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.