• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4268-4290
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• 2015

Future mobile communications face enormous challenges as traditional voice services are replaced with increasing mobile multimedia and data services. To address the vast data traffic volume and the requirement of user Quality of Experience (QoE) in the next generation mobile networks, it is imperative to develop efficient content distribution technique, aiming at significantly reducing redundant data transmissions and improving content delivery performance. On the other hand, in recent years cloud computing as a promising new content-centric paradigm is exploited to fulfil the multimedia requirements by provisioning data and computing resources on demand. In this paper, we propose a cooperative caching framework which implements State based Content Distribution (SCD) algorithm for future mobile networks. In our proposed framework, cloud service providers deploy a plurality of cloudlets in the network forming a Distributed Cloud Service Network (DCSN), and pre-allocate content services in local cloudlets to avoid redundant content transmissions. We use content popularity and content state which is determined by content requests, editorial updates and new arrivals to formulate a content distribution optimization model. Data contents are deployed in local cloudlets according to the optimal solution to achieve the lowest average content delivery latency. We use simulation experiments to validate the effectiveness of our proposed framework. Numerical results show that the proposed framework can significantly improve content cache hit rate, reduce content delivery latency and outbound traffic volume in comparison with known existing caching strategies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.91-98
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• 2020

The mobile agent, which handles a given task while migrating between the sensor nodes, moves including the execution commands and task processing results. This increases the size of the mobile agent, causing the network to load, leading to the migration time delay and the loss of migration reliability. This paper presents the method of lightening the mobile agent using distributed object technology and the algorithm for exploring and providing the optimal migration path that is actively performed in the event of network traffic, and it proposes a method to ensure the reliability of the mobile agent migration by applying them. In addition, through the comparative analysis experiments based on agent size and network traffic for the migration time of mobile agent equipped with active rules in sensor network-based mobile agent middleware environment, applying the proposed methods proves to ensure the autonomy and migration reliability of the mobile agent.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.395-406
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• 2015

This research carried out an analysis on input cost and leakage reduction effect by leakage reduction method, focusing on the project for establishing an optimal water pipe network management system in the Taebaek region, which has been executed annually since 2009. Based on the result, optimal cost-benefit analysis models for water distribution network rehabilitation project were developed using DEA(data envelopment analysis) and multiple regression analysis, which have been widely utilized for efficiency analysis in public and other projects. DEA and multiple regression analysis were carried out by applying 4 analytical methods involving different ratios and costs. The result showed that the models involving the analytical methods 2 and 4 were of low significance (which therefore were excluded), and only the models involving the analytical methods 1 and 3 were suitable. From the result it was judged that the leakage management method to be executed with the highest priority for the improvement of revenue water ratio was installation of pressure reduction valve, followed by replacement of water distribution pipe, replacement of water supply pipe, and then leakage detection and repair; and that the execution of leakage management methods in this order would be most economical. In addition, replacement of water meter was also shown to be necessary in case there were a large number of defective water meters.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.305-312
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• 2010

The aim of this study is to find an analytic solution to the problem of determining the optimal capacity of a batch-storage network to meet demand for finished products in a system undergoing joint random variations of operating time and batch material loss. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to joint random variations in the cycle time and batch size. The production processes have also joint random variations in cycle time and product quantity. The spoiled materials are treated through regeneration or waste disposal processes. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced. The proposed method has the potential to rapidly provide very useful data on which to base investment decisions during the early plant design stage. It should be of particular use when these decisions must be made in a highly uncertain business environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.77-83
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• 2013

Nowadays, the limitation of Lighting control and management skills is the excessive cost of equipments, the operational difficulties and wasting energy. To solve this problem is in need of communication and management S/W that is worked out complexly well as a information system of smart lighting energy, which is loaded wireless network facility. This paper made a study od the energy saving technology through energy monitoring and we developed LEIS(Lighting Energy Information Sysem) to converge this one. LEIS is monitoring and control lighting energy data that is collelcted from sensors by Zigbee mesh network and shows lighting use information by visualization to users. It is consists of lighting energy information data base based on LEM(Lighting Energy Metering) information and LEIS Web application, provide function scenario to manage energy optimization through LEIS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.83-93
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• 2014

It is a widespread concern that electrical interconnection based network-on-chip (NoC) will ultimately face the limitation in communication bandwidth, transmission latency and power consumption in the near future. With the development of silicon photonics technology, a hybrid optical network-on-chip (HONoC) which embraces both electrical- and optical interconnect, is emerging as a promising solution to overcome these problems. Today's leading edge systems-on-chips (SoCs) comprise heterogeneous many-cores for higher energy efficiency, therefore, extended study beyond regular topology based NoC is required. This paper proposes an energy and latency optimization topology design technique for HONoC taking into account the traffic characteristics of target applications. The proposed technique is implemented with genetic algorithm and simulation results show the reduction by 13.84% in power loss and 28.14% in average latency, respectively.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.183-196
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• 2015

The concept of energy-efficient networking has begun to spread in the past few years, gaining increasing popularity. A common opinion among networking researchers is that the sole introduction of low consumption silicon technologies may not be enough to effectively curb energy requirements. Thus, for disruptively boosting the network energy efficiency, these hardware enhancements must be integrated with ad-hoc mechanisms that explicitly manage energy saving, by exploiting network-specific features. The IEEE 802.3az Energy Efficient Ethernet (EEE) standard is one of such efforts. EEE introduces a low power mode for the most common Ethernet physical layer standards and is expected to provide large energy savings. However, it has been shown that EEE may not achieve good energy efficiency because mode transition overheads can be significant, leading to almost full energy consumption even at low utilization levels. Coalescing techniques such as packet coalescing and interrupt coalescing were proposed to improve energy efficiency of EEE, but their implementations typically adopt a simple policy that employs a few fixed values for coalescing parameters, thus it is difficult to achieve optimal energy efficiency. The paper proposes adaptive interrupt coalescing (AIC) that adopts an optimal policy that could not only improve energy efficiency but support performance. AIC has been implemented at the sender side with the Intel 82579 network interface card (NIC) and e1000e Linux device driver. The experiments were performed at 100 M bps transfer rate and show that energy efficiency of AIC is improved in most cases despite performance consideration and in the best case can be improved up to 37% compared to that of conventional interrupt coalescing techniques.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.178-202
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• 2012

In this paper, we first survey cross-layer architectures for Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs). Afterwards, we propose a novel cross-layer architecture for QoS provisioning in clustered and multi-hop based WMSNs. The proposed architecture provides support for multiple network-based applications on a single sensor node. For supporting multiple applications on a single node, an area in memory is reserved where each application can store its network protocols settings. Furthermore, the proposed cross-layer architecture supports heterogeneous flows by classifying WMSN traffic into six traffic classes. The architecture incorporates a service differentiation module for QoS provisioning in WMSNs. The service differentiation module defines the forwarding behavior corresponding to each traffic class. The forwarding behavior is primarily determined by the priority of the traffic class, moreover the service differentiation module allocates bandwidth to each traffic class with goals to maximize network utilization and avoid starvation of low priority flows. The proposal incorporates the congestion detection and control algorithm. Upon detection of congestion, the congested node makes an estimate of the data rate that should be used by the node itself and its one-hop away upstream nodes. While estimating the data rate, the congested node considers the characteristics of different traffic classes along with their total bandwidth usage. The architecture uses a shared database to enable cross-layer interactions. Application's network protocol settings and the interaction with the shared database is done through a cross-layer optimization middleware.