• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.253-259
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• 2008

Because a sensor node in wireless sensor networks is battery operated and energy constrained, reducing energy consumption of each node is one of important issues. The clustering technique can make network topology be hierarchical and reduce energy consumption of each sensor node. In this paper, we propose an efficient clustering mechanism considering overlap avoidance in wireless sensor networks. The proposed method consists of three parts. The first is to elect cluster heads considering each node's energy. Then clusters are formed by using signal strength in the second phase. Finally we can reduce the cluster overlap problem derived from two or more clusters. In addition, this paper includes performance evaluation of our algorithm. Simulation results show that network lifetime was extended up to 75 percents than LEACH and overlapped clusters are decreased down to nearly zero percents.

• Journal of KIISE:Information Networking
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• v.35 no.2
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• pp.139-148
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• 2008

Many types of sensor data exhibit strong correlation in both space and time. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not correlation of sensor data. In this paper, we propose a novel clustering algorithm with suppression techniques. To guarantee independent communication among clusters, we allocate multiple channels based on sensor data. Also, we propose a spatio-temporal suppression technique to reduce the network traffic. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the site of data which have been collected in the base-station. As a result, our experimental results show that the size of data was reduced by $4{\sim}40%$, and whole network lifetime was prolonged by $20{\sim}30%$.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.925-928
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• 2010

Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption. It can improve the scalability and lifetime of wireless sensor network. In this paper, we introduce a clustering protocol with mode selection (CPMS) for wireless sensor networks. Our scheme improves the performance of BCDCP (Base Station Controlled Dynamic Clustering Protocol) and BIDRP (Base Station Initiated Dynamic Routing Protocol) routing protocol. In CPMS, the base station constructs clusters and makes the head node with highest residual energy send data to base station. Furthermore, we can save the energy of head nodes using modes selection method. The simulation results show that CPMS achieves longer lifetime and more data messages transmissions than current important clustering protocol in wireless sensor networks.

• 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.

• 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 the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2370-2375
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• 2014

Wireless sensor networks (WSNs) have a big potential for distributed sensing for large geographical area. The improvement of the lifetime of WSNs is the important research topic because it is considered to be difficult to change batteries of sensor nodes. Clustering communication protocols are energy-efficient because each sensor node can send its packet to the cluster head near from itself rather than the sink far from itself. In this paper, we present an energy-efficient clustering communication protocol, which chooses cluster heads based on the expected residual lifetime of each sensor node. Simulation results show that our proposed scheme increases average lifetimes of sensor nodes as much as 20% to 30% in terms of the traffic quantity and as much as 30% to 40% in terms of the scalability compared to the existing clustering communication protocol, LEACH.

• Smart Media Journal
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• v.6 no.4
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• pp.24-31
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• 2017

In wireless sensor networks, sensor nodes are deployed in a remote, harsh environment. When the power of the sensor node is consumed in such a network, the sensor nodes become useless together with the deterioration of the quality and performance of the sensor network which may save human life. Although many clustering protocols have been proposed to improve the energy consumption and extend the life of the sensor network, most of the previous studies have shown that the overhead of the cluster head is quite large. It is important to design a routing protocol that minimizes the energy consumption of each node and maximizes the network lifetime because of the power limitations of the sensor nodes and the overhead of the cluster heads. Therefore, in this paper, we propose an efficient clustering scheme that reduces the burden of cluster heads, minimizes energy consumption, and uses algorithms that maximize network lifetime. Simulation results show that the proposed clustering scheme improves the energy balance and prolongs the network life when compared with similar techniques.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3675-3681
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• 2009

One of the most important factors in the lifetime of WSN(Wireless Sensor Network) is the limited resources and static control problem of the sensor nodes. In order to achieve energy efficiency and network utilities, sensor nodes can be well organized into one cluster and selected head node and normal node by dynamic conditions. Various clustering algorithms have been proposed as an efficient way to organize method based on LEACH algorithm. In this paper, we propose an efficient clustering algorithm using information entropy theory based on LEACH algorithm, which is able to recognize environmental differences according to changes from data of sensor nodes. To measure and analyze the changes of clusters, we simply compute the entropy of sensor data and applied it to probability based clustering algorithm. In experiments, we simulate the proposed method and LEACH algorithm. We have shown that our data balanced and energy efficient scheme, has high energy efficiency and network lifetime in two conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.465-468
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• 2008

Clustering techniques in wireless sensor networks is developed to minimize the energy consumption of node, show the effect that increases the network lifetime. Existing clustering techniques proposed the method that increases the network lifetime equalizing each node's the energy consumption by rotating the role of CH(Cluster Head), but these algorithm did not present the resolution that minimizes the energy consumption of neighboring nodes with sink. In this paper, we propose the clustering algorithm that prolongs the network lifetime by not including a part of nodes in POS(Personal Operating Space) of the sink in a cluster and communicating with sink directly to reduce the energy consumption of CH closed to sink.

• 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.