Deep Actor–Critic Learning-Based Robustness Enhancement of Internet of Things

The extensive applications in the Internet of Things (IoT) have inspired a growing network scale. However, due to the resource-limited IoT devices and the numerous cyber attacks against applications, maintaining the robustness and communication capabilities for the applications is increasingly challenging. In this article, we consider IoT network topologies that provide robust communication for heterogeneous networks and study the networking stability of IoT devices and the intelligent evolution computing in network architectures. We explicate the network robustness problem both for the network architecture and the resistance to cyber attacks. For the network architecture, we optimize the robustness of IoT network topology with a scale-free network model which has good performance in random attacks. In the case with the resistance to cyber attacks, a deep deterministic learning policy (DDLP) algorithm is proposed to improve the stability for large-scale IoT applications. Simulations show that the proposed algorithms greatly advance the robustness of IoT network topology compared to other algorithms, with a less computational cost.