AI-Driven Decentralized Storage Framework over Bluetooth Mesh: Performance and Resilience Evaluation for IoT Edge Systems
Bibliographic record
Abstract
Cloud-centric storage architectures are unsuitable for Internet-of-Things (IoT) edge environments where intermittent connectivity, long round-trip delays, and centralized points of failure limit operational reliability. This paper introduces an AI-driven decentralized storage framework built on Bluetooth Mesh to address these constraints. The proposed system partitions files into AES-256-GCM encrypted chunks and uses a lightweight neural predictor to evaluate node suitability based on link quality, hop distance, battery availability, memory capacity, and usage patterns. A constrained optimization engine allocates and replicates chunks across the mesh to ensure balanced distribution and resilience against independent and correlated node failures. Simulation results on a fifty-node mesh demonstrate a 32.7% reduction in access latency, a 25.3% improvement in memory utilization, and recovery probabilities exceeding 99% with multi-replica placement. These results indicate that AI-guided allocation significantly enhances decentralized storage performance in resource-constrained IoT edge systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".