Optimizing Latency in Air Quality Monitoring Architecture by Fog-Enabled Technology

In recent years, the rapid rise of the population and urbanization have raised the issue of air pollution. Because of its impact on human health, air quality monitoring is a crucial problem. Air pollution impacts our daily lives and quality of life. It presents a threat to the planet's environment and quality of life. Because industrial operations have grown in recent years, there is a clear need to monitor air quality. People must realize how their actions affect the quality of the air. With advances in sensing and embedded technology, the Internet of Things (IoT) has emerged as a cost-effective alternative to costly and stationary air quality monitoring stations for implementing air quality monitoring systems (AQMS). By developing an air pollution monitoring system, it will be possible to count the main pollutants and their sources with accuracy. Data is processed and cleaned close to IoT nodes' ends in the Fog computing paradigm, which is effective for addressing these problems and enhancing the quality of service (QoS) of IoT networks. The major reason for using fog computing in the suggested solution is to reduce network use and latency for the entire air pollution monitoring system. To solve the inadequacies of current methods, we suggest a three-phase pollution monitoring system. To demonstrate the effectiveness of the suggested technique for lowering latency and network consumption, the outcomes of simulations in iFogSim have been compared to those of the system's cloud-based application for monitoring air quality. Experimental findings demonstrate a considerable reduction in delay with the recommended fog-based deployment of an effective air quality monitoring system.