Wireless sensor network positioning technology based on improved sampling box and fuzzy reasoning.

As the wireless sensor network technology develops, precise positioning has become the key to achieving effective monitoring and data collection. This study proposes a wireless sensor network positioning technology based on improved sampling boxes and fuzzy reasoning to address the problem of low positioning accuracy and efficiency of traditional positioning methods in resource constrained environments. By using a fuzzy clustering algorithm based on time series optimization and a multidimensional Gaussian model to optimize the sampling box, the accuracy and efficiency of localization are significantly improved. The research results indicate that on the UCI and MIT Reality datasets, the Rand coefficient of the fuzzy clustering algorithm optimized for time series is stable at 0.89 when the weight allocation index is 5, which is superior to other comparative algorithms. When the proportion of beacon nodes reaches 40%, the average positioning error of the new model is as low as 0.21 m, which is lower than the comparison model. When the number of nodes is 50 and 200, the response times of the new model are 0.41 ms and 0.72 ms, respectively, which are also the fastest among all models. From this, the new model can maintain high positioning accuracy and fast response under different communication radii and beacon node ratios, especially in scenarios with a large number of nodes, demonstrating excellent versatility and efficiency. This study is of great significance for improving the application efficiency of wireless sensor networks in fields such as environmental monitoring and industrial automation, and provides an effective technical solution for achieving more accurate positioning.