Robust Wi-Fi location fingerprinting against device diversity based on spatial mean normalization

Wang, Chu-Hsuan; Kao, Tai-Wei; Fang, Shih-Hau; Tsao, Yu; Kuo, Lun-Chia; Shih-Wei, Kao; Lin, Nien-Chen

doi:10.1109/apsipa.2013.6694372

Cited by 11 publications

(9 citation statements)

References 21 publications

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“…Average RSS variation due to Wi-Fi devices heterogeneity may exceed 18 dBm [7]. Another experiment done in [8] supports this result.…”

Section: Related Worksupporting

confidence: 70%

“…It is shown that the two techniques perform almost the same. Although the issue of device diversity is addressed in [6][7][8][9][10][11], the performance of these schemes suffers from RSS variation due to change of environmental conditions.…”

Section: Related Workmentioning

confidence: 99%

“…Heterogeneous devices measure signal strength differently. In [7], an experiment is done to compare RSS of notebook and smartphone from the same location and at the same Wi-Fi AP. As shown from the results, the average RSS value from notebook is −47.6 dBm whereas from the smartphone is −66.2 dBm.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Another experiment done in [8] supports this result. Different localization methods are proposed in the literatures to address this issue [6][7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

“…Fingerprinting-based methods provide a better accuracy than propagation model-based methods even though collecting fingerprints to create a database is time-consuming [3]. Although fingerprinting-based methods show a great promise for indoor localization, RSS variation due to device diversity [6][7][8][9][10][11] and change of conditions in the environment [12][13][14] induce a significant localization error.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Bitew

Hsiao

Lin

et al. 2015

International Journal of Distributed Sensor Networks

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Indoor localization is used in many applications like security, healthcare, location based services, and social networking. Fingerprinting-based methods are widely used for indoor localization. But received signal strength (RSS) variation due to device diversity and change of conditions in the localization environment (e.g., distribution of furniture, people presence and movement, and opening and closing of doors) induce a significant localization error. To overcome this, we propose a hybrid indoor localization system using radio frequency (RF) and pyroelectric infrared (PIR) sensors. Our localization system has two stages. In the first stage, the zone of the target person is identified by PIR sensors. In the second stage, we apply K-nearest neighbor (K-NN) algorithm to the fingerprints within the zone identified and estimate position. Zone based processing of fingerprints will exclude deviated fingerprints because of RSS variation. We proposed two localization methods: Proposed 1 and Proposed 2 which use signal strength difference (SSD) and RSS, respectively. Simulation results show that the 0.8-meter accuracy of Proposed 1 achieves 84% and Proposed 2 achieves 65%, while traditional fingerprinting and SSD are 46% and 28%, respectively.

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“…Average RSS variation due to Wi-Fi devices heterogeneity may exceed 18 dBm [7]. Another experiment done in [8] supports this result.…”

Section: Related Worksupporting

confidence: 70%

Section: Related Workmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

“…Another experiment done in [8] supports this result. Different localization methods are proposed in the literatures to address this issue [6][7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%