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Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

Zhang P, Zhao Q, Li Y, Niu X, Zhuang Y, Liu J - Sensors (Basel) (2015)

Bottom Line: Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database.Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method.Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China. fenix@whu.edu.cn.

ABSTRACT
This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

No MeSH data available.


WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
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sensors-15-17534-f010: WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).

Mentions: The parameters were set asand in WiFi data processing. That is, only the RSS stronger than −85 dBm were used, and the WiFi fingerprinting results was used only when the minimal signal strength distance is smaller than 200 dBm. The k-NN estimation approach was used with. The indoor WiFi fingerprinting results are shown as yellow pins in Figure 10. Considering the good performance of GNSS in the outdoor environment, only the results of WiFi fingerprinting in the indoor environment are considered. Even though we set sampling rate of WiFi as 1 Hz, the real-world WiFi updating rate was less than 0.3 Hz. This might due to the restriction of the smartphone or the Android system.


Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

Zhang P, Zhao Q, Li Y, Niu X, Zhuang Y, Liu J - Sensors (Basel) (2015)

WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541948&req=5

sensors-15-17534-f010: WiFi fingerprinting result using proposed DB (a) and that using reference DB (b).
Mentions: The parameters were set asand in WiFi data processing. That is, only the RSS stronger than −85 dBm were used, and the WiFi fingerprinting results was used only when the minimal signal strength distance is smaller than 200 dBm. The k-NN estimation approach was used with. The indoor WiFi fingerprinting results are shown as yellow pins in Figure 10. Considering the good performance of GNSS in the outdoor environment, only the results of WiFi fingerprinting in the indoor environment are considered. Even though we set sampling rate of WiFi as 1 Hz, the real-world WiFi updating rate was less than 0.3 Hz. This might due to the restriction of the smartphone or the Android system.

Bottom Line: Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database.Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method.Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

View Article: PubMed Central - PubMed

Affiliation: GNSS Research Center, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China. fenix@whu.edu.cn.

ABSTRACT
This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

No MeSH data available.