We survey state-of-the-art researches on analyzed topics. We also describe directions for future research.

• Journal of IKEEE
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• v.19 no.1
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• pp.58-63
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• 2015

This study suggested the mobile intelligent management system based on the cloud service. The mobile intelligent management system are composed of cloud server, middleware and sensor networks. Each modules are controlled on mobile environment and observed operating status of each apparatus for environment. In this pater, the image-based moving detection algorithm applied in order to detect an intruder and average 12.3% are measured in moving detection experiments. it was confirmed the validity of the security device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5174-5190
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• 2016

Recent years have witnessed an explosive growth of mobile devices, mobile cloud computing services offered by these devices and the remote clouds behind them. In this paper, we noticed ultra-low latency service, as a type of mobile cloud computing service, requires extremely short delay constraints. Hence, such delay-sensitive applications should be satisfied with strong QoS guarantee. Existing solutions regarding this problem have poor performance in terms of throughput. In this paper, we propose an end-to-end bandwidth resource reservation via software defined scheduling inspired by the famous SDN framework. The main contribution of this paper is the end-to-end resource reservation and flow scheduling algorithm, which always gives priority to delay sensitive flows. Simulation results confirm the advantage of the proposed solution, which improves the average throughput of ultra-low latency flows.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.105-118
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• 2024

The decision to integrate mobile cloud computing (MCC) in higher education without first defining suitable usage scenarios is a global issue as the usage of such services becomes extensive. Consequently, this study investigates the security determinants of the educational use of mobile cloud computing among universities students. This study proposes and develops a theoretical model by adopting and modifying the Protection Motivation Theory (PMT). The studys findings show that a significant amount of variance in MCC adoption was explained by the proposed model. MCC adoption intention was shown to be highly influenced by threat appraisal and coping appraisal factors. Perceived severity alone explains 37.8% of students "Intention" to adopt MCC applications, which indicates the student's perception of the degree of harm that would happen can hinder them from using MCC. It encompasses concerns about data security, privacy breaches, and academic integrity issues. Response cost, perceived vulnerability and response efficacy also have significant influence on students "intention" by 18.8%, 17.7%, and 6.7%, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.507-516
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• 2013

Mobile devices have limited computing power and resources. Since mobile devices are equipped with rich network connectivity, an approach to subscribe cloud services can effectively remedy the problem, which is called Mobile Cloud Computing (MCC). Most works on MCC depend on a method to offload functional components at runtime. However, these works only consider the limited verion of offloading to a pre-defined, designated node. Moveover, there is the limitation of managing services subscribed by applications. To provide a comprehensive and practical solution for MCC, in this paper, we propose a self-stabilizing process and its management-related methods. The proposed process is based on an autonomic computing paradigm and works with diverse quality remedy actions such as migration or replicating services. And, we devise a pratical offloading mechanism which is still in an initial stage of the study. The proposed offloading mechanism is based on our proposed MCC meta-model. By adopting the self-stabilization process for MCC, many of the technical issues are effectively resolved, and mobile cloud environments can maintain consistent levels of quality in autonomous manner.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2352-2358
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• 2013

In this paper, we designed and implemented black box terminal with wireless communication function and cloud server for more efficient usage of black box video. Our system can store and manage all vehicle black box videos so public institutions can select videos with various conditions such as object, time-based and location based like integrated CCTV management system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.