Catching Whales and Minnows Using WiFiNet: Deconstructing Non-WiFi Interference Using WiFi Hardware

We present WiFiNet — a system to detect, localize, and quantify the interference impact of various non-WiFi interference sources on WiFi traffic using commodity WiFi hardware alone. While there are numerous specialized solutions today that can detect the presence of non-WiFi devices in the unlicensed spectrum, the unique aspects of WiFiNet are four-fold: First, WiFiNet quantifies the actual interference impact of each non-WiFi device on specific WLAN traffic in real-time, which can vary from being a whale — a device that currently causes a significant reduction in WiFi throughput — to being a minnow — a device that currently has minimal impact. WiFiNet continuously monitors changes in a device’s impact that depend on many spatio-temporal factors. Second, it can accurately discern an individual device’s impact in presence of multiple and simultaneously operating non-WiFi devices, even if the devices are of the exact same type. Third, it can pin-point the location of these non-WiFi in- terference sources in the physical space. Finally, and most importantly, WiFiNet meets all these objectives not by using sophisticated and high resolution spectrum sensors, but by using emerging off-the-shelf WiFi cards that provide coarse-grained energy samples per sub-carrier. Our deployment and evaluation of WiFiNet demonstrates its high accuracy — interference estimates are within ±10% of the ground truth and the median localization error is ≤ 4 meters. We believe a system such as WiFiNet can empower existing WiFi clients and APs to adapt against non-WiFi interference in ways that have not been possible before.