MyJournals Home  

RSS FeedsSensors, Vol. 19, Pages 1645: MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics (Sensors)


6 april 2019 11:04:26

Sensors, Vol. 19, Pages 1645: MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics (Sensors)

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. Digg Facebook Google StumbleUpon Twitter
39 viewsCategory: Chemistry, Physics
Sensors, Vol. 19, Pages 1646: A Seven-Rod Dielectric Sensor for Determination of Soil Moisture in Well-Defined Sample Volumes (Sensors)
Sensors, Vol. 19, Pages 1644: Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks (Sensors)
blog comments powered by Disqus
The latest issues of all your favorite science journals on one page


Register | Retrieve



Use these buttons to bookmark us: Digg Facebook Google StumbleUpon Twitter

Valid HTML 4.01 Transitional
Copyright © 2008 - 2019 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn