Local average consensus in distributed measurement of spatial–temporal varying parameters: 1D case

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dc.contributor.authorCai, Kai
dc.contributor.authorAnderson, Brian D. O.
dc.contributor.authorYu, Changbin (Brad)
dc.contributor.authorMao, Guoqiang
dc.date.accessioned2015-04-02T01:31:14Z
dc.date.available2015-04-02T01:31:14Z
dc.date.issued2014-12-06
dc.date.updated2015-12-10T09:47:16Z
dc.description.abstractWe study a new variant of consensus problems, termed ‘local average consensus’, in networks of agents. We consider the task of using sensor networks to perform distributed measurement of a parameter which has both spatial (in this paper 1D) and temporal variations. Our idea is to maintain potentially useful local information regarding spatial variation, as contrasted with reaching a single, global consensus, as well as to mitigate the effect of measurement errors. We employ two schemes for computation of local average consensus: exponential weighting and uniform finite window. In both schemes, we design local average consensus algorithms to address first the case where the measured parameter has spatial variation but is constant in time, and then the case where the measured parameter has both spatial and temporal variations. Our designed algorithms are distributed, in that information is exchanged only among neighbors. Moreover, we analyze both spatial and temporal frequency responses and noise propagation associated with the algorithms. The tradeoffs of using local consensus, as compared to standard global consensus, include higher memory requirement and degraded noise performance. Arbitrary updating weights and random spacing between sensors are also analyzed in the proposed algorithms.
dc.description.sponsorshipK. Cai’s work was supported by Program to Disseminate Tenure Tracking System and Grants-in-Aid for Scientific Research No. 26870169, MEXT, Japan. B.D.O. Anderson’s work was supported by the Australian Research Council through DP-130103610 and DP- 110100538 and by National ICT Australia. C. Yu’s work was supported by the Australian Research Council through DP-130103610 and a Queen Elizabeth II Fellowship under DP-110100538, the National Natural Science Foundation of China (61375072), and the Overseas Expert Program of Shandong Province.en_AU
dc.identifier.issn0005-1098en_AU
dc.identifier.urihttp://hdl.handle.net/1885/13163
dc.publisherElsevier
dc.relationhttp://purl.org/au-research/grants/arc/DP130103610
dc.relationhttp://purl.org/au-research/grants/arc/DP110100538
dc.rightshttp://www.sherpa.ac.uk/romeo/issn/0005-1098/..."Pre-print allowed on any website or open access repository." from SHERPA/RoMEO site (as at 07/04/15)
dc.sourceAutomatica
dc.subjectLocal average consensus
dc.subjectSensor networks
dc.subjectSpatial/temporal frequency response
dc.subjectNoise propagation
dc.subjectBandwidth
dc.titleLocal average consensus in distributed measurement of spatial–temporal varying parameters: 1D case
dc.typeJournal article
dcterms.dateAccepted2014-10-23
local.bibliographicCitation.lastpage145en_AU
local.bibliographicCitation.startpage135en_AU
local.contributor.affiliationAnderson, B. D. O., College of Engineering & Computer Science, The Australian National Universityen_AU
local.contributor.affiliationYu, Changbin, College of Engineering & Computer Science, The Australian National Universityen_AU
local.contributor.authoremailbrian.anderson@anu.edu.auen_AU
local.contributor.authoruidu8104642en_AU
local.identifier.absfor080500 - DISTRIBUTED COMPUTING
local.identifier.absfor090600 - ELECTRICAL AND ELECTRONIC ENGINEERING
local.identifier.ariespublicationa383154xPUB996
local.identifier.citationvolume52en_AU
local.identifier.doi10.1016/j.automatica.2014.11.002en_AU
local.identifier.scopusID2-s2.0-84922437226
local.identifier.uidSubmittedByu1005913en_AU
local.publisher.urlhttp://www.elsevier.com/en_AU
local.type.statusSubmitted Versionen_AU

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