Investigation of sensor placement optimization for indoor source identification by an entropy-based method

It is essential to deploy sensors effectively in indoor environments to provide high-quality measurements to successfully identify unknown sources of air pollution and protect indoor air quality. This paper investigated the optimal sensor placement strategy for source term estimation (STE) in an enclosed indoor space. An entropy-based optimization method was applied to design sensor configurations with 1, 2, 4, and 8 sensors on the ceiling for an indoor model without prior knowledge of pollution sources. The performance of optimal configurations was compared with uniform and random configurations by estimating 9 different point sources in the room. It was found that the optimal configuration is dependent on the airflow fields in the target domain. Because of the highly circulating flows in current cases, sensors near the inlet were downstream of the dispersion fields, which are preferred in optimal configurations. On average, the optimal configurations can improve location estimation accuracy by about 24% and 20% when compared with random and uniform configurations, respectively. Additionally, the limitations of strength estimation with 1 or 2 sensors were analyzed, revealing challenges for STEs under extremely sparse configurations. The study further examined the impact of sensor quantity on overall STE performance, showing that a proper combination of increased sensor number and optimized placements effectively enhances estimation reliability. Based on these findings, a practical criterion was proposed to guide the selection of an appropriate number of sensors for effective indoor STE.