• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2047-2051
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• 2009

In this paper, we propose the localization method on WSN(Wireless Sensor Network) using fuzzy model and Kalman filter. The proposed method is as follows: First, we estimate the distance of RSSI(Receive Signal Strength Index) by using fuzzy model in order to minimize the distance error. Second, we use a triangulation measurement for estimating the localization. And then, we minimize the localization error using a Kalman filter. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1059-1066
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• 2011

This paper makes a close inquiry into ill-conditioning that may be occurred in wireless localization of the sensor nodes based on network signals in the wireless sensor network and provides the clue for solving the problem. In order to estimate the location of a node based on the range information calculated using the signal propagation time, LS (Least Squares) method is usually used. The LS method estimates the solution that makes the squared estimation error minimal. When a nonlinear function is used for the wireless localization, ILS (Iterative Least Squares) method is used. The ILS method process the LS method iteratively after linearizing the nonlinear function at the initial nominal point. This method, however, has a problem that the final solution may converge into a LM (Local Minimum) instead of a GM (Global Minimum) according to the deployment of the fixed nodes and the initial nominal point. The conditions that cause the problem are explained and an adaptive method is presented to solve it, in this paper. It can be expected that the stable location solution can be provided in implementation of the wireless localization methods based on the results of this paper.

• Journal of the Korean Society of Industry Convergence
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• v.10 no.2
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• pp.105-113
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• 2007

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of application. Sensor position is used for its data to be meaningful and for energy efficient data routing algorithm especially geographic routing. The previous works for sensor localization utilize global positioning system(GPS) or estimate unknown-location nodes position with help of some small reference nodes which know their position previously. However, the traditional localization techniques are not well suited in the senor network for the cost of sensors is too high. In this paper, we propose the sensor localization method with a mobile robot, which knows its position, moves through the sensing field along pre-scheduled path and gives position information to the unknown-location nodes through wireless channel to estimate their position. We suggest using the sensor position estimation method and an efficient mobility path model. To validate our method, we carried out a computer simulation, and observed that our technique achieved sensor localization more accurately and efficiently than the conventional one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4971-4987
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• 2019

Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.699-705
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• 2012

In this paper, an infrastructure-based localization method using underwater wireless sensor network (UWSN) is addressed. A localization using the UWSN is necessary to widen the usage of underwater applications, however it is very difficult to establish the UWSN due to the restrictions of water. In this paper, to extend the usage of UWSN at the infrastructure, we propose a sophisticated UWSN localization method using the Received Signal Strength Indicator (RSSI) of the electromagnetic waves. During the electromagnetic waves propagating in underwater, there arises a lot of attenuation according to the distance, while the attenuation shows uniformity according to the distance. Using this characteristics, the localization system in underwater infrastructure is proposed and the experimental results show the effectiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.87-107
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• 2015

Considering the localization estimation issue in mixed line-of-sight (LOS)/non-LOS(NLOS) environments based on received signal strength (RSS) measurements in wireless sensor networks, a grid-based correlation method based on the relationship between distance and RSS is proposed in this paper. The Maximum-Likelihood (ML) estimator is appended to further improve the localization accuracy. Furthermore, in order to reduce computation load and enhance performance, an improved recursively version with NLOS mitigation is also proposed. The most advantages of the proposed localization algorithm is that, it does not need any prior knowledge of the propagation model parameters and therefore does not need any offline calibration effort to calibrate the model parameters in harsh environments, which makes it more convenient for rapid implementation in practical applications. The simulation and experimental results evidence that the proposed localization algorithm exhibits good localization performance and flexibilities for different devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.901-920
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• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.814-821
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• 2009

In an ultrawide band (UWB) indoor wireless localization, time of arrival (TOA) parameter estimation techniques have some difficulties in acquiring a reasonable TOA estimate because of the clustered multipath components overlapping or random time intervals mainly due to non line-of-sight (NLOS) environment. In order to solve that problem and achieve an excellent UWB indoor wireless localization, we propose a UWB signal model and a robust TOA parameter estimation technique that has little effect on the clustered problems unlike the conventional technique. Through simulation studies, the validity of the proposed model and the TOA estimation technique are examined. The performance of estimation error is also analyzed.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.497-498
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• 2007

The most of important thing when we design a Wireless Sensor Network is resources. You have to consider energy efficient operation When you design Wireless Sensor Network. Because Sensor devices have a limited resources. In this paper, we proposed energy efficiency localization technique in Wireless Sensor Network. We used Cell ID technique for location search. This method can reduce power consumption and the network life time will be extension.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.530-536
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• 2008

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on the angle of arrival (AOA) or distance measurements. In this paper, we propose an enhanced centroid localization method based on edge weights of adjacent nodes using fuzzy modeling and genetic algorithm when node connectivities are known. The simulation results shows that our proposed centroid method is more accurate than the simple centroid method using connectivity only.