• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3151-3168
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• 2021

Internet of Things (IoT) connects several objects with embedded sensors and they are capable of exchanging information between devices to create a smart environment. IoT smart devices have limited resources, such as batteries, computing power, and bandwidth, but comprehensive sensing causes severe energy restrictions, lowering data quality. The main objective of the proposal is to build a hybrid protocol which provides high data quality and reduced energy consumption in IoT sensor network. The hybrid protocol gives a flexible and complete solution for sensor selection problem. It selects a subset of active sensor nodes in the network which will increase the data quality and optimize the energy consumption. Since the unused sensor nodes switch off during the sensing phase, the energy consumption is greatly reduced. The hybrid protocol uses Dijkstra's algorithm for determining the shortest path for sensing data and Ant colony inspired variable path selection algorithm for selecting active nodes in the network. The missing data due to inactive sensor nodes is reconstructed using enhanced belief propagation algorithm. The proposed hybrid method is evaluated using real sensor data and the demonstrated results show significant improvement in energy consumption, data utility and data reconstruction rate compared to other existing methods.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.37-50
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• 2014

The purpose of this research is to look upon the smartphone market from the perspective of business ecosystems and to extract the critical success factors of OS platforms. Furthermore, this research aims to verify the effect of those factors on increasing utility resulting from the rising number of users as well as on intention of use. In order to do this, OS compatibility and OS upgradability were presented as the major characteristics of OS platforms and a logical causal relationship between network effect and intention to use which shows the increase of utility according to the number of users was established which was then followed by an empirical analysis. The results of the research showed that OS compatibility and OS upgradability both had positive effects on network effect and intention to use. By presenting the characteristics of OS platforms, a subject which has lacked pervious empirical studies, and establishing a logical causal relationship for the role platform characteristics play in the formation of business ecosystem in the smartphone market, it is expected that the findings of this research will contribute greatly not only academically but also in practical applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1749-1757
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• 2010

This paper considers the cost minimization problem to satisfy QoS (Quality of Service) requirements for a given network, in particular when communication resources to each link can be additionally assigned. For the purpose of quantifying QoS requirements such as data transfer delay and packet loss, we introduce the cost function considering both the link utilization factor and the additionally assigned resource. To minimize this cost function, we firstly formulate a Basic Capacity Planning (BCP) problem, a special case of Network Utility Maximization (NUM). We show that the solution of this BCP problem cannot be optimal via a counter example. In this paper, we suggest the cross-layer design of both additionally assigned resource and routing path, whose initial values are set to the result of BCP problem. This cross-layer design is based on a heuristic approach which presents an effective way to plan how much communication resources should be added to support the QoS requirements in future. By simulation study, we investigate the convergence of the cost function in a more general network topology as well as in a given simple topology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.61-68
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• 2018

As the smartphone and mobile environment develop, the time and space constraints for individual work performance are disappearing. Companies can reduce costs and expand their business quickly through cloud computing. As the use of various cloud expands, the boundaries of users, data, and applications are disappearing. Traditional security approaches based on boundaries (Perimeter) are losing their utility in the cloud environment. This paper describes the limitations of existing network access control (NAC) in a cloud environment and suggests network security technology that complements it. The study explains the SDP and combines SDP(Software Defined Perimeter) to overcome the limitations of NAC, while at the same time explaining its role as a new framework for supporting the cloud environment. The new framework proposed in this paper suggests a software-based network security solution that supports physical and software parts, providing identity-based access control, encrypted segment management, and dynamic policy management, not IP-based.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.145-170
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• 2008

The existence of network is indeed the single most important factor that brings about new business phenomena in the new digital economy, especially in the IT industry. Network effect refers to a phenomenon that the increase in size of the network leads to increased network value and user utility. It determines the competitive structure of an industry and the performances of industry competitors (Shapiro and Varian, 1999). The phenomenon of increasing returns and winner-take-all enjoyed by the early winner in the competition can be attributed to the existence of positive feedback which increases the value of network and induces more users into join the network (Arthur, 1996; Shapiro and Varian, 1999; Song and Lee, 2003). This research attempts to shed light on the topic of innovation and competitive strategy of network-based industries. We analyze the case of the Korean mobile communications industry, in which a shift in technological paradigm from 2G to 3G brought new changes to the competitive structure of the industry. The Korean mobile communications industry makes an ideal case for analysis since it is an industry whose value is inherently dependent upon its user network. It is characterized by the typical increasing returns, in which a monopolizing player is enjoying firstmover's network effects. Because of the existence of network in the mobile communications industry, latecomers' disruptive innovations could not outcompete the incumbent's sustaining innovations. The contribution of this research lies in laying a groundwork for future studies by introducing a numerical simulation model to analyze the complexity theory and network effect.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.261-273
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• 2008

Multimedia service whose use is rapidly increasing supports effective services to convert and transmit multimedia data based on network speed, noise circumstance, terminal computation, and type of contents for satisfying QoS. For supporting information protection of multimedia service, it offers middle level of singular security service or security mechanism which is based on policy of service provider, depending on present terminal computation and type of contents. It can support security mechanism for more effective multimedia service, if we study security of application layer and network layer for supporting multimedia service. In this paper, we propose Multimedia security framework reflected on quantitative analysis of the WLAN(Wireless Local Area Network) mesh network security using the utility function in the level of the sorority, violation and addictive compensation model.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.6
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• pp.1379-1397
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• 2013

We consider a system where a licensed radio spectrum is shared by multiple primary users(PUs) and secondary users(SUs). As the spectrum of interest is licensed to primary network, power and channel allocation must be carried out within the cognitive radio network so that no excessive interference is caused to PUs. For this system, we study the joint beamforming and power allocation problem via game theory in this paper. The problem is formulated as a non-cooperative beamforming and power allocation game, subject to the interference constraints of PUs as well as the peak transmission power constraints of SUs. We design a joint beamforming and power allocation algorithm for maximizing the total throughput of SUs, which is implemented by alternating iteration of minimum mean square error based decision feedback beamforming and a best response based iterative power allocation algorithm. Simulation results show that the algorithm has better performance than an existing algorithm and can converge to a locally optimal sum utility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.