• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.680-699
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• 2015

The use of web service has been increased rapidly, with an increase in the number of available services, finding the exact service is the challenging task. Service discovery is the most significant job to complete the service discoverers needs. In order to achieve the efficient service discovery, we focus on designing a cluster based service discovery model for service registering and service provisioning among all mobile nodes in a mobile ad hoc network (MANETs). A dynamic backbone of nodes (i.e. cluster heads) that forms a service repository to which MANET nodes can publish their services and/or send their service queries. The designed model is based on storing services with their service description on cluster head nodes that are found in accordance with the proposed cluster head election model. In addition to identifying and analyzing the system parameters for finding the effectiveness of our model, this paper studies the stability analysis of the network, overhead of the cluster, and bandwidth utilization and network traffic is evaluated using analytic derivations and experimental evaluation has been done.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.109-113
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• 2007

Second language learners' variable degree of production difficulty according to the cluster type has previously been accounted for in terms of sonority distance between adjacent segments. As an alternative to this previous model, I propose a Phonetically Based Consonant Cluster Acquisition Model (PCCAM) in which consonant cluster markedness is defined based on the articulatory and perceptual factors associated with each consonant sequence. The validity of PCCAM has been tested through Korean speakers' production of English consonant clusters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4991-4996
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• 2010

Clustering method in wireless sensor network is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. Even though cluster group model is based on clustering, it differs from previous method that reducing the total energy consumption by separating energy overload to cluster group head and cluster head. In this thesis, I calculate the optimal cluster group number and cluster number in this kind of cluster group model according to threshold of energy consumption model. By using that I can minimize the total energy consumption in sensor network and maximize the network lifetime. I also show that proposed cluster group model is better than previous clustering method at the point of network energy efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.35-41
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• 2017

This paper presents the development of a monitoring and control system for a vehicle cluster using a model-based design technique. For MBD(model-based design), MATLAB GUI(Graphic User Interface), M programs, simulink, state flow, and tool boxes are used to monitor a number of data such as warning, interrupts, and etc. connected to a real vehicle cluster. As a monitoring tool, a PC(Personal Computer) station interworks with the real vehicle cluster through the interface commands of tool boxes. Thus, unlike existing text-based designs, the MBD based vehicle cluster system provides very easy algorithm updates and addition, since it offers a number of blocks and state flow programs for each functional actions. Furthermore, the proposed MBD technique reduces the required time and cost for the development and modification of a vehicle cluster, because of verification and validation of the cluster algorithm on the monitor through a PC.

• Journal of Information Technology Applications and Management
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• v.26 no.1
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• pp.53-64
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• 2019

The purpose of this study is to find out the fast delivery route that several drones return a truck again after departing from it for delivery locations at each cluster while the truck goes through the cluster composed of several delivery locations. The main issue is to reduce the total delivery time composed of the delivery time by relatively slow trucks via clusters and the sum of maximum delivery times by relatively fast drones in each cluster. To solve this problem, we use a three-step heuristic approach. First, we cluster the nearby delivery locations with minimal number of clusters satisfying a constraint of drone flight distance to set delivery paths for drones in each cluster. Second, we set an optimal delivery route for a truck through centers of the clusters using the TSP model. Finally, we find out the moved centers of clusters while maintaining the delivery paths for the truck and drones and satisfying the constraint of drone flight. distance in the two-dimensional region to reduce the total delivery time. In order to analyze the effect of this study model according to the change of the number of delivery locations, we developed a R-based simulation prototype and compared the relative efficiency, and performed paired t-test between TSP model and the cluster-based models. This study showed its excellence through this experimentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2302-2324
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• 2014

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.447-456
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• 2012

For a sensitive survey in which the population is comprised of several clusters with a quantitative attribute, we present an additive quantitative randomized response model by cluster sampling that adapts a two-stage cluster sampling instead of a simple random sample based on Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's one. We also derive optimum values for the number of 1st stage clusters and the optimum values of observation units in a 2nd stage cluster under the condition of minimizing the variance given constant cost. We can see that Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of cluster sampling.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Journal of the Economic Geographical Society of Korea
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• v.20 no.2
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• pp.189-213
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• 2017

This study seeks to critically examine the significance and limits of the cluster adaptive cycle model for analysis of cluster evolution and to propose research issues for future analysis of cluster evolution based on this critical examination. Until the 1980s, research on industrial complexes including clusters was based on a 'static perspective' that focuses on the aspect of economic space at a specific point in time, but the research paradigm has recently shifted to a 'dynamic perspective' focusing on 'evolution' of 'complex adaptive systems'. As a result, the adaptive cycle model has attracted attention as an analysis tool of dynamically evolving clusters. However, the cluster adaptive cycle model has emerged by being appropriately modified and expanded according to the properties of the cluster and its evolution. The cluster adaptive cycle model is a comprehensive analysis framework that identifies the characteristics of cluster evolution in terms of resource accumulation, interdependence, and resilience and classifies cluster evolution paths into six different categories. Nevertheless, there is still a need for further discussion and supplementation in terms of theoretical and empirical research to expand and deepen the model. Therefore, research issues for future analysis of cluster evolution are to specify and elaborate the cluster evolution model, to emphasize the concept of resilience, and to verify the applicability and usefulness of the model through empirical research.