ReVo: A Hybrid Consensus Protocol for Blockchain in the Internet of Things through Reputation and Voting Mechanisms
Bibliographic record
Abstract
In the realm of the Internet of Things (IoT), in-tegrating blockchain technology has brought about significant enhancements in security and transparency. Nevertheless, the union of these two domains grapples with persistent challenges in performance and scalability. A dilemma confronts developers and researchers: the intricate interplay between security, scalability, and performance in various consensus protocols tailored for implementing blockchain within loT ecosystems. This research paper proposes an innovative consensus protocol to tackle these challenges while striking an optimal equilibrium among these tripartite factors. Central to this proposal is introducing a hybrid architectural framework that bridges the world of loT devices and cloud service providers via distinct regional entities, all united in the objective of consensus through a novel voting mechanism hinged on reputationbased mechanisms. The core of this voting mechanism is a dynamic ensemble of nodes, each endowed with unique roles - encompassing ordinary nodes, verifiers, and assemblers. The protocol employs a random selection mechanism through a verifiable random function (VRF) to designate assem-blers, ensuring a level playing field. At the heart of the reputation model lies an analysis of region-specific traffic patterns, granting privileges to nodes that demonstrate trustworthy behaviour and high rates of request fulfillment. Extending this framework is an incentive mechanism designed to maintain the network's organic and dynamic allocation of roles. Simulation results benchmarked against Ethereum provide results of the ReVo consensus protocol for latency and transaction throughput. This paper also analyses the protocol working through a novel use case of Taxi Providers and Taxi Ride Consumer Services. Index Terms-Blockchain, Consensus Algorithm, Reputation, Internet Of things, Hybrid blockchain, Voting.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".