This paper propose a deep regression model for WiFi localization using received signal
strength (RSS). In the offline phase, we first construct RSS fingerprints at all grid points in a …

We propose an accurate and calibration-free WiFi localization approach using received
signal strength (RSS) to mitigate the impact of RSS variations caused by changing …

Passive WiFi localization refers to determining the location of WiFi-enabled mobile devices
by deploying dedicated WiFi access points to sniff WiFi packets transmitted by these mobile …

With the increasing demand on Indoor Location-Based Services (ILBS), various positioning
technologies had emerged in the past decades, and WiFi-based approach is one of the most …

Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an
efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the …

Conventional Wi-Fi-based indoor localization methods rely on training a RSS fingerprint
model to predict user locations. Most fingerprinting models only consider the distribution of …

The main drawback for large-scale applications of WiFi-based localization is the varying
characteristics of received signal strength (RSS), which degenerates the localization …

Due to the free deployment of the additional network infrastructure, WiFi-based indoor
localization has drawn researchers' attention in recent years. However, the accuracy and …

This paper studies the indoor localisation of WiFi devices based on a commodity chipset and
standard channel sounding. First, we present a novel shallow neural network (SNN) in …

Indoor localization has been an active research field for decades, due to its wide range of
applications. WiFi fingerprinting, which estimates the user's locations using pre-collecting …