The Resource Bayesian prediction and adaptive sampling algorithms for mobile sensor networks : online environmental field reconstruction in space and time, Yunfei, Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks : online environmental field reconstruction in space and time, Yunfei, Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Label
Bayesian prediction and adaptive sampling algorithms for mobile sensor networks : online environmental field reconstruction in space and time
Title
Bayesian prediction and adaptive sampling algorithms for mobile sensor networks
Title remainder
online environmental field reconstruction in space and time
Statement of responsibility
Yunfei, Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Creator
Contributor
Author
Subject
Genre
Language
eng
Summary
This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Xu, Yunfei
Dewey number
681/.2
Index
no index present
LC call number
TK7872.D48
LC item number
X8 2016eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Choi, Jongeun
  • Dass, Sarat Chandra
  • Maiti, Tapabrata
Series statement
SpringerBriefs in electrical and computer engineering. Control, automation and robotics
http://library.link/vocab/subjectName
  • Sensor networks
  • Bayesian statistical decision theory
  • TECHNOLOGY & ENGINEERING
  • Bayesian statistical decision theory
  • Sensor networks
  • Engineering
  • Control, Robotics, Mechatronics
  • Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  • Artificial Intelligence (incl. Robotics)
  • Signal, Image and Speech Processing
  • Communications Engineering, Networks
  • Probability & statistics
  • Artificial intelligence
  • Imaging systems & technology
  • Communications engineering / telecommunications
  • Automatic control engineering
Label
Bayesian prediction and adaptive sampling algorithms for mobile sensor networks : online environmental field reconstruction in space and time, Yunfei, Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-21921-9
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion
Dimensions
unknown
Extent
1 online resource (xii, 115 pages).
File format
unknown
Form of item
online
Isbn
9783319219219
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-21921-9
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)927140666
  • (OCoLC)ocn927140666
Label
Bayesian prediction and adaptive sampling algorithms for mobile sensor networks : online environmental field reconstruction in space and time, Yunfei, Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-21921-9
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- Preliminaries -- Learning the Covariance Function -- Prediction with Known Covariance Function -- Fully Bayesian Approach -- Gaussian Process with Built-in Gaussian Markov Random Fields -- Bayesian Spatial Prediction Using Gaussian Markov Random Fields -- Conclusion
Dimensions
unknown
Extent
1 online resource (xii, 115 pages).
File format
unknown
Form of item
online
Isbn
9783319219219
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-21921-9
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)927140666
  • (OCoLC)ocn927140666

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