Adaptive Sampling with Mobile WSN
Simultaneous robot localisation and mapping of paramagnetic spatio-temporal fields
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.
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.