  We investigate the performance of concurrent remote sensing from independent
strategic sources, whose goal is to minimize a linear combination of the
freshness of information and the updating cost. In the literature, this is
often investigated from a static perspective of setting the update rate of the
sources a priori, either in a centralized optimal way or with a distributed
game-theoretic approach. However, we argue that truly rational sources would
better make such a decision with full awareness of the current age of
information, resulting in a more efficient implementation of the updating
policies. To this end, we investigate the scenario where sources independently
perform a stateful optimization of their objective. Their strategic character
leads to the formalization of this problem as a Markov game, for which we find
the resulting Nash equilibrium. This can be translated into practical smooth
threshold policies for their update. The results are eventually tested in a
sample scenario, comparing a centralized optimal approach with two distributed
approaches with different objectives for the players.
