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Empowering Sensors in the Internet of Things with Tiny Machine Learning

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 10 October 2024 | Viewed by 361

Special Issue Editors

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Guest Editor
School of Engineering, University of Mount Union, Alliance, OH 44601-3993, USA
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
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Special Issue Information

Dear Colleagues,

The aim of this special issue is to explore the potential of Tiny Machine Learning techniques in enhancing the capabilities of sensors in the context of the Internet of Things (IoT).

With the rapid growth of IoT applications, sensors play a crucial role in collecting and transmitting data. However, the limited resources of sensors often pose challenges in terms of data processing and analysis. Tiny Machine Learning offers a promising approach to address these challenges by providing lightweight machine learning algorithms and methods that can be deployed directly on sensors.

This special issue aims to bring together researchers and practitioners to discuss and showcase the latest advancements and applications of Tiny Machine Learning in empowering sensors within the IoT ecosystem. We invite original research articles, reviews, and perspectives on the following topics:

  • Tiny Machine Learning algorithms for sensor data processing and analysis.
  • Hardware and software platforms for implementing Tiny Machine Learning on sensors.
  • Applications of Tiny Machine Learning in various IoT domains, including healthcare, environmental monitoring, smart cities, and industrial automation.
  • Energy-efficient and resource-constrained machine learning models for sensors.
  • Performance evaluation and benchmarking of Tiny Machine Learning techniques on sensor devices.
  • Security and privacy considerations in deploying Tiny Machine Learning on sensors.

Dr. Dinh-Thuan Do
Prof. Dr. Cheng-Chi Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • tiny machine learning
  • Internet of Things
  • data processing
  • data analysis
  • energy efficiency
  • performance evaluation
  • security
  • privacy considerations

Published Papers (1 paper)

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30 pages, 3755 KiB  
An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models
by Amitabh Mishra, Lucas S. Liberman and Nagaraju Brahamanpally
Sensors 2024, 24(11), 3429; (registering DOI) - 26 May 2024
Viewed by 92
The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT [...] Read more.
The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity. Full article
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