A Method of Predicting the Link Quality of Wireless Sensor Networks Using GRU

A wireless sensor and link quality technology, applied in the network field, can solve problems such as the inability to efficiently predict the link quality of wireless sensor networks, and achieve the effect of improving data forwarding efficiency and improving efficiency

Active Publication Date: 2021-04-27
NANCHANG HANGKONG UNIVERSITY
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Problems solved by technology

[0004] The existing link quality prediction is mainly based on link characteristics and prediction methods based on probability estimation, which cannot efficiently predict the link quality of the wireless sensor network at the next moment

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  • A Method of Predicting the Link Quality of Wireless Sensor Networks Using GRU
  • A Method of Predicting the Link Quality of Wireless Sensor Networks Using GRU
  • A Method of Predicting the Link Quality of Wireless Sensor Networks Using GRU

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Embodiment Construction

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0035] These and other aspects of embodiments of the invention will become apparent with reference to the following description and drawings. In these descriptions and drawings, some specific implementation manners in the embodiments of the present invention are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited by this limit. On the contrary, the embodiments of the ...

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Abstract

A method for predicting the link quality of a wireless sensor network using GRU, the method comprising: obtaining a link quality parameter of the wireless sensor network at each moment within a period of time, and determining the link quality parameter of each moment according to the obtained link quality parameter Quality level; calculate the Pearson correlation coefficient between the historical link quality parameter before the current moment and the link quality parameter at the current moment; determine the quantity of the historical moment with the current moment Pearson correlation coefficient greater than the threshold value, and according to the The above quantity determines the size of the time window, and the link quality parameters are intercepted with the time window of the size to obtain the training sample set, and the training sample set with the link quality level label is applied to the GRU neural network model Training: using the trained GRU neural network model to predict the link quality of the wireless sensor. The invention can effectively and accurately predict the link quality at the next moment.

Description

technical field [0001] The invention relates to the field of network technology, in particular to a method for predicting the link quality of a wireless sensor network by using a GRU. Background technique [0002] Wireless sensor network (Wireless Sensor Networks, WSNs) is a network formed by self-organization of various cheap micro sensor nodes with perception, computing and communication capabilities through wireless communication. Due to its self-organizing, data-centric, dynamic and reliable characteristics, wireless sensor networks have important scientific research and application values ​​in military, industrial, agricultural, intelligent transportation, family, health, environmental protection and other fields. In WSNs, signal propagation is mainly affected by external and internal conditions, such as interference, noise, and weather. Link quality is easily affected by environmental factors such as temperature, humidity, and wind speed, resulting in temporal and spa...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/02H04W24/06H04W84/18G06K9/62G06N3/04G06N3/08
CPCH04W24/02H04W24/06H04W84/18G06N3/084G06N3/045G06F18/23213G06F18/22G06F18/214
Inventor 肖庭忠刘琳岚
Owner NANCHANG HANGKONG UNIVERSITY
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