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Study of mobile mixed sensing networks in an automotive context

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dc.contributor.author Chakravarthy, Animesh
dc.contributor.author Song, Kyungyeol
dc.contributor.author Peraire, Jaime
dc.contributor.author Feron, Eric
dc.date.accessioned 2012-06-21T19:23:40Z
dc.date.available 2012-06-21T19:23:40Z
dc.date.issued 2012
dc.identifier.citation A. 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.165-198 en_US
dc.identifier.isbn 9780387886183
dc.identifier.issn 1931-6828
dc.identifier.other WOS: 000301099400008
dc.identifier.uri http://hdl.handle.net/10057/5218
dc.identifier.uri http://dx.doi.org/10.1007/978-0-387-88619-0_8
dc.description Click on the DOI link below to access the article (may not be free). en_US
dc.description.abstract Mixed 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.iso en_US en_US
dc.publisher Springer en_US
dc.relation.ispartof Sensors: Theory, Algorithms, and Applications
dc.relation.ispartofseries Springer Optimization and Its Applications;2012, v.61, no.3
dc.subject.classification MATHEMATICS
dc.title Study of mobile mixed sensing networks in an automotive context en_US
dc.type Book chapter
dc.rights.holder Copyright © 2012, Springer New York

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