Towards a Sustainable Age of Information Metric: Carbon Footprint of Real-Time Status Updates

TL;DR

This work addresses the environmental impact of real-time status updates by integrating Carbon Footprint (CF) considerations into the Age of Information (AoI) framework. It derives closed-form expressions for the average AoI under CF budgets for the $M/M/1$ and $M/M/1^*$ queuing models and extends the analysis to time-varying Carbon Intensity (CI). The results reveal that naive AoI minimization does not guarantee minimal CF, and CI variability further reshapes the achievable AoI, necessitating joint optimization of CF budgets, SNR, and transmission scheduling. The findings lay the groundwork for carbon-aware information freshness optimization in next-generation networks, enabling greener operation of 6G and large-scale IoT systems.

Abstract

The timeliness of collected information is essential for monitoring and control in data-driven intelligent infrastructures. It is typically quantified using the Age of Information (AoI) metric, which has been widely adopted to capture the freshness of information received in the form of status updates. While AoI-based metrics quantify how timely the collected information is, they largely overlook the environmental impact associated with frequent transmissions, specifically, the resulting Carbon Footprint (CF). To address this gap, we introduce a carbon-aware AoI framework. We first derive closed-form expressions for the average AoI under constrained CF budgets for the baseline $M/M/1$ and $M/M/1^*$ queuing models, assuming fixed Carbon Intensity (CI). We then extend the analysis by treating CI as a dynamic, time-varying parameter and solve the AoI minimization problem. Our results show that minimizing AoI does not inherently minimize CF, highlighting a clear trade-off between information freshness and environmental impact. CI variability further affects achievable AoI, indicating that sustainable operation requires joint optimization of CF budgets, Signal-to-noise Ratio (SNR), and transmission scheduling. This work lays the foundation for carbon-aware information freshness optimization in next-generation networks.

Figures (7)

• Figure 1: Annual Carbon Intensity (CI) in Slovenia (2024).
• Figure 2: Illustration of the considered system.
• Figure 3: aoi as a function of arrival rate ($\lambda$) for $M/M/1$ and $M/M/1^*$ models.
• Figure 4: aoi versus cf for the $M/M/1$ and $M/M/1^*$ models.
• Figure 5: Average aoi over monthly CI for $M/M/1$ (blue line) and $M/M/1^*$ (orange line) models.