Precise localization and high throughput backscatter using WiFi signals
Thursday, October 15, 2015 - 12:15pm to 1:30pm
Gates 104
Manikanta Kotaru and Dinesh Bharadia (Stanford)
Abstract / Description: 

Indoor localization holds great promise to enable applications like location-based advertising, indoor navigation, inventory monitoring and management. SpotFi is an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems.

We then talk about BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter IoT sensors and WiFi APs using ambient WiFi transmissions as the excitation signal. We show via prototypes and experiments that it is possible to achieve communication rates of up to 5 Mbps at a range of 1 m and 1 Mbps at a range of 5 meters. Such performance is an order to three orders of magnitude better than the best known prior WiFi backscatter system.

Stanford's NetSeminar is a biweekly seminar covering networking-related topics. Speakers come from academia and industry and the talks are open to the public. When possible, talks will be recorded and posted online. We typically start at 12:15pm (lunch at 11:45am) at Gates Bld — Room 104, unless otherwise mentioned.

NetSeminar is run by graduate students, and it is generously supported by the Stanford Computer Forum.


Mani is a third year PhD candidate at Stanford University. His research interests are in developing wireless networks for communication, sensing, localization and imaging. He presented a portion of this talk in Sigcomm 2015.

Dinesh Bharadia is a Ph.D. candidate in the Electrical Engineering Department at Stanford University advised by Professor Sachin Katti. His research interests include advancing the theory and design of modern wireless communication systems, sensor networks, and data-center networks.