# Radio-Enabled Low Power IoT Devices for TinyML Applications

arXiv: https://arxiv.org/abs/2312.14947 · HTML: https://mlsanity.com/paper/2312.14947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Usability and Performance Analysis of Embedded Development Environment for On-device Learning](https://mlsanity.com/paper/2404.07948/) (2024)

## Abstract (arXiv)

The proliferation of Internet of Things (IoT) devices has intensified the demand for energyefficient solutions supporting ondevice and distributed learning applications. This re search presents a circumscribed comparative analysis of radioenabled ultralow power IoT devices, specifically focusing on their suitability for computationheavy use cases. Our analysis centers on middleend IoT devices that serve as a vital interface between the Electronics and Machine Learn ing communities. The evaluation encompasses a diverse range of IoT hardware equipped with inte grated radios. We established functional datasheetbased criteria completed with accessibility and communitywise criteria to study and offer valuable insights into each selected node's performance tradeoffs, strengths, and weaknesses. This study provides crucial guidance for TinyML practitioners seeking to make informed device selections for their applications.