Predicting WSN packet loss using machine learning: Applications in solid surroundings

The effectiveness of wireless communication systems exposed to radio propagation in their environment is shown by path loss, a key performance metric. For a long time, researchers have used the correlations they proposed to calculate route loss for waves moving across different environments with few operational factors. To swap out the log-normal shadowing model for route loss calculation on concrete surfaces, this study presents a new model based on weights of artificial neural networks. In the training phase, the neural network was provided the data of the physical separation between the transmitters and receivers of the wireless sensor nodes (d) and the radial angle of the reception node's position (Ϙ) as the target variable, path loss. Then, by utilizing the weights of the network, a novel PL prediction formula was developed. When tested across all ranges of experimental data, this formula outperforms the log-normal shadowing model, the FSPL model, and the Two-Ray model in predicting the average PL in concrete surfaces, with mean absolute deviation values of 0.51%, 4.1%, 40.58%, and 28.79%, respectively.