Enabling Intelligent and Scalable IoT Communications via LEO Satellite Networks

Dear Colleagues,

(a) Focus of this Special Issue

The central focus of this Special Issue is to advance the performance, reliability, and resource efficiency of low-Earth-orbit (LEO) satellite-based IoT communications through novel modeling techniques, intelligent algorithms, and cross-layer optimizations. The accelerating global demand for ubiquitous, large-scale IoT connectivity—especially in areas beyond the reach of terrestrial infrastructure—has positioned LEO nanosatellites as a compelling solution due to their wide coverage and cost-effectiveness. However, the practical deployment of direct-to-satellite (DtS) IoT links faces critical challenges: time-varying and complex channel conditions, severe Doppler shifts, multipath fading, and large-scale shadowing effects. Traditional models and protocols often fail to capture these dynamics, leading to suboptimal performance. This Special Issue is thus motivated by the need to develop next-generation techniques that respond to these unique characteristics of LEO satellite environments.

(b) Scope of this Special Issue

The scope of this Special Issue spans multiple layers of the communication system—from the physical layer to system-level service orchestration—with particular attention paid to AI/ML-driven solutions and resource-constrained optimization. Key topics of interest include the following:

• Channel Modeling: Improved models such as K-distribution-enhanced finite-state Markov channels (FSMC-TS) that account for large-scale fading and shadowing, surpassing traditional land-mobile satellite channel models.
• Random Access Protocols: Scalable and collision-resilient schemes for massive device populations, including the use of the static floor field method to better manage preamble congestion and access delay.
• Resource Management: Techniques for adaptive multibeam management, power control, and bandwidth allocation to reduce transmission power while satisfying traffic demands, with a focus on algorithmic efficiency.
• Edge Computing and Offloading: Solutions for collaborative task offloading in LEO satellite edge networks to reduce latency and computational bottlenecks, moving beyond single-satellite or neighbor-only models.
• AI/ML Integration: Implementation of DRL, federated learning, and other intelligent methods across multiple layers, for dynamic task allocation, spectrum sensing, link adaptation, interference mitigation, and payload flexibility.
• Security and Trust: The application of Blockchain-based decentralized federated learning frameworks to enhance spectrum pricing and power control, offering robust alternatives to centralized architectures.
• Performance Analysis and System Design:
  • Evaluation of Age of Information (AoI) and Peak AoI (PAoI) in multi-hop satellite IoT with backoff dynamics.
  • Use of OTFS-SDMA for Doppler-resilient, grant-free random access.
  • LoRa-based LEO satellite systems with spherical stochastic geometry for accurate interference and performance modeling.

(c) Purpose of this Special Issue

This Special Issue aims to bridge the critical research gaps in current LEO-based IoT communication systems by providing an integrative view that connects advanced theory with practical implementation challenges. By assembling diverse but complementary contributions, this Special Issue aims to provide the following:

• A unified foundation for understanding and designing robust LEO-IoT systems under realistic assumptions.
• Cross-disciplinary innovations that combine satellite communications, IoT networking, and AI-driven optimization.
• Deployable frameworks and algorithms that respond to pressing needs in large-scale connectivity, resource limitations, and real-time processing.

Ultimately, the purpose of this Special Issue is to equip researchers and practitioners with both deep analytical tools and realistic system designs that will shape the future of satellite IoT integration.

How This Special Issue Supplements the Existing Literature

While the literature on LEO satellite communications and AI for SATCOM has grown rapidly, several key limitations remain that this Special Issue explicitly addresses:

• Over-reliance on simplified or static models: Existing studies often neglect real-world channel dynamics like large-scale shadowing or Doppler variations. This Special Issue introduces more granular and dynamic channel models that reflect actual LEO environments.
• Limited scalability in access and resource protocols: Conventional RA schemes and centralized control frameworks struggle under massive IoT loads. We hope that our contributions will offer scalable, decentralized protocols and AI-enabled management schemes that support high device density.
• Insufficient cross-layer AI implementation: While surveys on AI in SATCOM exist, few works demonstrate concrete, multi-layer use cases of ML in spectrum sensing, task offloading, or interference management. This Special Issue provides end-to-end ML applications with implementable architectures and efficiency analysis.
• Underexplored topics in system freshness and latency: AoI/PAoI performance in multi-hop satellite IoT is rarely addressed, especially with regard to backoff mechanisms. This Special Issue delivers quantitative analyses of freshness metrics that inform real-time communication design.
• Sparse attention to LoRa-specific satellite modeling: Most LoRa-over-satellite studies use basic models. This Special Issue advances the field with spherical stochastic geometry and interference-aware evaluations, tailored to LoRa's unique properties.

Dr. Ala Arman
Dr. Zahra Ziran
Dr. Oras Baker
Guest Editors

Manuscript Submission Information

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• LEO satellite IoT
• direct-to-satellite (DtS)
• channel modeling
• shadowing
• random access (RA)
• artificial intelligence (AI)
• machine learning (ML)
• deep reinforcement learning (DRL)
• federated learning (FL)
• mobile edge computing (MEC)
• task offloading
• resource allocation
• power control
• information freshness (AoI)
• LoRa