Cloud-Enhanced Wireless Sensor Networks for Air Quality Monitoring

A growing trend in environmental monitoring involves the use of inexpensive wireless sensors to create densely dispersed networks that provide higher spatial resolution than traditional air quality control systems. In this study, a novel air quality monitoring system—a wireless sensors network, or WSN, made up of a dispersed sensor network connected to a cloud system—is presented. The sensor nodes use low-power ZigBee motes to send field measurements in real time to the cloud via a dedicated gateway. An optimally calibrated cloud computing framework has been instituted to effectively retain, oversee, handle, and present the data originating from the sensor system. Optimised chemical and contaminant identification is made possible by using artificial intelligence tools in the cloud for sophisticated data handling and analysis. This system is unique in that it is small, light, and energy-efficient, allowing for the deployment of several nodes for thorough air quality evaluations in various locations. The effectiveness of the method in identifying common volatile organic chemicals including the compounds benzene, toluene, ethane, and xylene to is validated by a practical example in a laboratory environment. Principal component analysis, perceptron networks with backpropagation learning, and help vector computation methods are all used in the data processing. The results show that the volatile organic compound concentrations may be distinguished and quantified with good accuracy. Cloud computing, air quality, chemical sensors, and wireless sensor networks are among the key themes.