Optimization Study of Wireless Sensor Positioning Based on Improved MCM Algorithm

With the rapid development of wireless sensor network, people need the positioning accuracy and use cost of wireless sensor nodes. To solve this problem, the wireless sensor positioning method to improve the fitness function of particle swarm algorithm, and adopts the cross variation processing to improve the sample filtering condition. The improved algorithm uses collinearity to filter the anchor nodes and use the mean offset for weight assignment. Experiments show that the localization error of the improved Monte Carlo algorithm based on particle swarm is 9.2% and 5.8% lower than the other algorithms when the number of anchor nodes is 20, and 15.2% and 11.4% lower for the sample number is 100, and the communication cost is 0.27 higher than the anchor node density of 3. Therefore, the improved Monte Carlo algorithm can effectively improve the positioning accuracy of the wireless sensor nodes, and compared with other algorithms, the use cost of the improved algorithm is less increased. This study provides a reference for node screening for the subsequent optimization of wireless sensor positioning. Key words: wireless sensor; Monte Carlo; mean offset; collinearity; localization optimization