The constant reduction in cost and increase in the power of computing machinery has resulted in an ever-increasing interest and deployment of Internet-enabled sensing systems. Such systems have the distinctly difficult task of making use of noisy data sampled from dynamic environments. While some natural processes may have very exact theoretical models, real actual data rarely holds to the model, making the comparison, improvement and iterative development of sensing applications extremely difficult. The inability to determine whether a sensing system’s error is the result of noisy data or algorithmic miscomputation, or the prevalence and significance of signal errors in a particular environment make the causes of the error inscrutable. In such cases strongly amortized or very general probabilistic analysis is often used as a last resort resulting in conclusions that are overly generic, heuristic, or strongly underdetermined. We present a systematic method that can be used to construct a holistic synthetic error model for sensed data, the algorithms that process it and the environment in which it is sampled. We demonstrate how this method can be applied to the problem of laterative localization to construct deductive, analytic and evaluative mechanisms that allow model misperception, algorithmic error and environmental character to be understood.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Algorithms
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6657
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xvii, 197 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by John-Austen Francisco
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. Graduate School - New Brunswick
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Type
License
Name
Author Agreement License
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