Method for analyzing utilization of mobile Apps on wireless network resources based on crowdsourcing mode

A technology of wireless network and crowdsourcing mode, applied in the field of analysis of wireless network resource utilization by mobile Apps, it can solve problems such as quantification of resource usage or difficulty in relative ranking, and achieve the goal of improving resource allocation rate and service quality level. Effect

Active Publication Date: 2016-01-06
NANJING HOWSO TECH
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AI Technical Summary

Problems solved by technology

For mobile applications, such characteristics as ambiguity, complexity, and dynamics pose challenges to the analysis of network resources, and this also makes it difficult for mobile operators to quantify or rank resources used by mobile applications. extremely difficult

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  • Method for analyzing utilization of mobile Apps on wireless network resources based on crowdsourcing mode
  • Method for analyzing utilization of mobile Apps on wireless network resources based on crowdsourcing mode
  • Method for analyzing utilization of mobile Apps on wireless network resources based on crowdsourcing mode

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Embodiment Construction

[0020] Such as figure 1 As shown, the present invention discloses a method for analyzing the utilization of wireless network resources by mobile Apps based on a crowdsourcing model. Through a crowdsourcing tool and an analysis algorithm located on a server, APP behavior indicators are collected, and the behavior indicators are analyzed. Data mining; establish a two-layer causality mapping model between APP behavior indicators, wireless network resources and network traffic (such as figure 2 ), to analyze the network resource utilization of mobile applications.

[0021] Specifically, the two-layer causality mapping model is to design a similarity matrix-assisted selection algorithm based on a random forest decision tree, select app measurable indicators that are highly related to network traffic, and develop a local weight scatter diagram smoothing algorithm based on a sliding window. Regression of the selected metrics establishes a mapping between mobile apps and network tra...

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Abstract

The invention provides a method for analyzing utilization of mobile Apps on wireless network resources based on a crowdsourcing mode. The method comprises the following steps: collecting behavior characteristic data of each mobile application App through a data collection tool based on the crowdsourcing technology and installed on a mobile client and an analysis algorithm located on a cloud server; establishing a three-level two-layer association mapping model among mobile application characteristic behaviors, wireless network traffic and the wireless network resources, and quantitatively analyzing how each mobile application service in a mobile communication network consumes the wireless network resources in a cell on a time dimension. The method provided by the invention is used for analyzing the condition of consumption of each mobile application service on the wireless network resources in the cell and obtaining a result through a machine learning algorithm to provide decision suggestions for mobile operators, for example, predicting, controlling and pricing the resources used by the mobile application App to improve the resource allocation rate and the quality of service level.

Description

technical field [0001] The invention relates to a method for analyzing the utilization of wireless network resources by mobile Apps, in particular to a method for analyzing the utilization of wireless network resources by mobile Apps based on crowdsourcing mode. Background technique [0002] Smart terminals have shortened the distance between people, and since they have various mobile application services for mobile networks, referred to as mobile Apps, smart terminals have enabled people to strengthen their relationship with each other through the rich service content provided by Apps. Contact, such as live video, push email, and live chat. However, the rapid growth of Apps and the sharp increase of network traffic have brought huge overhead to mobile networks. In 2013, global mobile data traffic grew by 81%, surpassing 2012 and reaching 15GB per month. In addition to data traffic, online chat programs, such as WeChat and Twitter, need to periodically send about 2400 hear...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04W24/04H04W4/60
CPCH04L41/142H04L41/145H04W24/04H04M15/58H04W4/60H04W24/08H04L43/04H04L41/16
Inventor 吴冬华欧阳晔王计斌胡岳
Owner NANJING HOWSO TECH
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