Research on Information Fusion Algorithm for Wireless Sensor Homogeneous Networks

Information fusion is an effective method to solve the energy and communication bandwidth limitations of wireless sensor networks. This article mainly studies relevant information fusion, and through simulation analysis of three commonly used data fusion algorithms: mean method, Bayesian estimation method, and Kalman filter method, it is analyzed that the data processed by mean method fusion is not easily affected by environmental interference and can meet a wide range of user needs. Bayesian estimation method can be used to eliminate measurement errors caused by inherent problems and sudden noise in sensor nodes, and Kalman filter method can effectively eliminate environmental noise interference. In operation, more suitable algorithms for homogeneous wireless sensor networks should be flexibly selected according to actual needs.