Sensor node positioning method

A technology of sensor nodes and positioning methods, applied in electrical components, wireless communication, network topology, etc., can solve the problems of high computing resource consumption of nodes to be positioned, algorithms falling into local optimal solutions, and low node positioning accuracy, so as to reduce computing costs. Effects of resource consumption, speed improvement, and burden reduction

Inactive Publication Date: 2010-09-01
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

Some positioning algorithms currently proposed, such as maximum likelihood estimation method (MLE), no-search quasi-Newton method (MNSQN), no-search steepest descent method (NSSD), etc., all run the positioning algorithm on the node S to be located, and the consumption is to be determined The computing resources of the bit node are also very strict on the initial value of the optimization
[0003] In addition, particle swarm optimizati

Method used

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Embodiment 1

[0043] Embodiment 1: After receiving the coordinates of the optimal particles of all beacon nodes, the node to be positioned calculates the corresponding optimal particle position information of each beacon node according to the optimal particle position information and the evaluation function F. F value.

[0044] Then, find out all the particles satisfying F<ε. The threshold ε can be selected according to actual conditions, such as experimental results, and the threshold ε is generally selected to be between 10 and 30.

[0045] Finally, take the coordinate average of the selected optimal particle position information as the coordinate position of the node to be located. In one embodiment, the position of the node to be positioned is obtained by the following formula:

[0046] x = Σ i = ...

Embodiment 2

[0048] Embodiment 2: After receiving the coordinates of the optimal particles of all beacon nodes, the node to be positioned calculates the corresponding optimal particle position information of each beacon node according to the optimal particle position information and the evaluation function F. F value. Then, select the minimum value of F value corresponding to each optimal particle position information as the coordinate position of the node to be located. That is, use the particle coordinates with the smallest F value as the coordinates of the nodes to be located.

[0049] From the experimental data, it can be concluded that compared to using the particle coordinate position with the smallest F value as the position of the node to be located in the embodiment 2, the average value of these particle coordinate positions used in the embodiment 1 will be more accurate.

[0050] In step 106, each beacon node corresponds to the obtained phased global optimal particles, and there...

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Abstract

The invention discloses a sensor node positioning method. The method comprises the following steps of: collecting the position information of each beacon node in a communication range at the node to be positioned to obtain the positioning information set of each beacon node; broadcasting the position information set to each beacon node by the node to be positioned; respectively independently operating a particle group algorithm by each beacon node according to the position information set to respectively obtain the corresponding the optimum particle position information; detecting the optimum particle position information by the node to be positioned to fix the coordinate position of the node to be positioned. The invention can obviously reduce the calculation consumption of the node to be positioned.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network, distributed and evolutionary computing intersecting, and in particular relates to a sensor node positioning method. Background technique [0002] With the application of sensors more and more widely, how to quickly and accurately locate sensor nodes becomes more and more important. Some positioning algorithms currently proposed, such as maximum likelihood estimation method (MLE), no-search quasi-Newton method (MNSQN), no-search steepest descent method (NSSD), etc., all run the positioning algorithm on the node S to be located, and the consumption is to be determined At the same time, the computing resources of bit nodes have strict requirements on the initial value of optimization. [0003] In addition, particle swarm optimization algorithms have been proposed in the application of sensor node positioning at present, but these algorithms have obvious shortcomings: they are centra...

Claims

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Application Information

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IPC IPC(8): H04W64/00H04W84/18
Inventor 王行甫曹仁之钱雷熊焰
Owner UNIV OF SCI & TECH OF CHINA
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