# Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

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## Abstract

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## 1. Introduction

- We specified the RE signature for a rich multipath indoor environment, which is suitable for the classification of wall materials;
- We defined an ML-based framework for the estimation of a wall material which takes advantages of three engineering domains, i.e., measurements, modeling and simulations (environment and communication system modeling and ray-tracing simulations), artificial intelligence (ML techniques) and electromagnetics (EM properties of matter and radio-propagation mechanisms);
- With ML we confirmed the domain-knowledge-based assumptions, stating that the EM properties of the materials and the transmitter/receiver position affect the wall-material classification performance;
- We analyzed and compared four ML techniques for the wall-material classification applied to the same dataset.

## 2. Background and Related Work

#### 2.1. Indoor Mapping

#### 2.2. Relative Permittivity Estimation

#### 2.3. Wireless Sensing

#### 2.4. Indoor Environment Classification

## 3. Radio-Environment Signature

## 4. ML-Based Wireless-Sensing Framework

- RE acquisition module: collects the propagation parameters of multiple wireless links in different indoor environments by measurements or computer simulations, pre-processes the results and transforms them into the form of partial RE signature;
- Propagation characteristics storage module: stores the partial RE signature, environment and radio system description in a database for the purposes of building a large dataset for the ML task and as open-access data for other RE-related studies.
- ML-based radio-analysis module: builds training and testing datasets from the main dataset according to a predefined scenario, extracts knowledge from the training set, builds a model and applies the model to classify the RE from the input RE signature.
- ML performance-evaluation module: calculates ML performance metrics and stores the statistics.
- Domain-knowledge-based interpretation module: Evaluates the classification results by taking into account the domain knowledge in the field of EM propagation.

#### 4.1. Radio-Environment Acquisition Module

#### 4.2. Propagation Characteristic Storage Module

- Description of the environment;
- Description of the radio system, (i.e., transmitters and receivers);
- RE signatures.

#### 4.3. Machine Learning Based Radio-Analysis Module

#### 4.4. Machine Learning Performance-Evaluation Module

- Classification accuracy is the ratio between the number of correct predictions and the total number of input instances. It is a relevant metric in our classification problem since the number of instances belonging to each class is the same. The classification accuracy for the ith class is calculated with (4):$$Accurac{y}_{i}=\frac{T{P}_{i}+T{N}_{i}}{T{P}_{i}+T{N}_{i}+F{P}_{i}+F{N}_{i}}.$$
- F-score (F) is the harmonic mean of precision and recall. It is the measure of classifier precision and robustness. It tells us how many instances are classified correctly and if it does not miss a significant number of instances. The large F values represent good performance of the model. F calculated for the ith class is obtained by using (5)–(7):$$Precisio{n}_{i}=\frac{T{P}_{i}}{T{P}_{i}+FPi},$$$$Recal{l}_{i}=\frac{T{P}_{i}}{T{P}_{i}+F{N}_{i}},$$$${F}_{i}=2\times \frac{Precisio{n}_{i}\times Recal{l}_{i}}{Precisio{n}_{i}+Recal{l}_{i}}.$$

#### 4.5. Domain-Knowledge-Based Interpretation Module

## 5. Framework Use Case Example

#### 5.1. Dataset Building

- Setting up an indoor scene, including the geometry and EM properties of the wall materials;
- Specification of the communication technology and its parameters and the location of the radio nodes;
- Specification or ray-tracing algorithm;
- Execution of simulations to obtain the dataset.

#### 5.2. Dataset Summary

#### 5.3. Simulation Scenarios

- ${\mathrm{C}}_{\mathrm{train}}{\mathrm{C}}_{\mathrm{test}}$: training and testing on the dataset that corresponds to the central transmitter position;
- ${\mathrm{M}}_{\mathrm{train}}{\mathrm{M}}_{\mathrm{test}}$: training and testing on the dataset that corresponds to multiple transmitter positions;
- ${\mathrm{C}}_{\mathrm{train}}{\mathrm{M}}_{\mathrm{test}}$: training on the dataset that corresponds to the central transmitter position and testing on the data that corresponds to the multiple non-central transmitter positions.

#### 5.4. Results and Analysis

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

6G | sixth generation |

ANN | artificial neural network |

AoA | angle of arrival |

CTF | channel transform function |

CIR | channel impulse response |

CM | confusion matrix |

CSI | channel state information |

DT | decision tree |

EM | electromagnetic |

F | F-score |

FCF | frequency coherence function |

FP | false positives |

FN | false negatives |

HP | Horizontal polarization |

IoT | Internet of Things |

KNN | k-nearest neighbor KNN |

LiDAR | light detection and ranging |

LoS | line of sight |

ML | machine learning |

MP | Multilayer Perceptron |

NB | naive Bayes |

RE | radio environment |

RF | random forest |

RTI | radio tomographic imaging |

RX | eceiver |

SLAM | simultaneous localization and mapping |

SBR | shooting and bouncing ray |

SVM | support vector machine |

TN | true negatives |

TP | true positives |

TX | transmitter |

UWB | ultra-wide-band |

VP | vertical polarization |

W | wall |

WSN | wireless sensor network |

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**Figure 6.**Confusion matrices for different ML algorithms and scenarios, (B—brick, C—concrete, G—glass, W—wood).

**Table 1.**Summary of the parameters used for DT, RF, and MP [80].

ML Algorithm | Parameter | Description | Value |
---|---|---|---|

DT | Confidence factor | Confidence factor used for pruning | 0.25 |

Number of objects | Minimum number of instances per leaf | 2 | |

Number of folds | Size of the pruning set | 3 | |

RF | Number of trees | Number of trees in the forest | 25 |

Number of instances | Minimum number of instances per leaf | 1 | |

Number of levels | Maximum number of levels in each decision tree | unlimited | |

MP | Learning rate | Learning rate for the backpropagation algorithm (value between 0 and 1) | 0.3 |

Momentum | Momentum rate for the backpropagating algorithm (value between 0 and 1) | 0.2 | |

Traininng time | Number of epochs to train trough | 500 |

**Table 2.**Electrical properties of wall materials [4].

Material | ${\mathit{\u03f5}}_{\mathit{r}}^{\prime}$ | $\mathit{\sigma}$ (S/m) |
---|---|---|

Brick | 3.75 | 0.038 |

Concrete | 5.31 | 0.120 |

Glass | 6.27 | 0.029 |

Wood | 1.99 | 0.026 |

Transmitter Position | Position Coordinates (x,y) |
---|---|

CC | (1.5 m, 1.5 m) |

DC | (1.5 m, 0.1 m) |

DL | (0.5 m, 0.5 m) |

DR | (2.5 m, 0.5 m) |

UC | (1.5 m, 2.9 m) |

UL | (0.5 m, 2.5 m) |

UR | (2.5 m, 2.5 m) |

Scenario | ML Algorithm | |||
---|---|---|---|---|

NB | DT | MP | RF | |

${\mathrm{C}}_{\mathrm{train}}{\mathrm{C}}_{\mathrm{test}}$ | 94.2% | 94.9% | 98.1% | 98.4% |

${\mathrm{M}}_{\mathrm{train}}{\mathrm{M}}_{\mathrm{test}}$ | 84.4% | 94.1% | 96.8% | 97.6% |

${\mathrm{C}}_{\mathrm{train}}{\mathrm{M}}_{\mathrm{test}}$ | 68.8% | 72.1% | 71.0% | 85.5% |

Material | Scenario | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

${\mathbf{C}}_{\mathbf{train}}{\mathbf{C}}_{\mathbf{test}}$ | ${\mathbf{M}}_{\mathbf{train}}{\mathbf{M}}_{\mathbf{test}}$ | ${\mathbf{C}}_{\mathbf{train}}{\mathbf{M}}_{\mathbf{test}}$ | ||||||||||

ML Algorithm | ||||||||||||

NB | DT | MP | RF | NB | DT | MP | RF | NB | DT | MP | RF | |

Brick | 0.90 | 0.89 | 0.97 | 0.97 | 0.72 | 0.89 | 0.94 | 0.96 | 0.43 | 0.55 | 0.51 | 0.76 |

Concrete | 0.92 | 0.92 | 0.97 | 0.98 | 0.80 | 0.93 | 0.96 | 0.97 | 0.60 | 0.60 | 0.58 | 0.80 |

Glass | 0.99 | 0.99 | 1.00 | 0.99 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.95 | 0.93 | 0.98 |

Wood | 0.96 | 0.98 | 0.99 | 0.99 | 0.89 | 0.96 | 0.97 | 0.98 | 0.70 | 0.74 | 0.82 | 0.88 |

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**MDPI and ACS Style**

Kocevska, T.; Javornik, T.; Švigelj, A.; Hrovat, A.
Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials. *Electronics* **2021**, *10*, 2843.
https://doi.org/10.3390/electronics10222843

**AMA Style**

Kocevska T, Javornik T, Švigelj A, Hrovat A.
Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials. *Electronics*. 2021; 10(22):2843.
https://doi.org/10.3390/electronics10222843

**Chicago/Turabian Style**

Kocevska, Teodora, Tomaž Javornik, Aleš Švigelj, and Andrej Hrovat.
2021. "Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials" *Electronics* 10, no. 22: 2843.
https://doi.org/10.3390/electronics10222843