Method for optimizing robustness of topological structure of Internet of Things through autonomous learning

Abstract

The invention discloses a method for optimizing robustness of a topological structure of the Internet of Things through autonomous learning. The method comprises: 1, initializing the topological structure of the Internet of Things; 2, compressing the topological structure; 3, initializing an autonomous learning model; constructing a deep deterministic learning strategy model to train the topological structure of the Internet of Things according to the features of deep learning and reinforcement learning; 4, training and testing a model; 5, periodically repeating the step 4 in one independent repeated experiment, and periodically repeating the steps 1, 2, 3 and 4 in multiple independent repeated experiments until the maximum number of iterations is reached. In the process, the maximum number of iterations is set, the experiment is independently repeated each time, and the optimal result is selected. Experiments are repeated for many times, andan average value is selected as a result ofthe experiment. According to the method, the attack resistance of the initial topological structure can be remarkably improved; the robustness of a network topology structure is optimized through autonomous learning, and high-reliability data transmission is ensured.

Description

technical field [0001] The present invention relates to the technical field of Internet of Things network technology, in particular to a method for optimizing the robustness of Internet of Things topology. Background technique [0002] The Internet of Things is an important part of the smart city network. Large-scale equipment nodes are connected together through the Internet of Things to provide people with high-quality services. However, the connected device nodes need to tolerate the threat of failure, such as random device failures, man-made malicious damage, natural disasters, energy exhaustion, etc., which cause the failure of some nodes in the network and paralyze the entire network. In the wide application scenarios of the Internet of Things, it is of great research significance to ensure that large-scale nodes can ensure high-quality data service communication on the premise that some nodes in the network fail. [0003] In traditional IoT network topology optimizat...

AI Technical Summary

Problems solved by technology

For example, the journal "Robustness optimization scheme with multi-population co-evolution for scale-freewireless sensor networks" proposes a multi-population genetic algorithm to solve the local optimal problem and obtain a globally optimal network topology. However, optimizing a network The time overhead of the topology structure is large, and the algorithm cannot accumulate optimization experience every time, so the algorithm needs to be restarted every time it runs

Secondly, some researchers use the neural network model to represent the learning behavior before and after network topology optimization, and reduce the topology optimization time, but this method requires label target data, which limits the maximum value of optimization.

Images