Research on node position estimation method for wireless sensor networks based on artificial neural network

In this paper, classical wireless sensor network self-localization algorithms (e.g., ranging algorithms vs. non-ranging algorithms) and position computation are firstly described. For the challenge of node localization in three-dimensional space, a model of RSSI ranging system in three-dimensional space is constructed, and a position estimation model based on RSSI ranging by the great likelihood method is established on this basis. In order to further improve the accuracy and robustness of the model, a node localization algorithm based on convolutional neural network for wireless sensor networks is innovatively proposed. The results show that: the algorithm in this paper can maintain high localization accuracy under different numbers of working nodes, and the prediction error of the nodes of the model is less than 13% under different heights from 20m to 200m, and the model has a good high degree of adaptability; at the same time, the algorithm effectively reduces the communication and computation overhead of the nodes, and significantly prolongs the survival time of the wireless sensor network.