A Blockchain-Enabled GNN Framework for Secure Routing in IoT Networks
Bibliographic record
Abstract
The Internet of Things (IoT) has made secure and reliable data communication more difficult due to its dynamic topologies, energy constrictions, and intelligent and sophisticated adversaries.To address these difficulties in IoT networks, we propose G-TrustChain, an integrated hybrid framework based on Graph Neural Networks (GNNs) for intelligent and dynamic routing and a light Blockchain for distributed trust.G-TrustChain makes use of node-level parameters including latency, remaining energy, and behavioural trust scores derived from a Graph Attention Network (GAT) for routing paths.A lightweight Directed Acyclic Graph (DAG)-structure Blockchain maintains trust scores with a distributed, scalable, and tamper-proof ledger that minimizes dependency on a centralized authority.Experimentation is done for 10,000 rounds, G-TrustChain demonstrated superior routing performance to other protocols such as Trust-based Routing, BBTR, and ROUTENET.It is achieving 95.6% packet delivery ratio, 91.2% detection rate of attacks, and energy consumption as low as 0.0110 J/bit.Also achieving more accurate and reliable trust scores despite energy constraints and higher/extensive attacks.These outcomes demonstrated G-TrustChain provides energy-efficient, secure, and intelligent data communication for the next generation of IoT networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".