Method for improving crowd sensing data quality based on preference degree of participants

Abstract

The invention discloses a method for improving crowd sensing data quality based on preference degrees of participants. According to the method, different participants have different perception qualities for different data due to the fact that data perception levels of data collection participants in the crowd-sourcing network are different due to owned perception equipment. Therefore, for different data acquisition, participants with high perception quality for the data need to be selected for acquisition, so that high data quality is obtained. According to the method, firstly, high-accuracy data perceived by participants serve as reference data, the preference degree of the participants for the perceived quality of different data attributes is calculated, and the preference degree of the participants for the perceived quality of different attribute data is calculated; during subsequent platform data acquisition, participants with high comprehensive completion quality are preferentially selected for different types of data, so that the data acquisition quality is improved.

Description

technical field [0001] The invention belongs to the field of high-quality data collection of swarm intelligence network, and particularly relates to a method for obtaining the difference of participants' perceived quality of different attributes and collecting accurate and real data in swarm intelligence network. Background technique [0002] Crowd sensing network is a new type of network, and its data collection method is a participatory method. That is, the system platform publishes information such as the location of data collection, the content of data collection, and the remuneration given to data participants. Data participants mainly refer to people holding mobile phones or other sensing devices. Data participants perceive data through mobile phones, and then submit it to the platform to get paid. Due to the large number of data participants, the crowdsensing network can obtain data for a long time, in a large range and at low cost, and is a good data collection and...

AI Technical Summary

Problems solved by technology

However, in the current swarm intelligence network, when selecting participants, they do not take into account the differences of participants in different objects and different attributes, which leads to the fact that the current method of participant selection cannot obtain accurate high-quality data

However, there are huge challenges in how to accurately select different objects and attributes according to the participants.

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