IoT deployments collect enormous amounts of sensor data, but making use of it still requires knowing which sensors to query, which machine learning models to run, and how to stitch the results together — expertise most organizations do not have.
Several of the group’s systems are deployed and evaluated on UMBC’s campus, which doubles as a living testbed for research on IoT data management and privacy. This page collects that campus-deployment work; new deployments are added here as they are published.
Smart devices talk to the network far more than their users expect, and the traffic they generate reveals a lot: which services they contact, how often, and what they do when nobody is using them.
Heating, ventilation and air conditioning accounts for roughly 40–60% of a building’s energy use, yet the reason to run it at all is the comfort of the people inside. Pervasive and mobile computing makes it possible to sense individual thermal sensations and manage HVAC around them, but evaluating such systems is hard: real-world experiments are expensive, slow, privacy-sensitive and rarely cover the diversity of occupants and climates, while simulations have to be shown to reflect reality.
Privacy policies are the main channel through which organizations tell people what happens to their data, yet they are long, intricate, and routinely skipped or misunderstood — and for smart devices they are scattered across manufacturers and e-commerce platforms, so even finding the right policy is hard.