• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.12
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• pp.52-60
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• 2010

Knowledge of the physical locations of access point of WLAN is becoming increasingly important with the rise of location-based services. In this article, we propose a new AP localization method using neural network approach. Basic theory and properties are derived for precise outdoor AP localization using GPS location information and RSSI. Rules for neural network model are derived and simulation has finally been done to demonstrate the efficiency of the proposed method. The simulation results show that the proposed method can result in AP localization with very. low error probability.

• Journal of IKEEE
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• v.23 no.4
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• pp.1360-1365
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• 2019

In this paper, we describe a sound source localization(SSL) system which can be applied to mobile robot and automatic control systems. Usually the SSL method finds the Interaural Time Difference, the Interaural Level Difference, and uses the geometrical principle of microphone array. But here we proposed another approach based on the deep neural network to obtain the horizontal directional angle(azimuth) of the sound source. We pick up the sound source signals from the two microphones attached symmetrically on both sides of the robot to imitate the human ears. Here, we use difference of spectral distributions of sounds obtained from two microphones to train the network. We train the network with the data obtained at the multiples of 10 degrees and test with several data obtained at the random degrees. The result shows quite promising validity of our approach.

• International Journal of Ocean System Engineering
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• v.1 no.2
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• pp.89-94
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• 2011

Underwater localization is a crucial capability for reliable operation of various types of underwater vehicles including submarines and underwater robots. However, sea water is almost impermeable to high-frequency electromagnetic waves, and thus absolute position fixes from Global Positioning System (GPS) are not available in the water. The use of acoustic telemetry systems such as Long Baseline (LBL) is a practical option for underwater localization. However, this telemetry network system needs to be pre-deployed and its availability cannot always be assumed. This study focuses on demonstrating the validity of terrain-based localization techniques in a GPS-denied underwater environment. Since terrain-based localization leads to a nonlinear estimation problem, nonlinear filtering methods are required to be employed. The extended Kalman filter (EKF) which is a widely used nonlinear filtering algorithm often shows limited performance under large initial uncertainty. The feasibility of using a particle filter is investigated, which can improve the performance and reliability of the terrain-based localization.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.429-437
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• 2005

Sensor localization has become an essential requirement for realistic applications over wireless sensor networks (WSN). In this paper we propose an ad hoc localization algorithm that is infrastructure-free, anchor-free, and computationally efficient with reduced communication. A novel combination of distance and direction estimation technique is introduced to detect and estimate ranges between neighbors. Using this information we construct unidirectional coordinate systems to avoid the reflection ambiguity. We then compute node positions using a transformation matrix [T], which reduces the computational complexity of the localization algorithm while computing positions relative to the fixed coordinate system. Simulation results have shown that at a node degree of 9 we get $90\%$ localization with $20\%$ average localization error without using any error refining schemes.

• Journal of Sensor Science and Technology
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• v.17 no.1
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• pp.53-60
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• 2008

Localization is the most important functions in mobile robots. There are so many approaches to realize this essential function in wheel based mobile robots, but it is not easy to find similar examples in small underwater robots. It is presented the sonar localization system using ubiquitous sensor network for a fish robot in this paper. A fish robot uses GPS and sonar system to find exact localization. Although GPS is essential tool to obtain positional information, this device doesn't provide reasonable resolution in localization. To obtain more precise localization information, we use several Ubiquitous Sensor Networks (USN) motes with sonar system. Experimental results show that a fish robot obtains more detailed positional information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.336-342
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• 2017

Traffic can be localized by reducing the traffic load on the physical network by causing traffic to be generated at the end of the packet network. By localizing traffic, the IoT-based sensitive data-related security issues can be supported effectively. In addition, it can be applied effectively to the next-generation smart network environment without changing the existing network infrastructure. In this paper, a content priority scheme was applied to smart network-based IoT data. The IoT contents were localized to efficiently pinpoint the flow of traffic on the network to enable smart forwarding. In addition, research was conducted to determine the effective network traffic routes through content localization. Through this study, the network load was reduced. In addition, it is a network structure that can guarantee user quality. In addition, it proved that the IoT service can be accommodated effectively in a smart network-based environment.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.91-100
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• 2009

Given the increased interest in ubiquitous computing, wireless sensor network has been researched widely. The localization service which provides the location information of mobile user, is one of important service provided by sensor network. Many methods to obtain the location information of mobile user have been proposed. However, these methods were developed for only one mobile user so that it is hard to extend for multiple mobile users. If multiple mobile users start the localization process concurrently, there could be interference of beacon or ultrasound that each mobile user transmits. In the paper, we propose IAL, the localization technique with interference avoidance for multiple mobile nodes in mobile wireless sensor networks. In IAL, interference is prevented by forcing the mobile node to get the permission of localization from anchor nodes. For this, we define LIP packet type for localization initiation by mobile node and LGP packet type for localization grant by anchor node. LRP packet type is used to reject localization by anchor node for interference avoidance. The experimental result shows that the number of interference between nodes are increased in proportion to the number of mobile nodes and IAL provides efficient localization.

• Journal of IKEEE
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• v.18 no.1
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• pp.57-63
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• 2014

This paper is to technically implement the sensing platform for Home-Wellness Robot. The self-Localization of indoor mobile robot is very important for the sophisticated trajectory control. In this paper, the robot's self-localization algorithm is designed by RF sensor network and fuzzy inference. The robot realizes its self-localization, using RFID sensors, through the collaboration algorithm which uses fuzzy inference for combining the strengths of triangulation and triangulation. For the triangulation self-Localization, RSSI is implemented. TOA method is used for realizing the triangulation self-localization. The final improved position is, through fuzzy inference, made by the fusion algorithm of the resultant coordinates from trilateration and triangulation in real time. In this paper, good performance of the proposed self-localization algorithm is confirmed through the results of a variety of experiments in the base of RFID sensor network and reader system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.575-582
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• 2009

The localization algorithm(LAtu) using the IDentification Code($C_{ID}$) is suggested in RSS(Received Signal Strength) based Wireless Sensor Network(WSN), and the localization system using the suggested algorithm is designed and implemented in this paper. In addition to this, the performance of ranging correction quality and localization error of the localization system(System(LAtu)) that is developed using the LAtu is analyzed and compared with that of the localization system(System(LAieee)) using the channel model of IEEE 802.15.4 standard(LAieee) by the actual experimentation. From the experimentation, the ranging correction quality is analyzed that the LAtu is highly better than the channel model of LAieee about 34% under the distance between the moving module and the beacon module($D_{MM-BM}$) is 2m, and is also a few better than that of the LAieee about average 5% under the $D_{MM-BM}$ is above 5m. The localization error quality of the System(LAtu) is lower than that of the System(LAieee)) about 1cm under the lecture room and 4cm in the large lecture room.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.6
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• pp.51-60
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• 2011

Researches on indoor localization using the wireless sensor network have been actively carried out to be used for indoor area where GPS signal is not received. Computationally efficient WCL(Weighted Centroid Localization) algorithm is shown to perform relatively well. However, to get the best performance for WCL all the anchor nodes must send signal with power to cover 96% of the network. The fact that outside the transmission range of the fixed nodes drastic localization error occurs results in large mean error and deviation. Due to these problems the WCL algorithm is not easily applied for use in the real indoor environment. In this paper we propose SRS(Succesive Region Setting) algorithm which sequentially reduces the estimated location area using the signal strength from the anchor nodes. The proposed algorithm does not show significant performance degradation corresponding to transmission range of the anchor nodes. Simulation results show that the proposed SRS algorithm has mean localization error 5 times lower than that of the WCL under free space propagation environment.