Coverart for item
The Resource Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks : Online Environmental Field Reconstruction in Space and Time, by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti, (electronic resource)

Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks : Online Environmental Field Reconstruction in Space and Time, by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti, (electronic resource)

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
by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
Creator
Contributor
Author
Provider
Subject
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
http://library.link/vocab/creatorName
Xu, Yunfei
Image bit depth
0
LC call number
  • TJ210.2-211.495
  • TJ163.12
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Choi, Jongeun.
  • Dass, Sarat.
  • Maiti, Tapabrata.
  • SpringerLink
Series statement
SpringerBriefs in Electrical and Computer Engineering,
http://library.link/vocab/subjectName
  • Engineering
  • Artificial intelligence
  • Statistics
  • Control engineering
  • Robotics
  • Mechatronics
  • Electrical engineering
  • 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
Label
Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks : Online Environmental Field Reconstruction in Space and Time, by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
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
Edition
1st ed. 2016.
Extent
XII, 115 p. 43 illus., 2 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783319219219
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-21921-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-21921-9
Label
Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks : Online Environmental Field Reconstruction in Space and Time, by Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
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
Edition
1st ed. 2016.
Extent
XII, 115 p. 43 illus., 2 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783319219219
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-21921-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-319-21921-9

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