Abstract
With the rapid development of social economy, the importance of ecological civilization is increasing day by day. The level of environmental monitoring will directly affect the control of total pollution sources and the evaluation of environmental quality. Crowd sensing is to use the group computing power of users with smart devices to quickly collect surrounding multidimensional data, analyze and calculate based on these massive perception data, and then dig out group behavior patterns and other regular information. In order to meet the basic requirements of environmental protection work put forward by the government in the new era, this paper proposes mobile positioning and crowd sensing technologies, explains the distribution map method and adaptive weighting data fusion related algorithms, and designs and develops a mobile positioning system based on mobile positioning. The system of crowd sensing of urban healthy street monitoring is designed, and then we use this PM2.5 monitoring system to monitor the relevant environmental data of a street in Shanghai and use normalization to process the experimental data. The experimental data and statistical results show that this performance of the system is good, and the accuracy of image PM2.5 monitoring reaches 90%–95%. More than 90% of street residents are also very satisfied with this system, believing that it can play a role in supervising urban healthy street.
Highlights
IntroductionComparing and discussing solutions to network design issues, such as scalability, time synchronization, sensor placement, and data processing, the survey outlines the test platform and actual deployment of wireless sensor network (WSN) for SH [4]
Design Experiment of the Smart Perception Urban Healthy Street Monitoring System Based on Mobile Positioning
Is paper proposes a fine-grained PM2.5 monitoring system based on the crowd sensing model. e system can provide citizens with a fine-grained PM2.5 concentration distribution map to help them learn the current local concentration of fine particles and take corresponding protective measures in time
Summary
Comparing and discussing solutions to network design issues, such as scalability, time synchronization, sensor placement, and data processing, the survey outlines the test platform and actual deployment of WSN for SH [4] They only used wireless sensors to monitor various infrastructure and facilities and did not conduct relevant research on mobile positioning technology. Is study first introduces the convolutional neural network model used to measure PM2.5 concentration through images, the Resnet-50 training model that uses image features to measure the concentration of PM2.5, and the machine learning platforms TensorFlow and TFLearm; the use of crowd sensing to monitor the environment data is fused, and the adaptive weighted data fusion method is introduced All these key technologies provide theoretical and technical support for the realization of the subsequent system
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