Blockchain-based message communication system for vehicular ad hoc networks
Bibliographic record
Abstract
Vehicular ad hoc networks (VANETs) are ephemeral networks that enables vehicles to share information about the travel route such as traffic congestion or accidents with other vehicles to help them make real time decisions. A VANET, however, is neither secure nor fault tolerant and its security is usually handled by central certificate authorities plagued with inefficiency. Hence, vehicles in these networks are prone to malicious attacks and messages may not be trustworthy. A blockchain network on the other hand ensures that data is secure and fault tolerant using its distributed and decentralized system. This thesis addresses the following question: How can we determine the trustworthiness of a message sent by a vehicle in the VANET? The answer lies in the blockchain and the methodology is described below. The large network is first partitioned into zones, each zone consisting of road side units (RSUs). Each RSU uses the calculated trust/credibility values of the received messages to form a block in the blockchain. In this thesis, we propose a trust inference model that combines the recommendation from other trusted vehicles as well as dynamic metrics like proximity to event location and the model aggregates them through a probabilistic Bayesian approach. We further study the feasibility of our model by practically implementing the RSUs as nodes on a Hyperledger blockchain framework. We conduct various experiments on blockchain and VANET parameters using the data from Winnipeg Public Works Department. We conclude that the model can tolerate a faulty RSU and that it is not detrimental to the network since the data in the blockchain is distributed.
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 | 0.001 | 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".