CuIoT: Advancing Network Connectivity With Motif Knowledge-Centric for Robust Topology

The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs' unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.