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)

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

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

Mer information

Senast uppdaterat

2024-05-21