  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.
