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WSN data layered fusion method for plant growth monitoring

A plant growth and fusion method technology, applied in the field of wireless sensor network data fusion processing, can solve the problems of heavy node burden and complex calculation methods

Inactive Publication Date: 2016-09-07
GUIZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

And put forward a method of single sensor data fusion, and then carry out weighted adaptive fusion of data between sensors, but the calculation method is more complicated when removing gross errors, and the data fusion between sensors is all completed on the same node, which makes the node a heavy burden

Method used

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  • WSN data layered fusion method for plant growth monitoring
  • WSN data layered fusion method for plant growth monitoring
  • WSN data layered fusion method for plant growth monitoring

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Experimental program
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Embodiment

[0085] A monitoring area is selected in the laboratory. A wireless sensor network is composed of 15 temperature sensor nodes and 3 sub-layer routing nodes. The temperature sensor nodes are numbered from 1 to 15. Each node collects data 10 times in a single experiment. Five experiments were carried out under the following conditions, and the benchmark true values ​​were 13.5°C, 17.2°C, 20.8°C, and 25.1°C. The 3 sub-layer nodes are labeled 1 to 3, and each sub-layer node is connected to 5 common nodes, corresponding to sensor nodes 1-5, 6-10, and 11-15 respectively.

[0086] Taking the fifth experimental data as an example to show the experimental data processing process, Table 2 to Table 4 are the temperature sensor node fusion values, variances and weights of Nos. 1 to 5, No. 6 to 10, and No. 11 to 15 respectively. ~3rd sublayer measures data fusion value, variance and weight. Calculated according to the above formula, the final data fusion value of the fifth experiment is 25...

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Abstract

The invention discloses a WSN data layered fusion method for plant growth monitoring. According to the method, a wireless sensor network is divided into a plurality of fixed layers, data transmission volume can be decreased, and errors can be reduced layer by layer. The method includes the following steps that: gross errors in a single node datum are eliminated by using Grubbs criterion, and the fusion value of the node datum per unit time is obtained through batched estimation; data adaptively weighted fusion at a sub layer level is carried out; and finally, data weighted adaptively fusion at a base station level is carried out. As indicated by a simulation experiment result, the fusion result of the method of the invention is closer to a true value compared with an arithmetic average method and a single adaptive weighting algorithm. With the method adopted, data transmission volume can be reduced. The method has high expansibility.

Description

technical field [0001] The invention relates to the field of wireless sensor network data fusion processing, in particular to a WSN data layered fusion method for plant growth monitoring. Background technique [0002] The Internet of Things, especially wireless sensing technology, has been widely used in agricultural production, industrial manufacturing, and logistics transportation. Applying wireless sensing technology to agricultural production, especially in the field of crop growth environment monitoring, can effectively reduce the consumption of manpower and material resources and The impact on the soil ecological environment [1]. In the monitoring of crop growth environment, the wireless sensor network has a large monitoring area and long data transmission distance, and most of the sensor nodes are powered by batteries, so the continuous working life is limited. At the same time, the growth environment of crops changes slowly and the real-time requirements are low. If...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/08H04W52/02H04W84/18G01D21/02
CPCH04L67/12G01D21/02H04W52/0248H04W84/18
Inventor 秦志远黄海松张慧
Owner GUIZHOU UNIV
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