Year: 2024
Type: conference-paper
Abstract: This demo uses a machine-learning-based hot word detection application to showcase the capabilities of Riotee, an open-source and commercially available hardware-software platform for the battery-free Internet of Things. We describe the Riotee hardware consisting of a base module, a debug probe for easy firmware updates, and several expansion boards that enhance functionality without the need for custom-designed printed circuit boards (PCBs). The demo features the classification of live audio recordings using TinyML deep neural network inference aboard a Riotee device. The Riotee device transmits the classification results via Bluetooth Low Energy (BLE) to a smartphone given to visitors. Visitors can also observe how the Riotee software checkpoints and restores critical state in case of power failures via the visualization of logic analyzer traces.
Authors: Kai Geissdoerfer, Marco Zimmerling
Institution: Technische Universität Darmstadt
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Deutsche Forschungsgemeinschaft
Award: G2106247187
OpenAlex ID: W4404034855