  Age of information (AoI) is one of the key performance metrics for Internet
of things (IoT) systems. Timely status updates are essential for many IoT
applications; however, they are subject to strict constraints related on the
available energy and unreliability of underlying information sources. Hence,
the scheduling of status updates must be carefully planned to preserve energy,
but at the same time, it may happen that when a status update is needed, there
is no reliable up-to-date information content to refresh. As a solution to
overcome these unpredictabilities, one can employ multiple sources that track
the same signal of interest, but with different energy costs and reliabilities,
which we call information source diversity. We consider an energy-harvesting
monitoring node equipped with a finite-size battery and collecting status
updates from multiple heterogeneous information sources. We investigate the
policies that minimize the average AoI, and evaluate the role of information
source diversity on the system performance. To do so, we formulate the problem
as a Markov decision process (MDP). The optimal policy represents the
scheduling of actions, such as an update from one of the sources or remaining
idle, based on the current energy level and the AoI at the monitoring node. We
analyze the structure of the optimal solution for different cost/AoI
distribution combinations, and compare the performance of the optimal policy
with an aggressive strategy that transmits whenever possible.
