Spatial Distribution of Mean PAoI in LEO Satellite-enabled IoT Networks
Abstract
Non-terrestrial networks (NTNs), such as Low Earth Orbit (LEO) satellites, can provide consistent coverage for Internet of Things (IoT) nodes, particularly in rural areas where cellular coverage is lacking. These IoT nodes are used to monitor and record their surroundings, supporting various applications, including real-time applications. In such cases, transmission of the most up-to-date data is essential, making the Age of Information (AoI) a critical metric to consider. The AoI measures the elapsed time from the data generation at the source node to its reception at the destination. Moreover, the Peak AoI (PAoI), which denotes the maximum AoI in the system before an update is received, provides valuable insights into the network's performance. Therefore, this encourages us to focus on the PAoI within a large-scale and fully loaded IoT network, which is covered by LEOSATs. Using tools from stochastic geometry, this paper derives the spatial distribution of the uplink mean PAoI in a LEOSAT-enabled IoT network. To achieve that purpose, the meta distribution of the uplink conditional coverage probability is derived. Numerical results articulate how varying the number of deployed LEOSATs and their altitudes affects the coverage probability meta distribution and the spatial distribution of the mean PAoI. Additionally, by examining various scenarios with different numbers of LEOSATs and altitudes, we can derive valuable insights to determine the optimal beamwidth for LEOSATs tailored to each specific scenario to minimize the conditional mean PAoI.
Related
- SN-ASMO: Satellite-Navigation Array Spatial-Manifold Precise Observation Theory A Mathematical Foundation for Observation Formation, Unified U(1) Geometry, Intrinsic Information, and Preservation of the RTK Integer Structure
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- Capacity-Aware Multi-UE Handover Optimization for LEO Satellite Networks Using Dueling DDQN
- Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: A Survey
- Absolute moments of the binomial distribution folded at its mean
BibTeX
@misc{gabr2026spatial,
title = {{Spatial Distribution of Mean PAoI in LEO Satellite-enabled IoT Networks}},
author = {Badiaa Gabr and Mustafa A. Kishk},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23597},
eprint = {2610.23597},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23597},
}
RIS
TY - UNPB
TI - Spatial Distribution of Mean PAoI in LEO Satellite-enabled IoT Networks
AU - Badiaa Gabr
AU - Mustafa A. Kishk
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.23597
UR - https://arxiv.org/abs/2610.23597
AB - Non-terrestrial networks (NTNs), such as Low Earth Orbit (LEO) satellites, can provide consistent coverage for Internet of Things (IoT) nodes, particularly in rural areas where cellular coverage is lacking. These IoT nodes are used to monitor and record their surroundings, supporting various applications, including real-time applications. In such cases, transmission of the most up-to-date data is essential, making the Age of Information (AoI) a critical metric to consider. The AoI measures the elapsed time from the data generation at the source node to its reception at the destination. Moreover, the Peak AoI (PAoI), which denotes the maximum AoI in the system before an update is received, provides valuable insights into the network's performance. Therefore, this encourages us to focus on the PAoI within a large-scale and fully loaded IoT network, which is covered by LEOSATs. Using tools from stochastic geometry, this paper derives the spatial distribution of the uplink mean PAoI in a LEOSAT-enabled IoT network. To achieve that purpose, the meta distribution of the uplink conditional coverage probability is derived. Numerical results articulate how varying the number of deployed LEOSATs and their altitudes affects the coverage probability meta distribution and the spatial distribution of the mean PAoI. Additionally, by examining various scenarios with different numbers of LEOSATs and altitudes, we can derive valuable insights to determine the optimal beamwidth for LEOSATs tailored to each specific scenario to minimize the conditional mean PAoI.
ER -