Adaptive Sampling with Mobile Wsn - Control, Robotics and Sensors by Koushil Sreenath & Muhammad F Mysorewala & Dan O Popa & Frank L Lewis

About the Book

Adaptive Sampling with Mobile WSN develops algorithms for optimal estimation of environmental parametric fields.

Book Synopsis

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.