BlueSeer: AI-Driven Environment Detection via BLE Scans

Valentin Poirot, Laura Harms, Hendric Martens, Olaf Landsiedel

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

pdf url code
Best paper nomination

Abstract

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.

Bibtex

@inproceedings{10.1145/3489517.3530519,
author = {Poirot, Valentin and Harms, Laura and Martens, Hendric and Landsiedel, Olaf},
title = {BlueSeer: AI-Driven Environment Detection via BLE Scans},
year = {2022},
isbn = {9781450391429},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3489517.3530519},
doi = {10.1145/3489517.3530519},
abstract = {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.},
booktitle = {Proceedings of the 59th ACM/IEEE Design Automation Conference},
pages = {871-876},
numpages = {6},
keywords = {BLE, environment classification, bluetooth low energy, embedded neural network, environment detection},
location = {San Francisco, California},
series = {DAC '22}
}