AP self-adaptive optimization selection method based on machine learning

A technology of optimization selection and machine learning, applied in machine learning, instruments, computer components, etc., can solve problems such as insufficient data flow requirements

Active Publication Date: 2019-07-12
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

Clearly, these two approaches may not be sufficient to ensure data flow requirements

Method used

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  • AP self-adaptive optimization selection method based on machine learning
  • AP self-adaptive optimization selection method based on machine learning
  • AP self-adaptive optimization selection method based on machine learning

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Embodiment Construction

[0026] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] A kind of AP adaptive optimization selection method based on machine learning, described method comprises steps:

[0028] Step 1. Collect connected device data in the current environment, establish training data sets, feature sets, and determine thresholds.

[0029] In the step 1, the feature set of the establishment includes but not limited to the time of connection, signal strength, mobile device model, whether it is a public AP, whether it is encrypted, the number of connected devices, and the algorithm used includes but not limited to ID3, C4 .5 and other decision tree algorithms.

[0030] Specifically, input: training data set D, feature set A (time of connection, signal strength, mobile device model, whether it is a public AP, whether it is encrypted, the number of connected devices, etc.), threshold ε; output: decision...

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Abstract

The invention discloses an AP self-adaptive optimization selection method based on machine learning, and the method is applied to the process of establishing WIFI connection between a mobile device and an AP and switching an Internet of Vehicles adaptive network, and the method comprises the steps: collecting connection device data in a current environment, establishing a training data set and a feature set, and determining a threshold value; determining whether the tree is a single-knot tree according to the data set and an ID3 algorithm; if the tree is not a single-knot tree, segmenting thesubset to construct a sub-node spanning tree; performing recursive call until a complete decision tree is generated so as to classify the APs into a FAST set and an SLOW set, and selecting the fastestAP in the FAST set to establish a connection. According to the invention, the AP access point is selected according to the machine learning model to shorten the connection time and reduce the WIFI connection setting time cost.

Description

technical field [0001] The invention relates to the fields of communication technology and vehicle networking adaptive switching, in particular to a machine learning-based AP adaptive optimization selection method. Background technique [0002] In recent years, due to the explosive growth of smart devices, wireless data traffic has increased exponentially. Among these wireless networks, 802.11 Wireless LAN (WiFi) has become a major part of today's wireless services. Over the past decade, more than 1 billion WiFi APs (Access Points) have been deployed to provide wireless connectivity. Even if users use smart devices that support 3G / 4G cellular networks, they can use WiFi hotspots that can be seen everywhere today. [0003] However, network performance and user experience in WiFi networks are still unsatisfactory: According to a measurement study of more than 5 million users using WiFi networks in urban areas, as many as 45% of mobile devices cannot establish a WiFi connecti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/24323G06F18/214
Inventor 赵海涛李嘉欣于建国张唐伟张晖朱洪波
Owner NANJING UNIV OF POSTS & TELECOMM
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