This brief introduces a class of problems and models for the prediction of the scalar field of
interest from noisy observations collected by mobile sensor networks. It also introduces the
problem of optimal coordination of robotic sensors to maximize the prediction quality subject
to communication and mobility constraints either in a centralized or distributed manner. To
solve such problems fully Bayesian approaches are adopted allowing various sources of
uncertainties to be integrated into an inferential framework effectively capturing all aspects
of variability involved. The fully Bayesian approach also allows the most appropriate values
for additional model parameters to be selected automatically by data and the optimal inference
and prediction for the underlying scalar field to be achieved. In particular spatio-temporal
Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of
observations measurement noise and prior distributions for obtaining the predictive
distribution of a scalar environmental field of interest. New techniques are introduced to
avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained
mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks
starts with a simple spatio-temporal model and increases the level of model flexibility and
uncertainty step by step simultaneously solving increasingly complicated problems and coping
with increasing complexity until it ends with fully Bayesian approaches that take into account
a broad spectrum of uncertainties in observations model parameters and constraints in mobile
sensor networks. The book is timely being very useful for many researchers in control
robotics computer science and statistics trying to tackle a variety of tasks such as
environmental monitoring and adaptive sampling surveillance exploration and plume tracking
which are of increasing currency. Problems are solved creatively by seamless combination of
theories and concepts from Bayesian statistics mobile sensor networks optimal experiment
design and distributed computation.
