Wireless sensor network topology optimization method based on asynchronous deep reinforcement learning

Abstract

The invention discloses a wireless sensor network topology optimization method based on asynchronous deep reinforcement learning. The method comprises the following steps: a step of generating an initialized wireless sensor network topology structure by utilizing a rule of a scale-free network model; a step of compressing the topological structure of the wireless sensor network; a step of initializing an asynchronous deep reinforcement learning model; a training and testing stage; a training stage, wherein firstly, a wireless sensor network topological structure is serialized, and a row vectoris used for representing the wireless sensor network topological structure; then, respectively inputting the row vectors of the network topology structure into different local network training models; secondly, the local network training model comprises two neural network models, namely an action selection strategy network and a strategy evaluation network; a test stage wherein the global networktraining model performs test evaluation on the test data set; a step of repeating the steps 1, 2, 3 and 4 till the maximum number of iterations is reached.

Description

Technical field [0001] The present invention relates to the present invention mainly relates to the technical field of wireless sensor network, in particular to a wireless sensor network topology optimization method based on asynchronous deep reinforcement learning. Background technique [0002] As an important network component of the Internet of Things in smart cities, wireless sensor networks play an important role in obtaining information in real time, enabling people to obtain more information they want. By analyzing these data, the service quality of smart cities can be improved and people can deal with emergencies. Wireless sensor networks are widely used, such as smart homes, smart wearable devices, smart transportation, environmental monitoring, homeland security, border detection, etc. The premise of the above application is that the network has high robustness and can send the sensed data to the server data center through the network, and relevant personnel or systems...

AI Technical Summary

Problems solved by technology

For example, the journal "Deep Actor-Critic Learning-basedRobustness Enhancement of Internet of Things" proposes a strategy for intelligent wireless sensor network topology based on deep reinforcement learning, but in the process of running the model, it is very dependent on GPU computing resources. Moreover, the isomorphism of the data will lead to the oscillation of the optimization results.

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