A quadratic fusion estimating model based on the clustering kernel for real-time data in web of things

Real-time data processing is a very important part of data processing in the web of things (WoT). The devices in WoT collect data and provide real-time information. The accuracy of the collected data is critical to provide valid results. Many existing methods are devoted to modifying filter algorithms. However, little attention is devoted to the inner relationship of data and data accuracy. In the present study, a quadratic filter model based on the clustering kernel is presented. First, the common filter method is used. Second, the clustering algorithm is adopted to deliver the clustering result. The attractor of the class is gained to the clustering kernel. Finally, the quadratic filter is processed according to the clustering kernel. The simulations show that the proposed model can increase the data accuracy.