Adaptive Sampling with Mobile Wsn : Simultaneous Robot Localisation and Mapping of Paramagnetic Spatio-Temporal Fields
Language: English
Published by Institution Of Engineering & Technology Feb 2011, 2011
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields. With a single mobile sensor, several approaches are presented to solve the problem of where to sample next to maximally and simultaneously reduce uncertainty in the field estimate and uncertainty in the localisation of the mobile sensor while respecting the dynamics of the time-varying field and the mobile sensor. A case study of mapping a forest fire is presented. Multiple static and mobile sensors are considered next, and distributed algorithms for adaptive sampling are developed resulting in the Distributed Federated Kalman Filter. However, with multiple resources a possibility of deadlock arises and a matrix-based discrete-event controller is used to implement a deadlock avoidance policy. Deadlock prevention in the presence of shared and routing resources is also considered. Finally, a simultaneous and adaptive localisation strategy is developed to simultaneously localise static and mobile sensors in the WSN in an adaptive manner. Experimental validation of several of these algorithms is discussed throughout the book.…
Seller Inventory # 9781849192576
- Title
- Adaptive Sampling with Mobile Wsn : Simultaneous Robot Localisation and Mapping of Paramagnetic Spatio-Temporal Fields
- Author
- Koushil Sreenath
- Publisher
- Institution Of Engineering & Technology Feb 2011
- Publication year
- 2011
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 184919257X
- ISBN 13
- 9781849192576
- Item weight
- 408 grams
- Dimensions
- 236x160x15 mm
- Series
- Book 9 of 32: Control, Robotics and Sensors
Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields. With a single mobile sensor, several approaches are presented to solve the problem of where to sample next to maximally and simultaneously reduce uncertainty in the field estimate and uncertainty in the localisation of the mobile sensor while respecting the dynamics of the time-varying field and the mobile sensor. A case study of mapping a forest fire is presented. Multiple static and mobile sensors are considered next, and distributed algorithms for adaptive sampling are developed resulting in the Distributed Federated Kalman Filter. However, with multiple resources a possibility of deadlock arises and a matrix-based discrete-event controller is used to implement a deadlock avoidance policy. Deadlock prevention in the presence of shared and routing resources is also considered. Finally, a simultaneous and adaptive localisation strategy is developed to simultaneously localise static and mobile sensors in the WSN in an adaptive manner. Experimental validation of several of these algorithms is discussed throughout the book.
"Synopsis" may belong to another edition of this title.
About the Author
Koushil Sreenath is a Ph.D. candidate in Electrical Engineering at the University of Michigan, Ann Arbor.
Muhammad F. Mysorewala is an Assistant Professor of Systems Engineering at King Fahd University of Petroleum and Minerals, Saudi Arabia.
Dan O. Popa is an Associate Professor of Electrical Engineering at the University of Texas, Arlington.
Frank L. Lewis is a Professor of Electrical Engineering and Moncrief-O'Donnell Chair at the University of Texas, Arlington.
"About the title" may belong to another edition of this title.
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