Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Nonparametric change detection and estimation in large-scale sensor networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Ting He ; Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA ; Ben-David, S. ; Lang Tong

The problem of detecting changes in the distribution of alarmed sensors is considered. Under a nonparametric change detection framework, several detection and estimation algorithms are presented based on the Vapnik-Chervonenkis (VC) theory. Theoretical performance guarantees are obtained by providing error exponents for false-alarm and miss detection probabilities. Recursive algorithms for the efficient computation of test statistics are derived. The estimation problem is also considered in which, after detection is made, the location with maximum distribution change is estimated.

Published in:

Signal Processing, IEEE Transactions on  (Volume:54 ,  Issue: 4 )