• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.615-616
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• 2022

Indoor positioning technology is being used in various application services due to the spread of smart devices. In addition to precisely positioning the location, simultaneous positioning of multiple users has been raised as a new problem. In addition, providing Internet services simultaneously with location measurement is essential for use in various applications. This paper aims to solve both of these problems by designing and implementing an indoor positioning system with the IEEE 802.11az standard

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.43-51
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• 2010

In this paper, we evaluate the system throughput and the method of optimal wireless resource allocation for the access zone (AZ) and relay zone (RZ) in downlink when the cell coverage is extended using the non-transparent Relay Station (RS) in a 2-hop cellular relay system based on IEEE802.16j, which uses the OFDMA-TDD structure. For the analyses, we first introduce the MAC (Media Access Control) frame structure in the non-transparent mode, then we investigate the interfering elements in both AZ and RZ for the network devices such as the Mobile Station (MS) and RS. Through computer simulation, we analyze the cell coverage extension and system throughput in terms of the distance between Base Station (BS) and RS, then we present the amount of the optimal allocation of wireless resource for the AZ and RZ in downlink using our results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.119-133
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• 2009

The purpose of this research is to accurately estimate the location of a device using the received signal strength indicator (RSSI) of IEEE 802.11 WLAN for location tracking in indoor environments. For the location estimation method, we adopted the calibration model. By applying the Adaptive Zone Based K-NNSS (AZ-NNSS) algorithm, which considers the velocity of devices, this paper presents a 9% improvement of accuracy compared to the existing K-NNSS-based research, with 37% of the K-NNSS computation load. The accuracy is further enhanced by using a Kalman filter; the improvement was about 24%. This research also shows the level of accuracy that can be achieved by replacing a subset of the calibration data with values computed by a numerical equation, and suggests a reasonable number of calibration points. In addition, we use both the mean error distance (MED) and hit ratio to evaluate the accuracy of location estimation, while avoiding a biased comparison.