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BlueSeer: AI-driven environment detection via BLE scans
Publikationstyp
Conference Paper
Date Issued
2022-07
Sprache
English
Author(s)
Start Page
871
End Page
876
Citation
Proceedings of the 59th ACM/IEEE Design Automation Conference: 871-876 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Association for Computing Machinery
ISSN
0738100X
ISBN
978-1-4503-9142-9
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.
Subjects
BLE | bluetooth low energy | embedded neural network | environment classification | environment detection
MLE@TUHH
DDC Class
600: Technology