Energy -Efficient Sensor Networks for Detection Applications
Appadwedula, Swaroop
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/80813
Description
Title
Energy -Efficient Sensor Networks for Detection Applications
Author(s)
Appadwedula, Swaroop
Issue Date
2003
Doctoral Committee Chair(s)
Jones, Douglas L.
Veeravalli, Venugopal V.
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
Sensor networks powered by batteries or charged by energy-scavenging devices are to be used in a wide variety of applications where detection is the primary step. Faced with tight constraints on energy, bandwidth, and other system resources, the new paradigm of decentralized detection under resource constraints has emerged. To address some of the system-level costs in a sensor network, we formulate detection problems with constraints on the cost arising from transmission (sensor nodes to a fusion center) and measurement (at each sensor node). For transmission, a send/nosend scheme effectively reduces how often sensor nodes must communicate while maintaining small error and false-alarm probabilities. We find that randomization over the choice of measurement and rate of transmission can meet resource constraints and maximize the detection performance. In practice, the distribution of the observations is time varying and only partially known. Invariant tests with thresholds or parameters that can be determined independently at each sensor node are necessary to avoid frequent communication between nodes. For distance metrics and for sufficiently small communication rates in Bayesian and Neyman-Pearson problems, we find that the censoring regions can be determined independently at each sensor node, eliminating the joint optimization over the sensor nodes. The sensor decision rule is simply to transmit the likelihood ratio when it is above some threshold, chosen to meet the communication-rate constraint. Under the censoring scenario, we show that robust, locally optimum, and maximum-likelihood approaches result in simple tests and provide parameter invariance for parametric distributions. Using power measurements for existing testbeds and acoustic data collected outdoors with microphones, our approach is demonstrated in the problem of detecting machine sounds.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
