Stabilize IoT Blockchain using Smart Rewarding Mechanism: Incremental Block Reward
Bibliographic record
Abstract
Bitcoin's block reward is dependent on miner hardware profiles such as computation capacity and mining hardware identities, therefore, incentivizing miners to upgrade mining rigs in order to mine more blocks and maximize the payoff. As a result, miners who refuse to upgrade mining hardware suffer decreasing payoff because the mining difficulty increases automatically as the computation power of the entire network grows. However, this phenomenon is highly counter-productive in Internet-of-Things (IoT) networks, where CPUs are embedded into various devices and hardware upgrading require non-trivial efforts, in addition to the constrained computational and power capabilities. As a result, we propose the Incremental Block Reward (IBR) to encourage miners to reuse mining hardware, thereby helping stabilize the network, reduce energy consumption, and encourage new miners (i.e. more devices) to join. The simulation results show IBR facilitates even wealth distribution and promotes more fair and long-term mining establishment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".