A Systematic Literature Review of TinyML for Environmental Radiation Monitoring System

DOI

10.2991/978-94-6463-134-0_44How to use a DOI?

Keywords

TinyML; machine learning; systematic review; radiation monitoring

Abstract

Tiny machine learning (TinyML) is an important and growing field, but academic research related to the term is still at a very early stage at this time. As a result, efforts to synthesize TinyML research into broad knowledge integration are relatively limited. Therefore, the development of tinyML in the environmental radiation monitoring system is carried out in stages and relies on the latest developments in tinyML technology. The application of TinyML for environmental radiation monitoring systems has not been used or has not been discovered in a published paper. To meet the needs of developing systems using tinyML, we started by conducting a systematic literature review (SLR). Until now there is no direct literature related to tinyML and environmental radiation monitoring systems, so SLR focuses on the literature on developing tinyML technology. The review results obtained are expected to meet the requirements and specifications of the developed system. In particular, this article contributes to the TinyML literature by synthesizing current research on the following aspects: scientific publication trends, hardware, and use case experiment sets. This review is carried out by selecting papers from journals and proceedings in scientific databases, namely Scopus, ScienceDirect, IEEE Xplore, and Web of Science. The process of selecting papers using special keyword strings for each database, using inclusion and exclusion criteria, and extracting data using Mendeley and Microsoft Excel. Paper selection resulted in 34 selected papers.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Istofa
AU  - Prawito Prajitno
AU  - I. Putu Susila
PY  - 2023
DA  - 2023/04/19
TI  - A Systematic Literature Review of TinyML for Environmental Radiation Monitoring System
BT  - Proceedings of the 6th Mechanical Engineering, Science and Technology International conference (MEST 2022)
PB  - Atlantis Press
SP  - 461
EP  - 473
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-134-0_44
DO  - 10.2991/978-94-6463-134-0_44
ID  - 2023
ER  -