Adaptive ON/OFF Scheduling to Minimize Age of Information in an Energy-Harvesting Receiver

This article considers a timely information updating problem where an energy-harvesting (EH) Internet-of-Things (IoT) receiver node interacts with an information source having a state-dependent time-varying update generation rate. This model and problem are motivated by the interaction of a random or controlled state change (represented by lazy and prolific modes) in monitoring a physical process and the ability of the IoT node to monitor and track in a timely fashion using harvested energy. Time is slotted and in every time slot, the EH IoT receiver node can either turn on to receive status updates, if any, or turn off to save energy. With the aim of minimizing average age of information (AoI) at the receiving end with available state information, we determine the optimal ON-OFF scheduling policy of the EH receiver for the single-unit capacity (infinite capacity) battery case through a Markov decision process (MDP) [constrained MDP (CMDP)] framework. We obtain resulting dynamic programming algorithms that yield optimal ON-OFF scheduling policies. Furthermore, we consider an age-threshold-based scheme called "state-adapted waiting before turning on" scheduling policy and obtain closed-form expressions of average AoI (AAoI) for the single-unit and infinite battery capacity cases. To study the effect of battery presence and optimal waiting time, we also consider the case of no battery and another policy that waits until the occurrence of state transition in the information source. We consistently observe in our numerical results that the AAoI of the state-adapted age-threshold-based ON- OFF scheme matches those of the optimal policy.