Robust Topology Generation of Internet of Things Based on PPO Algorithm Using Discrete Action Space

The rapid proliferation of Internet of Things (IoT) devices has led to the deployment of numerous sensor nodes in various industrial scenarios. These nodes play a crucial role in collecting, relaying, processing, and transmitting data through wireless communication. The interconnections between these nodes form diverse topologies, each exhibiting varying degrees of robustness against different types of attacks. To make the robustness of topology more robustness, this article proposes a robust topology generation method for IoT nodes. The method leverages the proximal policy optimization (PPO) algorithm in reinforcement learning, combined with a discrete action space that closely aligns with real-world deployment environments. By utilizing PPO and discrete actions, the method effectively optimizes the topology of IoT nodes, considering the constraints and limitations of the deployment scenario. In addition, this article introduces a novel metric called
$S$
-value to evaluate the robustness of the generated topology. Unlike existing metrics, the
$S$
-value provides a more practical and meaningful assessment of topology robustness, taking into account the ability of the topology to withstand attacks and maintain connectivity to the server even when nodes with high degrees fail. Experimental results demonstrate the effectiveness of the proposed method in generating robust topologies for IoT nodes.