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

Reza Shahbazian¹, Giusy Macrina¹, Edoardo Scalzo¹

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

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

Machine learning enhances Internet of Things (IoT) localization accuracy. This review covers recent learning algorithms for IoT systems, addressing challenges and future trends in device positioning.

Keywords:

Industry 4.0 Internet of things fingerprinting localization machine learning

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

The proliferation of the Internet of Things (IoT) and diverse hardware fuels the need for advanced localization systems.
Machine learning (ML) offers high prediction accuracy, making it a key technology for solving complex localization challenges.

Purpose of the Study:

To review machine learning-based approaches for Internet of Things (IoT) localization systems.
To identify and discuss current challenges and future trends in ML-driven IoT localization.

Main Methods:

A systematic literature review of papers published between 2020 and 2022.
Classification of studies based on learning algorithms, environments, IoT protocols, and measurement techniques.

Main Results:

Machine learning methods are increasingly adopted for IoT localization due to their superior accuracy.
The review categorizes existing ML approaches for IoT localization across various parameters.

Conclusions:

Machine learning algorithms are pivotal for advancing IoT localization capabilities.
Further research into ML applications and addressing current challenges will shape the future of IoT positioning.