Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review

Abstract

Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.

A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks

“…Many more examples of using machine learning in indoor positioning using the Wi-Fi radio interface can be found in very extensive review articles [ 22 , 23 ]. It is somehow interesting that majority of the solutions mentioned in both these publications rely on RSS and channel state information (CSI) estimation, with no angle- or time-based solutions.…”

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

“…Machine learning in WiFi-based indoor positioning was reviewed by Bellavista-Parent, Torres-Sospedra, and Pérez-Navarro [ 20 ]. The authors conducted a literature review and categorized existing methods based on machine learning techniques such as deep reinforcement learning (DRL), extreme learning machine (ELM), convolutional neural networks (CNNs), deep neural networks (DNNs), backpropagation neural networks (BPNNs), capsule neural networks (CapsNets), stacked denoising autoencoders (SDAs), variational autoencoder (VAEs), and deep belief networks (DQNs).…”

Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables

“…The aim of this paper is to fill the gap that still exists in the literature. In [ 33 ], which was published in June 2022, the authors surveyed 119 papers on machine learning (ML) algorithms applied to indoor positioning. In 114 of the 119 papers published between 2016 and 2021, the metric used for positioning was the RSS.…”

“…In 114 of the 119 papers published between 2016 and 2021, the metric used for positioning was the RSS. In fact, the survey [ 33 ] does not mention any work that uses the FTM by assessing the performance of several state of the art (SoA) ML classifiers when applied to an RTT-based fingerprinting solution. The results shown in this paper are also compared with SoA RSS-based fingerprinting approaches.…”

“…The radio map stores the ranges from IEEE 802.11mc compatible APs located in the auditorium of our university. To the best of the authors’ knowledge, this paper is the first in the literature at studying whether the IEEE 802.11mc FTM can contribute as an observable to fingerprint-based positioning [ 7 , 33 ], rather than providing a complete location system definition. The raw performance of six of the most popular supervised learning techniques [ 34 ] when applied to FTM observables is assessed and compared to what RSS-based fingerprinting solutions would achieve under the same conditions.…”