Before you leave...
Take 20% off your first order
20% off
Enter the code below at checkout to get 20% off your first order
Discover summer reading lists for all ages & interests!
Find Your Next Read

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.
Thanks for subscribing!
This email has been registered!
Take 20% off your first order
Enter the code below at checkout to get 20% off your first order