Research on Optimization of Adaptive Positioning and Routing Algorithm for Industrial Internet of Things Engineering Based on Improved Neural Network

Abstract

The Internet of Things is one of the key technologies leading the development of modern industry. There is great uncertainty in the industrial production process, which causes great difficulties in the process of node perception and information transmission. Therefore, based on the improved neural network, this study designed the industrial Internet of Things engineering adaptive positioning and routing algorithm optimization methods. Based on the analysis of industrial IoT wireless sensor network node type, on the basis of exploring the advantages of back propagation neural network for the shortcomings of slow convergence speed and so on, we establish a discrete time Markov chain, determine its transition probability and the matrix, through interval differentiate, and calculate the estimate step error correction neural network. Then, training samples are selected to build an adaptive positioning model to obtain the absolute position of engineering nodes. Then, the shortest path constraints are set and independent variables are selected. After establishing the matrix form of the two-layer recursive neural network, the route traffic is updated by calculating the connection weight, so as to complete the routing optimization. The experimental results show that this method has high positioning accuracy and low overhead, and the optimized routing algorithm has a higher transmission success rate.