• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.6
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• pp.446-453
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• 2013

Clustering schemes have been adopted as an efficient solution to prolong network lifetime and improve network scalability. In such clustering schemes cluster ratio is represented by the rate of the number of cluster heads and the number of total nodes, and affects the performance of clustering schemes. In this paper, we mathematically analyze an optimal clustering ratio in wireless sensor networks. We consider a multi-hop to one-hop transmission case and aim to provide the optimal cluster ratio to minimize the system hop-count and maximize packet reception ratio between nodes. We examine its performance through a set of simulations. The simulation results show that the proposed optimal cluster ratio effectively reduce transmission count and enhance energy efficiency in wireless sensor networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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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.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.47-55
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• 2016

Stratified cluster sampling has been widely used for effective parameter estimations due to reductions in time and cost. The probability proportional to size (PPS) sampling method is used when the number of cluster element are significantly different. However, simple random sampling (SRS) is commonly used for simplicity if the number of cluster elements are almost the same. Also it is known that the ratio estimator produces a good performance when the total number of population elements is known. However, the two stage cluster estimator should be used if the total number of elements in population is neither known nor accurate. In this study we suggest a composite estimator by combining the ratio estimator and the two stage cluster estimator to obtain a better estimate under a certain population circumstance. Simulation studies are conducted to compare the superiority of the suggested estimator with two other estimators.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.207-224
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• 2000

Generally cluster size is predetermined when we use the stratified two-stage cluster sampling But in case that the sizes of clusters vary greatly one may want to make the sizes to be about equal. In this paper we study the optimal cluster size in stratified twostage cluster sampling. Also we find the optimal primary sampling unit sizes and optimal secondary sampling unit sizes under the given cost restriction.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.29-33
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• 1996

A categorization for cluster is necessary when an unsupervised classfication is used for remote sensing image classification. It is desirable that this method is performed automatically, because manual categorization is a highly time consuming process. In this paper, several automatic determination methods were proposed and evaluated. They are four methods. a) maximum number method : which assigns the tharget cluster to the category which occupies the largest area of that cluster b) maximum percentage method : which assigns the target cluster to the category which shows the maximum percentage within the category in that cluster. c) minmun distance method : which assigns the target cluster to the category having minmum distance with that cluster d) element ratio matching method : which assigns local regions to the category having the most similar element ratio of that region From the results of the experiments, it was certified that the result of minimum distance method was almost the same as the result made by a human operator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.224-242
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• 2010

Data routing in wireless sensor networks must be energy-efficient because tiny sensor nodes have limited power. A cluster-based hierarchical routing is known to be more efficient than a flat routing because only cluster-heads communicate with a sink node. Existing hierarchical routings, however, assume unrealistically large radio transmission ranges for sensor nodes so they cannot be employed in real environments. In this paper, by considering the practical transmission ranges of the sensor nodes, we propose a clustering and routing method for hierarchical sensor networks: First, we provide the optimal ratio of cluster-heads for the clustering. Second, we propose a d-hop clustering scheme. It expands the range of clusters to d-hops calculated by the ratio of cluster-heads. Third, we present an intra-cluster routing in which sensor nodes reach their cluster-heads within d-hops. Finally, an inter-clustering routing is presented to route data from cluster-heads to a sink node using multiple hops because cluster-heads cannot communicate with a sink node directly. The efficiency of the proposed clustering and routing method is validated through extensive simulations.

• Journal of Periodontal and Implant Science
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• v.26 no.3
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• pp.725-734
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• 1996

The present study has been performed to revise the forms of early-onset periodontitis(EOP) into the homogeneous phenotypic subsets by cluster analysis using sets of clinical parameters. Retrospective radiographic interproximal alveolar bone levels were measured from cemento-enamel junctions on patients who have previously been diagnosed as having one of EOP during last 5 years. Mean interproximal bone levels(BL) and mesial bone level(Ratio) of 1st molars relative to mean interproximal bone levels of adjacent teeth(lst and 2nd premolars and canines)were calculated on each patient. Using parameters BL and Ratio(BR group) or BL, Ratio and age(BRA group), cluster analysis was performed to revise EOP patients into homogeneous subsets. At least three or four cluster could be homogeneously formed both in BR or BRA groups with statistically significant differences in parameters used among clusters as evidenced by MANOVA test. It was shown that the greater the BL, the smaller the Ratio was. It was also evident that mean interproximal bone levels were lowest aroud 1st molars and/or incisors regardless of cluster types. The results has provided cluster-based studies for identifying laboratory markers responsible for the development of EOP subsets.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1885-1890
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• 2008

3-D heterogeneous nucleation was simulated by classical molecular dynamics (MD), where the Lennard-Jones (LJ) gas and solid cluster-seed molecules have argon and aluminum properties, respectively. There are three shapes of cluster-seeds, cube, rod, and sphere, and three classes of masses and the simulation took place under nine supersaturation ratios, making a total of 81 calculations. Results show that the dimension of the cluster-seed highly affects the rates of cluster development. In order to analyze heterogeneous nucleation above and below the critical supersaturation ratio, growth rate and liquefaction rate were separately defined to supplement the investigation. Design of experiments (DOE) was used for analysis which displayed that the shape and mass of the cluster-seed are prominent for the growth rate, while the supersaturation ratio is most significant followed by the mass for liquefaction rate. The significance of the supersaturation ratio for overall liquefaction suggests that thermal diffusion is more dominant than mass interactions for this system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4223-4249
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• 2015

A novel jamming detection technique to detect the presence of jamming in the downstream direction for cluster based wireless sensor networks is proposed in this paper. The proposed technique is deployed in base station and in cluster heads. The proposed technique is novel in two aspects: Firstly, whenever a cluster head receives a packet it verifies whether the source node is legitimate node or new node. Secondly if a source node is declared as new node in the first step, then this technique observes the behavior of the new node to find whether the new node is legitimate node or jammed node. In order to monitor the behavior of the existing node and new node, the second step uses two metrics namely packet delivery ratio (PDR) and received signal strength indicator (RSSI). The rationality of using PDR and RSSI is presented by performing statistical test. PDR and RSSI of every member in the cluster is measured and assessed by the cluster head. And finally the cluster head determines whether the members of the cluster are jammed or not. The CH can detect the presence of jamming in the cluster at member level. The base station can detect the presence of jamming in the wireless sensor network at CH level. The simulation result shows that the proposed technique performs extremely well and achieves jamming detection rate as high as 99.85%.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.675-677
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• 2005

This study proposes a fuzzy classification using EM algorithm. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes.