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001 978-3-319-21921-9
003 DE-He213
005 20200420221301.0
007 cr nn 008mamaa
008 151027s2016 gw | s |||| 0|eng d
020 _a9783319219219
_9978-3-319-21921-9
024 7 _a10.1007/978-3-319-21921-9
_2doi
050 4 _aTJ210.2-211.495
050 4 _aTJ163.12
072 7 _aTJFM
_2bicssc
072 7 _aTJFD
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTEC037000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aXu, Yunfei.
_eauthor.
245 1 0 _aBayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks
_h[electronic resource] :
_bOnline Environmental Field Reconstruction in Space and Time /
_cby Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXII, 115 p. 43 illus., 2 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
505 0 _aIntroduction -- 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.
520 _aThis 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aStatistics.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aCommunications Engineering, Networks.
700 1 _aChoi, Jongeun.
_eauthor.
700 1 _aDass, Sarat.
_eauthor.
700 1 _aMaiti, Tapabrata.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319219202
830 0 _aSpringerBriefs in Electrical and Computer Engineering,
_x2191-8112
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-21921-9
912 _aZDB-2-ENG
942 _cEBK
999 _c53183
_d53183