Abstract
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.
Highlights
Recent advances in wireless communication and computing technologies have resulted in successful low-cost low-power wireless sensor networks (WSNs), in the fields of cyber physical systems (CPSs) and the Internet of Things (IoT)
This paper presented MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity
MuCHLoc extracts the channel diversity from the received signal strength (RSS) of Wi-Fi access points (APs) measured on multiple ZigBee channels during fingerprinting localization
Summary
Recent advances in wireless communication and computing technologies have resulted in successful low-cost low-power wireless sensor networks (WSNs), in the fields of cyber physical systems (CPSs) and the Internet of Things (IoT). In a WSN, sensor location is important information for recognizing and tracking a sensing target, and for the routing of a sensor network. We can locate sensor nodes using Global Positioning System (GPS). We need to manually measure the sensor nodes. When we build a large-scale sensor network, we need to locate an extremely large number of sensor nodes. Indoor sensor localization systems have been proposed to reduce the efforts regarding sensor localization in an indoor environment [1,2,3]. Studies have primarily focused on improved accuracy [4,5,6,7,8,9]
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