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Conference Paper: Evaluating continuous probabilistic queries over imprecise sensor data

TitleEvaluating continuous probabilistic queries over imprecise sensor data
Authors
Issue Date2010
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2010, v. 5981 LNCS PART 1, p. 535-549 How to Cite?
AbstractPervasive applications, such as natural habitat monitoring and location-based services, have attracted plenty of research interest. These applications deploy a large number of sensors (e.g. temperature sensors) and positioning devices (e.g. GPS) to collect data from external environments. Very often, these systems have limited network bandwidth and battery resources. The sensors also cannot record accurate values. The uncertainty of these data hence has to been taken into account for query evaluation purposes. In particular, probabilistic queries, which consider data impreciseness and provide statistical guarantees in answers, have been recently studied. In this paper, we investigate how to evaluate a longstanding (or continuous) probabilistic query. We propose the probabilistic filter protocol, which governs remote sensor devices to decide upon whether values collected should be reported to the query server. This protocol effectively reduces the communication and energy costs of sensor devices. We also introduce the concept of probabilistic tolerance, which allows a query user to relax answer accuracy, in order to further reduce the utilization of resources. Extensive simulations on realistic data show that our method reduces by address more than 99% of savings in communication costs. © Springer-Verlag Berlin Heidelberg 2010.
Persistent Identifierhttp://hdl.handle.net/10722/151982
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorZhang, Yen_US
dc.contributor.authorCheng, Ren_US
dc.contributor.authorChen, Jen_US
dc.date.accessioned2012-06-26T06:32:00Z-
dc.date.available2012-06-26T06:32:00Z-
dc.date.issued2010en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2010, v. 5981 LNCS PART 1, p. 535-549en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/151982-
dc.description.abstractPervasive applications, such as natural habitat monitoring and location-based services, have attracted plenty of research interest. These applications deploy a large number of sensors (e.g. temperature sensors) and positioning devices (e.g. GPS) to collect data from external environments. Very often, these systems have limited network bandwidth and battery resources. The sensors also cannot record accurate values. The uncertainty of these data hence has to been taken into account for query evaluation purposes. In particular, probabilistic queries, which consider data impreciseness and provide statistical guarantees in answers, have been recently studied. In this paper, we investigate how to evaluate a longstanding (or continuous) probabilistic query. We propose the probabilistic filter protocol, which governs remote sensor devices to decide upon whether values collected should be reported to the query server. This protocol effectively reduces the communication and energy costs of sensor devices. We also introduce the concept of probabilistic tolerance, which allows a query user to relax answer accuracy, in order to further reduce the utilization of resources. Extensive simulations on realistic data show that our method reduces by address more than 99% of savings in communication costs. © Springer-Verlag Berlin Heidelberg 2010.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleEvaluating continuous probabilistic queries over imprecise sensor dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailCheng, R:ckcheng@cs.hku.hken_US
dc.identifier.authorityCheng, R=rp00074en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/978-3-642-12026-8_41en_US
dc.identifier.scopuseid_2-s2.0-78650500608en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650500608&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume5981 LNCSen_US
dc.identifier.issuePART 1en_US
dc.identifier.spage535en_US
dc.identifier.epage549en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridZhang, Y=35185810300en_US
dc.identifier.scopusauthoridCheng, R=7201955416en_US
dc.identifier.scopusauthoridChen, J=36695906500en_US

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