BlueSeer: AI-Driven Environment Detection via BLE Scans
Paper i proceeding, 2022
IoT devices rely on environment detection to trigger specific actions, e.g., for headphones to adapt noise cancellation to the surroundings. While phones feature many sensors, from GNSS to cameras, small wearables must rely on the few energy-efficient components they already incorporate. In this paper, we demonstrate that a Bluetooth radio is the only component required to accurately classify environments and present BlueSeer, an environment-detection system that solely relies on received BLE packets and an embedded neural network. BlueSeer achieves an accuracy of up to 84% differentiating between 7 environments on resource-constrained devices, and requires only ~12 ms for inference on a 64 MHz microcontroller-unit.
embedded neural network
Bluetooth Low Energy
environment detection
BLE
environment classification
Författare
Valentin Poirot
Chalmers, Data- och informationsteknik, Nätverk och system
Christian-Albrechts-Universität zu Kiel
Laura Harms
Christian-Albrechts-Universität zu Kiel
Chalmers, Data- och informationsteknik, Nätverk och system
Hendric Martens
Christian-Albrechts-Universität zu Kiel
Olaf Landsiedel
Christian-Albrechts-Universität zu Kiel
Chalmers, Data- och informationsteknik, Nätverk och system
Proceedings - Design Automation Conference
0738100X (ISSN)
871-876978-1-4503-9142-9 (ISBN)
59th ACM/IEEE Design Automation Conference (DAC 22),
San Francisco, USA,
San Francisco, USA,
AgreeOnIT: Lättvikts konsensus och distribuerat datakunskap i resursbegränsade sakernas Internet
Vetenskapsrådet (VR) (37200024), 2019-01-01 -- 2022-12-31.
Ämneskategorier
Datorteknik
Kommunikationssystem
Inbäddad systemteknik
DOI
10.1145/3489517.3530519