Abstract:

In order to solve the problem that the information collected from wireless sensor network nodes has a great similarity and some errors exist in the data results, this study proposes a data fusion algorithm based on Kalman filter for wireless sensor network, which improves the efficacy and accuracy of uploaded data by filtering invalid data and shrunk data packets. The algorithm uses the Kalman filter algorithm with high real-time performance to integrate the data in the wireless sensor network according to the time series. On the basis of time data fusion, according to the characteristics of spatial distribution, the data fusion of multi-sensor at the gateway layer is further carried out according to the weight. In view of the characteristics of real-time changes of different position errors, the gateway layer uses spatial data as the basis to dynamically adjust the weight of each node using an adaptive weighting algorithm. Simulation experiments show that the algorithm is easy to implement, can effectively remove redundant information, and improve data accuracy and reliability. Compared with the improved batch estimation and adaptive weighting method, the root mean square error is reduced by about 7.9% and the accuracy is improved by 2.1% after using this method.

Key words: data fusion, wireless sensor network, Kalman filter, adaptive weighting, time series, spatial series, internet of things, consistency test