Bleep
| Autor: | Adeola Bannis, Hae Young Noh, Pei Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Heartbeat Situation awareness Computer science Real-time computing 020206 networking & telecommunications 02 engineering and technology Sight 020901 industrial engineering & automation Encoding (memory) 0202 electrical engineering electronic engineering information engineering Chirp ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS Detection theory Side channel attack Decoding methods |
| Zdroj: | MobiCom |
| DOI: | 10.1145/3372224.3419183 |
| Popis: | Small unmanned autonomous vehicles (UAVs) swarms are becoming ubiquitous in a number of applications (e.g., surveying, monitoring, and situational awareness). Indoor environments may contain metal equipment that temporarily disrupts radio reception. During these momentary interruptions, a small UAV needs to be able to broadcast a 'heartbeat' to indicate that it is not damaged or lost. Considering alternative messaging modalities, we observe that light-based methods require line-of sight, which is not guaranteed when UAVs are moving through a cluttered environment, while a naive sound-based method is easily drowned out by the UAV's own loud motor and propeller noise. We present Bleep, a side-channel messaging system that modulates UAV motors sounds to enable multiple UAVs to communicate when radio is unavailable. Bleep accomplishes this by 1) embedding linear chirps within the PWM frequency while maintaining the controllability of the UAV and 2) decoding the received sounds utilizing chirp multiplication to enhance signal detection in loud motor noise. Our chirp-based encoding allows multiple simultaneous transmissions to be detected and decoded in the presence of loud sounds from multiple UAV motors. Through real-world experiments, we show that Bleep is able to achieve over 95% signal detection and decoding accuracy with simultaneous transmissions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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