Application Exploration and Optimization of Artificial Intelligence in Smart Sensor Networks

Smart sensor networks play an increasingly important role in many fields such as environmental monitoring and health care, but the differences in data distribution in different application scenarios pose challenges to the generalization ability and prediction accuracy of the model. This paper focuses on transfer learning and cross-domain adaptation techniques, aiming to improve model performance by reducing the distributional discrepancy between the source and target domains. We selected three methods based on feature transfer, model transfer, and instance transfer, and introduced the Maximum Mean Discrepancy (MMD) minimization technique and feature selection algorithm for optimization. The experimental design covers three different geographical environments: urban areas, mountainous areas, and coastal areas, and uses multiple sensor parameters including temperature, humidity, air pressure, and air quality index as input. The results show that the method based on feature transfer not only achieves the lowest mean square error (MSE), but also shows high accuracy and F1 score, proving its stability and reliability in different environments. In addition, this paper records resource consumption indicators such as training time, GPU usage, memory usage, and energy consumption in detail, providing a comprehensive cost-effectiveness evaluation for practical applications. The study found that the transfer learning method significantly improves the generalization ability of the model, solves the problem of data distributional discrepancy in different environments, and provides a theoretical basis and technical support for the application of smart sensor networks.