Opportunity network topology prediction method and device based on cyclic generative adversarial network

Abstract

The invention discloses an opportunity network topology prediction method and device based on a cyclic generative adversarial network, and the method comprises the steps: carrying out the slicing of the communication data of an opportunity network through a time sequence analysis method, and obtaining a series of discrete network snapshot data; randomly extracting and grouping all nodes in the opportunity network to obtain a plurality of observation node groups; intercepting the network snapshot by using a sliding window with a preset size, and extracting a network topology structure change sequence of each observation node group under each sliding window; constructing a generative adversarial network model by utilizing the generative model and the discrimination model, training the generative adversarial network model by taking the network topology structure change sequence as a training sample, and performing topology structure prediction on the opportunity network by taking the generative model in the trained generative adversarial network model as a prediction model. According to the method, the prediction model is used to extract the high-order spatial-temporal characteristicsof the topological structure evolution law of the opportunity network, and topology prediction of the opportunity network at the future moment is realized.

Description

technical field [0001] The invention relates to the field of network technology, in particular to a method and device for predicting the topology of an opportunistic network based on a cyclic generative confrontation network. Background technique [0002] The opportunistic network uses the movement of nodes to transmit data from the source node to the target node in a multi-hop manner, and realizes inter-node communication in a "store-carry-forward" routing mode. The dynamic nature of opportunistic networks and the fact that they do not require full network connectivity are more suitable for actual ad hoc network requirements, and are more universal and can meet the needs of network communications under harsh conditions. Therefore, in the field of military and civilian wireless communications All have important research and application value, and the development of related research is becoming more and more extensive. [0003] Due to the movement of nodes in opportunistic n...

AI Technical Summary

Problems solved by technology

[0005] In view of the above situation, it is necessary to provide a method and device for predicting the topology of opportunistic networks based on recurrent generative confrontation networks for the problem that the topology of opportunistic networks cannot be predicted in the prior art

Images