Transmission Performance Optimization of Wireless Sensor Networks Driven by Artificial Intelligence

Abstract

In this article, the transmission performance optimization of wireless sensor networks (WSN) is deeply studied, aiming at improving the transmission efficiency of WSN, reducing energy consumption and prolonging the network life through artificial intelligence (AI) technology. To accomplish the aforementioned goals, we devise an artificial intelligence-driven optimization model. This encompasses a detailed elucidation of the model’s conceptual framework, encompassing data preprocessing, feature selection, AI algorithm formulation, as well as verification and evaluation methodologies. Our experimental approach involves selecting representative WSN (Wireless Sensor Network) application contexts, with model efficacy validated through both simulated experiments and empirical investigations. The outcomes indicate that, when compared to the BPNN (Backpropagation Neural Network) technique, our proposed model notably enhances data transmission rates, mitigates latency and packet loss, and accomplishes superior energy management. It is concluded that artificial intelligence technology has obvious advantages in optimizing the transmission performance of WSN. A more efficient and reliable WSN system can be realized by intelligently sensing changes in network environment and dynamically adjusting transmission strategies.