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Research on Particle Swarm Optimization-based and Neural Network-based Nodes Positioning Algorithm for Wireless Sensor

Author: ZhouShuWang
Tutor: WangYingLong
School: Shandong Normal University
Course: Computer Software and Theory
Keywords: Wireless sensor networks Node Localization Particle Swarm BP neural network Link quality
CLC: TN929.5
Type: Master's thesis
Year: 2011
Downloads: 135
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Abstract


Since the 1990s, wireless sensor networks become increasingly subject to the attention of the world, was named the ten kinds of change in the future emerging technologies in the world first. The United States, European Union, Japan and other developed countries have made significant deployment in wireless sensor networks. In recent years, China also attaches great importance to the research of wireless sensor networks, November 3, 2009, Premier Wen Jiabao instructions \Traditional positioning technology (GPS) have a critical application, both in the military and civilian aspects of the same node positioning technology is also one of the key technologies of the sensor network, the network information collected is meaningful protection. In order to further improve the accuracy of the positioning of the nodes as well as to further improve the robustness of node positioning method proposed node positioning method based on particle swarm search node positioning method and the improved method - based on geometric constraints and particle swarm search. In addition, we are still using BP neural network node localization to do some meaningful exploring, proposed a model based on BP neural network node localization. The main work of this paper are as follows: (1) study the basic principles of the node localization, the classic method of node localization. (2) study the link quality (LQI) based ranging technology through several experiments were collected LQI and distance relationship between data, and by means of fitting and other mathematical mapping between distance and LQI relationship. Actual authentication, the method ranging error is less than 20%, better than the RSSI ranging effects. (3) by the basic principles of particle swarm algorithm to analyze the optimal solution in the search path of the particle swarm search and analysis of the practical application of the node localization is proposed node localization method based on particle swarm search. In order to verify the effectiveness of node positioning applications, the paper first cases ranging error unknown node location search, simulation results show that regardless of the anchor nodes is particularly placed, or placed randomly unknown node precisely positioning. Then search in the case of different ranging error, the simulation results show that under 20% of the ranging error than the least squares positioning method accuracy is 19.01% higher. Particle swarm search for node localization is feasible and effective. (4) In order to further improve the positioning accuracy of unknown node, in the analysis of the particle swarm search constraint domain to meet the conditions of the location and anchor node on unknown node search based on the geometric constraints and particle swarm node positioning method (CIL). The simulation results show that in the 20% range error is 30.33% higher than the positioning accuracy of the method of least squares. In addition, the method is not only static node having good positioning effect can also be well applied to the tracking positioning of the mobile node. (5) In the application of the BP neural network node localization meaningful exploration. After a number of experiments and training 160,000 finalize the localization model based on BP network. In summary, the results of this study indicate that the node localization method after the introduction of particle swarm optimization, to further improve the accuracy of the node localization, and the ability to enhance the robustness of the algorithm.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Wireless communications > Mobile Communications
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