Predicting K-Coverage in Wireless Multihop Networks with Boundary Effects Using Support Vector Regression: A Feature Sensitivity Analysis for Smart City Applications

Achieving the optimal network coverage is very essential for wireless multihop networks (WMNs), mainly in finite rectangular shape areas where the boundary effects (BEs) can distort the coverage metrics. Traditionally the simulations approach many times struggle very much to scale effectively as the network parameters grow increasingly complex. In this study, we used a Support Vector Regressor (SVR) model for prediction of the k-coverage probability in those scenarios where we see both with and without boundary effects. The model we used is trained using key features such as region dimensions, sensing ranges, and the density of node. Compared to other models like GRNN, Random Forest and Gradient Boosting, the SVR is demonstrating superior performance. It is acquiring a root mean square error (RMSE) of 0.1287 and $R^{2}$ value of 0.7752 in the scenarios with boundary effects, and an RMSE of 0.1371 with an $R^{2}$ value of 0.7610 in scenarios without them. Additionally, our feature sensitivity analysis gives valuable insights for the optimization of network. This makes the proposed framework not only scalable but also highly accurate for multiple applications, including urban sensor networks, disaster management, proving effective for applications and other critical infrastructure monitoring.