A Deep Learning Approach to Accurately Predict the κ-Coverage Probability in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) is one of the most widely employed technology because it has numerous applications in almost every walk of life. The analytical results available for large-scale WSNs cannot be utilised to estimate the performance of WSNs deployed in a finite region due to Boundary Effects (BEs). In addition, wireless channel characteristics are affected by diverse environmental phenomena such as the presence of impediments, interference, reflection, and refraction, etc. Therefore, we render an analytical model by considering BEs in the shadowed environments to estimate the $\kappa$-coverage metric of a WSN installed in a circular region (CR). Validation of the analytical models is a time-consuming and tedious task and requires hours. To overcome this problem, in this study, we proposed a framework based on feed-forward Artificial Neural Network (ANN) to map the $\kappa$-coverage probability using nodes, sensing range, the standard deviation of shadowing denoted by sigma, and required $\kappa$ as features. These features were extracted through Monte Carlo simulations. We estimated the feature importance and performed the feature sensitivity analysis before training the ANN model. We trained two feed-forward ANN models for with and without BEs. We found sensing range is the most important feature in predicting the $\kappa$-coverage probability. Further, the proposed feed-forward framework performs equally well for both cases, with correlation coefficient (R) = 0.98 and Root Mean Square Error (RMSE) = 0.07. Furthermore, it also outperforms the results obtained through the Adaptive Neuro-Fuzzy Inference System (ANFIS).