Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback

dc.contributor.author Dogandžić, Aleksandar
dc.contributor.author Qiu, Kun
dc.contributor.department Department of Electrical and Computer Engineering
dc.date 2018-02-14T00:56:18.000
dc.date.accessioned 2020-06-30T02:03:27Z
dc.date.available 2020-06-30T02:03:27Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2008
dc.date.embargo 2014-04-30
dc.date.issued 2008-12-01
dc.description.abstract <p>In large-scale wireless sensor networks, sensor-processor elements (nodes) are densely deployed to monitor the environment; consequently, their observations form a random field that is highly correlated in space. We consider a fusion sensor-network architecture where, due to the bandwidth and energy constraints, the nodes transmit quantized data to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. In addition to saving energy, this feedback ensures reliability and robustness to node and fusion-center failures. We assume that the sensor observations follow a linear-regression model with known spatial covariances between any two locations within a region of interest. We propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the random field and its parameters. We also derive a simple suboptimal scheme for estimating the unknown parameters, apply our estimation approach to the no-feedback scenario, discuss field prediction at arbitrary locations within the region of interest, and present numerical examples demonstrating the performance of the proposed methods.</p>
dc.description.comments <p>This is a post-print of an article from <em>IEEE Transactions on Signal Processing</em> 56 (2008): 6069–6085, doi:<a href="http://dx.doi.org/10.1109/TSP.2008.2005753" target="_blank">10.1109/TSP.2008.2005753</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ece_pubs/6/
dc.identifier.articleid 1008
dc.identifier.contextkey 5540323
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ece_pubs/6
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/21119
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ece_pubs/6/2008_DogandzicA_DecentralizedRandomfieldEstimation.pdf|||Sat Jan 15 01:05:42 UTC 2022
dc.source.uri 10.1109/TSP.2008.2005753
dc.subject.disciplines Signal Processing
dc.subject.keywords Parameter estimation
dc.subject.keywords Random processes
dc.subject.keywords Wireless sensor networks
dc.title Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication c910f7d3-c386-4c37-8143-4e653a539aa9
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
File
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2008_DogandzicA_DecentralizedRandomfieldEstimation.pdf
Size:
3.82 MB
Format:
Adobe Portable Document Format
Description:
Collections