• 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.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.1-10
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• 2020

Service robots providing services according to user's needs in dynamically changing environments should be able to utilize external services such as cloud services. Cloud services are, however, changing and expanding continuously and thus the interface to the services must be general and flexible to adapt to the changes of the functionality and data of the services. In order to facilitate the adaptation of the interface to the changes, a model-based general interface to various cloud services is proposed. In this approach, a general and extensible interface is realized by defining standard service profiles that can be easily extended to adapt to the changed services. Experiments with intelligent service robots show satisfying results exhibiting flexible adaptations to new or changed external services.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.345-350
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• 2014

According to the necessity of smart working with various mobile devices, and the increasing services based on the converged infrastructures such as Cloud, Wearable Computing, Next Generation Wired/Wireless Mobile Networks, the network reliability has been one of the most important things. However, the research related to the network reliability is still insufficient. To solve these problems, we propose the ISNTC (Intelligent Safe Network Technology based on Cloud), which uses the safe network technique based on SDN, to be adopted to the smart working environments. The proposed ISNTC guarantees secure data forwarding through the synchronized transmission path and timing. We have verified the throughput which outperformed the existing techniques through the computer simulations using OPnet.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.59-68
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• 2012

Zero client-based cloud storage technology is gaining much interest as a tool to centralized management of organizational documents nowadays. Besides the well-known cloud storage's defects such as security and privacy protection, users of the zero client-based cloud storage point out the difficulty in browsing and selecting the storage category because of its diversity and complexity. To resolve this problem, this study proposes a method of intelligent document archiving by applying an algorithm-based automatic topic identification technology. Without user's direct definition of category to store the working document, the proposed methodology and prototype enable the working documents to be automatically archived into the predefined categories according to the extracted topic. Based on the proposed ideas, more effective and efficient centralized management of electronic documents can be achieved.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2294-2314
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• 2011

The key issue in providing fast and reliable access on cloud services is the effective management of resources in a cloud system. However, the high variation in cloud service access rates affects the system performance considerably when there are no default routines to handle this type of occurrence. Adaptive techniques are used in resource management to support robust systems and maintain well-balanced loads within the servers. This paper presents an adaptive resource management for cloud systems which supports the integration of intelligent methods to promote quality of service (QoS) in provisioning of cloud services. A technique of dynamically assigning cloud services to a group of cloud servers is proposed for the adaptive resource management. Initially, cloud services are collected based on the excess cloud services load and then these are deployed to the assigned cloud servers. The assignment function uses the proposed proportional ordering which efficiently assigns cloud services based on its resource consumption. The difference in resource consumption rate in all nodes is analyzed periodically which decides the execution of service assignment. Performance evaluation showed that the proposed dynamic service assignment (DSA) performed best in throughput performance compared to other resource allocation algorithms.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.1-12
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• 2019

The role of Internet of Things (IoT) has been evolving from connectivity to intelligent and autonomous functions. The increase in the number of connected things and the volume of data has revealed the limit of cloud-based intelligent IoT. Meanwhile, the development of microprocessors for the IoT has enabled their intelligent decision making and reactions without the intervention of the cloud; this phase is referred to as the "autonomous IoT era." However, intelligence is not the only function of the IoT. When a cyber physical system (CPS) is running on the cloud, the real-time synchronization between the real and virtual worlds cannot be guaranteed. If a CPS is running on the IoT, both the worlds can be synchronized closely enough for a zero- time gap, i.e., achieving the goals of autonomous IoT. ETRI implements intelligence into the role of IoT and collaborates their decision making and reactions without the intervention of humans. Then, we focus on the development of a new IoT computing paradigm that enables human-like discussions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.239-244
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• 2014

As the usefulness of mobile device is kept growing the privacy of the cloud computing is receiving more attentions. Even though many researches and solutions for privacy matters are suggested we are still worrying about the security problems. In addition most of cloud computing systems uses file-level synchronization which make it difficult to modify a part of a file. If we use data-centric app that stores data on embedded DBMS such as SQLite, a simple synchronization may incur some loss of information. In this paper we propose a solution to build a personal cloud that supports record-level synchronization. And we show a prototype system which uses RESTful web services and the same schema on mobie devices and the cloud storage. Synchronization is achieved by using a kind of optimistic concurrency control.