• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Convergence Security Journal
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• v.8 no.1
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• pp.91-100
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• 2008

The previous works in sensor networks security have focused on the aspect of confidentiality, authentication and integrity based on cryptographic primitives. There has been no prior work to assess the survivability in systematic way. Accordingly, this paper presents a survivability model of wireless sensor networks using software rejuvenation for dual adaptive cluster head. The survivability model has state transition to reflect status of real wireless sensor networks. In this paper, we only focus on a survivability model which is capable of describing cluster head compromise in the networks and able to switch over the redundant cluster head in order to increase the survivability of that cluster. Second, this paper presents how to enhance the survivability of sensor networks using software rejuvenation methodology for dual cluster head in wireless sensor network. We model and analyze each cluster as a stochastic process based on Semi Markov Process (SMP) and Discrete Time Markov Chain (DTMC). The proof of example scenarios and numerical analysis shows the feasibility of our approach.

• Proceedings of the IEEK Conference
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• summer
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• pp.330-333
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• 2004

Microsensor nodes is energy limited in sensor networks. If nodes had been stop in working, sensor network can't acquire sensing data in that area as well as routing path though the sensor can't be available. So, it's important to maximize the life of network in sensor network. In this paper, we look at communication protocol, which is modified by LEACH(Low-Energy Adaptive Clustering Hierarchy). We extend LEACH's stochastic cluster-head selection algorithm by a Position-based Selection (PB-Leach). This method is that the sink divides the topology into several areas and cluster head is only one in an area. PB-Leach can prevent that the variance of the number of Cluster-Head is large and Cluster-Heads are concentrated in specific area. Simulation results show that PB-Leach performs better than leach by about 100 to $250\%.$

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.359-373
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• 2019

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

• 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 the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.77-86
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• 2008

Traditional cluster-based routing method is a representative method for increasing the energy efficiencies. In these cluster-based routing methods, the selected cluster head collect/aggregate the information and send the aggregated information to the base station. But they have to solve the unnecessary energy dissipation of frequent information exchange between the cluster head and whole member nodes in cluster. In this paper, we minimize the frequency of the information exchange for reducing the unnecessary transmit/receive frequencies as calculate the overlapped area or number of overlapped member nodes between the selected cluster head and previous cluster head in the setup phase. And besides, we consider the direction of super cluster head for optimal cluster formation. So, we propose the modified cluster selection scheme that optimizes the energy dissipation in the setup phase and reuses the saved energy in the steady phase efficiently that prolongs the whole wireless sensor network lifetime by uniformly selecting the cluster head.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.998-1007
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• 2018

The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.1-7
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• 2019

In this paper, we implemented a Wi-Fi based cluster system using raspberry pi for multidisciplinary education. The cluster implementation on the desktop was more difficult to maintain the complexity, big size, high price, power consumption as the number of nodes increased. In this paper, we implemented a cluster using Raspberry Pi, which is developed for educational purposes, to reduce the cost of connecting nodes. In addition, the complexity of system construction is reduced by replacing the connection between each node with Wi-Fi. Also, the inconvenience of configuration due to node increase was reduced. It is expected that the implementation of the cluster will be a good alternative in the educational environment where distributed processing and parallel processing are performed in the embedded environment. Also, it is confirmed that it can be applied to the multidisciplinary education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.2042-2060
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• 2013

In wireless sensor networks (WSNs), energy efficiency is one of the most essential design considerations, since sensor nodes are resource constrained. Group communication can reduce WSNs communication overhead by sending a message to multiple nodes in one packet. In this paper, in order to simultaneously resolve the transmission security and scalability in WSNs group communications, we propose a hierarchical cluster-based secure and scalable group key management scheme, called HRKT, based on logic key tree and route key tree structure. The HRKT scheme divides the group key into cluster head key and cluster key. The cluster head generates a route key tree according to the route topology of the cluster. This hierarchical key structure facilitates local secure communications taking advantage of the fact that the nodes at a contiguous place usually communicate with each other more frequently. In HRKT scheme, the key updates are confined in a cluster, so the cost of the key updates is reduced efficiently, especially in the case of massive membership changes. The security analysis shows that the HRKT scheme meets the requirements of group communication. In addition, performance simulation results also demonstrate its efficiency in terms of low storage and flexibility when membership changes massively.