A Layered Multi-Rate Wireless Sensor Network Data Fusion Algorithm Based on Missing Measurements

To solve the problem of low data fusion accuracy in wireless sensor networks, a hierarchical multi-rate wireless sensor network data fusion algorithm based on missing measurement is proposed in this paper. Firstly, the measured data of each sensor node is estimated by Kalman filter, then the estimated and measured values are sent to the head node for consistency analysis and weighted data fusion. Under the premise of meeting the accuracy of data fusion, an adaptive multi-rate measurement model is proposed, which can reduce the energy consumption of wireless sensors. Finally, the cluster data fusion value of the head node is sent to the sink node, and the weighted data fusion value of the cluster data fusion value from different head nodes is performed on the sink node based on consistency analysis. The validity of improved algorithm is proved by an application instance, and the results show that the data transmission volume has been reduced, and the energy consumption of the wireless sensors has been reduced.