  In this work, we consider a real-time IoT monitoring system in which an
energy harvesting sensor with a finite-size battery measures a physical process
and transmits the status updates to an aggregator. The aggregator, equipped
with caching capabilities, can serve the external requests of a destination
network with either a stored update or a fresh update from the sensor. We
assume the destination network acts as a gossiping network in which the update
packets are forwarded among the nodes in a randomized setting. We utilize the
Markov Decision Process framework to model and optimize the network's average
Version Age of Information (AoI) and obtain the optimal policy at the
aggregator. The structure of the optimal policy is analytically demonstrated
and numerically verified. Numerical results highlight the effect of the system
parameters on the average Version AoI. The simulations reveal the superior
performance of the optimal policy compared to a set of baseline policies.
