Bibliographic record
Abstract
The Internet of Things (IoT) enables the interconnection of resource-constrained devices, or "things," through the Internet, and it is rapidly becoming one of the most popular technologies. However, attackers could take advantage of the centralized architecture to create disruptions in the network, alter collected data, or even affect the reputations of trusted devices. Since the blockchain ledger is decentralized, verifiable, and secure, it has been used in a variety of IoT application scenarios. As the nodes in the network do not trust each other, they must use a consensus protocol to ensure the validity and availability of accepted data items. In this dissertation, I propose a set of consensus protocols that address the main issues that practical Byzantine fault tolerance (PBFT) and other Byzantine fault-tolerant (BFT) protocols may face. The proposed BFT consensus protocols eliminate the dependence on a single leader node. In addition, they improve the system's scalability and reduce the long latency in the communication between the nodes and the clients, who may be located geographically far from the single leader node in leader-based BFT protocols. The first BFT-based protocol proposed in this dissertation eliminates the single leader node problem and can run multiple consensus rounds concurrently without a performance penalty. This eliminates the long latency and limited bandwidth that originate from the individual processes in most BFT-based protocols. This dissertation also introduces a spot reservation mechanism that processes multiple consecutive requests without contention, which improves the protocol throughput and scalability. The second proposed protocol provide a solution for blockchain-based IoT applications that may require wide geographic coverage. Finally, in the last proposed BFT-based consensus protocol, a mechanism is proposed that incentivizes the nodes to act honestly. To analyze and evaluate the performance of the proposed protocols, we develop detailed analytical models based on discrete-time Markov chain (DTMC) and queuing theory. The models reveal improvements in propagation delays, throughput, and mean response time compared with the standard single-stream PBFT protocol.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".