Noncooperative Topology Inference of Wireless Networks With Monitoring Sensors

Abstract

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a non-cooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatio-temporal features (TDSTF). Specifically, the monitoring sensor network monitors the power of the non-cooperative network and locates the nodes of the non-cooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the non-cooperative nodes are used as characteristics to infer the topology of the non-cooperative network based on K-Nearest Neighbors (KNN). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.