Age of Information Minimization for an Energy Harvesting Source with Updating Erasures: With and Without Feedback

TL;DR

This paper studies how to optimally schedule status updates from an energy-harvesting sensor to minimize the average Age of Information, considering erasures and feedback, proposing and proving the optimality of specific policies.

Contribution

It introduces and proves the optimality of the Best-effort Uniform updating policies with and without feedback for AoI minimization in energy-harvesting sensors with erasures.

Findings

Best-effort Uniform updating (BU) is optimal without feedback.

Best-effort Uniform updating with Retransmission (BUR) is optimal with perfect feedback.

Derived lower bounds and constructed policies approach optimal AoI.

Abstract

Consider an energy harvesting (EH) sensor that continuously monitors a system and sends time-stamped status update to a destination. The sensor harvests energy from nature and uses it to power its updating operations. The destination keeps track of the system status through the successfully received updates. With the recently introduced information freshness metric "Age of Information" (AoI), our objective is to design optimal online status updating policy to minimize the long-term average AoI at the destination, subject to the energy causality constraint at the sensor. Due to the noisy channel between the sensor and the destination, each transmitted update may be erased with a fixed probability, and the AoI at the destination will be reset to zero only when an update is successfully received. We first consider status updating without feedback available to the sensor and show that the Best-effort Uniform updating (BU) policy is optimal. We then investigate status updating with perfect feedback to the sensor and prove the optimality of the Best-effort Uniform updating with Retransmission (BUR) policy. In order to prove the optimality of the proposed policies, for each case, we first identify a lower bound on the long-term average AoI among a broad class of online policies, and then construct a sequence of virtual policies to approach the lower bound asymptotically. Since those virtual policies are sub-optimal to the original policy, the original policy is thus optimal.