Project Airshark

Understanding interference in a wireless environment is a challenging problem. Our work in this domain started with asking a basic question in WiFi environments --- can we tell apart the reason for a packet loss, e.g., is it due to a weak signal or is it due to a collision? We defined a post-mortem approach to diagnosing the cause for a packet's loss, called COLLIE, which was the first experimental attempt to distinguish the root cause of packet losses. Since then, we have looked at a range of problems that try to infer in real-time which other WiFi as well as non-WiFi sources are causing interference to users in a WLAN environment in project Airshark. The following papers capture some of the main results until date.

Goal: How can we detect the existence of non-WiFi transmitters in the unlicensed spectrum, using only off-the-shelf WiFi cards? Examples of non-WiFi transmitters are Bluetooth gadgets, ZigBee units, various game controllers (PS2, Xbox, etc.), analog phones, frequency hopping phones, and even microwave ovens. Past work and various commercial products do this but often use more sophisticated spectrum sensing chips or specialized embedded hardware (e.g., WiSpy). If we do this without using any such additional hardware, then every WiFi Access Point (AP) and client can do this in software we can build great interference awareness in WiFi APs and clients. Our current system has been designed for doing this using Atheros 9280 WiFi chipsets, all in software.

Many wireless devices occupy spectrum.

[IMC 2011 paper], [YouTube video]
Media coverage: Network World, Slashdot, CRA Highlight of the week, PC Magazine, The Register, BoingBoing, ...

Device detection accuracy using our lab prototype.
Our continued work in this domain, called WiFiNet, provides even deeper analysis of non-WiFi transmitters in two ways still using off-the-shelf WiFi cards: (i) it can now quantify the impact each individual non-WiFi interferer has on WiFi traffic, including when there are multiple devices of the same or different type For example, there may be two Bluetooth headsets in operation and one analog phone, WiFiNet tries to separate the contribution of each such device on how much it impacts specific WiFi users (maybe 10%, 12%, and 25% respectively); (ii) it can localize the position of this non-WiFi interferer. The core challenge we have solved in both these cases is how to meet both these goals using only WiFi cards, and hence with the constraint that our system cannot decode the transmissions from these non-WiFi devices in the air.
Paper and video coming soon.

Locate Non-WiFi devices in space.

Project team:-
Faculty Member: Suman Banerjee
Graduate Students: Ashish Patro, Shravan Rayanchu
Undergraduate Students: Nick Butch, Wess Miller