Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment's safety and health

Sensor monitoring plays a very important role in indoor environment control and energy efficiency. However, it is always a challenging task when discussing the methods and strategy for sensor deployment, considering its numbers and locations. Thus, this work aims to provide a systematic methodology of sensor deployment for efficient indoor environment monitoring by using clustering method of Fuzzy C-means (FCM) algorithm. An experimental chamber was considered for ventilation basis. Low-dimensional Linear Ventilation Models (LLVM) were applied to generate a series of low-dimensional pollutant concentrations, representing the potential hypothetic monitoring data. FCM algorithm was then adopted to cluster these data as well as corresponding grid points classified by three zones, i.e., main flow, main diffusion and well-mixed zones. Then the cluster centers of each zone were determined representing as the locations of sensor deployment. Lastly, taking the cluster centers as the hypothetic sensor locations and corresponding concentrations as the input for LLVM-based ANN (Artificial Neural Network), the predicted volume-averaged CO2 concentrations agree well with the simulated ones directly from low-dimensional CFD results, with the maximal error of 6.5 %. This work will be further facilitating the realization of artificial intelligent ventilation system and efficient indoor environment control.