• Journal of Convergence Society for SMB
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• v.4 no.1
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• pp.7-15
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• 2014

In this paper, we propose an efficient routing protocol to achieve an optimal route searching process of the network lifetime by balancing power consumption per node. The proposed protocols aim at finding energy-efficient paths at low protocol power. In our protocol, each intermediate node keeps power level and branch number of child nodes and it transmits the data the nearest neighbor node. Our protocol may minimize the energy consumption at each node, thus prolong the lifetime of the system regardless of the location of the sink outside or inside the cluster. In the proposed protocol for inter-cluster communication, a cluster head chooses a relay node from its adjacent cluster heads according to the node's residual energy and its distance to the base station. Simulation results show that proposed protocol successfully balances the energy consumption over the network, and achieves a remarkable network lifetime improvement as highly as 7.5%.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.61-69
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• 2009

Most existing clustering protocols have been aimed to provide balancing the residual energy of each node and maximizing life-time of wireless sensor networks. In this paper, we present the threshold based cluster head replacement strategy for clustering protocols in wireless sensor networks. This protocol minimizes the number of cluster head selection by preventing the cluster head replacement up to the threshold of residual energy. Reducing the amount of head selection and replacement cost, the life-time of the entire networks can be extended compared with the existing clustering protocols. Our simulation results show that our protocol outperformed than LEACH in terms of balancing energy consumption and network life-time.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.206-216
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• 2010

Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. A spatial clustering may reduce energy consumption of data collection by partitioning the WSN into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to a base station (BS). The BS may predict the missed data of non-samplers using the spatial correlations between sensor nodes. ASAP is a representative data collection algorithm using the spatial clustering. It periodically reconstructs the entire network into new clusters to accommodate to the change of spatial correlations, which results in high message overhead. In this paper, we propose a new data collection algorithm, name EPDC (Energy-efficient Periodic Data Collection). Unlike ASAP, EPDC identifies a specific cluster consisting of many dissimilar sensor nodes. Then it reconstructs only the cluster into subclusters each of which includes strongly correlated sensor nodes. EPDC also tries to reduce the message overhead by incorporating a judicious probabilistic model transfer method. We evaluate the performance of EPDC and ASAP using a simulation model. The experiment results show that the performance improvement of EPDC is up to 84% compared to ASAP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.6
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• pp.49-54
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• 2009

Sensor nodes in wireless sensor networks operate in distributed environments with limited resources and sensing capabilities. Especially, a sensor node has a small energy. After the sensor nodes are distributed in some area, it is not accessible to the area. AIso, a battery of sensor node cannot change. One of the hot issues in wireless sensor networks maximizes the network lifetime through minimizing the energy dissipation of sensor nodes. In LEACH, the cluster head is elected based on a kind of probability method without considering remaining energy of sensor node. In this paper, we propose a cluster formation scheme that the network elect the node, which has higher energy level than average energy level of overall sensor network, as cluster head node. We show the superiority of our scheme through computer simulation.

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.779-786
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• 2018

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3683-3703
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• 2018

Wireless sensor networks (WSNs) consist of set of sensor nodes. These sensor nodes are deployed in unattended area which are able to sense, process and transmit data to the base station (BS). One of the primary issues of WSN is energy efficiency. In many existing clustering approaches, initial centroids of cluster heads (CHs) are chosen randomly and they form unbalanced clusters, results more energy consumption. In this paper, an energy efficient clustering protocol to prevent unbalanced clusters based on firefly and midpoint algorithms called EEC-FM has been proposed, where midpoint algorithm is used for initial centroid of CHs selection and firefly is used for cluster formation. Using residual energy and Euclidean distance as the parameters for appropriate cluster formation of the proposed approach produces balanced clusters to eventually balance the load of CHs and improve the network lifetime. Simulation result shows that the proposed method outperforms LEACH-B, BPK-means, Park's approach, Mk-means, and EECPK-means with respect to balancing of clusters, energy efficiency and network lifetime parameters. Simulation result also demonstrate that the proposed approach, EEC-FM protocol is 45% better than LEACH-B, 17.8% better than BPK-means protocol, 12.5% better than Park's approach, 9.1% better than Mk-means, and 5.8% better than EECPK-means protocol with respect to the parameter half energy consumption (HEC).

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.13-20
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• 2005

An ad hoc wireless networks forms temporary network without the aid of fixed networks or centralized administration with a collection of wireless mobile hosts. In this case, it is necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its destination. This paper presents an efficient cluster-based routing protocol scheme for ad hoc networks. The cluster is used for path setup and data delivery. Our cluster-based routing algorithm is designed for the improvement of the load balance. Our simulation results show the improved performance for low mobility networks comparing with the previous works.

• The KIPS Transactions:PartC
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• v.13C no.4 s.107
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• pp.481-490
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• 2006

In a super-peer based P2P network, The network is clustered and each cluster is managed by a special peer, called a super-peer which has information of all peers in its cluster. This clustered P2P model is known to have efficient information search and less traffic load. In this paper, we first estimate the message traffic cost caused by peer's query, join and update actions within a cluster as well as between the clusters and with these values, we present the optimal number of super-peers that minimizes the traffic cost for the various size of super-peer based P2P networks.rks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2469-2490
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• 2019

A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).