Sciweavers

Share
PR
2006

Similarity-based analysis for large networks of ultra-low resolution sensors

8 years 10 months ago
Similarity-based analysis for large networks of ultra-low resolution sensors
By analyzing the similarities between bit streams coming from a network of motion detectors, we can recover the network geometry and discover structure in the human behavior being observed. This means that a low-cost network of sensors can provide powerful contextual information to building systems: improving the efficiency of elevators, lighting, heating, and cooling; enhancing safety and security; and opening up new opportunities for human-centered information systems. This paper will show how signal similarity can be used to calibrate a sensor network to accuracies below the resolution of the individual sensors. This is done by analyzing the similarity structures in the unconstrained movement of people in the observed space. We will also present our efficient behavior-learning algorithm that yields 90% correct behavior-detection in data from a sensor network comprised of motion detectors by employing similarity-based clustering to automatically decompose complex activities into det...
Christopher Richard Wren, David C. Minnen, Sriniva
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Christopher Richard Wren, David C. Minnen, Srinivasa G. Rao
Comments (0)
books