• Journal of information and communication convergence engineering
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• v.4 no.2
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• pp.67-70
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• 2006

A distributed healthcare monitoring system prototype for clinical and trauma patients was developed, using wireless sensor network node. The proposed system aimed to measure various vital physiological health parameters like ECG and body temperature of patients and elderly persons, and transfer his/her health status wirelessly in Ad-hoc network to remote base station which was connected to doctor's PDA/PC or to a hospital's main Server using wireless sensor node. The system also aims to save the cost of healthcare facility for patients and the operating power of the system because sensor network is deployed widely and the distance from sensor to base station was shorter than in general centralized system. The wireless data communication will follow IEEE 802.15.4 frequency communication with ad-hoc routing thus enabling every motes attached to patients, to form a wireless data network to send data to base-station, providing mobility and convenience to the users in home environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.473-493
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• 2019

5G vehicular communication is one of key enablers in next generation intelligent transportation system (ITS), that require ultra-reliable and low latency communication (URLLC). To meet this requirement, a new hybrid vehicular network structure which supports both centralized network structure and distributed structure is proposed in this paper. Based on the proposed network structure, a new vehicular network utility model considering the latency and reliability in vehicular networks is developed based on Euclidean norm theory. Building on the Pareto improvement theory in economics, a vehicular network uplink optimization algorithm is proposed to optimize the uplink utility of vehicles on the roads. Simulation results show that the proposed scheme can significantly improve the uplink vehicular network utility in vehicular networks to meet the URLLC requirements.

• International Journal of Knowledge Content Development & Technology
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• v.8 no.4
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• pp.75-98
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• 2018

In this study, the network analysis of researchers, institutions, and research principal agent was conducted to understand structure characteristics of international cooperation research project implemented from 1997 to 2018. The network of researchers and institutions were decentralized structure. On the other hands, the network of research principal agent was centralized structure. The Soul National University is the leading organization of international cooperation research project. In terms of research principal agent, corporation is the leading principal agent. In additions, the results of the network centroid analysis of the researchers and institutions were correlated with the research funds. As a result, it was confirmed that the network centroid of research organization was linearly related to research funds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2895-2921
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• 2018

Security has become one of the major concerns in mobile adhoc networks (MANETs). Data and voice communication amongst roaming battlefield entities (such as platoon of soldiers, inter-battlefield tanks and military aircrafts) served by MANETs throw several challenges. It requires complex securing strategy to address threats such as unauthorized network access, man in the middle attacks, denial of service etc., to provide highly reliable communication amongst the nodes. Intrusion Detection and Prevention System (IDPS) undoubtedly is a crucial ingredient to address these threats. IDPS in MANET is managed by Command Control Communication and Intelligence (C3I) system. It consists of networked computers in the tactical battle area that facilitates comprehensive situation awareness by the commanders for timely and optimum decision-making. Key issue in such IDPS mechanism is lack of Smart Learning Engine. We propose a novel behavioral based "Smart Multi-Instance Multi-Label Intrusion Detection and Prevention System (MIML-IDPS)" that follows a distributed and centralized architecture to support a Robust C3I System. This protocol is deployed in a virtually clustered non-uniform network topology with dynamic election of several virtual head nodes acting as a client Intrusion Detection agent connected to a centralized server IDPS located at Command and Control Center. Distributed virtual client nodes serve as the intelligent decision processing unit and centralized IDPS server act as a Smart MIML decision making unit. Simulation and experimental analysis shows the proposed protocol exhibits computational intelligence with counter attacks, efficient memory utilization, classification accuracy and decision convergence in securing C3I System in a Tactical Battlefield environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.97-105
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• 2015

Recently, due to an explosive growth of the internet traffic, the limitations of a current framework for a mobility management have been focused. The current centralized mobility management is prone to several problems and limitations: suboptimal routing, low scalability, signaling overhead, and a single point of failure. To overcome these problems and limitations, IETF is working about the distributed mobility management scheme that the centralized mobility functions of HA(Home Agents) are distributed to networks edges such as access routers. These distributions of mobility functions overcome the limitations of the centralized mobility managements and go with the trend of flat networks e.g. more simple network architecture. This paper analyzes the distributed mobility management based on Proxy Mobile IPv6 and demonstrates the performance superiority.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• The KIPS Transactions:PartC
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• v.14C no.6
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• pp.525-536
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• 2007

Network Control Platform(NCP) and Qualify of Service Switch(QSS) are being developed to realize centralized control and management technology, which is essential for guaranteeing traffic engineering and service quality in a next generation network. This paper adopts a parallel mechanism, and a thread and object pool to achieve high-speed connection management in the existing SNMP interface between NCP and QSS. We built up a connection management test-bed in laboratory environment to validate the functionality of high-speed connection management. We also measured and analyzed a performance of connection setup delay and a completion ratio using the test-bed. We ascertain that the parallel mechanism and the object pool are the most important performance parameters to achieve high-speed connection management in the SNMP interface between NCP and QSS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.666-669
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• 2008

A number of intrusion detection systems have been developed to detect intrusive activity on individual hosts and networks. The systems developed rely almost exclusively on a software approach to intrusion detection analysis and response. In addition, the network systems developed apply a centralized approach to the detection of intrusive activity. The problems introduced by this approach are twofold. First the centralization of these functions becomes untenable as the size of the network increases.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.494-497
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• 2008

In Mobile ad hoc networks (MANET), nodes could transmit packets for each other to communicate beyond their transmission range without centralized control. Unlike infrastructure-based wireless networks, due to the unique characteristics of MANETs such as open network architecture, stringent resource constraints and highly dynamic network topology, networks are vulnerable to wormhole attacks launched through colluding nodes. In this paper, we develop an wormhole detection and prevention algorithm for MANET.