A new technology that has received a lot of interest lately is wireless sensor networks (WSNs). WSNs are made up of a sizable number of tiny sensor nodes with wireless communication capabilities that can collect and transfer data from their surroundings. Numerous industries, including environmental monitoring, healthcare, smart homes, agriculture, and military surveillance, have adopted these networks for a variety of purposes.

In-depth information about the use of wireless sensor networks in many fields is presented in this study. This survey seeks to give a general overview of the various applications of WSNs, along with their benefits and drawbacks. The survey will evaluate how WSNs are used for environmental monitoring, healthcare, smart homes, agriculture, and military surveillance, and it will provide a thorough analysis of all the problems associated with these applications.

Each sensor mode is equipped with a number of heterogeneous sensors that can detect a range of parameters, including particulate matter, ozone, sulfur oxides, volatile organic compounds, ammonia, carbon oxides, nitrogen oxides, and other meteorological variables. Many sensor motes are densely dispersed over the research region to obtain thorough coverage and excellent spatiotemporal resolution of the acquired data.

The performance of the monitoring system must thus be improved by fusing the wireless sensor data. Preconditioning and filtering techniques have been used to address imperfect conditions like missing information, packet losses, stochastic uncertainty, and sensor noise associated with the time series of air quality data that have been gathered. Data assimilation is crucial in Low-power Wireless Sensor Networks (LWSN).

Kalman filtering techniques, including a fundamental form and several forms of Kalman filters, have been suggested to increase accuracy in the monitoring and forecast of various air contaminants. In this research, an IoT-enabled LWSN that focuses on monitoring particulate matter emissions at a suburban construction site has been created and deployed with enhanced accuracy and reliability for local air quality monitoring.

To deal with missing data and climatic volatility and improve system performance, the suggested system combines a wireless dependable control (W-DepC) scheme with extended fractional-order Kalman filtering (EFKF) for data assimilation and imputation. The capacity of the suggested system to offer a dependable and accurate solution for microclimate responsiveness in air quality monitoring is the key contribution of this work. Results from the field are gathered and examined to show how well this method predicts regional air pollution.