Optimizing Sensor Placement for Air Quality Monitoring System Using Particle Swarm Optimization

In this study, a Particle Swarm Optimization (PSO) framework for placing sensors in air quality monitoring systems is introduced. Effective sensor deployment has become crucial due to the growing significance of precise air quality data for environmental wellbeing and public health. The complexity of PSO, a bio-inspired optimization algorithm, and its applicability to the problem of carefully placing sensors to optimize data coverage while reducing resource use, are explored in depth in this work. The study demonstrates the PSO-based strategy's superior performance over conventional methods through extensive experimentation and evaluation. The outcomes show enhanced data coverage and accuracy, as well as cost-efficiency improvements. This study also highlights the larger effects of optimum sensor location, emphasizing how it affects urban planning, sensible environmental regulations, and pro-active public health initiatives. The study contributes to the continuous development of sensor location optimization approaches and emphasizes the need of data-driven decision-making in solving current air quality concerns. In conclusion, the incorporation of PSO into air quality monitoring systems is a viable way to solve crucial monitoring needs in a world that is changing quickly.