Optimization and Simulation of Atmospheric Environment Monitoring System Based on Wireless Sensor

Abstract

With the acceleration of urbanization, the problems in urban construction are becoming increasingly prominent, especially in air pollution. In order to deal with a series of problems brought by urbanization, the state has formulated the strategic layout of smart city construction. As an important measure and practice for the development of smart city, atmospheric environment monitoring is the premise of controlling atmospheric environment problems and plays a great role in environmental protection. The traditional automatic atmospheric environment monitoring station has complex structure, expensive price, and harsh working conditions, which is difficult to be popularized throughout the country. Aiming at the problems of poor expansibility and low intelligence of atmospheric environment monitoring system, an atmospheric environment monitoring system based on wireless sensor network is proposed. The system designs sensor module, networking module, gateway module, and monitoring interface, studies the accuracy of data collected by the system and the coverage of wireless sensor network, filters the environmental data collected by the sensor module, optimizes the layout of networking module by using improved virtual force algorithm, and finally tests the system. The experimental results show that the system realizes the remote monitoring of temperature, humidity, air pressure, and PM2.5 data, and the monitoring data is real and reliable. The improved virtual force algorithm improves the coverage of wireless sensor networks. Comparing the data collected by the system with the monitoring data of the cause control station, the average relative errors of PM2.5 and other particle parameters and NOx and other gas parameters monitored by the system are 3.81% and 3.48%, respectively; the system can be widely used in various environmental monitoring fields.

Design of Gas Monitoring Terminal Based on Quadrotor UAV

“…During backpropagation, the parameters θ (including the sensitivity factor w and bias b) are updated by minimizing the loss product of the gradient θ and the learning rate a as shown in Equation (13). Backpropagation is automatically handled by the Adam optimizer under the pytorch framework.…”

“…This atmospheric monitoring system is wireless, in real-time, reliable, continuously recording, and portable [ 12 ]. Feng Zhenqiang proposed the design of a wireless sensor-based environmental monitoring system capable of the remote monitoring of temperature, humidity, air pressure, and PM2.5 data [ 13 ]; Philips has been working on the research and development of environmental monitoring and purification equipment [ 14 ], which can monitor and predict the atmospheric environment in real-time and is greatly convenient for managers; L. Capezuto put forward a monitoring method that allows people to obtain their own sensor nodes and participate in aerial environment monitoring through the mobile terminal [ 15 ]; Popoola constructed a sensor network platform composed of low-cost air quality sensors for quantifying air quality in urban environments [ 16 ]. However, in the future of rapid development, mobile terminals have been widely used in actual production and daily life [ 17 ], such as UAVs for intelligent agricultural irrigation [ 18 ] and real-time traffic monitoring [ 19 ] to strengthen the support of the environmental protection industry and accelerate the improvement of the technical equipment level of the environmental protection industry.…”