A Scalable End-to-End IoT Data Pipeline with Dynamic Bucketing and Blockchain Verification
Bibliographic record
Abstract
The rapid advancement of wireless and cyber-physical systems has driven a growing demand for real-time sensor data in cyber environments. While existing solutions attempt to meet these demands, achieving both high-throughput processing and robust data integrity remains a significant challenge. This paper proposes an end-to-end distributed data pipeline and blockchain-enabled framework that integrates online anomaly detection, scalable data aggregation, and secure verification to ensure reliable cyber evolution. An Apache Kafka-based streaming pipeline ingests high-velocity sensor data and employs a dynamic bucketing strategy that finalizes buckets based on data volume, elapsed time, and network gas costs. Once validated, each bucket’s canonical sensor data representation is hashed and committed on-chain for tamper-evident storage. To enhance efficiency and security, the framework supports both Merkle tree and Verkle tree cryptographic data structures for comparative analysis. Implemented on a private Ethereum-like blockchain, our system efficiently handles large-scale sensor ingestion while enabling per-record verification. By integrating real-time anomaly correction, cryptographic proof mechanisms, and on-chain commitments, our solution delivers trustworthy, verifiable sensor streams tailored to the low-latency and high-reliability needs of next-generation systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".