Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends

Author:

Shahbazian Reza1ORCID,Macrina Giusy1ORCID,Scalzo Edoardo1ORCID,Guerriero Francesca1ORCID

Affiliation:

1. Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, 87036 Rende, Italy

Abstract

The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper’s main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.

Funder

project “Soluzioni efficienti di Logistica Industriale per la Distribuzione Organizzata (SOLIDO)”

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference76 articles.

1. Internet of things-IOT: Definition, characteristics, architecture, enabling technologies, application & future challenges;Patel;Int. J. Eng. Sci. Comput.,2016

2. Devezas, T., and Sarygulov, A. (2017). Industry 4.0, Springer.

3. Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities;Gama;Artif. Intell. Rev.,2021

4. Mirtaheri, S.L., and Shahbazian, R. (2022). Machine Learning: Theory to Applications, CRC Press.

5. Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure;Shit;IEEE Commun. Surv. Tutor.,2018

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