AI-Driven Security for Hierarchical Edge Computing: Combining D2D, WRR, and Queue-Based Load Distribution
Bibliographic record
Abstract
This paper proposes an AI-driven framework for Hierarchical Edge Computing (HEC) that integrates Device-to-Device (D2D) communication, Weighted Round Robin (WRR) scheduling, and queue-based load distribution to improve efficiency, scalability, and security in Internet of Things (IoT) environments. The objective is to minimize latency, balance workloads, and enhance secure task allocation across hierarchical edge layers. The methodology combines AI-based anomaly detection for security, WRR for fair resource allocation, and queue-based scheduling for dynamic load balancing. Experimental evaluation demonstrates that the proposed framework achieves 95% accuracy, 94% recall, and 20 ms latency, outperforming existing CoAP, SDN, and hybrid mesh networking models. The findings indicate that the integration of AI with D2D and WRR significantly improves real-time processing, throughput, and resource utilization, ensuring a scalable and secure IoT infrastructure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".