  Suppose that a mobile sensor describes a Markovian trajectory in the ambient
space. At each time the sensor measures an attribute of interest, e.g., the
temperature. Using only the location history of the sensor and the associated
measurements, the aim is to estimate the average value of the attribute over
the space. In contrast to classical probabilistic integration methods, e.g.,
Monte Carlo, the proposed approach does not require any knowledge on the
distribution of the sensor trajectory. Probabilistic bounds on the convergence
rates of the estimator are established. These rates are better than the
traditional "root n"-rate, where n is the sample size, attached to other
probabilistic integration methods. For finite sample sizes, the good behaviour
of the procedure is demonstrated through simulations and an application to the
evaluation of the average temperature of oceans is considered.
