Wireless sensor network node positioning method based on compressed sensing theory

Abstract

The invention provides a wireless sensor network node positioning method based on a compressed sensing theory. Firstly, special information sent by beacon nodes is used for obtaining communication information of all nodes to the beacon nodes; and then a sampling matrix is obtained through the communication information obtained by the utilization of the beacon nodes, compressed communication information is obtained through target nodes, and correlation coefficients of the target nodes and all beacon nodes are obtained through a compressed sensing algorithm. Lastly, a weight coefficient of each beacon node to the target nodes is obtained by using the correlation coefficients, and an estimate position of each target node is obtained by the utilization of a centroiding algorithm. According to the wireless sensor network node positioning method based on the compressed sensing theory, the compressed sensing theory is introduced, and the correlation of the target nodes and the beacon nodes on geographic positions is fully dug. Due to the fact that the wireless sensor network node positioning method based on the compressed sensing theory fulfills four conditions, the wireless sensor network node positioning method based on the compressed sensing theory is reliable, effective, general in application, and suitable for node self-positioning of large scale networks.

Description

technical field [0001] The invention relates to a wireless sensor network node positioning method, in particular to a node self-location method based on compressed sensing. Background technique [0002] Wireless sensor network is a brand-new information acquisition platform, which can realize complex and large-scale monitoring and tracking tasks in a wide range of application fields, and the self-location of network nodes is the basis and premise of most applications. However, wireless sensor networks are composed of cheap energy-limited sensors, and only a small number of sensor nodes know their own positions. Therefore, using these small amount of location information to accurately, effectively and quickly locate the locations of all nodes has become a research hotspot. At present, many algorithms have been developed to solve the problem of node self-localization. However, each algorithm is usually only suitable for certain types of applications, and there is no general ...

AI Technical Summary

Problems solved by technology

However, the positioning accuracy of the Diffsion algorithm at the edge of the perception area is very poor, and the overall positioning effect of the LSVM algorithm is better than that of the Diffusion algorithm, and the positioning effect of the edge area is also better than that of the Diffsion algorithm.

However, the LSVM algorithm also has its own disadvantages

First, when there are fewer beacon nodes, it has a greater impact on the classification accuracy, which will seriously affect the positioning accuracy

Second, because it is necessary to select a beacon node to establish a classification model in front of the head beacon node, the head beacon node will consume a lot of energy, which is very unfavorable for sensor networks with high energy consumption requirements.

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