The Application Effect of Improved CS-RBF Neural Network in Industrial Internet of Things Node Localization

Node positioning technology can help industrial Internet of Things systems control production processes more accurately, monitor product quality in real time, discover and handle problems timely, thereby improving product quality. It is significant for improving the efficiency and safety of industrial production. However, existing node localization technologies have low accuracy in node localization in complex environments, which cannot effectively ensure localization effectiveness and directly affect device monitoring in industrial Internet of Things. To address this issue, a node localization method is constructed based on the radial basis function neural network function. Then the cuckoo algorithm is applied to optimize it. From the results, the improved CS-RBF model had a smaller change in absolute error value, with a minimum absolute error value of -9.3 and an average absolute error value of 0.8. When the proportion of beacon nodes was 35%, the average positioning errors of DE, APTT, and DV Hop were 0.27, 0.23, and 0.22, respectively. The node positioning error of CS-RBF was 0.18. This indicates that the node positioning method designed in the industrial Internet of Things can effectively achieve accurate positioning of the required nodes, thereby effectively scheduling and managing equipment, reducing equipment waiting time, and improving production efficiency.