Research on Machine Learning Energy Consumption Optimization Model in Wireless Sensor Networks

Wireless sensor network (WSN) has attracted wide attention due to its wide application in the fields of Internet of Things, environmental monitoring, smart home and so on. How to optimize energy consumption effectively and extend the service life of sensor networks has become a key research issue. Traditional energy consumption optimization methods usually rely on static strategy or simple heuristic algorithm, which is difficult to cope with complex and variable network environment and load demand. Therefore, an energy consumption optimization model WSN-LR-EOM based on logistic regression algorithm is proposed in this paper, which aims to optimize the energy management strategy of nodes through dynamic learning and prediction of network state. In this model, the energy consumption behavior of nodes is modeled by logistic regression algorithm, and the changes of real-time network environment are forecasted to achieve energy scheduling and load balancing. By introducing logistic regression model, WSN-LR-EOM can significantly improve network energy efficiency, reduce energy consumption, and extend network life cycle on the basis of accurate prediction of energy consumption. An experimental platform is designed and implemented to simulate different scales and types of wireless sensor networks. The experimental results show that compared with the traditional energy consumption optimization method, WSN-LR-EOM can significantly improve the accuracy, reduce energy consumption, and effectively extend the life cycle of the network. In addition, the model has strong adaptability and can adapt to the changes of network load and environment, which has good practical value and application prospect.