Design and Optimization of an IoT-Based Air Pollution Sensing Network for Seasonal Monitoring in Industrial Zones

Simulation modeling is essential for designing effective air pollution monitoring systems, especially in industrial zones where pollutant behavior varies seasonally. This paper presents a MATLAB-based framework for optimizing the layout of an IoT-enabled sensor network for gas dispersion monitoring around a power plant. a Gaussian plume model was used to simulate pollutant concentration under four seasonal wind profiles (January, April, July, October), and sensor effectiveness was evaluated for a fixed layout. to improve performance, two evolutionary algorithms, particle swarm optimization (PSO) and genetic algorithm (GA) were applied to maximize exposure while minimizing node count and deployment cost. results showed that both methods significantly outperformed the fixed layout, with PSO offering slightly better coverage-efficiency trade-offs. The framework enables robust, season-aware planning of air quality monitoring networks and supports smart environmental decision-making. Future extensions may incorporate energy-aware constraints and real-time deployment strategies.