Aiming at the problem that the error of traditional DV-Hop algorithm is too large and the particle swarm optimization（ PSO） algorithm is easy to fall into local optimal

a node localization algorithm for the centroid oppositional particle swarm wireless sensor networks（ WSNs） is proposed. Firstly

PSO algorithm is combined with centroid opposition-based learning

calculate the opposite solution by taking the center of gravity of the whole group as the reference point

and combine the forward and opposite solutions of the particles to select the particles with better fitness values as the next generation to further improve the diversity of the population. Secondly

the particle running speed is improved

and a linearly increasing term of random perturbation is added to the velocity to improve the convergence speed of the algorithm and avoid falling into local optimum. Finally

the centroid oppositional PSO algorithm is combined with the DV-Hop localization method to construct network node localization method based on the centroid oppositional PSO algorithm. The simulation results show that compared with the traditional DV-Hop algorithm

the centroid oppositional PSO algorithm has higher positioning precision and better effect

and is suitable for scenes with high positioning precision requirements.