FM Backscatter: Enabling Connected Cities and Smart Fabrics
February 22, 2017 ยท Declared Dead ยท ๐ Symposium on Networked Systems Design and Implementation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Anran Wang, Vikram Iyer, Vamsi Talla, Joshua R. Smith, Shyamnath Gollakota
arXiv ID
1702.07044
Category
cs.NI: Networking & Internet
Citations
255
Venue
Symposium on Networked Systems Design and Implementation
Last Checked
3 months ago
Abstract
This paper enables connectivity on everyday objects by transforming them into FM radio stations. To do this, we show for the first time that ambient FM radio signals can be used as a signal source for backscatter communication. Our design creates backscatter transmissions that can be decoded on any FM receiver including those in cars and smartphones. This enables us to achieve a previously infeasible capability: backscattering information to cars and smartphones in outdoor environments. Our key innovation is a modulation technique that transforms backscatter, which is a multiplication operation on RF signals, into an addition operation on the audio signals output by FM receivers. This enables us to embed both digital data as well as arbitrary audio into ambient analog FM radio signals. We build prototype hardware of our design and successfully embed audio transmissions over ambient FM signals. Further, we achieve data rates of up to 3.2 kbps and ranges of 5-60 feet, while consuming as little as 11.07ฮผW of power. To demonstrate the potential of our design, we also fabricate our prototype on a cotton t-shirt by machine sewing patterns of a conductive thread to create a smart fabric that can transmit data to a smartphone. We also embed FM antennas into posters and billboards and show that they can communicate with FM receivers in cars and smartphones.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Networking & Internet
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
R.I.P.
๐ป
Ghosted
A Survey of Indoor Localization Systems and Technologies
R.I.P.
๐ป
Ghosted
Survey of Important Issues in UAV Communication Networks
R.I.P.
๐ป
Ghosted
Network Function Virtualization: State-of-the-art and Research Challenges
R.I.P.
๐ป
Ghosted
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted