TY - JOUR
T1 - MuCHLoc
T2 - Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics
AU - Kimoto, Ryota
AU - Ishida, Shigemi
AU - Yamamoto, Takahiro
AU - Tagashira, Shigeaki
AU - Fukuda, Akira
N1 - Funding Information:
Funding: This work was partially supported by JSPS KAKENHI Grant Numbers JP15H05708, JP17H01741, and JP18K18041 as well as the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University.
Funding Information:
This work was partially supported by JSPS KAKENHI Grant Numbers JP15H05708, JP17H01741, and JP18K18041 as well as the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/4/6
Y1 - 2019/4/6
N2 - The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.
AB - The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.
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U2 - 10.3390/s19071645
DO - 10.3390/s19071645
M3 - Article
C2 - 30959881
AN - SCOPUS:85064576303
SN - 1424-3210
VL - 19
JO - Sensors
JF - Sensors
IS - 7
M1 - 1645
ER -