Abstract
Device-Free Indoor Localization Based on Data Mining Classification Algorithms
Highlights
Indoor localization has shown rapid development with many methods proposed in the literature, most of which consider a target object to carry a device
The effect of ZigBee and WiFi interference on ZigBee-based device-free localization (DFL) systems is discussed
The statistical classifiers k-nearest neighbors (k-NN), support vector machine (SVM), logistic regression (LogR), naïve Bayes multinomial (NBM), and naïve Bayes (NB) have been applied to three datasets collected from three experiment areas
Summary
Indoor localization has shown rapid development with many methods proposed in the literature, most of which consider a target object to carry a device. A target object does not always carry a device. Device-free localization (DFL) has been used as a way of detecting and tracking subjects without the need to carry any tag or device. It is suitable for applications such as security, surveillance, assisted living, and elderly care. Researchers have proposed DFL systems using different technologies such as RF,(1–3) camera,(4) infrared,(5) and ultrasonic.[6] Radio frequency (RF)-based schemes have advantages of long range, low cost, and the ability to work through nonconducting walls and obstacles
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