• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.665-683
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• 2011

Wireless sensor networks (WSN) consist of a large amount of sensor nodes distributed in a certain region. Due to the limited battery power of a sensor node, lots of energy-efficient schemes have been studied. Clustering is primarily used for energy efficiency purpose. However, clustering in WSNs faces several unattained issues, such as ensuring connectivity and scheduling inter-cluster transmissions. In this paper, we propose a location-based spiral clustering (LBSC) algorithm for improving connectivity and avoiding inter-cluster collisions. It also provides reliable location aware routing paths from all cluster heads to a sink node during cluster formation. Proposed algorithm can simultaneously make clusters in four spiral directions from the center of sensor field by using the location information and residual energy level of neighbor sensor nodes. Three logical addresses are used for categorizing the clusters into four global groups and scheduling the intra- and inter-cluster transmission time for each cluster. We evaluated the performance with simulations and compared it with other algorithms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.99-106
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• 2017

In the WSN environment, the sensor node is selected as the cluster header and consumes a lot of energy. Therefore, we proposed a method to automatically select a cluster algorithm using the sensor field environment that can improve the reliability of the whole network by applying an energy efficient clustering algorithm based on already deployed sensor field. Experimental results show that FDN is extended about 3 times by using the proposed algorithm. In addition, the network energy is extended by up to 30% compared to the conventional method, thereby improving the reliability of the sensor network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3950-3957
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• 2010

Wireless sensor network is composed of sensor node with limited sources, and to maintain and repair is vexatious once made up. Accordingly it is important matter to maximize the network lifetime by minimizing the energy consumption in wireless sensor network, and utilizing the limited sources efficiently. In this paper, I propose a technique arranging the cluster number with efficiency in clustering method to optimize the energy consumption. The energy usage needed for wireless transmission varies in distance(threshold). This technique reduces the energy consumption considering the threshold when arranging the cluster number. I verify that the clustering method organized through the valid processes outperform the LEACH(Low-Energy Adaptive Clustering Hierarchy) in total energy consumption.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1082-1092
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• 2008

In wireless sensor networks, clustering protocol such as LEACH is an efficient method to increase whole networks lifetime. However, this protocol result in high energy consumption at the cluster head node. Hence, this protocol must changes the cluster formation and cluster head node in each round to prolong the network lifetime. But this method also causes a high amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new cluster formation algorithm. In this algorithm, we define a intra cluster as the sensor nodes within close proximity of each other. In a intra cluster, a node senses and transmits data at a time on the round-robin basis. In a view of whole network, intra cluster is treated as one node. During the setup phase of a round, intra clusters are formed first and then they are re-clustered(network cluster) by choosing cluster-heads(intra clusters). In the intra cluster with a cluster-head, every member node plays the role of cluster-head on the round-robin basis. Hence, we can lengthen periodic round by a factor of intra cluster size. Also, in the steady-state phase, a node in each intra cluster senses and transmits data to its cluster-head of network cluster on the round-robin basis. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1056-1059
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• 2003

Self-Oranizing Map(SOM) is an unsupervised neural network providing cluster analysis of high dimensional input data. The output from the SOM is represented in map that help us to explore data. The weak point of conventional SOM is when the map is large, it take a long time to train the data. The computing time is known to be O(MN) for trainning to find the winning node (M,N are the number of nodes in width and height of the map). This paper presents a new method to reduce the computing time by creating new map. Each node in a new map is the centroid of nodes' group that are in the original map. After create a new map, we find the winning node of this map, then find the winning node in original map only in nodes that are represented by the winning node from the new map. This new method is called "High Speed Self-Oranizing Map"(HS-SOM). Our experiment use HS-SOM to cluster documents and compare with SOM. The results from the experiment shows that HS-SOM can reduce computing time by 30%-50% over conventional SOM.

• Korean Management Science Review
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• v.26 no.3
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1201-1206
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• 2007

In this paper, we propose an algorithm that improve network lifetime by adjusting cluster size according to location information of sensor node in wireless sensor network (WSN) using clustering algorithm. The collected sensing information by sensor nodes in each cluster are transferred to sink node using inter-cluster communications method. Cluster head (CH) that located nearby sink node spend much more energy than those of far from sink node, because nearer CH forwards more data, so network lifetime has a tendency to decrease. Proposed algorithm minimizes energy consumption in adjacent cluster to sink node by decreasing cluster size, and improve CH lifetime by distributing transmission paths. As a result of mathematical analysis, the proposed algorithm shows longer network lifetime in WSN.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.75-84
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• 2014

In this paper, we constructed a sensor network environment where various sensors are used. Then, we evaluated the performance when this environment adopted existing clustering algorithms that are designed for only single type sensors network. In our experiments, we considered two different types of the networks. In the first, all nodes are equipped with identical sensors. In the second, all nodes are equipped with three different types of sensors. We measured performance variations of several clustering schemes in accordance with sensor data accuracy, sensor node resource depletion timing, amount of available energy, node isolation ratio, and network lifetime. According to our performance analysis, we proved that existing clustering algorithms are partially inefficient to maintain the various-sensor network. Consequently we suggest that a new algorithm is required to take aim at the various sensor network.