• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.1-11
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• 2018

The Blockchain is a technique that prevents data from being manipulated and guarantees the integrity and reliability of the data by all participants in the network jointly owning and validating the data. Since the Blockchain characterized by security, scalability and transparency, it is used in a variety of fields including logistics, distribution, IoT and healthcare, including remittance. In particular, there is a growing interest in smart contract that can create different forms of contracts and automate implementation based on Blockchain. Smart Contract can be used to pre-programme contracts and are implemented immediately when conditions are met. As a result, digital data can be more reliable. In this paper, we are conducting a study on the smart contract design as a way to solve such problems as illegal misuse of funds on virtual currency, which has become an issue recently. Through this process, we applied the customer identification and money laundering prevention process using smart contract, and then check the possibility of preventing money laundering and propose the ASM (AML SmartContract Mechant) design.

• The Journal of Society for e-Business Studies
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• v.25 no.2
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• pp.1-14
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• 2020

ISO 15118 is an international standard that defines communication between electric vehicles and electric vehicle chargers. Plug & Charge (PnC) was also defined as a technology to automatically authenticate users when using charging services. PnC indicates automatic authentication technology where all processes such as electric vehicle user authentication, charging and billing are automatically processed. According to the standard, certificates for chargers and CPSs (Certificate Provisioning Services) should be under the V2G (Vehicle to Grid) Root certificate. In Korea, the utility company operates its own PKI (Public Key Infrastructure), making it difficult to provide chargers under the V2G Root Certificate. Therefore, a method that can be authenticated is necessary even when you have different Root Certificates. This paper proposes to apply cross-certificate technology to PnC authentication. Automatic authentication of Cross Certification is to issue a cross-certificate of the Root CA and include it in the certificate chain to proceed with automatic authentication, even if you have different Root certificates. Applying cross-certificate technology enables verification of certificates under other Root certificates. In this paper, the PnC automatic authentication and cross certificate automatic authentication is implemented, so as to proceed with proof of concept proving that both methods are available. Define development requirements, certificate profiles, and user authentication sequences, and implement and execute them accordingly. This experiment confirms that two automatic authentication are practicable, especially the scalability of automatic authentication using cross-certificate PnC.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.121-133
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• 2013

Defect-free transfer service on the Next-generation wireless network extensive roaming mobile node (MN) to provide efficient mobility management has become very important. MIPv6(Mobility IPv6) is one of mobility management scheme proposed by IETF(Internet Engineering Task Force), and IPv6-based mobility management techniques have been developed in various forms. One of each management techniques, IPv6-based mobility management techniques for PMIPv6 (MIPv6) system to improve the performance of a variety of F-PMIPv6 (Fast Handover for Proxy MIPv6) is proposed. However, the F-PMIPv6 is cannot be excellent than PMIPv6 in all scenarios. Therefor, to select a proper mobility management scheme between PMIPv6 and F-PMIPv6 becomes an interesting issue, for its potenrials in enhancing the capacity and scalability of the system. In this paper, we develop an analytical model to analyze the applicability of PMIPv6 and F-PMIPv6. Based on this model, we design an Secure Smart Mobility Support(SSM) scheme that selects the better alternative between PMIPv6 and F-PMIPv6 for a user according to its changing mobility and service characteristics. When F-PMIPv6 is adopted, SSM chooses the best mobility anchor point and regional size to optimize the system performance. Numerical results illustrate the impact of some key parameters on the applicability of PMIPv6 and F-PMIPv6. Finally, SSM has proven even better result than PMIPv6 and F-PMIPv6.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.94-100
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• 2016

ESS(Energy Storage System) is an important source that keeps power supply stable and utilizes electricity efficiently. For example, ESS contributes to resolve power supply imbalance, stabilize new renewable energy output and regulate frequency. ESS is predicted to be expanded to 55.9GWh of installed capacity by 2023, which is 30 times more than that of 2014. To raise competitiveness of domestic ESS industry in this increasing world market, we have disseminated load-shift ESS for continuous power supply imbalance with FR ESS, and also necessity to secure domestic track record is required. However in case of FR ESS, utility of installing thermal power plant is generally generated within 5% range of rated capacity, so that scalability of domestic market is low without dramatic increase of thermal power plant. Necessity of load-shift ESS dissemination is also decreasing effected by surplus backup power securement policy, raising demand for new dissemination model. New dissemination model is promising for $CO_2$ reduction effect in spite of intermittent output. By stabilizing new renewable energy output in connection with new renewable energy, and regulating system input timing of new renewable energy generation rate, it is prospected model for 'post-2020' regime and energy industry. This research presents a policy alternatives of REC multiplier calculation method to induce investment after outlining PV-connected ESS charge/discharge mode to reduce GHG emission, This alternative is projected to utilize GHG emission reduction methodology for 'Post-2020' regime, big issue of new energy policy.

• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.447-454
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• 2003

Optical burst switching (OBS) has been proposed to reduce the use of fiber delay lines (FDLs) and to realize the optical switching paradigm of the next-generation ail optical networks. The OBS can provide improvements over wavelength routing in terms of bandwidth efficiency and core network scalability via statistical multiplexing of bursts. Recently, another challenging issue is how to upport quality of service (QoS) in the optical burst switching networks. In this paper, we propose a deflection routing scheme to guarantee the QoS for the OBS networks to detour lower priority burst forward to the deflection routing path when congested. A big advantage of the proposed scheme is the simplicity of QoS provision, that comes from the simple QoS provisioning algorithm. Also, the QoS provisioning scheme be able to make efficient networks by fairly traffic distributing with the reduce of the use of FDLs at core routers. The QoS provisioning scheme has been verified to reliably guarantee the QoS of priority 0, 1, 2 burst and to efficiently utilize network resources by computer simulations using OPNET As results, the end-to-end delay of high priority burst is improved, and the network efficiency is also improved.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.377-391
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• 2019

Recently, system studies using tokens and block chains for authentication, access control, etc in IoT environment have been going on at home and abroad. However, existing token-based systems are not suitable for IoT environments in terms of security, reliability, and scalability because they have centralized characteristics. In addition, the system using the block chain has to overload the IoT device because it has to repeatedly perform the calculation of the hash et to hold the block chain and store all the blocks. In this paper, we intend to manage the access rights through tokens for proper access control in the IoT. In addition, we apply the Tangle to configure the P2P distributed ledger network environment to solve the problem of the centralized structure and to manage the token. The authentication process and the access right grant process are performed to issue a token and share a transaction for issuing the token so that all the nodes can verify the validity of the token. And we intent to reduce the access control process by reducing the repeated authentication process and the access authorization process by reusing the already issued token.

• The Journal of Bigdata
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• v.8 no.2
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• pp.125-131
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• 2023

Globally, drug side effects rank among the top causes of death. To effectively respond to these adverse drug reactions, a shift towards an active real-time monitoring system, along with the standardization and quality improvement of data, is necessary. Integrating individual institutional data and utilizing large-scale data to enhance the accuracy of drug side effect predictions is critical. However, data sharing between institutions poses privacy concerns and involves varying data standards. To address this issue, our research adopts a federated learning approach, where data is not shared directly in compliance with privacy regulations, but rather the results of the model's learning are shared. We employ the Common Data Model (CDM) to standardize different data formats, ensuring accuracy and consistency of data. Additionally, we propose a drug monitoring system that enhances security and scalability management through a cloud-based federated learning environment. This system allows for effective monitoring and prediction of drug side effects while protecting the privacy of data shared between hospitals. The goal is to reduce mortality due to drug side effects and cut medical costs, exploring various technical approaches and methodologies to achieve this.

• Journal of IKEEE
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• v.28 no.3
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• pp.458-464
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• 2024

This study proposes a fault-tolerant caching system to address the issue of caching content imbalance caused by the dynamic departure and participation of cache servers in a heterogeneous cache server network, and validates it in both real and virtual environments. With the increase of large-scale media content requiring various types and resolutions, the necessity of cache servers as key components to reduce response time to user requests and alleviate network load has been growing. In particular, research on heterogeneous cache server networks utilizing edge computing and low-power devices has been actively conducted recently. However, in such environments, the irregular departure and participation of cache servers can occur frequently, leading to content imbalance among the cache servers deployed in the network, which can degrade the performance of the cache server network. The fault-tolerant caching algorithm proposed in this study ensures stable service quality by maintaining balance among media contents even when cache servers depart. Experimental results confirmed that the proposed algorithm effectively maintains content distribution despite the departure of cache servers. Additionally, we built a network composed of seven heterogeneous cache servers to verify the practicality of the proposed caching system and demonstrated its performance and scalability through a large-scale cache server network in a virtual environment.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.