Tactical communication network key node identification method based on environmental feedback

Abstract

The invention discloses a tactical communication network key node identification method based on environmental feedback, and relates to the technical field of network communication, and the method comprises the steps: selecting a network node from a tactical communication network as a target interference node; the target interference node is interfered, in the interference process, physical data are collected from the tactical communication network, and an environment feedback value v of the target interference node is obtained according to the number of frames of the collected physical data;calculating an average environment feedback value reware of the target interference node according to the interference times Playcount of the target interference node and the environment feedback value v; and repeating the operation process until the selected target interference node converges and the obtained average environment feedback value reware is stable, and taking the current physical interference node as a key node of the tactical communication network. In the process of identifying the key node, only the interfered physical layer data is required to be acquired, a network topology structure is not required to be acquired, the physical data is not required to be analyzed, and feedback information required to be acquired is simple and easy to obtain.

Description

technical field [0001] The invention relates to the technical field of network communication, in particular to a method for identifying key nodes of a tactical communication network based on environmental feedback. Background technique [0002] At present, most of the important node evaluation methods are based on graph theory and graph data mining, which are mainly divided into two categories: the first category thinks that nodes connected with other nodes in the network have distinctive features different from other nodes, and propose betweenness, Measurement indicators such as eigenvectors and subgraph centers; the second category proposes measurement indicators such as kernel degree and spanning tree number by evaluating the degree of influence on the connectivity of the entire network after a node is deleted. On the basis of the above, a small number of studies believe that a single measurement standard cannot fully measure the importance of nodes, so methods such as gr...

AI Technical Summary

Problems solved by technology

Due to the characteristics of the tactical communication network, the network topology and clear network load information are difficult to obtain, so the above method is not perfectly applicable to the tactical communication network

Images