MERL – Ambient Intelligence for Better Buildings

Ambient Intelligence for Better Buildings

Sensor networks have the potential to allow the development of truly intelligent buildings that improve productivity, efficiency, safety, and security. To be practical, such networks must be efficient, scalable to very large spaces, and economical to manufacture, install and maintain. One answer is networks of passive infrared motion detectors. Sensors could be manufactured onto building infrastructure elements such as light fixtures. We are developing technology that enables cost-effective networks to recognize, predict, and index human activity in building-scale environments.

Background & Objective: The falling cost of sensors, microprocessors, and radios combined with the rising interest in safe, secure, and efficient buildings leads us to believe that holistic building systems are the future of building management. Many groups across the globe are focused on the problem of better packet routing, better sensing modalities, and lower-power computing. At MERL we are leveraging that work by focusing on the perceptual question: what is it possible to sense with these systems, what are the fundamental structures of human activity at the building scale, what can we learn, and what benefit can we gain? We have assembled a host of experiments and demonstrations that illustrate the usefulness of these systems for security, efficiency, safety and situational awareness within buildings.

Technical Discussion: These results rest on the technical innovations of several other projects. Ultra-low power, easy to manufacture and easy to install, the Reduced Operating Cost Sensors (ROCkS) platform has enabled a host of experiments by allowing us to collect data from large, multi thousand square meter installations, over year-scale spans of time to assemble a unique dataset about human behavior at the building-scale. The Scalable Activity Recognition for Sensor Networks (SARSEN) project has focused on finding structures in that data, and probabilistic models to detect and analyze those structures. The Integrated Event Recognition project has focused using sensor networks to help solve problems that are hard given only classical sensor modalities.

Future Direction: We are pursuing several technical and business directions for the project. Technically, we are pursuing more detailed models of crowds, further refinements and validation of the ROCkS platform, and deeper interaction between sensor networks and classical modalities.

Publications:

	Wigdor, D.; Ivanov, Y.; Wren, C.R., “Soda Pop Zombies: Soft Drink Consumption and Motion”, Workshop on Massive Datasets (MD), ISBN: 978-1-59593-871-8, pp. 8-9, November 2007 (ACM Press, TR2007-081)
	Wren, C.R.; Ivanov, Y.A.; Leigh, D.; Westhues, J., “The MERL Motion Detector Dataset”, Workshop on Massive Datasets (MD), ISBN: 978-1-50593-981-8, pp. 10-14, November 2007 (ACM Press, TR2007-069)
	Ivanov, Y.; Wren, C.; Sorokin, A.; Kaur, I., “Visualizing the History of Living Spaces”, IEEE Transactions on Visualization and Computer Graphics, ISSN: 1077-2626, Vol. 13, Issue 6, pp. 1153-1160, Nov-Dec 2007 (IEEE Xplore, TR2007-068)
	Wren, C.; Ivanov, Y.; Kaur, I.; Leigh, D.; Westhues, J., “SocialMotion: Measuring the Hidden Social Life of a Building”, Third International Symposium on Location- and Context-Awareness (LoCA 2007), ISBN: 978-3-540-75159-5, Volume 4718, pp. 85-102, September 2007 (Lecture Notes in Computer Science, TR2007-034)
	Wren, C.R.; Ivanov, Y.A.; Leigh, D.; Westhues, J., “Buzz: Measuring and Visualizing Conference Crowds”, ACM SIGGRAPH, Session Emerging Technologies, Article 25, August 2007 (ACM Press, TR2007-035)
	Ivanov, Y.; Sorokin, A.; Wren, C.; Kaur, I., “Tracking People in Mixed Modality Systems”, SPIE Conference on Visual Communications and Image Processing (VCIP), Vol. 6508, January 2007 (SPIE Publications, TR2007-011)
	Reynolds, C.J.; Wren, C.R., “Worse is Better for Ambient Sensing”, Pervasive 2006: Workshop on Privacy Trust and Identity Issues for Ambient Intelligence, May 2006 (Pervasive 2006, TR2006-005)
	Wren, C.R.; Erdem, M.; Azarbayejani, A.J., “Automatic Pan-Tilt-Zoom Calibration in the Presence of Hybrid Sensor Networks”, ACM International Workshop on Video Surveillance and Sensor Networks (VSSN), ISBN: 1-59593-242-9, pp. 113-120, November 2005 (VSSN 2005, TR2005-084)

Technical Reports:

Research Areas:
Sensor and Data Systems
Computer Vision

Modification Date: September 2, 2011