Abstract: To solve the issues of that swarm intelligence optimization algorithm has low iterative efficiency and is easy to fall into local optimum in wireless sensor network ( WSN) localization, WSN localization algorithm is proposed based on temporary best and worst centroid opposite cross mutation and sparrow optimization. The algorithm uses the estimated distance of nodes to build a box model, determine the location area of the initial node, and narrow the scope of the previous search area, The Circle chaotic map is used to initialize the population and make the population distribution more uniform. A reverse learning strategy of temporary best and worst centroid is proposed to make full use of the search experience of the population while maintaining the diversity of the population. By combining the cross mutation strategy, the proposed algorithm is easier to jump out of the local optimum, which improves the efficiency of global search. The simulation results show that the proposed algorithm has better positioning accuracy and convergence efficiency.

Key words: wireless sensor network, location algorithm, temporary best and worst centroid , sparrow search algorithm