CARE: an efficient modelling for topology robustness of an IoT based healthcare network using Go-GA

The proliferation of the Internet of Things (IoT) in healthcare necessitates networks that are robust against potential node or link failures to ensure uninterrupted patient care. This research concentrates on enhancing the topology robustness of IoT-based smart healthcare networks. Utilizing Schneider R as a robustness metric, a system model is developed, where IoT nodes' geographic information is centrally stored. Introducing the Convergence And Robustness Efficiency (CARE) solution, we exploit a geometric approach within genetic algorithms (GA) to enhance the network's topology robustness. CARE's nested strategy tackles the slow convergence and high computational costs seen in contemporary techniques, leading to optimized results. Simulations show that CARE surpasses conventional GA by an impressive 21% in Schneider R and only degrades by 7.5% (compared to 16% of existing solutions) when node density surges to 200 in healthcare settings. By tapping into efficient computing methods, this research paves the way for robust healthcare IoT networks, ensuring patient safety and data integrity.