Bibliographic record
Abstract
Bitcoin is the first cryptocurrency based on blockchain technology emerged in 2008 to provide a peer-to-peer (P2P) electronic cash system without a financial third party. Much research has been done to analyze different aspects of blockchain-based networks and improve their performance. This dissertation presents an innovative approach to evaluating the churning process (the dynamic participation of nodes in the P2P network) in the Bitcoin network. We propose an analytical model based on the Continuous Time Markov Chain (CTMC) and queuing model to evaluate node churn impact on Bitcoin network performance and calculate the synchronization time needed for nodes when rejoining the net- work. This dissertation also proposes an analytical model for the Bitcoin network’s churning process with relay nodes that is the first to the best of our knowledge. Relay nodes only distribute blocks in the network and have a higher number of connections. We introduce two different CTMCs for ordinary and relay nodes to model each type of node’s behavior separately. We analyze the transaction and block propagation in the network and calculate synchronization time. We show that the churn of relay nodes has a higher impact on traffic performance than ordinary nodes. Moreover, we introduce an analytical model to evaluate the impact of node churn in the Bitcoin network when compact block protocol is in use. Compact block protocol aims to reduce bandwidth usage and probably latency by propagating a smaller version of blocks in the network. We model the node’s behavior with CTMC and calculate synchronization time including transaction deficit recovery. Finally, we propose an analytical model to evaluate the impact of selfish behavior on Bitcoin network performance. We evaluate the Bitcoin network’s performance metrics, including network connectivity, block arrival rate, delivery time, and block response time in the presence of selfish miners. We also calculate the probability of intentional forking caused by selfish behavior compared to unintentional forking caused by network delay and show that intentional forking probability is higher than unintentional forking, which may result in ledger inconsistency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".