Blockchain-Empowered Space-Air-Ground Integrated Networks: Opportunities, Challenges, and Solutions
Bibliographic record
Abstract
The terrestrial networks face the challenges of severe cost inefficiency and low feasibility to provide seamless services anytime and anywhere, especially in the extreme or hotspot areas (e.g., disaster areas, mountains, and oceans) due to limited service coverage and capacity. The integration of multi-dimensional networks consisting of space, air, and ground layers is expected to provide solutions in delivering cost-effective and ubiquitous Internet of things (IoT) services for billions of users and interconnected smart devices. Autonomous data collection, exchange, and processing across different network segments with minimal human interventions in space-air-ground IoT (SAG-IoT) can bring great convenience to consumers, however, it also suffers new attacks from intruders. Severe privacy invasion, reliability issues, and security breaches of SAG-IoT can hinder its wide deployment. The emerging blockchain holds great potentials to address the security concerns in SAG-IoT, thanks to its prominent features of decentralization, transparency, immutability, traceability, and auditability. Despite of the benefits of blockchain-empowered SAG-IoT, there exist a series of fundamental challenges in terms of efficiency and regulation due to the intrinsic characteristics of SAG-IoT (e.g., heterogeneity, time-variability, and poor interoperability) and the limitations of existing blockchain approaches (e.g., capacity and scalability). This article presents a comprehensive survey of the integration of blockchain technologies for securing SAG-IoT applications. Specifically, we first discuss the architecture, characteristics, and security threats of SAG-IoT systems. Then, we concentrate on the promising blockchain-based solutions for SAG-IoT security. Next, we discuss the critical challenges when integrating blockchain in SAG-IoT security services and review the state-of-the-art solutions. We further investigate the opportunities of blockchain in artificial intelligence and beyond 5G networks and provide open research directions for building future blockchain-empowered SAG-IoT systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".