• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.39-44
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• 2020

In this paper, we propose a SDN-based fog computing service mobility support scheme. Fog computing architecture has been attracted because it enables task offloading services to IoT(Internet of Things) devices which has limited computing and power resources. However, since static as well as mobile IoT devices are candidate service targets for the fog computing service, the efficient task offloading scheme considering the mobility should be required. Especially for the IoT services which need low-latency response, the new connection and task offloading delay with the new fog computing node after handover can occur QoS(Quality of Service) degradation. Therefore, this paper proposes an efficient service mobility support scheme which considers both task migration and flow rule pre-installations. Task migration allows for the service connectivity when the fog computing node needs to be changed. In addition, the flow rule pre-installations into the forwarding nodes along the path after handover enables to reduce the connection delay and service interruption time.

• Journal of the Korea Society for Simulation
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• v.16 no.1
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• pp.11-19
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• 2007

Under ubiquitous environment, applications can gather and utilize various sensing information. There are many issues such as energy management, protocol standardization, independency on sensor fields, and security to be resolved for the complete ubiquitous computing. Especially, the independent information access in the sensor field is one of the most important issues to maximize the usability of sensors in various sensor fields. However, existing frameworks are not suitable for the ubiquitous computing environment because of data heterogeneity between data elements in sensor fields. Existing applications are dependent to sensor fields and sensors in the existing ubiquitous computing on environment is dependent to the application in the sensor field. In other word, an application can utilize just information from a specific sensor field. To overcome this restriction, many issues from a hardware or software view must be resolved. In this paper, we provide the design and implementation of the Metadata Registry-based framework (UbiMDR) of the Ubiquitous environment. This framework can provides the semantic interoperability among ubiquitous applications or various sensor fields. In addition, we describe comparison evaluation between conventional Ubiquitous computing framework and UbiMDR framework with data accuracy of interoperability.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.195-204
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• 2010

In this paper, we propose an efficient indoor environment management system which provides the environment management service in ubiquitous computing environment. It is designed to gather data from the user's environment and keep it pleasant with respect to indoor air quality regulation. In order to offer such service Ubiquitous Computing Environment used distributed framework based on the active model for its development and support, while supporting service integration and also we used service oriented architecture. In order to verify the performance of the environment management system, components showed operation while environment management service showed the result through GUI screen.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.395-401
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• 2004

In the current high network-centralized computing and enterprising environment, it is getting essential to transmit data reliably at very high rates. Until now previous client/server model based NFS(Network File System) or AFS(Andrew's Files System) have met the various demands but from now couldn't satisfy those of the today's scalable high-performance computing environment. Not only performance but data sharing service redundancy have risen as a serious problem. In case of NFS, the locking issue and cache cause file system to reboot and make problem when it is used simply as ip-take over for H/A service. In case of AFS, it provides file sharing redundancy but it is not possible until the storage supporting redundancy and equipments are prepared. Lustre is an open source based cluster file system developed to meet both demands. Lustre consists of three types of subsystems : MDS(Meta-Data Server) which offers the meta-data services, OST(Objec Storage Targets) which provide file I/O, and Lustre Clients which interact with OST and MDS. These subsystems with message exchanging and pursuing scalable high-performance file system service. In this paper, we compare the transmission speed of gigabytes file between Lustre and NFS on the basis of concurrent users and also present the high availability of the file system by removing more than one OST in operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.368-371
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• 2000

The distributed object computing is possible to distributed computing on same or heterogeneous machine environment with growth of distributed computing and object-oriented technology. The typical of model, CORBA is int solved the interoperability in heterogeneous machine environment over the network But, the CORBA has a problem that the server is can't rely on request of client occurs network overhead by extinction of network A plan was on the rise to solve the problem that is combination of mobile agent and distributed computing technology.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.67-75
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• 2014

In this paper, we proposed offloading delay method which determines effectively offloading timing by measuring of handoff delay and offloading time at mobile node side in mobile environment. The propose method measures each of handoff delay and offloading time and making decision of proper offloading timing on mobile node side. Therefore, it is possible to support cloud computing without changing previous implemented cloud computing structure for fixed node in a mobile environment. We compare the energy consumption of server and node to analyze efficiency of proposed method by using existing method of energy consumption measurement. Simulation results shows the reducing energy consumption more than previous method and operation time similar to previous method.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.12a
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• pp.669-685
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• 2003

Nowadays mobile phones and PDAs are part and parcel of our lives. By carrying a portable mobile device with us all the time we are already living in partial Pervasive Computing Environment (PCE) that is waiting to be exploited very soon. One of the advantages of pervasive computing is that it strongly supports the deployment of Location-Based Service(s) (LBSs). In PCE, there would be many competitive service providers (SPs) trying to sell different or similar LBSs to users. In order to reserve a particular service, it becomes very difficult for a low-computing and resource-poor mobile device to handle many such SPs at a time, and to identify and securely communicate with only genuine ones. Our paper establishes a convincing trust model through which secure job delegation is accomplished. Secure Job delegation and cost effective cryptographic techniques largely help in reducing the burden on the mobile device to securely communicate with trusted SPs. Our protocol also provides users privacy protection, replay protection, entity authentication, and message authentication, integrity, and confidentiality. This paper explains our protocol by suggesting one of the LBSs namely“Secure Automated Taxi Calling Service”.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.119-124
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• 2022

Successful underwater localization requires a large-scale, parallel computing environment that can be mounted on various underwater robots. Accordingly, we propose a design method for an artificial intelligence computing platform for underwater localization. The proposed platform consists of a total of four hardware modules. Transponder and hydrophone modules transmit and receive sound waves, and the FPGA module rapidly pre-processes the transmitted and received sound wave signals in parallel. Jetson module processes artificial intelligence based algorithms. We performed a sound wave transmission/reception experiment for underwater localization according to distance in an actual underwater environment. As a result, the designed platform was verified.