• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7B
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• pp.488-501
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• 2008

Convergence of various wireless systems can be cost effectively achieved through enhancement of existing technology. The emergence of Wireless Mesh Network (WMN) entails the interoperability and interconnection of various wireless technologies in one single system. Furthermore, WMN can be implemented with multi-radio and multi-channel enhancement. A multi-radio, multi-channel wireless mesh network could greatly improve certain networking performance metrics. In this research, two approaches namely, clustering and topology control mechanisms are integrated with multi-radio multi-channel wireless mesh network. A Clustering and Topology Control Algorithm (CTCA)is presented that would prolong network lifetime of the client nodes and maintain connectivity of the routers.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.909-922
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• 2000

High sped messaging layer for application's feeling of low level network performance is needed by Clustering System based on high speed network fabrics. It should have the mechanism to directly pass messages between network card and application space, and provide flexible affodabilities for many diverse applications. In this paper, CROWN (Clustering Resources On Workstations' Network) which is designed and implemented for multi-purpose clustering system will be introduced briefly, and CLCP(CROWN Lean Communication Primitives)which is the high speed messaging layer for CROWN will be followed. CLCP consists of a firmware for controlling Myrinet card, device drier, and user libraries. CLCP supports various application domains as a result of pooling and interrupt receive mechanism. In case of polling based receive, 8 bytes short message, and no other process, CLCP has 262 micro-second response time between two nodes, and IM bytes large message, it shows 442Mbps bandwidth.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.163-177
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• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1136-1140
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• 2022

This paper is a method for efficient traffic prediction in mobile edge computing, where many studies have recently been conducted. For distributed processing in mobile edge computing, tasks offloading from each mobile edge must be processed within the limited computing power of the edge. As a result, in the mobile nodes, it is necessary to efficiently select the surrounding edge server in consideration of performance dynamically. This paper aims to suggest the efficient clustering method by selecting edges in a cloud environment and predicting mobile traffic. Then, our dynamic clustering method is to reduce offloading overload to the edge server when offloading required by mobile terminals affects the performance of the edge server compared with the existing offloading schemes.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.163-176
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• 2015

Self-organization of distributed wireless sensor nodes is a critical issue in wireless sensor networks (WSNs), since each sensor node has limited energy, bandwidth, and scalability. These issues prevent sensor nodes from actively collaborating with the other types of sensor nodes deployed in a typical heterogeneous and somewhat hostile environment. The automated self-organization of a WSN becomes more challenging as the number of sensor nodes increases in the network. In this paper, we propose a dynamic self-organized architecture that combines tree topology with a drawn-grid algorithm to automate the self-organization process for WSNs. In order to make our proposed architecture scalable, we assume that all participating active sensor nodes are unaware of their primary locations. In particular, this paper presents two algorithms called active-tree and drawn-grid. The proposed active-tree algorithm uses a tree topology to assign node IDs and define different roles to each participating sensor node. On the other hand, the drawn-grid algorithm divides the sensor nodes into cells with respect to the radio coverage area and the specific roles assigned by the active-tree algorithm. Thus, both proposed algorithms collaborate with each other to automate the self-organizing process for WSNs. The numerical and simulation results demonstrate that the proposed dynamic architecture performs much better than a static architecture in terms of the self-organization of wireless sensor nodes and energy consumption.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.135-139
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• 2003

Sensor networks consist of sensor nodes with small-size, low-cost, low-power, and multi-functions to sense, to process and to communicate. Minimizing power consumption of sensors is an important issue in sensor networks due to limited power in sensor networks. Clustering is an efficient way to reduce data flow in sensor networks and to maintain less routing information. In this paper, we propose a multi-hop clustering mechanism using global and local ID to reduce transmission power consumption and an efficient routing method for improved data fusion and transmission.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.155-161
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• 2015

Efficient energy consumption is a one of the key issues in wireless sensor networks. Clustering-based routing algorithms have been popular solutions for such an issue. Re-clustering is necessary for avoiding early energy drain of cluster head nodes in such routing strategies. The re-clustering process itself, however, is another source of energy consumption. It is suggested in this work to adaptively set the frequency of re-clustering by comparing the energy levels of cluster heads and a threshold value. The algorithm keeps the clusters if all the cluster heads' energy levels are greater than the threshold value. We confirm through simulations that the suggested algorithm shows better energy efficiency than the existing solutions.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.7
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• pp.235-238
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• 2015

IEEE 802.15.4 is the well-established standard enabling wireless connectivities among wireless sensor nodes. However, the wireless sensor networks based on IEEE 802.15.4 are inherently vulnerable to hidden nodes collision because the wireless sensor nodes have very limited communication range and battery life time. In this paper, we propose the advanced method of mitigating hidden nodes collision in IEEE 802.15.4 base wireless sensor networks by clustering sensor nodes according to channel quality information. Moreover, we deal with the problem of resource allocation for each cluster.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1609-1622
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• 2009

In wireless sensor networks, a clustering protocol is an efficient method to prolong network lifetime. In general, it results in more energy consumption at the cluster-head node. Hence, such a protocol must changes the cluster formation and cluster-head node in each round to prolong the network lifetime. But, this method also causes large 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 clustering algorithm. In this algorithm, we exclude duplicated data of adjacent nodes and transmits the threshold value. We define a group as the sensor nodes within close proximity of each other. In a group, a node senses and transmits data at a time on the round-robin basis. In a view of whole network, group 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(group). In the group 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 group size. 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.