three dimensional localization algorithm Research Articles (Page 1)

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. 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. Then, the parameters of RBF kernel function and grid width of LSSVR are optimized. 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. 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. 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.

Location information is generally considered to be indispensable information in wireless sensor networks, but existing algorithms have notable limitations in their positioning accuracy in 3-dimensional (3D) spaces. To improve the positioning accuracy of nodes in a 3D scenario, this paper proposes a new method called N2-3DDV-Hop (non-dominated sorting genetic algorithm II with 3D distance-vector hop) that builds on the 3D-DV-Hop algorithm by adding multi-objective model and NSGA-II. In this paper, it is analyzed that the limitations of the traditional single-objective positioning model and showed the relationship among the average hop distance, the number of sensor nodes, and the theoretical average distance. In addition then, the multi-objective positioning model incorporating the NSGA-II algorithm is presented. To evaluate its performance relative to other current methods, we compared our method by testing all of the methods with three different complex network topologies. Simulation results demonstrate that the N2-3DDV-Hop offered the best overall positioning performance compared with other algorithms and better robustness than the 3DDV-Hop algorithm.

In wireless sensor network, determine the position of the node position or events is very important to its surveillance activities, however, in the traditional wireless sensor network, are using two-dimensional spatial positioning technology, aiming at this problem, this article will wireless sensor network (WSN) 2 d algorithm is extended to 3 d space, analyzes the main factors affecting the positioning accuracy of wireless sensor network. In order to further improve the stability and precision of the algorithm, by introducing the Newton's method, gives the quasi-newton three-dimensional node localization hybrid localization algorithm. The simulation analysis shows that the proposed hybrid algorithm can under the same conditions with high positioning accuracy and stability, at the same time the CRLB are obtained. The simulation results show that the proposed algorithm can get g Latin American community, simulation analysis of the localization method is effective

Three-dimensional localization algorithm of wireless sensor networks base on particle swarm optimization