Using Classification in the Preprocessing Step on Wi-Fi Data as an Enabler of Physical Analytics

In this research we present a remote localization technique as an essential preprocessing step to enable Physical Analytics in the retail and hospitality sector. We studied two crowdsourced Wi-Fi data sources as potential inputs for fingerprinting-based positioning systems. These sources are non intrusively crowdsourced and can be easily acquired at almost any retail store. We evaluated our hypothesis on large, real-world datasets using statistical and machine learning techniques. With the use of these sources, we built a fingerprinting-based positioning system that achieved reliable and accurate physical positioning results. Our method is capable of estimating positions without any prior knowledge about the store plan or the antennas' location with, only one off-the-shelf access point. Unlike other positioning techniques, instead of estimating a relative position of a device from an antenna, we provide an absolute position for a device as inside or outside of a venue without making any assumption about the site nor the positioned devices. To investigate its practicality, we evaluated our method with datasets of five different stores.