Research Scientist Bill Eason will lead a discussion on some of the latest Internet of Things (IoT) work within Georgia Tech's Research Network Operations Center. Specifically, he has been working on alternative sensing methods to solve the "urban canyon" problem encountered when using GPS to track Georgia Tech bus movement between midtown highrises. How can we simply use a high-resolution barometric pressure sensor to determine the location of a bus along its route? Can this really be more accurate than GPS? What other ecosystems can we apply such methods to?
We will also open the broader, related topic of what might be called "orthogonal inference," where one type of sensor data can be used to infer (apparently) unrelated information. Whether it's a single data stream by itself, or aggregated streams from various sources, sometimes we're indirectly giving away more information than we might realize.