Enhanced Salama Algorithm for Randomized Network Topology Generation

In the enhanced Salama algorithm for random network topology generation, we developed a universal MATLAB program that incorporates K-means clustering during the node placement phase. This implementation strategically partitions the deployment area using centroid-based clustering, ensuring nodes are distributed with optimal uniformity and appropriate density gradients. The algorithm employs MATLAB's kmeans() function to iteratively adjust node positions, minimizing intra-cluster distances while maintaining global distribution balance. This clustering approach not only achieves superior node spatial distribution but also optimizes edge connectivity patterns through calculated proximity thresholds. The resulting topology demonstrates significant performance improvements in network robustness and efficiency, making it adaptable to diverse application requirements. Key implementation aspects include cluster validation using silhouette analysis and dynamic threshold adjustment for edge formation based on cluster densities.