  This paper presents a novel information value function that can be used in
online sensor planning to monitor a spatial phenomenon in which the spatial
phenomenon is modeled by nonparametric Gaussian processes. The information
value function is derived from the Kullback-Leibler (KL) divergence and
represents the information value brought by sensor decision. The sensor
decision at every time step is to select the sensing location that maximizes
the information value function associated with the measurement taken. Gaussian
processes (GPs) are employed to obtain the posterior distribution of the
spatial phenomenon given a number of sensor measurements, because GPs have
sufficient flexibilities to adopt the complexity from data. Furthermore, a
greedy algorithm is designed based on the information value function. By
comparing the greedy algorithm with the random algorithm, it is shown that the
error decreases faster defined as the difference between the estimated
posterior distribution and the true distribution of the spatial phenomenon via
the greedy algorithm.
