BlueSeer: AI-Driven Environment Detection via BLE Scans
Paper in 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.

environment classification

environment detection

BLE

Bluetooth Low Energy

embedded neural network

Author

Valentin Poirot

Network and Systems

Oliver Harms

Network and Systems

Hendric Martens

University of Kiel

Olaf Landsiedel

Network and Systems

Proceedings - Design Automation Conference

0738100X (ISSN)

871-876
978-1-4503-9142-9 (ISBN)

59th ACM/IEEE Design Automation Conference (DAC 22),
San Francisco, USA,

AgreeOnIT: Lightweight Consensus and Distributed Computing in the Resource-Constrained Internet of Things

Swedish Research Council (VR) (37200024), 2019-01-01 -- 2022-12-31.

Subject Categories

Computer Engineering

Communication Systems

Embedded Systems

DOI

10.1145/3489517.3530519

More information

Latest update

3/18/2024