Identifying Critical Nodes in Heterogeneous Networks for IoT Systems Based on Global and Local Centrality

In heterogeneous networks within industrial IoT systems, critical nodes are typically decision-making nodes or those possessing robust information reception and transmission capabilities. To identify high-influence nodes in heterogeneous networks, this paper proposes a node importance evaluation method that integrates betweenness centrality and degree centrality, aiming to identify nodes that exert a key impact on the functionality of heterogeneous networks. First, nodes possessing capabilities such as recognition, decision-making, and attack are constructed into a heterogeneous directed network. Subsequently, degree centrality and betweenness centrality are employed to measure the local and global influence of nodes. Then, the Euclidean distance formula is used to calculate the distance between the node and the origin in the two-dimensional centrality space formed by betweenness centrality and degree centrality, yielding a node importance evaluation metric. Finally, by comparing the importance metrics of all nodes, the critical nodes within the heterogeneous network are identified. Experimental results show that this algorithm achieves higher accuracy and faster speed in identifying critical nodes within heterogeneous networks of IoT systems.