• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.515-522
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• 2009

In this paper, we have investigated the technology of location-aware applicable for ubiquitous environment and considered IEEE 802.15.4a IR UWB of location-aware WPAN specification which is capable of both communication of 1Mbps and wireless localization. We analyzed both system structure and the characteristic of transmission pulse as comprising of the transmitter for wireless localization of IR UWB.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.477-488
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• 2013

Wireless Sensor Networks (WSNs) are comprised of sensor nodes that forward data in the shape of packets inside a network. Proficient packet forwarding is a prerequisite in sensor networks since many tasks in the network, together with redundancy evaluation and localization, depend upon the methods of packet forwarding. With the motivation to develop a fault tolerant packet forwarding scheme a Self-Localized Packet Forwarding Algorithm (SLPFA) to control redundancy in WSNs is proposed in this paper. The proposed algorithm infuses the aspects of the gossip protocol for forwarding packets and the end to end performance of the proposed algorithm is evaluated for different values of node densities in the same deployment area by means of simulations.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.9-17
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• 2014

This paper proposes a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization using the value of the measured distances from three or more neighboring anchors, i.e., nodes that know their location information. The node that is localized during the localization process is then used as another anchor for remaining nodes. The performances of particle swarm optimization (PSO) and BPSO in terms of localization error and computation time are compared by using simulations in Matlab. The simulation results indicate that PSO-based localization is more accurate. In contrast, BPSO algorithm performs faster for finding the location of unknown nodes for distributed localization. In addition, the effects of transmission range and number of anchor nodes on the localization error and computation time are investigated.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.179-182
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• 2001

This paper has been studied a wavelet based still image transmission over the wireless channel. EZW(Embedded Zerotree Wavelet) is an efficient and scalable wavelet based image coding technique, which provides progressive transfer of signal resulted in multi-resolution representation. It reduces therefore the reduce cost of storage media. Although EZW has many advantages, it is very sensitive on error. Because coding are performed in subband by subband, and it uses arithmetic coding which is a kind of variable length coding. Therefore only 1∼2bit error may degrade quality of the entire image. So study of error localization and recovery are required. This paper investigates the use of reversible variable length codes(RVLC) and data partitioning. RVLC are known to have a superior error recovery property due to their two-way decoding capability and data partitioning is essential to applying RVLC. In this work, we show that appropriate data partitioning length for each SNR(Signal-to-Noise Power Ratio) and error localization in wireless channel.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.209-216
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• 2005

In wireless sensor networks, an estimation method is proposed for distances between nodes within two hops. The method uses only proximity information of nodes without physiccal distance measurements. It drastically improves the performance of localization algorithms based on Proximity information. In addition, it is the first method that estimates distances between nodes exactly in two hops. The distances are estimated from the number of common neighbors under an assumption that the number of common neighbors is proportional to the intersection of two unit disks centered at the two nodes. Simulation analysis shows that the estimation error is roughly from 10 to 20 percent of real distances. Meanwhile, the number of messages required by a distributed algorithm realizing this method is only two times the number of nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1337-1362
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• 2020

Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1295-1302
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• 2010

This paper proposed indoor location recognition method based on RSSI(received signal strength indication) using the LVQ network. In order to verify the effectiveness of the proposed method, we performed experiments, and then compared to the conventional triangularity measurement method. In the experiments, we set up the system to the laboratory, divided the 40 section, and installed 6 nodes as a reference node. We obtained the log-normal path loss model of wireless channels, RSSI converted into the distance. The distance values used as the input of LVQ. To learn the LVQ network, we set the target values as section indices. In the experiments, we determined the optimal number of subclass, and confirmed that the success rate of training phase was 96%, test phase was 91%.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.31-37
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• 2021

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.12
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• pp.97-104
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• 2008

Location-based services provide customized information or services according to the user's location. The existing localization schemes for outdoor environment are not applicable to the indoor localization system which requires higher accuracy of location estimation than that of the outdoor localization system. In this paper, we employ the received signal strength(RSS) to approximate the distance between a moving target and a reference point and use the triangulation method to estimate the location of the moving target for the indoor localization system in IEEE 802.15.4 wireless PAN(personal area network). For the indoor localization system, we propose a scheme which selects the best reference points to enhance the localization accuracy and adaptively reflects the changes in propagation environments of a moving target to the distance approximation. Through the implementation of the localization system, we have verified the performance of the proposed scheme in terms of the estimation accuracy.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.