  We consider an energy harvesting (EH) IoT network, where users make on-demand
requests to a cache-enabled edge node to send status updates about various
random processes, each monitored by an EH sensor. The edge node serves users'
requests by either commanding the corresponding sensor to send a fresh status
update or retrieving the most recently received measurement from the cache. We
aim to find a control policy at the edge node that minimizes the average
on-demand AoI over all sensors subject to per-slot transmission and energy
constraints under partial battery knowledge at the edge node. Namely, the
limited radio resources (e.g., bandwidth) causes that only a limited number of
sensors can send status updates at each time slot (i.e., per-slot transmission
constraint) and the scarcity of energy for the EH sensors imposes an energy
constraint. Besides, the edge node is informed of the sensors' battery levels
only via received status update packets, leading to uncertainty about the
battery levels for the decision-making.We develop a low-complexity algorithm --
termed relax-then-truncate -- and prove that it is asymptotically optimal as
the number of sensors goes to infinity. Numerical results illustrate that the
proposed method achieves significant gains over a request-aware greedy policy
and show that it has near-optimal performance even for moderate numbers of
sensors.
