Probabilistic Approach to the Problem of Choosing Sensor Types

Selecting the appropriate sensors for large-scale air quality monitoring systems presents a considerable challenge due to the dynamic nature of environmental conditions, varying sensor performance, and budgetary limitations. This paper introduces a probabilistic approach to tackle this issue by modeling the uncertainties associated with sensor performance and environmental variability. By employing a penalty function, this methodology enables the assessment of different sensor types based on crucial factors such as accuracy, reliability, and overall system cost. The penalty function facilitates an optimal trade-off between cost and data quality. The proposed method thus paves the way for the development of air quality monitoring networks that are both efficient and cost-effective.