Variations in Wireless Network Topology Inference: Recent Evolution, Challenges, and Directions

Abstract

Wireless networks, as the foundation of the modern information society, rely crucially on network topology with the development of 6th generation mobile networks technologies. The network topology structure not only shapes the mechanism and functional dynamics of network evolution, but also reflects the communication relationship and information exchange among nodes. For this reason, wireless network topology inference has become a key research field in network science and the Internet of Things. Wireless network topology inference methods can be roughly divided into cooperative methods and non-cooperative methods. The former needs to directly participate in the communication process of the target network to obtain detailed internal information, and its applicability is limited. In contrast, the latter infers the topology through external observation of data packet timing without the need to know the internal information of the network in advance, and has broader practicability. This paper first outlines the basic concepts and scope of topology inference, and briefly reviews the cooperative methods. Then, three types of non-cooperative methods were comprehensively summarized: based on statistical learning, based on machine learning, and based on rule analysis. Using a unified dataset and evaluation metrics, the performance of four representative non-cooperative topology inference algorithms is compared. Finally, this paper points out the challenges faced by network topology inference and proposes potential future research directions, aiming to provide theoretical support for the continuous development of this field.