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

Abstract

This study presents an ambient intelligence oriented, topological robustness scheme for internet of things (IoT). The scheme primarily exploits the underlying geometric properties of scale-free IoT networks for a substantial improvement of genetic algorithm (GA) based state of the art robustness techniques. The geometrically optimized GA (Go-GA) is subsequently extended to traditional heuristics algorithms by proposing their geometrically optimized variants. All three techniques are comparatively evaluated over a simulated scale-free IoT architecture employing Schneider R 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 R. Traditional techniques of hill climbing algorithm (HCA), simulated annealing algorithm (SAA) and ROSE also improve by a margin of 11%, 12% and 14% respectively with consideration of geometric aspects. Moreover, as the network size increases, a mere decline of 7.6% in robustness R is observed for Go-GA as compared to 18% degradation for classical algorithms.