Towards Automatic Spatial Verification of Sensor Placement in Buildings

Abstract

Most large, commercial buildings contain thousands of sensors that are manually deployed and managed. These sensors are used by software and firmware processes to analyze and control building operations. Many such processes rely on sensor placement information in order to perform correctly. However, as buildings evolve and building subsystems grow and change, managing placement information becomes burdensome and error-prone. An automatic verification process is needed. We investigate empirical methods to automate spatial verification. We find that a spatial clustering algorithm is able to classify relative sensor locations -- for 15 sensors, spread across five rooms in a building -- with 93.3% accuracy, 13% better than a k-means clustering-based baseline method. Analysis on the raw time series data has a classification accuracy of only 53%. By decomposing the signal into intrinsic modes and performing correlation analysis, an observable, statistical boundary emerges that corresponds to a physical one. These results may suggest that automatic verification of placement information is possible.