The growing commercial interest in indoor location-based services (ILBS) has spurred
recent development of many indoor positioning techniques. Due to the absence of Global …

A major assumption in many machine learning and data mining algorithms is that the
training and future data must be in the same feature space and have the same distribution …

The last few decades have witnessed a growing interest in location-based services. Using
localization systems based on Radio Frequency (RF) signals has proven its efficacy for both …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Indoor positioning systems have received increasing attention for supporting location-based
services in indoor environments. WiFi-based indoor localization has been attractive due to …

Conventional metric learning methods usually assume that the training and test samples are
captured in similar scenarios so that their distributions are assumed to be the same. This …

WLAN-based indoor location fingerprinting has been attractive owing to the advantages of
open access and high accuracy. Most fingerprinting-based systems so far rely on the …

To verify the validity of our previously reported autonomous indoor localization system in an
actual environment, we implemented it in a wireless sensor network based on the ZigBee …

RFID technologies are gaining much attention as they are attractive solutions to many
application domains. Localization based on active RFID technologies provides a much …

In traditional radio-based localization methods, the target object has to carry a transmitter
(eg, active RFID), a receiver (eg, 802.11 x detector), or a transceiver (eg, sensor node) …