New trends in indoor positioning based on WiFi and machine learning: A systematic review

TL;DR

This systematic review analyzes recent WiFi-based indoor positioning methods using machine learning, highlighting prevalent techniques, their accuracy, testing environments, and identifying trends and limitations in current research.

Contribution

It provides a comprehensive overview of recent machine learning approaches for WiFi indoor positioning, emphasizing common techniques, evaluation practices, and research gaps.

Findings

Neural networks are the most commonly used models.

Most studies evaluate in small areas, affecting generalizability.

Empirical experiments are the primary evaluation method.

Abstract

Currently there is no standard indoor positioning system, similar to outdoor GPS. However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so. But what are the most commonly used techniques in machine learning? What accuracy do they achieve? Where have they been tested? This article presents a systematic review of works between 2019 and 2021 that use WiFi as the signal for positioning and machine learning models to estimate indoor position. 64 papers have been identified as relevant, which have been systematically analyzed for a better understanding of the current situation in different aspects. The results show that indoor positioning based on WiFi trends use neural network-based models, evaluated in empirical experiments. Despite this, many works still conduct an assessment in small areas, which can influence the goodness of the results presented.