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dc.contributor.authorMartínez, Borja-
dc.contributor.authorVilajosana i Guillén, Xavier-
dc.contributor.authorVilajosana Guillén, Ignasi-
dc.contributor.authorDohler, Misha-
dc.contributor.otherUniversitat Autònoma de Barcelona-
dc.contributor.otherKing's College London-
dc.contributor.otherUniversitat Oberta de Catalunya (UOC)-
dc.date.accessioned2019-04-02T13:44:39Z-
dc.date.available2019-04-02T13:44:39Z-
dc.date.issued2015-10-
dc.identifier.citationMartinez, B., Vilajosana, X., Vilajosana, I. & Dohler, M. (2015). Lean sensing: exploiting contextual information for most energy-efficient sensing. IEEE Transactions on Industrial Informatics, 11(5), 1156-1165. doi: 10.1109/TII.2015.2469260-
dc.identifier.issn1551-3203MIAR
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dc.identifier.issn1941-0050MIAR
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dc.identifier.urihttp://hdl.handle.net/10609/92810-
dc.description.abstractCyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherIEEE Transactions on Industrial Informatics-
dc.relation.ispartofIEEE Transactions on Industrial Informatics, 2015, 11(5)-
dc.relation.urihttps://kclpure.kcl.ac.uk/portal/files/51708026/Binder1.pdf-
dc.rights(c) Author/s & (c) Journal-
dc.subjecturban areasen
dc.subjectenergy consumptionen
dc.subjectmonitoringen
dc.subjectinformaticsen
dc.subjectsensor systemsen
dc.subjectoptimizationen
dc.subjectareas urbanases
dc.subjectconsumo de energíaes
dc.subjectvigilanciaes
dc.subjectinformáticaes
dc.subjectsistemas de sensoreses
dc.subjectoptimizaciónes
dc.subjectàrees urbanesca
dc.subjectconsum d'energiaca
dc.subjectmonitoritzacióca
dc.subjectinformàticaca
dc.subjectsistemes de sensorsca
dc.subjectoptimitzacióca
dc.subject.lcshEnergy consumptionen
dc.titleLean sensing: exploiting contextual information for most energy-efficient sensing-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/acceptedVersion-
dc.subject.lemacEnergia -- Consumca
dc.subject.lcshesEnergía -- Consumoes
dc.identifier.doi10.1109/TII.2015.2469260-
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