• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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• pp.1618-1637
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• 2014

Clustering wireless sensor network is an efficient way to reduce the energy consumption of individual nodes in a cluster. In clustering, multihop routing techniques increase the load of the Cluster head near the sink. This unbalanced load on the Cluster head increases its energy consumption, thereby Cluster heads die faster and create an energy hole problem. In this paper, we propose an Energy Balancing Cluster Head (EBCH) in wireless sensor network. At First, we balance the intra cluster load among the cluster heads, which results in nonuniform distribution of nodes over an unequal cluster size. The load received by the Cluster head in the cluster distributes their traffic towards direct and multihop transmission based on the load distribution ratio. Also, we balance the energy consumption among the cluster heads to design an optimum load distribution ratio. Simulation result shows that this approach guarantees to increase the network lifetime, thereby balancing cluster head energy.

• ETRI Journal
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• 제35권3호
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• pp.414-424
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• 2013

We consider a new load balancing routing for delay-guaranteed services in the network in which the traffic is dynamic and network topologies frequently change. For such an ever-changing network, we propose a new online load balancing routing called AntLBR, which exploits the ant colony optimization method. Generally, to achieve load balancing, researchers have tried to calculate the traffic split ratio by solving a complicated linear programming (LP) problem under the static network environment. In contrast, the proposed AntLBR does not make any attempt to solve this complicated LP problem. So as to achieve load balancing, AntLBR simply forwards incoming flows by referring to the amount of pheromone trails. Simulation results indicate that the AntLBR algorithm achieves a more load-balanced network under the changing network environment than techniques used in previous research while guaranteeing the requirements of delay-guaranteed services.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1645-1654
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• 2007

In this paper, we study the problem of load balancing routing in clustered-based wireless mesh network in order to enhance the overall network throughput. We first address the problems of cluster allocation in wireless mesh network to achieve load-balancing state. Due to the complexity of the problem, we proposed a simplified algorithm using gradient load-balancing model. This method searches for a localized optimal solution of cluster allocation instead of solving the optimal solution for overall network. To support for load-balancing algorithm and reduce complexity of topology control, we also introduce limited broadcasting between two clusters. This mechanism maintain shortest path between two nodes in adjacent clusters while minimizing the topology broadcasting complexity. The simulation experiments demonstrate that our proposed model achieve performance improvement in terms of network throughput in comparison with other clustering methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• 전기전자학회논문지
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• 제7권2호
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• pp.127-134
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• 2003

라우팅 기능을 가진 이동노드로 구성된 자치 분산 네트워크인 애드 혹 네트워크에서는 여러 개의 경로 서비스 제공으로 인하여 특정 노드에 네트워크 부하가 증가되었을 경우에 대한 방법이 고려되지 않았다. 본 논문에서는 네트워크 트래픽 혼잡을 줄임과 동시에 데이터 전송과정에서의 네트워크 부하를 분산 시킬 수 있는 트래픽 관리 라우팅 프로토콜을 제안하고 평가하였다. NS 네트워크 시물레이터를 통해 제안한 알고리즘을 적용한 결과 네트워크 부하의 감소와 데이터 전송율의 증가를 얻을 수 있었다

• 한국멀티미디어학회논문지
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• 제6권7호
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• pp.1209-1218
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• 2003

다수의 사용자 요구에 의해 비디오 타이틀을 실시간으로 제공해야 하는 VOD 서비스는 클라이언트의 버퍼 안정화와 재생 질 보증을 위해서, 네트워크의 상태에 따라 드로핑 (Dropping)이나 스키핑(Skipping) 알고리즘으로 프레임을 전송하는 메카니즘이 필요하다. 본 논문에서는 네트워크의 부하에 따라 저장 장치에서 생략 인출한 MPEG 프레임 (I, P, B 프레임)을 클라이언트에 전송하는 알고리즘을 제시한다. 또한 시뮬레이션을 통해 네트워크의 부하에 적응적으로 대처하여 네트워크의 부하를 줄이고 클라이언트의 수신 버퍼를 안정화시킬 수 있음을 검증한다.

• 한국전자통신학회논문지
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• 제17권6호
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• pp.1047-1054
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• 2022

네트워크 장비의 하드웨어 영역과 소프트웨어 영역을 분리하고 오픈소스 기반의 소프트웨어를 사용하여 네트워크를 정의하는 차세대 네트워크 기술인 SDN의 등장으로 기존 네트워크 체계가 가지고 있던 복잡성과 확장성의 문제를 해결하고 저비용으로 사용자의 환경과 요구조건에 맞춤형 네트워크 구성이 가능해졌다. 하지만, 컨트롤러와 스위치 간에 발생하는 많은 제어 통신으로 인한 네트워크의 부하가 발생할 수 있다는 구조적 단점을 가지고 있어 이를 효과적으로 해결하기 위한 네트워크 부하분산에 대한 많은 연구가 선행되었다. 특히 플로우 테이블과 관련된 부하분산 기법의 기존 연구에서는 플로우 엔트리에 대한 고려 없이 진행된 연구가 많아서 플로우 수가 많아지게 되면 패킷 처리속도가 떨어져 오히려 부하를 가중시키는 결과를 가져오기도 했는데, 본 논문에서는 이러한 문제점을 해결하기 위해 실시간으로 플로우를 모니터링하고 동적 플로우 관리 기법을 적용하여 플로우 수를 적정 수준으로 조절하면서도 높은 패킷 처리속도를 유지할 수 있는 새로운 네트워크 부하분산 기법을 제안한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• 전기학회논문지
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• 제67권1호
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.