• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.875-880
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• 2005

One of the most important issues in the sensor network with resource-constrained sensor nodes is prolonging the network lifetime by efficiently utilizing the given energy of nodes. The most representative mechanism to achieve a long-lived network is the clustering mechanism. In this paper, we propose a new dynamic-size multi-hop clustering mechanism in which the burden of a node acting as a cluster head(CH) is balanced regardless of the density of nodes in a sensor network by adjusting the size of a cluster based on the information about the communication load and the residual energy of the node and its neighboring nodes. We show that our proposed scheme outperforms other single-hop or fixed-size multi-hop clustering mechanisms by carrying out simulations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6081-6088
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• 2012

An unequal clustering method in wireless sensor networks is the technique that forms the cluster of different size. This method decreases whole energy consumption by solving the hot spot problem. In this paper, I propose a layer-based dynamic unequal clustering using the unequal clustering model. This method decreases whole energy consumption and maintain that equally using optimal cluster's number and cluster head position. I also show that proposed method is better than previous clustering method at the point of network lifetime.

• The KIPS Transactions:PartC
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• v.14C no.6
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• pp.519-524
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• 2007

One of the most important issues on the sensor network with resource limited sensor nodes is prolonging the network lifetime by effectively utilizing the limited node energy. The most representative mechanism to achieve a long lived sensor network is the clustering mechanism which can be further classified into the single hop mode and the multi hop mode. The single hop mode requires that all sensor nodes in a cluster communicate directly with the cluster head(CH) via single hop md, in the multi hop mode, sensor nodes communicate with the CH with the help of other Intermediate nodes. One of the most critical factors that impact on the performance of the existing multi hop clustering mechanism is the cluster size and, without the assumption on the uniform node distribution, finding out the best cluster size is intractable. Since sensor nodes in a real sensor network are distributed non uniformly, the fixed size mechanism may not work best for real sensor networks. Therefore, in this paper, we propose a new dynamic size multi hop clustering mechanism in which the cluster size is determined according to the distance from the sink to relieve the traffic passing through the CHs near the sink. We show that our proposed scheme outperforms the existing fixed size clustering mechanisms by carrying out numerical analysis and simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1728-1742
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• 2023

In this paper, we extended a hierarchical clustering technique, which is the most researched in the sensor network field, and studied a dynamic differential clustering technique to minimize energy consumption and ensure equal lifespan of all sensor nodes while considering the mobility of sinks. In a sensor network environment with mobile sinks, clusters close to the sinks tend to consume more forwarding energy. Therefore, clustering that considers forwarding energy consumption is desired. Since all clusters form a hierarchical tree, the number of levels of the tree must be considered based on the size of the cluster so that the cluster size is not growing abnormally, and the energy consumption is not concentrated within specific clusters. To verify that the proposed DDC protocol satisfies these requirements, a simulation using Matlab was performed. The FND (First Node Dead), LND (Last Node Dead), and residual energy characteristics of the proposed DDC protocol were compared with the popular clustering protocols such as LEACH and EEUC. As a result, it was shown that FND appears the latest and the point at which the dead node count increases is delayed in the DDC protocol. The proposed DDC protocol presents 66.3% improvement in FND and 13.8% improvement in LND compared to LEACH protocol. Furthermore, FND improved 79.9%, but LND declined 33.2% when compared to the EEUC. This verifies that the proposed DDC protocol can last for longer time with more number of surviving nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4228-4247
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• 2018

Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.

• Journal of Digital Contents Society
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• v.9 no.1
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• pp.87-99
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• 2008

High Altitude Platform network systems utilize Unmanned Aerial Vehicle as routers for ground node communication. For this purpose, geographical clustering of ground nodes must be required. In this paper, we assume mobile ground nodes over wide area and the clusters composed of ground nodes are identified. UAVs can be positioned at the point of centroid of clusters. The number of UAVs are derived from the area size and the number of ground nodes deployed in that area. From the simulation and application of clustering algorithms, we showed visual clustering results with dynamic variance of number of ground nodes.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.22-30
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• 2007

Most real word systems such as world economy, stock market, and medical applications, contain a series of dynamic and complex phenomena. One of common methods to understand these systems is to build a model and analyze the behavior of the system. In this paper, we investigated methods for best clustering over time series data. As a first step for clustering, BIC (Bayesian Information Criterion) approximation is used to determine the number of clusters. A search technique to improve clustering efficiency is also suggested by analyzing the relationship between data size and BIC values. For clustering, two methods, model-based and similarity based methods, are analyzed and compared. A number of experiments have been performed to check its validity using real data(stock price). BIC approximation measure has been confirmed that it suggests best number of clusters through experiments provided that the number of data is relatively large. It is also confirmed that the model-based clustering produces more reliable clustering than similarity based ones.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3E
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• pp.21-25
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• 1999

This paper proposes methods that replace a missing triphone with a new one selected or created by existing triphones, and compares the results. The recognition system uses DMS (Dynamic Multisection) model for acoustic modeling. DMS is one of the statistical recognition techniques proper to a small - or mid - size vocabulary system, while HMM (Hidden Markov Model) is a probabilistic technique suitable for a middle or large system. Accordingly, it is reasonable to use an effective algorithm that is proper to DMS, rather than using a complicated method like a polyphone clustering technique employed in HMM-based systems. In this paper, four methods of filling missing triphones are presented. The result shows that a proposed replacing algorithm works almost as well as if all the necessary triphones existed. The experiments are performed on the 500＋ word DMS speech recognizer.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.115-122
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• 2011

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.