IoT-based multi-channel information integration method for wireless sensor networks

Abstract

The speedy progress of the Internet of Things has connected a huge network of smart devices in series and also intensified the pressure of multi-channel information transmission in wireless sensor networks. To solve the transmission of large amount of information, a multi-channel information integration method based on Radial Basis Function neural network, Particle Swarm Optimization algorithm and Dempster-Shafer evidence theory is studied. The method designs an improved counter propagation Radial Basis Function neural network prediction algorithm to correct the abnormal data based on the normal and abnormal values detected by the Grubbs criterion. And then it uses the Dempster-Shafer evidence inference method for information integration on the basis of the complete monitored values after correction obtained using the above method. The lab result indicated that mean absolute error value of the improved counter propagation Radial Basis Function model for anomaly data prediction findings was 4.399, mean square error was 26.820, and root mean squared error value was 5.180. The average time delay of information integration of this research method was 0.24 μs. Compared with other methods, this method has the shortest integration time delay and the highest integration accuracy. The study provides a new method in the multi-channel information integration for wireless sensor networks in the context of internet of things.