#
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review^{ †}

^{1}

^{2}

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^{†}

## Abstract

**:**

## 1. Introduction

- The current work extends the analyzed period to the last five years, analysing a total of 119 published research works, 57 more than in [11];
- An analysis of solutions based on Artificial Neural Networks (ANN), Suport Vector Machines (SVM), and Random Forest (RF) is included;
- A comprehensive analysis of the most widely used public datasets (radio maps) and how they have been integrated in experiments performed by the research community;
- A discussion of the size of the operational areas considered in experiments performed in the reviewed works;
- Extended context, discussion, and conclusions.

## 2. Related Work

## 3. Methodology

**RQ1.**- Which machine learning algorithms provide the best results in Wi-Fi-based indoor positioning?
**RQ2.**- What kind of Wi-Fi signal parameters provide the best results?
**RQ3.**- What are the most commonly used metrics in indoor positioning studies?
**RQ4.**- Are there substantial differences between simulated and experimental studies?
**RQ5.**- Which public radio signal maps are the most commonly used in simulations?

**IC1.**- Written in English
**IC2.**- Coming from a conference or journal article
**IC3.**- Dealing with Wi-Fi-based positioning
**IC4.**- Positioning through Machine Learning algorithms
**IC5.**- Published between 2016 to 2021

**EC1.**- Workshops and book chapters
**EC2.**- Positioning that is not 100% Wi-Fi or is based on Sensor Fusion
**EC3.**- Positioning that has part of the work outdoors
**EC4.**- Positioning based on classic multilateration (TOA, AOA, etc.)
**EC5.**- Positioning that uses a KNN-based algorithm or Particle Filter, as this is not considered Machine Learning

## 4. Results

- Features not explained in the articles appear as $N/A$.
- Articles that include different experiments and/or simulations are grouped together.
- Articles that do not display a clear metric are marked in the column oError (Other Errors).
- Articles that are based on or use algorithms different from the main one are marked in the column sAlg (Secondary Algorithm)

## 5. Discussion

#### 5.1. Methods: Algorithms and Machine Learning Models

#### 5.1.1. Neural Networks

#### 5.1.2. Support Vector Machines

#### 5.1.3. Random Forest

#### 5.1.4. Comparison of Models

#### 5.2. Types of Wi-Fi Signal Parameters Used

#### 5.3. Evaluation Metrics

#### 5.4. Experimental and Full Simulated Results

#### 5.5. Most Widely Used Public Datasets

#### UJIIndoor Results Analysis

#### 5.6. Experimental Scenarios

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Conflicts of Interest

## Abbreviations

ANN | Artificial Neural Networks |

AoA | Angle of Arrival |

AP | Access Point |

BPNN | Back Propagation Neural Network |

CapsNet | Capsule Neural Network |

CNN | Convolutional Neural Networks |

CSI | Channel State Information |

DBN | Deep Belief Network |

DNN | Deep Neural Networks |

DQN | Deep Q-Networks |

DRL | Deep reinforcement learning |

ELM | Extreme learning machine |

GNSS | Global Navigation Satellite Systems |

MLP | Multilayer Perceptron |

MSE | Mean Squared Error |

NN | Neural Network |

PoA | Phase of Arrival |

RF | Random Forest |

RMSE | Root Mean Squared Error |

RNN | Recurrent Neural Networks |

RSSI | Received Signal Strength Indicator |

SDA | Stacked Denoising Autoencoders |

SMN | Single Multiplicative Neuron |

SNR | Signal-to-Noise Ratio |

SVM | Suport Vector Machines |

ToF | Time Of Flight |

VAE | Variational Autoencoder |

## Appendix A. Full Features of Reviewed Articles

Art | Year | Est | AP | rPoint | fMap | fmRoom | mAlg | sAlg | mError | oError | sType |
---|---|---|---|---|---|---|---|---|---|---|---|

[41] | 2021 | pMap | 168 | 57 | IPIN2016 | N | DRL | $0.92\mathrm{m}$ | RSSI | ||

pMap | 589 | 1452 | UTSIndoorLoc | Y | DRL | $1.72\mathrm{m}$ | RSSI | ||||

pMap | 520 | 993 | UJIIndoorLoc | Y | DRL | $3.06\mathrm{m}$ | Only Building B1 | RSSI | |||

[22] | 2021 | pMap | 96 | 80 | JUIndoorLoc | Y | BayesNet | Dempster–Shafer | Accuracy = 80% between 3 and 3.6 m | RSSI | |

pMap | 520 | 993 | UJIIndoorLoc | Y | BayesNet | Accuracy = 98% in 2 m | RSSI | ||||

[87] | 2021 | exp | 7 | 116 | 1052 ${\mathrm{m}}^{2}$ | Y | SISAE (NN) | $1.93\mathrm{m}$ | std = 1.34 m | RSSI | |

[44] | 2021 | exp | 1 | 32 | $49.9{\mathrm{m}}^{2}$ | N | CNN | $1.76\mathrm{m}$ | CSI | ||

exp | 1 | 45 | 40 ${\mathrm{m}}^{2}$ | N | CNN | $1.16\mathrm{m}$ | CSI | ||||

exp | 1 | 66 | $48.8{\mathrm{m}}^{2}$ | Y | CNN | $2.54\mathrm{m}$ | CSI | ||||

exp | 1 | 15 | 32 ${\mathrm{m}}^{2}$ | N | CNN | $0.91\mathrm{m}$ | CSI | ||||

[45] | 2021 | sim | 15 | 158 | 1160 ${\mathrm{m}}^{2}$ | Y | ASDELM (ELM) | Accuracy = 85,90% in 1 m | CSI | ||

exp | 22 | 47 | 384 ${\mathrm{m}}^{2}$ | Y | ASDELM (ELM) | Accuracy = 77% in 1 m | CSI | ||||

[88] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | DNNIP | Accuracy = 89% building and floor | RSSI | ||

[80] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | CHISEL (CNN) | autoencoder | $6.95$$\mathrm{m}$ | Accuracy = 99.6% building, 83.97% floor | RSSI |

[46] | 2021 | exp | 1 | 40 | 131.3 ${\mathrm{m}}^{2}$ | Y | BPNN | adaptive genetic algorithm | Accuracy = 90.47% in 4 m | CSI | |

[30] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | NNELILS (NN) | 67% to 78% localization accuracies | RSSI | ||

pMap | 96 | 80 | JUIndoorLoc | Y | NNELILS (NN) | $2.2\mathrm{m}$ to $2.6\mathrm{m}$ | RSSI | ||||

[89] | 2021 | pMap | 309 | 3951 | Tampere | Y | CMDRNN (cnn) | $8.26\mathrm{m}$ | std = 1.31 m | RSSI | |

[21] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | CDAE i CNN | 12.4 $\mathrm{m}$ | RSSI | ||

pMap | 152 | 670 | Alcala Tutorial 2017 | N | CDAE-CNN | 1.05 $\mathrm{m}$ | RSSI | ||||

[89] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | CMDRNN (cnn) | $8.26\mathrm{m}$ | std = 1.31 m | RSSI | |

[90] | 2021 | exp | 113 | 30 | 3600 ${\mathrm{m}}^{2}$ | Y | WiFiNet (cnn) | Accuracy = 91.89% in 2 m | RSSI | ||

[81] | 2021 | pMap | 520 | 993 | UJIIndoorLoc | Y | DeepLocBox (NN) | 9.07 $\mathrm{m}$ | RSSI | ||

[33] | 2021 | exp | 15 | 150 | 200 ${\mathrm{m}}^{2}$ | Y | SVM | M-LS | 2.7 $\mathrm{m}$ | RSSI | |

[47] | 2021 | exp | 1 | $N/A$ | 14 ${\mathrm{m}}^{2}$ | N | NN | 0.18 $\mathrm{m}$ | CSI | ||

exp | 2 | $N/A$ | 18 ${\mathrm{m}}^{2}$ | N | NN | 0.03 $\mathrm{m}$ | CSI | ||||

exp | 2 | $N/A$ | 6.7 ${\mathrm{m}}^{2}$ | Y | NN | 0.08 $\mathrm{m}$ | CSI | ||||

[48] | 2021 | exp | 1 | 317 | 148.5 ${\mathrm{m}}^{2}$ | Y | BLS(NN) | 2.54 $\mathrm{m}$ | CSI | ||

exp | 1 | 176 | 126 ${\mathrm{m}}^{2}$ | N | BLS(NN) | 1.48 $\mathrm{m}$ | CSI | ||||

[82] | 2021 | exp | 6 | 132 | 460 ${\mathrm{m}}^{2}$ | Y | Edgeloc(CapsNet) | 99% under 2 m | RSSI | ||

pMap | 520 | 993 | UJIIndoorLoc | Y | Edgeloc(CapsNet) | 7.93 $\mathrm{m}$ | RSSI | ||||

[91] | 2021 | exp | 1 | 210 | 600 ${\mathrm{m}}^{2}$ | Y | MLR | 4.03 $\mathrm{m}$ | RSSI | ||

[77] | 2021 | exp | 436 | 654 | WIFINE | Y | RNN | 3.05 $\mathrm{m}$ | RSSI | ||

[92] | 2021 | exp | 191 | 349 | 360 ${\mathrm{m}}^{2}$ | N | DNN | 1.08 $\mathrm{m}$ | RSSI | ||

[49] | 2021 | exp | 1 | $17,486$ | CTW 2019 challenge | N | CNN | 0.12 m | CSI | ||

[93] | 2021 | exp | $N/A$ | 292 | 600 ${\mathrm{m}}^{2}$ | Y | CNN | 1.86 $\mathrm{m}$ | Accuracy = 95% in 5.41 m | RSSI | |

exp | $N/A$ | 262 | 1360 ${\mathrm{m}}^{2}$ | Y | CNN | 1.86 $\mathrm{m}$ | Accuracy = 95% in 5.41 m | RSSI | |||

[94] | 2021 | exp | 12 | 680 | 6000 ${\mathrm{m}}^{2}$ | N | DNN | 3.6 $\mathrm{m}$ | RSSI | ||

exp | 12 | 170 | 6000 ${\mathrm{m}}^{2}$ | N | DNN | 3.7 $\mathrm{m}$ | RSSI | ||||

exp | 12 | 40 | 6000 ${\mathrm{m}}^{2}$ | N | DNN | 3.8 $\mathrm{m}$ | RSSI | ||||

[95] | 2021 | exp | 4 | 54 | 69.35 ${\mathrm{m}}^{2}$ | Y | ANN | Accuracy = 13.84% < 0.5 m & 23.07% 0.5 < 1 m | RSSI | ||

[50] | 2020 | exp | 3 | 21 | 45 ${\mathrm{m}}^{2}$ | Y | CNN | 1.27 $\mathrm{m}$ | std = 0.68 m | CSI | |

[36] | 2020 | exp | 4 | 264 | 112 ${\mathrm{m}}^{2}$ | N | RF | 1.68 $\mathrm{m}$ | RSSI | ||

[51] | 2020 | exp | 4 | 63 | 75.6 ${\mathrm{m}}^{2}$ | N | CNN | 1.61 $\mathrm{m}$ | CSI | ||

exp | 4 | $N/A$ | 44.8 ${\mathrm{m}}^{2}$ | N | CNN | 1.11 $\mathrm{m}$ | CSI | ||||

exp | 4 | $N/A$ | 16 ${\mathrm{m}}^{2}$ | N | CNN | 0.98 $\mathrm{m}$ | CSI | ||||

[96] | 2020 | exp | 4 | 10 | 169 ${\mathrm{m}}^{2}$ | Y | CNN | 0.98 $\mathrm{m}$ | RSSI | ||

[54] | 2020 | exp | 5 | 34 | 55 ${\mathrm{m}}^{2}$ | N | MLP | Regression | 0.37 $\mathrm{m}$ | RMSE = 0.84 m | SNR |

**art**: Article;

**mAlg**: Main algorithm used;

**est**: Experimental or pMapulated study;

**sAlg**: Other algorithms used in the study;

**AP**: APs used;

**mError**: Mean Error;

**rPoint**: Reference Points used in offline phase;

**oError**: Other metrics reported in the study;

**fMap**: Size of experimental room or radio-map used;

**sType**: Signal type used;

**fmRoom**: Rooms used in exp/pMap.

Art | Year | Est | AP | rPoint | fMap | fmRoom | mAlg | sAlg | mError | oError | sType |
---|---|---|---|---|---|---|---|---|---|---|---|

[23] | 2020 | pMap | 520 | 993 | UJIIndoorLoc | N | KNN, LR, SVM, RF | RMSE = 1.87 m | RSSI | ||

[55] | 2020 | exp | 6 | 112 | 460 ${\mathrm{m}}^{2}$ | Y | capsnet | 0.68 $\mathrm{m}$ | RSSI | ||

[31] | 2020 | exp | 8 | 133 | 512 ${\mathrm{m}}^{2}$ | N | Deep Fuzzy Forest | 1.36 $\mathrm{m}$ | RMSE = 1.79 m | RSSI | |

[52] | 2020 | exp | 1 | 32 | 50 ${\mathrm{m}}^{2}$ | N | CNN | 1.77 $\mathrm{m}$ | CSI | ||

exp | 1 | 24 | 40 ${\mathrm{m}}^{2}$ | N | CNN | 1.16 $\mathrm{m}$ | CSI | ||||

exp | 1 | 66 | 49 ${\mathrm{m}}^{2}$ | N | CNN | 2.54 $\mathrm{m}$ | CSI | ||||

[97] | 2020 | exp | 6 | 50 | 60 ${\mathrm{m}}^{2}$ | N | RF | Bernoulli distribution | RMSE = 2.50 m | RSSI | |

[98] | 2020 | exp | 25 | 240 | 315 ${\mathrm{m}}^{2}$ | N | RF | Co-forest | 2.44 $\mathrm{m}$ | RSSI | |

exp | 5 | $N/A$ | NULL | N | RF | 4.44 $\mathrm{m}$ | RSSI | ||||

[24] | 2020 | pMap | 7 | 1000 | Rajen Bhatt | Y | MLP | Accuracy = 94.4% | RSSI | ||

[25] | 2020 | pMap | 520 | 993 | UJIIndoorLoc | Y | CNN | Accuracy = 88% | RSSI | ||

[99] | 2020 | exp | 195 | 300 | 800 ${\mathrm{m}}^{2}$ | N | DNN | HMM | 1.22 $\mathrm{m}$ | RMSE = 1.43 m | RSSI |

[32] | 2020 | exp | 3 | 56 | 87.75 ${\mathrm{m}}^{2}$ | N | DNN | LC | 0.78 $\mathrm{m}$ | std = 1.96 m | CSI |

[28] | 2020 | exp | 4 | 236 | 1148 ${\mathrm{m}}^{2}$ | Y | BPNN | GA-PSO | 0.22 $\mathrm{m}$ | RSSI | |

[26] | 2020 | exp | 10 | 102 | 568.4 ${\mathrm{m}}^{2}$ | Y | LSTM | LF-D | 1.48 $\mathrm{m}$ | RSSI | |

exp | 30 | 353 | 2750 ${\mathrm{m}}^{2}$ | Y | LSTM | 1.75 $\mathrm{m}$ | RSSI | ||||

[27] | 2020 | pMap | $N/A$ | $N/A$ | Cramariuc | Y | SEQ2SEQ | LSTM | 5.5 $\mathrm{m}$ | RSSI | |

pMap. | $N/A$ | $N/A$ | Cramariuc | Y | SEQ2SEQ | 3.08 $\mathrm{m}$ | RSSI | ||||

[100] | 2020 | pMap | $N/A$ | $N/A$ | IPIN2016 | Y | CNN, LSTM | 4.93 $\mathrm{m}$ | RSSI | ||

pMap | $N/A$ | $N/A$ | IPIN2016 | Y | CNN, LSTM | 5.4 $\mathrm{m}$ | RSSI | ||||

pMap | 520 | 993 | UJI Library | Y | CNN, LSTM | 3.2 $\mathrm{m}$ | RSSI | ||||

pMap | 520 | 993 | UJI Library | Y | CNN, LSTM | 4.98 $\mathrm{m}$ | RSSI | ||||

[56] | 2020 | exp | 5 | 22 | 293 ${\mathrm{m}}^{2}$ | Y | DNN | Accuracy = 95.45% in 3.65 × 3.65 m | RSSI | ||

[29] | 2020 | exp | $N/A$ | 157 | 5500 ${\mathrm{m}}^{2}$ | Y | RNN | DL | 3.05 $\mathrm{m}$ | std = 2.818 m | RSSI |

pMap | 520 | 993 | UJIIndoorLoc | Y | RNN | 4.92 $\mathrm{m}$ | std = 3.719 m | RSSI | |||

sim | 4 | 00 | 1681 ${\mathrm{m}}^{2}$ | Y | RNN | DL | 2.42 $\mathrm{m}$–2.92 $\mathrm{m}$ | RSSI | |||

[101] | 2020 | sim | 54 | 54 | 10,000 m ${}^{2}$ | N | MLP | 3.35 $\mathrm{m}$ | RSSI | ||

[9] | 2020 | exp | 3 | 7 | 25 ${\mathrm{m}}^{2}$ | Y | DNN | RESNET | 0.11 $\mathrm{m}$ | RMSE = 0.08 m | SNR |

[102] | 2020 | pMap | $N/A$ | 40 | UJI Library | N | CNN | SVR | 2.15 $\mathrm{m}$ | RSSI | |

[66] | 2019 | exp | 3 | 30 | 540 ${\mathrm{m}}^{2}$ | N | DBN | cross entropy and the mean squared | NULL | RSSI | |

[34] | 2019 | exp | 2 | 59 | 125 ${\mathrm{m}}^{2}$ | Y | SVM | 0.7 $\mathrm{m}$ | RSSI | ||

[57] | 2019 | exp | $N/A$ | 206 | NULL | Y | DNN | Stacked AutoEncoder | Accuracy = 85% | RSSI | |

[35] | 2019 | exp | 1 | 100 | 100 ${\mathrm{m}}^{2}$ | N | SVM | 1.9 $\mathrm{m}$ | std = 0.07 m | CSI | |

[103] | 2019 | exp | $N/A$ | $N/A$ | NULL | NULL | CNN | RMSE = 0.31 m | RSSI | ||

[104] | 2019 | exp | 1 | $N/A$ | 63 ${\mathrm{m}}^{2}$ | Y | SVM | 96.4% | RSSI | ||

exp | 1 | $N/A$ | 63 ${\mathrm{m}}^{2}$ | Y | MLP | 96.5% | RSSI | ||||

[53] | 2019 | exp | $N/A$ | $N/A$ | NULL | NULL | SVM | RMSE = 0.42 m | CSI | ||

[58] | 2019 | exp | 16 | 83 | 305 ${\mathrm{m}}^{2}$ | Y | DNN | 2 $\mathrm{m}$ | RSSI | ||

[59] | 2019 | pMap | 520 | 993 | UJIIndoorLoc | Y | CNN | Accuracy = 95.92% | RSSI | ||

pMap | 309 | 3951 | Tampere | Y | CNN | Accuracy = 94.13% | RSSI | ||||

[105] | 2019 | exp | 6 | 300 | 300 ${\mathrm{m}}^{2}$ | N | MEA-BP | 0.72 $\mathrm{m}$ | RSSI | ||

[67] | 2019 | exp | 50 | $N/A$ | NULL | NULL | ELM | NULL | RSSI | ||

[61] | 2019 | exp | 256 | 74 | 1664 ${\mathrm{m}}^{2}$ | Y | CNN | Accuracy = 95.4% in 4 m | RSSI | ||

[62] | 2019 | exp | 54 | 180 | 1209 ${\mathrm{m}}^{2}$ | Y | RDF | Accuracy = 89% at room level | RSSI | ||

[64] | 2019 | exp | 256 | 74 | 300 ${\mathrm{m}}^{2}$ | Y | CNN | 1.46 $\mathrm{m}$ | Accuracy = 94% std = 2.24 m | RSSI |

**art**: Article;

**mAlg**: Main algorithm used;

**est**: Experimental or pMapulated study;

**sAlg**: Other algorithms used in the study;

**AP**: APs used;

**mError**: Mean Error;

**rPoint**: Reference Points used in offline phase;

**oError**: Other metrics reported in the study;

**fMap**: Size of experimental room or radio-map used;

**sType**: Signal type used;

**fmRoom**: Rooms used in exp/pMap.

Art | Year | Est | AP | rPoint | fMap | fmRoom | mAlg | sAlg | mError | oError | sType |
---|---|---|---|---|---|---|---|---|---|---|---|

[106] | 2019 | exp | 4 | 42 | 80 ${\mathrm{m}}^{2}$ | N | RBF | LM | 1.42 $\mathrm{m}$ | RMSE = 1.459 m | RSSI |

[107] | 2019 | exp | $N/A$ | 300 | 302 ${\mathrm{m}}^{2}$ | Y | SVM | 4.6 $\mathrm{m}$ | RSSI | ||

[60] | 2019 | exp | 5 | 10 | NULL | N | RF | Accuracy = 97.5% in 2 m | RSSI | ||

[108] | 2019 | exp | 8 | 107 | 512 ${\mathrm{m}}^{2}$ | Y | K-ELM | RMSE = 1.7123 m std = 2.418 m | RSSI | ||

[109] | 2019 | exp | 9 | 96 | 560 ${\mathrm{m}}^{2}$ | Y | QKMMCC | average = 0.76m | RSSI | ||

[65] | 2019 | pMap | 520 | 993 | UJIIndoorLoc | Y | RNN | Accuracy = 87.41%floor std = 0.83 m | RSSI | ||

exp | 7 | $N/A$ | 4 Rooms | Y | RNN | Accuracy = 95.8% std = 0.60 m | RSSI | ||||

[83] | 2019 | pMap | 520 | 993 | UJIIndoorLoc | Y | RNN | 4.2 $\mathrm{m}$ | std = 3.2 m | RSSI | |

exp | 6 | 365 | 336 ${\mathrm{m}}^{2}$ | Y | RNN | 0.75 $\mathrm{m}$ | std = 0.64 m | RSSI | |||

[110] | 2019 | exp | 9 | 261 | 300 ${\mathrm{m}}^{2}$ | N | BPNN | 2.7 $\mathrm{m}$ | Accuracy = 90% | RSSI | |

[111] | 2019 | exp | 8 | 66 | 736 ${\mathrm{m}}^{2}$ | Y | SDA | 3.7 $\mathrm{m}$ | Accuracy = 84% | RSSI | |

[112] | 2019 | exp | 1 | 42 | 50 ${\mathrm{m}}^{2}$ | N | CNN | 0.46 $\mathrm{m}$ | without obstacles | RSSI | |

exp | 1 | 42 | 50 ${\mathrm{m}}^{2}$ | N | CNN | 1.11 $\mathrm{m}$ | with some obstacles | RSSI | |||

[113] | 2019 | exp | 1 | 15 | 20 ${\mathrm{m}}^{2}$ | Y | MLP | 1.42 $\mathrm{m}$ | RSSI | ||

exp | 1 | 15 | 20 ${\mathrm{m}}^{2}$ | Y | CNN | 1.67 $\mathrm{m}$ | RSSI | ||||

exp | 1 | 15 | 14.4 ${\mathrm{m}}^{2}$ | N | MLP | 1.43 $\mathrm{m}$ | RSSI | ||||

exp | 1 | 15 | 14.4 ${\mathrm{m}}^{2}$ | N | CNN | 1.51 $\mathrm{m}$ | RSSI | ||||

[114] | 2019 | exp | 258 | 9 | 125 ${\mathrm{m}}^{2}$ | Y | CNN | 3.91 $\mathrm{m}$ | Accuracy = 84% | RSSI | |

[63] | 2019 | pMap | $N/A$ | $N/A$ | NULL | Y | BPNN | ACO | Accuracy = 91.4% | RSSI | |

[115] | 2019 | pMap | 520 | 993 | UJI Library | Y | CNN, GRP | 3.6 $\mathrm{m}$ | 90% less 2m | RSSI | |

[42] | 2019 | exp | 1 | 25 | 26.4 ${\mathrm{m}}^{2}$ | N | BPNN | PCA-PD | 1.42 $\mathrm{m}$ | std = 1.1511 m | CSI |

[84] | 2019 | exp | $N/A$ | 20 | 1200 ${\mathrm{m}}^{2}$ | Y | MLP | SDAE | 3.05 $\mathrm{m}$ | 1day | RSSI |

exp | $N/A$ | 57 | 2400 ${\mathrm{m}}^{2}$ | Y | MLP | SDAE | 3.39 $\mathrm{m}$ | 2 days | RSSI | ||

pMap | 520 | 993 | UJIIndoorLoc | Y | MLP | SDAE | 5.64 $\mathrm{m}$ | 10 days | RSSI | ||

[116] | 2019 | pMap | 520 | 993 | UJIIndoorLoc | Y | VAE | RMSE = 4.65 m | RSSI | ||

[117] | 2019 | exp | 6 | 49 | 1600 ${\mathrm{m}}^{2}$ | Y | DNN | 0.95 $\mathrm{m}$ | Open Doors | RSSI | |

exp | 6 | 49 | 1600 ${\mathrm{m}}^{2}$ | Y | DNN | 1.26 $\mathrm{m}$ | Closed Doors | RSSI | |||

[118] | 2019 | exp | 4 | 228 | 1200 ${\mathrm{m}}^{2}$ | Y | ANN | 1.22 $\mathrm{m}$ | RSSI | ||

exp | $N/A$ | $N/A$ | $N/A$ | Y | ANN | 1.90 $\mathrm{m}$ | RSSI | ||||

[119] | 2019 | exp | 7 | 25 | 1728 ${\mathrm{m}}^{2}$ | N | RNN | LSTM | 1.05 $\mathrm{m}$ | std = 0.8856 m | RSSI |

[120] | 2019 | exp | 15 | 71 | 4000 ${\mathrm{m}}^{2}$ | Y | NN | GA | 3.47 $\mathrm{m}$ | RSSI | |

[121] | 2019 | exp | 4 | 50 | 1100 ${\mathrm{m}}^{2}$ | Y | BGM | 2.9 $\mathrm{m}$ | RSSI | ||

[122] | 2019 | exp | 122 | 48 | 629 ${\mathrm{m}}^{2}$ | Y | DNN | 2.64 $\mathrm{m}$ | RSSI | ||

exp | 59 | 139 | 65 ${\mathrm{m}}^{2}$ | N | DNN | 1.21 $\mathrm{m}$ | RSSI | ||||

[123] | 2018 | pMap | 520 | 993 | UJIIndoorLoc | Y | CNN | 95.76% floor level | RSSI | ||

[124] | 2018 | pMap | 7 | 1000 | Rajen Bhatt | Y | RF | 98.3% floor level | RSSI | ||

[125] | 2018 | exp | 20 | 2100 | 8250 ${\mathrm{m}}^{2}$ | Y | DNN | 3.95 $\mathrm{m}$ | std = 2.72 m | RSSI | |

[126] | 2018 | exp | 16 | 202 | 806 ${\mathrm{m}}^{2}$ | Y | SMN | PCA | 1.85 $\mathrm{m}$ | std = 1.04 m | RSSI |

[127] | 2018 | pMap | 520 | 993 | UJIIndoorLoc | Y | DQN | 78.79% in 1 m | RSSI | ||

[37] | 2018 | exp | 50 | 180 | 75 ${\mathrm{m}}^{2}$ | Y | RF | 1.29 $\mathrm{m}$ | 90% in 3 m | RSSI | |

[128] | 2018 | exp | $N/A$ | $N/A$ | NULL | Y | DNN | 83.6% floor with people, 99.6% without | RSSI |

**art**: Article;

**mAlg**: Main algorithm used;

**est**: Experimental or pMapulated study; sAlg: Other algorithms used in the study;

**AP**: APs used;

**mError**: Mean Error;

**rPoint**: Reference Points used in offline phase;

**oError**: Other metrics reported in the study;

**fMap**: Size of experimental room or radio-map used;

**sType**: Signal type used;

**fmRoom**: Rooms used in exp/pMap.

Art | Year | Est | AP | rPoint | fMap | fmRoom | mAlg | sAlg | mError | oError | sType |
---|---|---|---|---|---|---|---|---|---|---|---|

[129] | 2018 | pMap | $N/A$ | $N/A$ | UJI Library | Y | RNN | 2.48 $\mathrm{m}$ | 99.6% floor level | RSSI | |

pMap | $N/A$ | $N/A$ | UJI Library | Y | LSTM | 2.6 $\mathrm{m}$ | 99.5% floor level | RSSI | |||

[85] | 2018 | pMap | 520 | 993 | UJIIndoorLoc | Y | RDF | 6.72 $\mathrm{m}$ | std = 4.82 m | RSSI | |

[130] | 2018 | exp | 7 | 101 | 404.5 ${\mathrm{m}}^{2}$ | Y | FF-DNN | RMSE = 0.32 m, 53.123% in 0.5 m | RSSI | ||

[43] | 2018 | exp | 4 | 25 | 80 ${\mathrm{m}}^{2}$ | N | RF | 0.40 $\mathrm{m}$ | CSI | ||

[131] | 2018 | exp | 4 | 67 | 1664 ${\mathrm{m}}^{2}$ | Y | SVM | 1.34 $\mathrm{m}$ | RSSI | ||

[86] | 2018 | pMap | 520 | 993 | UJIIndoorLoc | Y | CNN | 2.77 $\mathrm{m}$ | 100% for floor prediction | RSSI | |

[132] | 2018 | exp | $N/A$ | $N/A$ | NULL | NULL | SVR | RBF Kernel | 95% in 1.81 m | RSSI | |

[133] | 2018 | exp | 40 | 180 | 1209 ${\mathrm{m}}^{2}$ | Y | RF | 95% accuracy 1.5 × 1.5 m | RSSI | ||

[134] | 2018 | exp | 8 | 40 | 580 ${\mathrm{m}}^{2}$ | Y | RVFL | 0.43 $\mathrm{m}$ | RMSE = 0.5830 m | RSSI | |

[69] | 2018 | sim | 4 | 36 | 441 ${\mathrm{m}}^{2}$ | N | RVM | PLS | 0.84 $\mathrm{m}$ | RSSI | |

exp | 6 | 25 | 156 ${\mathrm{m}}^{2}$ | Y | RVM | PLS | 41% in 1 m and 91% in 2 m | RSSI | |||

[135] | 2017 | exp | 3 | 110 | 109.25 ${\mathrm{m}}^{2}$ | N | FF-DNN | RMSE = 0.6782 m | RSSI | ||

[136] | 2017 | exp | 4 | $N/A$ | NULL | N | ANN | RMSE = 1.1045 m | RSSI | ||

exp | 6 | $N/A$ | NULL | N | ANN | RMSE = 1.2288 m | RSSI | ||||

[137] | 2017 | exp | 16 | 126 | 304 ${\mathrm{m}}^{2}$ | Y | SVM | 1.43 $\mathrm{m}$ | RSSI | ||

[138] | 2017 | sim | 6 | 441 | 100 ${\mathrm{m}}^{2}$ | N | LS-SVM | 2.56 $\mathrm{m}$ | RSSI | ||

[139] | 2017 | exp | 38 | 411 | 600 ${\mathrm{m}}^{2}$ | Y | ELM | 1.91 $\mathrm{m}$ | RSSI | ||

[140] | 2017 | exp | 28 | 67 | 30 ${\mathrm{m}}^{2}$ | N | ANN | 2.2 $\mathrm{m}$ | RSSI | ||

[141] | 2017 | exp | 185 | 480 | NULL | Y | SVM | 100% shop level | RSSI | ||

[142] | 2017 | pMap | 520 | 993 | UJIIndoorLoc | Y | DNN | 92% floor recognition | RSSI | ||

[143] | 2017 | exp | 8 | 48 | 53.35 ${\mathrm{m}}^{2}$ | N | SVR | 86.2% in 1.5 m and 90.4% in 2 m | RSSI | ||

[144] | 2017 | exp | $N/A$ | $N/A$ | NULL | Y | SVM | 97.31% flat and 88.38% floor | RSSI | ||

[145] | 2016 | exp | 22 | 84 | 387.75 ${\mathrm{m}}^{2}$ | Y | BPNN | 0.98 $\mathrm{m}$ | RSSI | ||

[146] | 2016 | sim | 4 | 25 | 400 ${\mathrm{m}}^{2}$ | N | MLP-ANN | 0.27 $\mathrm{m}$ | std = 0.36 m | RSSI | |

[147] | 2016 | sim | $N/A$ | $N/A$ | NULL | NULL | EB-ANN | RMSE = 0.4991 m | RSSI | ||

[148] | 2016 | exp | 5 | 54 | 150 ${\mathrm{m}}^{2}$ | Y | SVR | 70% in 5 m | RSSI | ||

[149] | 2016 | exp | 16 | 188 | 1125 ${\mathrm{m}}^{2}$ | Y | ANN | 1.89 $\mathrm{m}$ | 90% in 2.971 m | RSSI | |

[70] | 2016 | sim | 12 | 1600 | 1600 ${\mathrm{m}}^{2}$ | N | SVR | RMSE = 1.42 m | RSSI | ||

exp | 13 | 116 | 1000 ${\mathrm{m}}^{2}$ | Y | SVR | RMSE = 1.8 m, 74% in 2 m | RSSI | ||||

[150] | 2016 | exp | $N/A$ | 112 | 460 ${\mathrm{m}}^{2}$ | Y | SVM | 1.2 $\mathrm{m}$ | RSSI |

**art**: Article;

**mAlg**: Main algorithm used;

**est**: Experimental or pMapulated study; sAlg: Other algorithms used in the study;

**AP**: APs used;

**mError**: Mean Error;

**rPoint**: Reference Points used in offline phase;

**oError**: Other metrics reported in the study;

**fMap**: Size of experimental room or radio-map used;

**sType**: Signal type used;

**fmRoom**: Rooms used in exp/pMap.

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Public Radio Map | Year | Size | APs | rPoints | Others |
---|---|---|---|---|---|

UJIIndoorLoc | 2014 | 110,000 ${\mathrm{m}}^{2}$ | 520 | 993 | three buildings with four or five floors depending on the building. |

IPIN2016 | 2016 | 150 ${\mathrm{m}}^{2}$ | 168 | 57 | a university corridor |

UTSIndoorLoc | 2019 | 44,000 ${\mathrm{m}}^{2}$ | 589 | 1452 | a building with sixteen floors, including three basement levels |

JUIndoorLoc | 2019 | 2646 ${\mathrm{m}}^{2}$ | 172 | 2646 | faculty rooms, classrooms, seminar rooms, research labs, and corridor |

Rajen Bhatt | 2019 | 4 rooms | 7 | 1000 | conference room, kitchen, or indoor sports room |

Cramariuc | 2016 | 2 university building | 663 | 2651 | data divided into two different University buildings. |

WiFine | 2020 | 9000 ${\mathrm{m}}^{2}$ | 436 | 26,418 | based on 260 trajectories |

UJI Library | 2020 | 308.4 ${\mathrm{m}}^{2}$ | 448 | 212 | data taken across fifteen months at the same positions and directions |

Tampere | 2017 | 22,570 ${\mathrm{m}}^{2}$ | 992 | 4648 | 882 rooms on six floors |

Art | Year | mAlg | mError |
---|---|---|---|

[41] | 2021 | DRL | $3.06\mathrm{m}$ |

[80] | 2021 | CHISEL (CNN) | $6.95\mathrm{m}$ |

[21] | 2021 | CNN | $12.4\mathrm{m}$ |

[81] | 2021 | DeepLocBox (NN) | $9.07\mathrm{m}$ |

[82] | 2021 | Edgeloc(CapsNet) | $7.93\mathrm{m}$ |

[29] | 2020 | RNN | $4.91\mathrm{m}$ |

[83] | 2019 | RNN | $4.2\mathrm{m}$ |

[84] | 2019 | MLP | $5.64\mathrm{m}$ |

[85] | 2018 | RDF | $6.72\mathrm{m}$ |

[86] | 2018 | CNN—Single RSS vector | $10.25\mathrm{m}$ |

CNN—Time Series | $2.77\mathrm{m}$ |

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Bellavista-Parent, V.; Torres-Sospedra, J.; Pérez-Navarro, A.
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review. *Sensors* **2022**, *22*, 4622.
https://doi.org/10.3390/s22124622

**AMA Style**

Bellavista-Parent V, Torres-Sospedra J, Pérez-Navarro A.
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review. *Sensors*. 2022; 22(12):4622.
https://doi.org/10.3390/s22124622

**Chicago/Turabian Style**

Bellavista-Parent, Vladimir, Joaquín Torres-Sospedra, and Antoni Pérez-Navarro.
2022. "Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review" *Sensors* 22, no. 12: 4622.
https://doi.org/10.3390/s22124622