An Intelligent Robustness Optimization Method for Internet of Things Using Graph Neural Networks

Abstract

The limited resources and complex application environment of Internet of Things (IoT) devices, making them vulnerable to cyberattacks and natural disasters. Thus, how to improve the robustness of the IoT topology becomes a critical issue. Existing research on the robustness of IoT topology mostly uses heuristic algorithms, and the high computational cost cannot meet the needs of topology optimization in low-latency IoT scenarios. To solve this problem, this paper proposes an intelligent robustness optimization method for IoT using graph neural networks (TRO-GNN). The method first uses the graph neural network to extract the evolution characteristics from the initial IoT topology to the highly robust topology from the data set, and then the output of the graph neural network is transformed into an effective predicted topology by using the designed robustness search strategy. The experimental results show that TRO-GNN effectively improve the robustness of scale-free IoT topology against malicious attacks, and the computational cost is low.