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Semantics-Aware Updates from Remote Energy Harvesting Devices to Interconnected LEO Satellites

Erfan Delfani, Nikolaos Pappas

TL;DR

The paper addresses timely delivery of informative data in energy-constrained IoT-to-LEO satellite networks by optimizing Version AoI (VAoI) under energy harvesting. It develops a stochastic, MD-based framework across ring and star LEO topologies, proving a threshold structure for the optimal policy and showing significant energy savings while maintaining target VAoI. Numerical results reveal that semantics-aware updates can reduce unnecessary transmissions by up to 50% compared with Greedy under limited energy, and that finite-horizon results converge rapidly to the infinite-horizon optimum. The approach provides a principled method to balance freshness, relevance, and energy in non-terrestrial networks with practical implications for satellite-ground data dissemination.

Abstract

Providing timely and informative data in integrated terrestrial and non-terrestrial networks is critical as data volume grows while the resources available on devices remain limited. To address this, we adopt a semantics-aware approach to optimize the Version Age of Information (VAoI) in a status update system in which a remote Energy Harvesting (EH) Internet of Things (IoT) device samples data and transmits it to a network of interconnected Low Earth Orbit (LEO) satellites for dissemination and utilization. The optimal update policy is derived through stochastic modeling and optimization of the VAoI across the network. The results indicate that this policy reduces the frequency of updates by skipping stale or irrelevant data, significantly improving energy efficiency.

Semantics-Aware Updates from Remote Energy Harvesting Devices to Interconnected LEO Satellites

TL;DR

The paper addresses timely delivery of informative data in energy-constrained IoT-to-LEO satellite networks by optimizing Version AoI (VAoI) under energy harvesting. It develops a stochastic, MD-based framework across ring and star LEO topologies, proving a threshold structure for the optimal policy and showing significant energy savings while maintaining target VAoI. Numerical results reveal that semantics-aware updates can reduce unnecessary transmissions by up to 50% compared with Greedy under limited energy, and that finite-horizon results converge rapidly to the infinite-horizon optimum. The approach provides a principled method to balance freshness, relevance, and energy in non-terrestrial networks with practical implications for satellite-ground data dissemination.

Abstract

Providing timely and informative data in integrated terrestrial and non-terrestrial networks is critical as data volume grows while the resources available on devices remain limited. To address this, we adopt a semantics-aware approach to optimize the Version Age of Information (VAoI) in a status update system in which a remote Energy Harvesting (EH) Internet of Things (IoT) device samples data and transmits it to a network of interconnected Low Earth Orbit (LEO) satellites for dissemination and utilization. The optimal update policy is derived through stochastic modeling and optimization of the VAoI across the network. The results indicate that this policy reduces the frequency of updates by skipping stale or irrelevant data, significantly improving energy efficiency.

Paper Structure

This paper contains 11 sections, 5 theorems, 36 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Average VAoI within the Satellite Network
  4. Optimization Problem
  5. Numerical Results
  6. The Structure of the Optimal Policy
  7. The Impact of Energy Arrival Probability $(\beta)$
  8. The Impact of the Time Horizon $(T)$
  9. Conclusion
  10. Transition Probabilities
  11. The Optimal Policy Structure for Problem $\mathcal{P}_2$

Key Result

Proposition 1

The VAoI at the $n$-th satellite for the ring and star topologies is given by: where $\Delta_0(t)$ denotes the VAoI at the Connected Satellite, $\mathit{m}_n$ is a Geometric Random Variable (RV) with parameter $\rho_n$, i.e., $\mathit{m}_n \sim Geom(\rho_n)$, and $\zeta_{k}$ is a Binomial RV with parameters $k$ and $p_g$, i.e., $\zeta_{k} \sim Bin(k, p_g), \ k \in \{0,1,2, \cd

Figures (4)

  • Figure 1: Status updates from an IoT device to an $(N\!+\!1)$-satellite LEO network: (a) ring, (c) star topology. (b) shows the direct link from the device to the CS.
  • Figure 2: The structure of the optimal policy for the problem $\mathcal{P}_2$.
  • Figure 3: The average VAoI for various policies vs. $\beta$.
  • Figure 4: Impact of $T$ on optimal finite- and infinite-horizon policies ($\beta=0.2$).

Theorems & Definitions (12)

  • Proposition 1
  • proof
  • Lemma 1
  • proof
  • Definition 1
  • Proposition 2
  • proof
  • Proposition 3
  • proof
  • Definition 2
  • ...and 2 more