Abstract
In order to improve the flexibility of GPS measurement, a high-precision GPS measurement method that is not restricted by the geographical location under crowd-sensing technology was proposed. The performance of the crowdsensing network was improved through a regular hexagon-based crowd-smart big data sensing network deployment mechanism. The GPS /SINS/DR fast and high-precision combined measurement methods were used to achieve high-precision measurement without geographical restrictions. It has been verified that the proposed method in this paper has much better stability in the deployment strategy of a regular hexagon than that of the square. The proposed method can achieve fast acquisition of satellite signals and high-precision positioning, and its measurement accuracy in the low-latitude city and high-latitude city is higher than the online measurement method based on Google Earth, indicating that it has significant application value.
Highlights
Crowdsensing refers to the large-scale and complex society sensing tasks that use the mobile devices of ordinary users as the basic sensing unit, through conscious or unconscious collaboration on mobile Internet, to realize the distribution of sensory tasks and the collection of sensory data as well as complete those complex social sensory tasks
Based on crowd-sensing technology, this paper proposes a high-precision GPS measurement method that is not restricted by geographical location
A crowd-sensing big data network deployment mechanism based on regular hexagon was designed
Summary
Crowdsensing refers to the large-scale and complex society sensing tasks that use the mobile devices (mobile phones, tablets, etc.) of ordinary users as the basic sensing unit, through conscious or unconscious collaboration on mobile Internet, to realize the distribution of sensory tasks and the collection of sensory data as well as complete those complex social sensory tasks. Crowd-sensing technology can be used to collect information such as CO2 and PM2.5, noise, and water quality in the air. Trajectories corresponding to the time and place of mobile users are collected, and known models are used to calculate and collect the content of CO2 and PM2.5 in the atmosphere. This information is collected through the air quality sensor of the mobile phone to realize the function of environmental monitoring. With crowd-sensing technology, the method in this paper realizes a high-precision GPS measurement that is not restricted by geographical location (Cai et al, 2016)
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