M2MHub: A Blockchain-Based Approach for Tracking M2M Message Provenance
Bibliographic record
Abstract
The Internet of Things (IoT) is a fast growing and popular subset of technology. One of the main features of IoT is device autonomy, the ability for the machines embedded in the devices to function without human intervention. This includes communications with other devices through Machine-to-Machine (M2M) communication. Unfortunately, M2M communications are only stored with what behaviours they have observed, without the causal relationship as to how or why they observed those behaviours. Since M2M messages can trigger more M2M messages, the provenance of issues inside an IoT system can be hidden behind a long chain of messages, so finding the root source of any problem, such as malicious or defective devices, is almost impossible to detect. To solve this problem, in this paper we introduce M2MHub, a centralized auditing system which collects M2M messages in an IoT system and stores them in a blockchain. Devices in the system can tell the hub if they wish to open, continue, or end transactions, allowing the hub to keep track of who is the provenance of the transaction and how their transaction affects other devices. A proof-of-concept simulation has been constructed, demonstrating how M2MHub may function in a real-world implementation. The current implementation is not scalable enough to be deployed to actual IoT networks, so several ideas for future work are offered.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".