Sensor system design for building indoor air protection

Many new biological and chemical sensors have been or are continuously being developed for infrastructure and environmental protection, e.g., for protecting the quality of water and indoor and outdoor air. However, there is a lack of fundamental system-level research leading to the development of sensor networks that both maximize protection and minimize the system cost for indoor air protection. Four key parameters are usually used to evaluate sensor performance: sensor sensitivity, probability of correct detection, false positive rate, and response time. The optimal design of a sensor system is affected by the above sensor performance parameters. This paper describes a preliminary study to: (1) identify simplified simulation and optimization strategies that can be used for sensor system design; (2) examine the relationships between sensor location, sensitivity, and quantity, and (3) use both detection time and total occupant exposure as optimization objective functions for sensor system design. Common building attack scenarios, using a typical chemical and biological warfare (CBW) agent, are simulated for a small commercial building. Genetic algorithm (GA) is then applied to optimize the sensor sensitivity, location, and quantity, thus achieving the best system behavior while also reducing the total system cost. Assuming that each attack scenario has the same probability for occurrence, optimal system designs that account for the simulated possible attack scenarios are obtained.