GA-based geometrically optimized topology robustness to improve ambient intelligence for future internet of things

as metric of robustness. The study follows a data-driven approach where information about nodes and edges is pulled from a central big data server, and topological robustness of a given scale-free IoT is tested against existing benchmarks. The proposed scheme aims to achieve convergence to global optima and conserve computational costs by efficient edge swapping (EES) and node removal based thresholding (NRT). Performance evaluations show that Go-GA outperforms state of the art variants of GA by a margin of 20% for Schneider is observed for Go-GA as compared to 18% degradation for classical algorithms.