• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.225-232
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• 2001

In this paper, we describe a mechanism that supports the mobility service for VPN(Virtual Private Network) users on MPLS(Multiprotocol Label Switching) network. The MPLS VPN considered in this study is controlled by CE(Customer Edge) routers. In such a VPN, CE routers have additional functions to support mobile VPN users, i.e., Home Agent function, foreign Agent function, Correspondent Agent function. This mechanism is applied when a VPN node moves to other site of the saute VPN, or when it moves to other site of a different VPN, or to a non-VPN site. We perform a simulation study to compare the performance of CE based MPLS VPN with that of PE(Provider Edge) based MPLS VPN with mobility support.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.10a
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• pp.68-72
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• 2000

In order to estimate spatial charging process in the corona charging which has been used to make polymer electret, the electrical properties of polypropylene film were obtained from Thermally Stimulated Current (TSC) measurements after corona charging between knife electrode and cylinder electrode with the voltages of -5, -6, -7 and -8[kV], respectively. And then the electrostatic contour and the electric field vector were also simulated by using Finite Element Method (FEM). The edge effect around edge of knife electrode affected the electrostatic contour on surface of specimen and the electric field concentration inside specimen. The uneven charging state in the electret due to the mistake on design could be calculated and so the optimal design of corona charging device which is appropriate to various materials is come to be practicable.

• Journal of Advanced Navigation Technology
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• v.16 no.1
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• pp.96-102
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• 2012

The goal of mobile internet system is to provide a high-data-rate, low-latency and optimized packet radio access technology supporting flexible bandwidth deployments. Therefore, network architecture is designed with the goal to support packet-switched traffic with seamless mobility, quality of service and minimal latency. An important requirement for the mobile internet system is improved cell-edge BER performance and data throughput. This is to provide some level of service consistency in terms of geographical coverage as well as in terms of available data throughput within the communication coverage area. In a cellular system, however, the signal to interference plus noise power ratio gap between cell-center and cell-edge users can be of the order of 20 [dB]. The disparity can be even higher in a communication coverage limited cellular system. This leads to vastly lower data throughputs for the cell-edge users relative to cell-center users creating a large QoS gap. This paper proposes a analytical approach that tries to reduce inter-cell interference, and shows the SIR and BER performance according to the OFDM system parameters in mobile Internet environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.2
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• pp.102-113
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• 2014

Present communication and network environment have been moving rapidly toward wireless and mobile base from existing wired internet base. This change of trend influences greatly on business methods accordingly. Therefore many enterprises are trying hard to adopt mobile business in order to gain competitive edge of their products and they are in need of more effective and stable mobile solutions. However, the method of establishing optimal mobile computing environment and how to handle existing business process and use vast amount of database are still needed. Therefore this paper tries to realize a mobile middleware system as a mobile business establishment supporting tool that could link various computational resource on wired internet with wireless LAN, mobile phone network, and mobile devices. To accomplish that specific goal, this paper provides a powerful tool of mobile and wireless application data access that could expand the line of business already set up in general enterprises easily and rapidly into mobile environment. When this suggested solution is applied in the field of industry, it can economically change legacy business process into mobile environment without having to change existing logic and resources at all.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.794-815
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• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• Smart Structures and Systems
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• v.29 no.1
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• pp.141-151
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• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.113-119
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• 2019

The key features of 5G mobile communications recently commercialized can be represented by High Data Rate, Connection Density and Low Latency, of which the features most distinct from the existing 4G will be low Latency, which will be the foundation for various new service offerings. AR and self-driving technologies are being considered as services that utilize these features, and 5G Network Latency is also being discussed in related standards. However, it is true that the discussion of E2E Latency from a service perspective is much lacking. The final goal to achieve low Latency at 5G is to achieve 1ms of air interface based on RTD, which can be done through Ultra-reliable Low Latency Communications (URLLC) through Rel-16 in early 20 years, and further network parity through Mobile Edge Computing (MEC) is also being studied. In addition to 5G network-related factors, the overall 5G E2E Latency also includes link/equipment Latency on the path between the 5G network and the IDC server for service delivery, and the Processing Latency for service processing within the mobile app and server. Meanwhile, it is also necessary to study detailed service requirements by separating Latency for initial setup of service and Latency for continuous service. In this paper, the following three factors were reviewed for initial setup of service. First, the experiment and analysis presented the impact on Latency on the Latency in the case of 1 Data Lake Setup, 2 CRDX On/Off for efficient power, and finally 3H/O on Latency. Through this, we expect Low Latency to contribute to the service requirements and planning associated with Latency in the initial setup of the required services.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.92-98
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• 2019

In this paper, we propose a deep learning system based on morphological neural network(MNN). The deep learning layers are morphological operation layer, pooling layer, ReLU layer, and the fully connected layer. The operations used in morphological layer are erosion, dilation, and edge detection, etc. Unlike CNN, the number of hidden layers and kernels applied to each layer is limited in MNN. Because of the reduction of processing time and utility of VLSI chip design, it is possible to apply MNN to various mobile embedded systems. MNN performs the edge and shape detection operations with a limited number of kernels. Through experiments using database images, it is confirmed that MNN can be used as a deep learning system and its performance.