mlsanityData to 14 Oct 2026

Device management and network connectivity as missing elements in TinyML landscape

arXiv 2304.11669 ↗First posted 23 Apr 2023cs.NI cs.LGcited by 0

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Abstract (arXiv)

Deployment of solutions based on TinyML requires meeting several challenges. These include hardware heterogeneity, microprocessor (MCU) architectures, and resource availability constraints. Another challenge is the variety of operating systems for MCU, limited memory management implementations and limited software interoperability between devices. A number of these challenges are solved by dedicated programming libraries and the ability to compile code for specific devices. Nevertheless, the challenge discussed in the paper is the issue of network connectivity for such solutions. We point out that more emphasis should be placed on standard protocols, interoperability of solutions and security. Finally, the paper discusses how the LwM2M protocol can solve the identified challenges related to network connectivity and interoperability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 23 Apr 2023.

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3 of 8 references.

References · 3

  1. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  2. 2021Widening Access to Applied Machine Learning with TinyMLcited by 17
  3. 2022Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devicescited by 1

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