Minimization of Age-of-Information in Remote Sensing with Energy Harvesting

TL;DR

This paper develops optimal policies for minimizing the average age-of-information in energy harvesting remote sensing systems, balancing data freshness with energy constraints using Markov decision processes.

Contribution

It introduces a threshold-based policy framework for AoI minimization in energy harvesting sensors with channel probing, considering both single and multiple process scenarios.

Findings

Optimal policies are threshold-based, depending on age, energy, and channel state.

Numerical results illustrate the effectiveness of the proposed policies.

Trade-offs between energy use and information freshness are characterized.

Abstract

In this paper, the minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source-equipped remote sensing setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a time-varying wireless link. At any discrete-time instant, the EH node decides whether to probe the link quality using its stored energy and further decides whether to sample a process and communicate the data based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the energy buffer of the source node. To this end, an infinite horizon Markov decision process theory is used to formulate the problem of minimization of time-averaged expected AoI for a single energy harvesting source node. The following two scenarios are considered: (i) single process with channel state information at the transmitter (CSIT), (ii) multiple processes with CSIT. In each scenario, for probed channel state, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process(es), the available energy in the buffer, and the instantaneous channel quality as the decision variables. Finally, numerical results are provided to demonstrate the policy structures and trade-offs.