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dc.contributor.authorChakravarthy, Animesh
dc.contributor.authorSong, Kyungyeol
dc.contributor.authorPeraire, Jaime
dc.contributor.authorFeron, Eric
dc.date.accessioned2012-06-21T19:23:40Z
dc.date.available2012-06-21T19:23:40Z
dc.date.issued2012
dc.identifier.citationA. Chakravarthy, K.Y. Song, J. Peraire and E. Feron, “Study of mobile mixed sensing networks in an automotive context”, book chapter in “Sensors: Theory, Algorithms and Applications”, Springer Optimization and its Applications 61, edited by V. Boginski, C. Commander, P. Pardalos, Y. Ye, New York : Springer, 2012, p.165-198en_US
dc.identifier.isbn9780387886183
dc.identifier.issn1931-6828
dc.identifier.otherWOS: 000301099400008
dc.identifier.urihttp://hdl.handle.net/10057/5218
dc.identifier.urihttp://dx.doi.org/10.1007/978-0-387-88619-0_8
dc.descriptionClick on the DOI link below to access the article (may not be free).en_US
dc.description.abstractMixed sensing mobile networks comprise of mobile sensors that have different sensing capabilities. We look at such sensor networks in an automotive context; wherein automobiles with two levels of sensing (and consequently with two different dynamics) are ‘mixed’ among one another. The two levels of sensing considered are local, near-neighbor information sensing; and advance, far-ahead information sensing. We look for conditions governing the way the two types of sensors should be mixed (i.e., required minimum number and distribution of the far-ahead information sensing vehicles in a mixed N-vehicle string) in order to meet certain performance objectives. In this regard, two types of models are considered – microscopic models (using ODEs) governing individual vehicle behavior; and macroscopic models (using PDEs) governing average behavior of groups of vehicles. The performance objective that we address is related to the safety of the overall network, and depends on the type of model being adopted – thus in the microscopic model, the performance metric is one of achieving zero collisions, in conditions where there otherwise would have been multi-vehicle collisions; while in the macroscopic model, the metric is one of weakening the shock waves that otherwise would have existed.en_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.ispartofSensors: Theory, Algorithms, and Applications
dc.relation.ispartofseriesSpringer Optimization and Its Applications;2012, v.61, no.3
dc.subject.classificationMATHEMATICS
dc.titleStudy of mobile mixed sensing networks in an automotive contexten_US
dc.typeBook chapter
dc.rights.holderCopyright © 2012, Springer New York


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