A fluid reservoir model for the Age of Information through energy-harvesting transmitters

TL;DR

This paper models the Age of Information in energy-harvesting transmitters using a fluid-reservoir approach, revealing how energy and buffer management affect update freshness in various regimes.

Contribution

It introduces a fluid-reservoir model for AoI analysis, providing detailed results for different buffer and energy reservoir configurations, which better reflect real energy storage dynamics.

Findings

Minimizing transmitter buffer reduces queueing delays.

High update rates benefit energy-rich regimes.

In energy-poor regimes, frequent updates increase AoI.

Abstract

We apply a fluid-reservoir model to study the Age-of-Information (AoI) of update packets through energy-harvesting transmitters. The model is closer to how energy is stored and depleted in reality, and can reveal the system behavior for different settings of packet arrival rates, service rates, and energy charging and depletion rates. We present detailed results for both finite and infinite transmitter buffers and an infinite energy reservoir, and some indicative results for a finite reservoir. The results are derived for the mean AoI in the case of an infinite transmitter buffer and an infinite reservoir, and for the mean peak AoI for the remaining cases. The results show that, similar to a system without energy constraints, the transmitter buffer should be kept to a minimum in order to avoid queueing delays and maintain freshness of updates. Furthermore, a high update packet rate is only helpful in energy-rich regimes, whereas in energy-poor regimes more frequent updates deplete the energy reservoir and result in higher AoI values.