Research on Optimization of Node Deployment in Wireless Sensor Networks Driven by Artificial Intelligence

Abstract

The security deployment of WSN (Wireless Sensor Network) nodes also needs to ensure the performance of node coverage, which affects the accuracy and integrity of information detected by the network and the quality of service of the network. It is not enough to improve the network coverage and network performance of nodes only by adjusting the network topology of WSN, so there will be a problem of excessive node density in the network deployment area, which will lead to node redundancy and coverage hole, which will eventually affect the accuracy and integrity of monitoring information. In this paper, the deployment of WSN nodes driven by AI(Artificial Intelligence) is optimized. The data fusion theory is introduced, and the data fusion model is used to integrate node synchronization task scheduling, dynamic node deployment and dynamic network topology recovery.