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

Abstract

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 (e.g., cordless phones, Bluetooth and ZigBee de- vices) in the unlicensed spectrum, the unique aspects of WiFiNet are three-fold. First, it 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 --- that which currently has minimal impact. In particular, WiFiNet can continuously track changes in device?s interference impact that depend on many spatio-temporal factors. Second, it can accurately discern the interference estimates in presence of multiple and simultaneously operating non-WiFi devices, even if the devices are of the exact same type. Third, it can determine the location of these non-WiFi interference sources in the physical space. Finally, and most importantly, it can meet all these objectives without using sophisticated and high resolution spectrum sensors, and simply by using emerging off-the-shelf WiFi cards that provide some coarse-grained energy samples per sub-carrier. Our deployment and evaluation of the WiFiNet system 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.