In wireless sensor network (WSN) applications, such as environment monitoring and target tracking, most sensor nodes are randomly deployed, and most of their locations are unknown (Langendoen & Reijers, Citation2013). At the same time, a node three dimensional localization experiment platform was built to verify the proposed algorithm in the actual environment, and the test results verified the effectiveness and superiority of the proposed algorithm.

The simulation results show that the average localization error of the algorithm without parameter optimization is 21.82%, and the localization error of the algorithm after parameter optimization is 11.70%, which has higher localization accuracy. Finally, the RSSI range values are used as the input of LSSVR localization model, and the LSSVR regression model is used to solve, in this way, the location estimation of unknown WSN nodes is realized. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. First, the RSSI average values at 1–25m in four different directions are collected by experiments and the weighted recursive mean optimization method is used to optimize the values of RF factor and propagation factor.

To reduce the influence of received signal strength indication (RSSI) on ranging error, as well as the influence of least squares support vector regression (LSSVR) on localization algorithm, a node three-dimensional localization algorithm based on RSSI and LSSVR parameter optimization is proposed.

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