Large-Scale Network Lifetime Inference Based on Universal Scaling Function

Reliability evaluation of complex network is one of main topics in complex engineering systems, especially for Internet of Things (IoT). The reliability of IoT partially depends on its large-scale network. Especially, the lifetime distribution of large-scale network is critical for its health management. However, the large scale of the network usually leads to an expensive simulation time cost. Instead of direct simulation, we propose a method to infer the large-scale network lifetime using small-scale networks with the universal scaling function. We first find the scaling relationships between network lifetime and network size in a network model with failure coupling for two-dimensional square lattice network and Cayley tree network. Network lifetime with different size can be described by one universal scaling function. Then we perform theoretical analysis to derive the scaling relationships. Finally we apply these scaling relationships to wireless sensor network with coupled failures in more realistic situation as case study. From the simulation results of smaller-scale networks, we can infer the lifetime distribution of large-scale networks based on universal scaling function. The computation time and accuracy are compared with standard Monte Carlo simulation which shows that our method is faster and accurate. Our research shows that the proposed method using universal scaling functions can help us to infer the lifetime properties of large-scale networks with low computational cost. Our method can help fast reliability evaluations of large-scale complex networks with high accuracy.