SUMMARY
Aiming at the problem of node localization in wireless sensor networks, a location algorithm for optimizing distance vector hopping (DV-hop) by constructing a quantum neural network model based on particle swarm optimization (PSO) is proposed. According to the average distance obtained by the traditional DV-HOP and the distance from the measured nodes, the quantum neural network model is constructed, and the average distance is trained by the particle swarm optimization algorithm which would shorten the training time of the traditional artificial neural network and accelerate the convergence speed. By using the proposed model, the optimal mean value is obtained, and the optimization of the DV-HOP algorithm is realized. The simulation results show that compared with the traditional DV-HOP algorithm, the proposed algorithm can reduce the positioning error by about 20%, and the positioning accuracy is significantly improved.