HyPoPE: A Hybrid Proof of Participation and Efficiency Protocol for Secure IoT Blockchain Networks
Bibliographic record
Abstract
The Internet of Things and Blockchain are considered two major technologies. They face numerous challenges, including poor interoperability, security flaws, privacy concerns, and a lack of industry standards. Most IoT devices require a constant Internet connection, making them vulnerable to various attack vectors. Blockchain technology provides authentication and a decentralized environment, preventing malicious third parties from accessing the network. However, traditional consensus schemes like Proof of Work (PoW), PBFT, etc., restrict performance, processing time, and energy efficiency. In these systems, a miner must devote substantial time and money to obtaining the reward-based structure of the consensus mechanism. Recent research on blockchains has focused on accelerating the speed and scalability and enhancing the security level to overcome these limitations. In this paper, we propose a new hybrid consensus scheme, HyPoPE. In this model, the consensus algorithm has a more straightforward form while maintaining higher energy efficiency, faster throughput time, and higher security than traditional algorithms like PoW, PBFT, etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".