1 / 7

Research Ideas

Research Ideas. Lei Rao Jan. 17 th , 2009. Inspired By Two Papers. C. Guestrin, A. Krause, and A.P. Singh, “Near-optimal sensor placements in Gaussian processes,” International Conference on Machine Learning (ICML 2005), 2005, p. 265.

keene
Download Presentation

Research Ideas

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Research Ideas Lei Rao Jan. 17th, 2009

  2. Inspired By Two Papers • C. Guestrin, A. Krause, and A.P. Singh, “Near-optimal sensor placements in Gaussian processes,” International Conference on Machine Learning (ICML 2005), 2005, p. 265. • A. Krause, C. Guestrin, A. Gupta, and J. Kleinberg, “Near-optimal sensor placements: maximizing information while minimizing communication cost,” Proceedings of the fifth international conference on Information processing in sensor networks (IPSN 2006), ACM New York, NY, USA, 2006, pp. 2-10.

  3. Review of The First Paper: Problem Statement Assumption: the temperatures have a Gaussian joint distribution. Problem: How to find a subset of Locations that are most informative about un-sensed locations to place sensor nodes?

  4. General Idea of The Solution • Greedy Algorithm • V is a finite location set, and A is the placement set; • Adding sensors in sequence, choosing the next sensor which provides the maximum increase in mutual information ( I(A;V\A) ); • The greedy algorithm can be proved to give a certain bound approximation to the optimal sensor placement (Based on Sub-modularity); • The complexity of computing is reduced by local kernels.

  5. Review of The Second Paper: Improvements From The First One Solutions borrow the idea of Machine Learning Problem: How to find a subset of Locations that are most informative about un-sensed locations to place sensor nodes with communication cost under a certain bound? OR: How to find a subset of Locations that minimize the network communication cost while guarantee the placements are above a certain informative bound?

  6. General Research Ideas • Idea 1: Power Management, how to adjust the power of each sensor so that a certain bound of information collection can be guaranteed based on the methods in previous papers. • Idea 2: Add a different constraint (as communication cost in the 2nd paper) to the problem in the 1st paper. • Idea 3: Explore SIFT to solve problems in CPS applications following a similar way.

  7. THANKS!Q & A

More Related