  Age of Information (AoI) has recently received much attention due to its
relevance in IoT sensing and monitoring applications. In this paper, we
consider the problem of minimizing the AoI in a system in which a set of
sources are observed by multiple sensors in a many-to-many relationship, and
the probability that a sensor observes a source depends on the state of the
source. This model represents many practical scenarios, such as the ones in
which multiple cameras or microphones are deployed to monitor objects moving in
certain areas. We formulate the scheduling problem as a Markov Decision
Process, and show how the age-optimal scheduling policy can be obtained. We
further consider partially observable variants of the problem, and devise
approximate policies for large state spaces. Our evaluations show that the
approximate policies work well in the considered scenarios, and that the fact
that sensors can observe multiple sources is beneficial, especially when there
is high uncertainty of the source states.
