• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.109-118
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• 2023

Energy awareness is an essential design flaw in wireless sensor network. Clustering is the most highly regarded energy-efficient technique that offers various benefits such as energy efficiency and network lifetime. Clusters create hierarchical WSNs that introduce the efficient use of limited sensor node resources and thus enhance the life of the network. The goal of this paper is to provide an analysis of the various energy efficient clustering algorithms. Analysis is based on the energy efficiency and network lifetime. This review paper provides an analysis of different energy-efficient clustering algorithms for WSNs.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.53-75
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• 2004

First, we present two classes of sequential algorithms for minimum flow problem: decreasing path algorithms and preflow algorithms. Then we describe another approach of the minimum flow problem, that consists of applying any maximum flow algorithm in a modified network. In section 5 we present several parallel preflow algorithms that solve the minimum flow problem. Finally, we present an application of the minimum flow problem.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.369-381
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• 1991

In this paper, design algorithms for SMP(Single Mated Pair) and MMP (Multipli Mated Pair) structure of CCS (Common Channel Signaling) network are proposed through the study of the structure of CCS network. High reliability and fast messagy transfer time are the most important requirements for the CCS network. Based on it, three parameters such as monotraffic, reliability (maximum isolated SP(Signalling Point) number when any two STP(Signalling Transfer Points) fail and total network cost are defined. And the proposed algorithms different from preexisted algorithm that minimizes total network cost, maximize monotraffic with two constraints, reliability and total network cost. Comparing the experimental results of the proposed algorithms with those of the preexisted algorithm that minimizes total network cost, shows that the proposed algorithms produce a more reliable topology that has more monotraffic and a little higher total network cost. Additionaly, with the results of the proposed algorithms, SMP and MMP structures are compared.

• Journal of Communications and Networks
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• v.10 no.4
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• pp.396-402
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• 2008

The use of network coding in wireless networks has been proposed in the literature for energy efficient broadcast. However, the decoding complexity of existing algorithms is too high for low-complexity devices. In this work we formalize the all-to-all information exchange problem and shows how to optimize the transmission scheme in terms of energy efficiency. Furthermore, we prove by construction that there exists O(1) -complexity network coding algorithms for grid networks which can achieve such optimality. We also present low-complexity heuristics for random. topology networks. Simulation results show that network coding algorithms outperforms forwarding algorithms in most cases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.625-628
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• 1998

This paper presents an application of genetic algorithms(GAs) for optimal configuration of distribution network. Three problems have been used to show how genetic algorithms are modified and applied. Solutions to the problems are found by minimizing the cost function which is directly related with balancing the loads. Simulation results show that genetic algorithms are technically feasible if they are tailored to meet the needs of real problems.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.10
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• pp.947-955
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• 2012

In this paper, we propose iterative algorithms to obtain the transmit and the receive vectors for interference alignment in cellular network. Although the conventional interference alignment algorithms for interference channels can be applied to cellular network, the number of iterations required to achieve a high sum rate is very large. The key idea in the proposed algorithms is to ignore intra-cell interference in updating the transmit vector for uplink and the receive vector for downlink. Numerical results show that the proposed algorithms achieve higher sum rates than the conventional algorithms for given iteration numbers when multiple antennas and a single carrier are used for interference alignment. It is also shown that the proposed algorithms outperform the conventional algorithms when a single antenna and multiple subcarriers are used for interference alignment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.626-648
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• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• ETRI Journal
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• v.27 no.1
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• pp.110-113
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• 2005

The resilient packet ring (RPR) is a data optimized ring network, where one of the key issues is on load balancing for competing streams of elastic traffic. This paper suggests three efficient traffic loading algorithms on the RPR. For the algorithms, we evaluate their efficiency via analysis or simulation.