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Modern buildings consist of hundreds of sensors and actuators for monitoringand operation of systems such as HVAC, light and security. To enable portableapplications in next generation smart buildings, we need models andstandardized ontologies that represent these sensors across diverse types ofbuildings. Recent research has shown that extracting information such as sensortype with available metadata and timeseries data analysis is difficult due toheterogeneity of systems and lack of support for interoperability. We proposeperturbations in the control system as a mechanism to increase theobservability of building systems to extract contextual information and developstandardized models. We design Quiver, an experimental framework for actuationof building HVAC system that enables us to perturb the control system safely.Using Quiver, we demonstrate three applications using empirical experiments ona real commercial building: colocation of data points, identification of pointtype and mapping of dependency between actuators. Our results show that we cancolocate data points in HVAC terminal units with 98.4 % accuracy and 63 %coverage. We can identify point types of the terminal units with 85.3 %accuracy. Finally, we map the dependency links between actuators with anaccuracy of 73.5 %, with 8.1 % and 18.4 % false positives and false negativesrespectively.

Quiver: Using Control Perturbations to Increase the Observability of Sensor Data in Smart Buildings