Automatic fusion method for perceptual data of internet of things based on Kalman filter Online publication date: Thu, 23-Jan-2025
by Andi Gao; Xiaojing Guo; Ketong Liu; Kuntai Meng
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 6, 2024
Abstract: Due to the low fusion accuracy of traditional internet of things (IoT) sensing data automatic fusion methods, an automatic fusion method of IoT sensing data based on Kalman filter is proposed. Firstly, we adopt the error correction mechanism and time registration to deal with the structure deviation of the IoT sensing data and the asynchronous problem of the IoT sensing data. Then, the Kalman filtering algorithm is used to fuse the time data and space data at the terminal node and gateway layer. Finally, the Kalman filtering algorithm is optimised by using the distribution diagram method to determine the abnormal or missing data in the fused data, so as to obtain the optimised data automatic fusion results. The test results show that the precision of this method for automatic fusion of IoT sensing data is above 94%, and the fusion effect is good.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com