# Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks

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## Abstract

**:**

## 1. Introduction

- We propose a new hybrid ANN model, and compare its performance with conventional fully-connected ANN in reconstructing EMF exposure. In order to make better use of key inputs of ANN, including distance to the source, direction of the transmitting antennas, blockage in the surrounding environment, time variation and background noise, we propose a hybrid ANN. The idea is to process information from one BSA locally, then concentrate the data from different BSA together.
- We propose a new reconstruction method based on the one-time drive testing with the help of sensor networks. We compare its performance against the reconstruction method based on sensor networks only. The new reconstruction method has the advantage of achieving good predictions with lower cost.

## 2. System Model

#### 2.1. Exposure Reference Map Construction and Setting

- -
- the direction of each antenna is uniformly distributed;
- -
- the transmit power for each BSA is equal.

#### 2.2. Parametric Exposure Reference Map

#### Stochastic Block-Based Path Loss Model

**Remark**

**1.**

**Remark**

**2.**

#### 2.3. Time Variation

**Remark**

**3.**

#### 2.4. Adding Noise to Exposure

## 3. Regression Neural Network Set Up

^{2}[25]. MSE is used to minimize the residual sum of squares (RSS) and R

^{2}indicates how close two sets of data are in terms of distribution. To be more specific, R

^{2}is defined as:

#### Tuning Hyper-Parameters

## 4. New Hybrid-Connected Neural Networks

**Remark**

**4.**

## 5. Reconstruction Methods

#### 5.1. Case 1: Through Sensor Networks

#### 5.2. Case 2: Through Combined Drive Testing and Sensor Networks

## 6. Results

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AI | Artificial Intelligence |

ANN | Artificial Neural Network |

AWGN | Added White Gaussian Noise |

BSA | Base Station Antennas |

EEM | EMF Exposure Map |

EMF | Electromagnetic Field |

ERM | Exposure Reference Map |

FC | Fully-Connected |

LC | Locally-Connected |

LoS | Line-of-Sight |

MSE | Mean Square Errors |

NLoS | Non Line-of-Sight |

PL | Path Loss |

PLE | Path Loss Exponent |

Q-Q Plot | Quantile-to-Quantile Plot |

reLU | Refined Linear Unit |

RF | Radiofrequency |

RSS | Residual Sum of Squares |

STD | Standard Deviation |

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**Figure 1.**Base station antennas (BSA) and street lamps inside and around the 14th district in Paris.

**Figure 2.**Block-based path loss (PL) model. (

**Left**): Example of deterministic four-block based PLE model in the 14th district. (

**Middle**): Map of the Block-based stochastic Path Loss Exponent (PLE) model. (

**Right**): Receivers lie inside each block have normally-distributed PLE.

**Figure 7.**Averaged mean square error (MSE) and R${}^{2}$ with standard deviation (STD) based on sensor network inputs.

**Figure 8.**Exposure Reference Map (ERM), reconstructed electromagnetic field exposure map (EEM) and absolute error maps obtained from hybrid ANN for ${N}_{training}=50$ cases.

**Figure 9.**ERM, reconstructed EEM and absolute error maps obtained from hybrid ANN for ${N}_{training}=1758$ cases.

**Figure 11.**Scattering plot between predictions and targets from ANN. From left to right: (

**a**) conventional ANN without considering sensor information as inputs of ANN; (

**b**) conventional ANN considering sensor information as inputs of ANN; (

**c**) hybrid ANN considering sensor information as inputs.

**Figure 12.**Quantile-to-Quantile (Q-Q) plot between predictions and targets from ANN. From left to right: (1) conventional ANN without considering sensor information as inputs of ANN; (2) conventional ANN considering sensor information as inputs of ANN; (3) hybrid ANN considering sensor information as inputs.

Total number of sensors in case 1 | 3516 |

Number of sensors selected in case 2 | 50 |

Additive Noise level | SNR = 15dB |

PLE in Block-based PL Model | ${\alpha}_{x}=\{2.5,3,3.5,4\}$ and $\{2,2.8,4.4,3.6\}$ |

${\sigma}^{2}$ in normal distribution | ${\sigma}^{2}=\{0.1,0.15\}$ |

${N}_{training}$, ${N}_{validation}$ and ${N}_{testing}$ in case 2 | 469, 201 and 670 |

Hyper-Parameters | Conv.-Case 1 | Hybrid-Case 1 | Conv.-Case 2 | Hybrid-Case 2 |
---|---|---|---|---|

Pre-processing | Standardization | |||

Num. of hidden layers | 4 | 2(LC), 1(FC) | 4 | 2(LC), 3(FC) |

Num. of neurons | 50 | 20(LC), 30(FC) | 50 | 10(LC), 40(FC) |

Learning rate | $1\times {10}^{-4}$ | |||

Batch size | 10 | |||

Patience in early stopping | 30 |

Scenarios | Num. of Para. | MSE (±STD) | R${}^{2}$ (±STD) |
---|---|---|---|

Conventional ANN without considering sensors | 8851 | 11.2 (±0.91) | 0.81 (±0.02) |

Conventional ANN considering sensors | 8951 | 9.2 (±0.73) | 0.85 (±0.01) |

Hybrid ANN with considering sensors | 5431 | 7.9 (±1.06) | 0.87 (±0.02) |

Conventional ANN without considering sensors with noise | 8851 | 16.3(±0.85) | 0.72 (±0.01) |

Conventional ANN considering sensors with noise | 8951 | 14.6 (±0.76) | 0.75 (±0.01) |

Hybrid ANN without considering sensors with noise | 5431 | 12.8 (±0.96) | 0.78 (±0.02) |

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**MDPI and ACS Style**

Wang, S.; Wiart, J. Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks. *Int. J. Environ. Res. Public Health* **2020**, *17*, 3052.
https://doi.org/10.3390/ijerph17093052

**AMA Style**

Wang S, Wiart J. Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks. *International Journal of Environmental Research and Public Health*. 2020; 17(9):3052.
https://doi.org/10.3390/ijerph17093052

**Chicago/Turabian Style**

Wang, Shanshan, and Joe Wiart. 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks" *International Journal of Environmental Research and Public Health* 17, no. 9: 3052.
https://doi.org/10.3390/ijerph17093052