• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10B
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• pp.845-854
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• 2008

In this paper, we propose a clustering algorithm for efficient energy management of sensor network consisted of sensor nodes that have restricted energy to solve these problem. Proposed algorithm improves energy efficiency by controlling sensing power. And it has distinctive feature that is applied in various network environment. The performance evaluation result shows that the energy efficiency is improved by 5% in the case of all sensor node fixed and by $10{\sim}15%$ in the case of all sensor node moving. It is confirmed through experiment process that the proposed algorithm brings energy efficiency ratio improvement of $5{\sim}15%$ more than the existing algorithm. Proposed algorithm derived an upper bound on the energy efficiency for Ubiquitous Computing environment that have various network environment that is with ZigBee technology of IEEE 802.15.4 bases. Also, we can blow bring elevation for lifetime of sensor network greatly for lifetime of sensor node as is small. And we think that may expand practical use extent of a sensor network technology more in fast changed network environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.731-738
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• 2011

In this paper, we introduce an advanced architecture of K-Means clustering-based polynomial Radial Basis Function Neural Networks (p-RBFNNs) designed with the aid of SSOA (Space Search Optimization Algorithm) and develop a comprehensive design methodology supporting their construction. In order to design the optimized p-RBFNNs, a center value of each receptive field is determined by running the K-Means clustering algorithm and then the center value and the width of the corresponding receptive field are optimized through SSOA. The connections (weights) of the proposed p-RBFNNs are of functional character and are realized by considering three types of polynomials. In addition, a WLSE (Weighted Least Square Estimation) is used to estimate the coefficients of polynomials (serving as functional connections of the network) of each node from output node. Therefore, a local learning capability and an interpretability of the proposed model are improved. The proposed model is illustrated with the use of nonlinear function, NOx called Machine Learning dataset. A comparative analysis reveals that the proposed model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Convergence Security Journal
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• v.14 no.2
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• pp.43-50
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• 2014

Recently, lot of researches for the multi-level protocol have been done to balance the sensor node energy consumption of WSN and improve the node efficiency to extend the life of the entire network. Especially in multi-hop protocol, a variety of models have been proposed to improve energy efficiency and apply it to WSN protocol. In this paper, we analyze LEACH algorithm and propose new method based on center of local clustering routing algorithm in wireless sensor networks. We also perform NS-2 simulation to show the performance of our model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.514-537
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• 2020

Wireless sensor network (WSN) is a distributed network composed of many sensory nodes. It is precisely due to the clustering unevenness and cluster head election randomness that the energy consumption of WSN is excessive. Therefore, a many-objective optimization WSN energy balance model is proposed for the first time in the clustering stage of LEACH protocol. The four objective is considered that the cluster distance, the sink node distance, the overall energy consumption of the network and the network energy consumption balance to select the cluster head, which to better balance the energy consumption of the WSN network and extend the network lifetime. A many-objective optimization algorithm to optimize the model (LEACH-ABF) is designed, which combines adaptive balanced function strategy with penalty-based boundary selection intersection strategy to optimize the clustering method of LEACH. The experimental results show that LEACH-ABF can balance network energy consumption effectively and extend the network lifetime when compared with other algorithms.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1661-1669
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• 2010

In this paper, I propose an Energy efficient Clustering based on Genetic Algorithm(ECGA) which reduces energy consumption by distributing energy overload to cluster group head and cluster head in order to lengthen the lifetime of sensor network. ECGA algorithm calculates the values like estimated energy cost summary, average and standard deviation of residual quantity of sensor node and applies them to fitness function. By using the fitness function, we can obtain the optimum condition of cluster group and cluster. I demonstrated that ECGA algorithm reduces the energy consumption and lengthens the lifetime of network compared with the previous clustering method by stimulation.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.640-648
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• 2017

Sensors have limited resources in sensor networks, so efficient use of energy is important. In order to use the energy of the sensor node efficiently, researches applying mobile sink to the sensor network have been actively carried out. The sink node moves the sensor network, collects data from each sensor node, which spread the energy concentrated around the sink node, thereby extending the entire life cycle of the network. But, when the sink node moves, it requires a reset of the data transmission path, which causes a lot of control messages and delays. In this paper, we propose a CMS(Cluster-based Mobile Sink) method to support the movement of mobile sink in a cluster sensor environment. The proposed scheme minimizes an amount of control messages without resetting the routing paths of entire sensor networks by supporting the sink mobility path using the neighbor cluster list. And, it simplifies the routing path setup process by setting a single hop path between clusters without a gateway. The experiment results show that the proposed scheme has superior energy efficiency in processing and network structure, compared with existing clustering and mesh routing protocols.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.29-37
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• 2008

Clustering allows hierarchical structures to be built on the nodes and enables more efficient use of scarce resources, such as frequency spectrum, bandwidth, and energy in wireless sensor networks (WSNs). This paper proposes a hierarchical clustering algorithm called EEHC which is more energy efficient than existing algorithms for WSNs, It introduces region node selection as well as cluster head election based on the residual battery capacity of nodes to reduce the costs of managing sensor nodes and of the communication among them. The role of cluster heads or region nodes is rotated among nodes to achieve load balancing and extend the lifetime of every individual sensor node. To do this, EEHC clusters periodically to select cluster heads that are richer in residual energy level, compared to the other nodes, according to clustering policies from administrators. To prove the performance improvement of EEHC, the ns-2 simulator was used. The results show that it can reduce the energy and bandwidth consumption for organizing and managing WSNs comparing it with existing algorithms.

• The KIPS Transactions:PartC
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• v.10C no.3
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• pp.377-386
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• 2003

This study suggested a technique to maintain an efficient core-based multicast tree using weighted clustering factors in mobile Ad-hoc networks. The biggest problem with the core-based multicast tree routing is to decide the position of core node. The distance of data transmission varies depending on the position of core node. The overhead's effect on the entire network is great according to the recomposition of the multicast tree due to the movement of core node, clustering is used. A core node from cluster head nodes on the multicast tree within core area whose weighted factor is the least is chosen as the head core node. Way that compose multicast tree by weighted clustering factors thus and propose keeping could know that transmission distance and control overhead according to position andmobility of core node improve than existent multicast way, and when select core node, mobility is less, and is near in center of network multicast tree could verification by simulation stabilizing that transmission distance is short.

• 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.