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.
embedded neural network
Bluetooth Low Energy
environment detection
BLE
environment classification
Author
Valentin Poirot
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
University of Kiel
Laura Harms
University of Kiel
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
Hendric Martens
University of Kiel
Olaf Landsiedel
University of Kiel
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
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: 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