Optimal Deployment of Movable Sensors for Air Quality Monitoring

Monitoring air quality is crucial to obtaining health-related information but is challenging due to spatially and temporally varying pollution levels. This paper addresses the problem of optimally relocating air quality sensors over time to effectively capture these variations, prioritizing areas with higher pollution concentrations and adhering to constraints on sensor deployment and relocation. Unlike traditional static sensor deployment methods, our approach dynamically relocates sensors to adapt to changing pollution patterns, providing a more accurate and efficient monitoring solution. To solve the proposed nonlinear integer programming problem, we developed a genetic algorithm that efficiently explores the solution space and provides near-optimal solutions within reasonable computation time. We demonstrate that the proposed method effectively adapts to dynamic pollution patterns through two case studies—a simulated road network and a real-world urban scenario. Numerical tests show that the proposed algorithm achieves solutions within 0.01% of the optimal value and reduces computation time by two orders of magnitude compared to enumeration even when only three sensors are deployed. This research provides a framework for urban planners and environmental agencies to deploy air quality sensors more effectively, enabling better decision making in pollution monitoring and mitigation.