Random Walk and Lighting Control

This visualization represents an average 24 hour day on the 5th floor of the MIT Media Lab. The network is made of 34 sensors which measure motion and light. The sensor data are normalized with respect to the busiest time of day and fitted to a convex hull. Low areas are denoted in blue with increased activity in red.

We pose the problem of turning off a single luminaire (or group) as an optimal stopping problem. We present the stationary and first-passage analysis of motion data obtained using custom wireless nodes in an open office floor plan. These calculations allow us to estimate the state of the network and calculate the probability and expected number of steps to visit a state from any arbitrary state. We also investigate if there is any evidence of clustering amongst the nodes by studying the covariance of the dataset. The data indicate the existence of clustering within the lattice. In other words, the analysis of random walk prevents luminaires from accidentally shutting off and dimensionality reduction determines the correct zoning of lighting via the occupants' movements.

publications:

Aldrich, M., Badshah, A., Mayton, B., Zhao, N., and Paradiso, J.A., "Random Walk and Lighting Control," IEEE Sensors 2013

Principle Investigator: Joseph Paradiso

Research Group: Responsive Environments group at the MIT Media Lab

Research Assistants: Matt Aldrich, Brian Mayton, Nan Zhao

Previous Undergraduate Researchers: Akash Badshah (2012)