• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.929-935
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• 2017

All sensor nodes generally determine their positions using anchor nodes that are located in underwater sensor networks. This paper proposes a Tabu search algorithm to determine the minimum number of anchor nodes for the location of all sensor nodes in underwater sensor networks. As the number of the sensor nodes increases in the network, the amount of calculation that determines the number of anchor nodes would be too much increased. In this paper, we propose a Tabu search algorithm that determines the minimum number of anchor nodes within a reasonable computation time in a high dense network, and propose an efficient neighborhood generating operation of the Tabu search algorithm for efficient search. The proposed algorithm evaluates those performances through some experiments in terms of the minimum number of anchor nodes and execution time. The proposed algorithm shows 5-10% better performance than the conventional algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.493-502
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• 2010

In this paper we propose a scalable sensor network system that makes mesh network among the sink nodes to solve the scalability problem of existing sensor network which is caused by multi-hop networking between the sensor nodes. In the proposed system, the sink nodes have the wireless networking ability to communicate with another sink nodes in mesh fashion, and with the monitoring nodes which is located in the local area or internet area. Especially, the system includes L4(Application Layer) routing mechanism that provides subscribe/publish service to serve selective transmission of sensor data to the specific monitoring nodes. The collected sensor data is transmitted to the monitoring nodes when the sensor data is matched with the monitoring node's interesting value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1549-1555
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• 2009

The Relay node placement problem is one of the most important requirements for many wireless sensor networks because the lifetime of sensor networks is closely related with the placement of relay nodes which receive sensed data from sensor nodes and forward them to the base station. Relay node placement problem has focused at minimization of dissipated total energy of the sensor nodes in whole networks. However, minimum total energy causes the unbalance of consumed energy in sensor nodes due to different distances between relay nodes and sensor nodes. This paper proposes the concept of energy balance ratio and finds the locations of relay nodes using objective functions which maximize the energy balance ratio. Maximizing this ratio results in maximizing the network lifetime by minimizing the energy consumption of large-scale sensor networks. However, finding a solution to relay node placement problem is NP-hard and it is very difficult to get exact solutions. Therefore, we get approximate solutions to EBR-RNP problem which considers both energy balance ratio and relay node placement using constraint programming.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.59-64
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• 2017

A wireless sensor network is a network in which nodes equipped with sensors capable of collecting data from the real world are configured wirelessly. Because the sensor nodes are configured wirelessly, they have limited power such as batteries. If the battery of the sensor node is exhausted, the node is no longer usable. If more than a certain number of nodes die, the network will not function. There are many wireless sensor network protocols to improve energy efficiency, among which LEACH Protocol is a typical example. The LEACH protocol is a cluster-based protocol that divides sensor space into clusters and transmits and receives data between nodes. Therefore, depending on how the cluster is structured, the shape of the energy cow may decrease or increase. We compare the network lifetimes of the existing LEACH protocols and the three types of protocols that have been improved using fuzzy methods for cluster selection.

• 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.12 no.5
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• pp.728-736
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• 2009

We propose a method using the genetic algorithm to solve the maximum set cover problem. It is needed for scheduling the power of sensor nodes in extending the lifetime of the wireless sensor network with variable sensing range. The existing Greedy Heuristic method calculates the power scheduling of sensor nodes repeatedly in the process of operation, and so the communication traffic of sensor nodes is increased. The proposed method reduces the amount of communication traffic of sensor nodes, and so the energies of nodes are saved, and the lifetime of network can be extended. The effectiveness of this method was verified through computer simulation, and considering the energy losses of communication operations about 10% in the network lifetime is improved.

• Journal of IKEEE
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• v.11 no.3
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• pp.100-107
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• 2007

One of the fundamental problems in wireless sensor networks is the efficient deployment of sensor nodes. The Fuzzy C-Means(FCM) clustering algorithm is proposed to determine the optimum location and minimum number of sensor nodes for the specific application space. We performed a simulation and a experiment using two rectangular and one L shape area. We found the minimum number of sensor nodes for the complete coverage of modeled area, and discovered the optimum location of each nodes. The real deploy experiment using sensor nodes shows the 94.6%, 92.2% and 95.7% error free communication rate respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1322-1329
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• 2010

As body sensor network (BSN) research becomes mature, the need for managing power consumption of sensor nodes has become evident since most of the applications are designed for continuous monitoring. Real time Electrocardiograph (ECG) analysis on sensor nodes is proposed as an optimal choice for saving power consumption by reducing data transmission overhead. Smart sensor nodes with the ability to categorize lately detected ECG cycles communicate with base station only when ECG cycles are classified as abnormal. In this paper, ECG classification algorithms are described, which categorize detected ECG cycles as normal or abnormal, or even more specific cardiac diseases. Our Euclidean distance (ED) based classification method is validated to be most power efficient and very accurate in determining normal or abnormal ECG cycles. A close comparison of power efficiency and classification accuracy between our ED classification algorithm and generalized linear model (GLM) based classification algorithm is provided. Through experiments we show that, CPU cycle power consumption of ED based classification algorithm can be reduced by 31.21% and overall power consumption can be reduced by 13.63% at most when compared with GLM based method. The accuracy of detecting NSR, APC, PVC, SVT, VT, and VF using GLM based method range from 55% to 99% meanwhile, we show that the accuracy of detecting normal and abnormal ECG cycles using our ED based method is higher than 86%.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.615-620
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• 2005

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, there will often be some faulty nodes within the network. As nodes in some certain regions rely on each other to route the information gathered by different sensors to a base station (sink), the network should be able to detect a non-operational node in order to determine new paths for routing the information. Failure detection, which identifies the faulty nodes, is rather necessary in sensor networks and a very important research issue. The detection of a non-operational node can be performed using Consensus Algorithm with the purpose of achieving agreement about a node which is supposed to be faulty (non-operational). In this work, we discuss the application of a Consensus Algorithm to sensor node called 'mote'. Our experimental results show that it works efficiently for identifying faulty nodes in sensor networks.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.27-34
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• 2013

In wireless sensor networks, sensors need to communicate with each other to send their sensing data to the administration node and so they are susceptible to many attacks like garbage packet injection that cannot be prevented by using traditional cryptographic approaches. A behavior-based detection is used to defend against such attacks in which some specialized monitoring nodes overhear the communications of their neighbors to detect bad packets. As monitoring nodes use more energy, it is desirable to use the minimal number of monitoring nodes to cover the whole or maximal part of the network. The monitoring nodes can either be selected among the deployed normal nodes or differ in type from normal nodes. In this study, we have developed an algorithm for selecting the predefined number of monitoring nodes needed to cover the maximum number of normal nodes when the different types of normal nodes and monitoring nodes are deployed. We also have investigated experimentally how the number of monitoring nodes and their transmission range affect the connection ratio of the monitoring nodes and the coverage of the normal nodes.