Age of Information in Non-Terrestrial Networks with Energy Harvesting

2026-08-03

AI Summary

This work addresses the timeliness of status updates in low Earth orbit satellite-aided passive Internet-of-Things networks, where satellite mobility and uncertain energy harvesting degrade information freshness. To tackle this challenge under unknown satellite visibility, the authors propose a novel "sense-before-transmit" mechanism for age-of-information (AoI) optimization. By integrating spherical stochastic geometry with a semi-Markov process, they model the coupled evolution of the energy buffer and connectivity states, and—uniquely—incorporate sensing into AoI analysis for non-terrestrial, energy-constrained networks. Leveraging long-term connectivity probability to approximate instantaneous states, they derive a low-complexity analytical expression for the time-average AoI. Simulations demonstrate that the proposed scheme significantly outperforms blind transmission strategies, particularly under sparse satellite deployment, high decoding thresholds, or stringent energy constraints.

Abstract

We analyze the timeliness of status-update delivery in a low Earth orbit (LEO) satellite-assisted energy-harvesting Internet of Things network using the Age of Information (AoI) metric. A ground source harvests ambient energy and sends status updates to a remote destination through LEO satellites. Because of satellite mobility, source-to-satellite connectivity alternates between on and off periods whose durations depend on the satellite-ground geometry. The source does not know the connectivity state a priori and therefore employs a probe-before-transmission mechanism: it first expends one energy unit to sense satellite availability and transmits an update only after a successful probe. We combine spherical stochastic geometry with semi-Markov analysis to characterize the coupled evolution of satellite connectivity and the source energy buffer, and derive an analytical expression for the time-average AoI. We then develop a lower-complexity approximation by replacing the instantaneous connectivity state in the energy process with the long-term on-state probability. The resulting approximation is accurate when the energy constraint is weak or satellite connectivity is highly intermittent. Numerical results show that probing can substantially reduce AoI relative to blind transmission by preventing energy expenditure during off periods, particularly under sparse satellite deployment, stringent decoding requirements, or limited energy harvesting.

Problem

• Age of Information
• Non-Terrestrial Networks
• Energy Harvesting
• LEO Satellite
• Status Update

Innovation

• Age of Information
• energy harvesting
• LEO satellite
• probe-before-transmission
• stochastic geometry