Formal Verification and Performance Analysis of a New Data Exchange Protocol for Connected Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, we focus on the usage of MQTT (Message Queuing Telemetry Transport) within Connected Vehicles (CVs). Indeed, in the original version of MQTT protocol, the broker is responsible “only” for sending received data to subscribers; abstracting then the underlying mechanism of data exchange. However, within CVs context, subscribers (i.e., the processing infrastructure) may be overloaded with irrelevant data, in particular when the requirement is real or near real-time processing. To overcome this issue, we propose MQTT-CV; a new variant of MQTT protocol, in which the broker is able to perform local processing in order to reduce the workload at the infrastructure; i.e., filtering data before sending them. In this article, we first validate formally the correctness of MQTT-CV protocol (i.e., the three components of the proposed protocol are correctly interacting), through the use of Promela language and its system verification tool; the model checker SPIN. Secondly, using real-world data provided by our car manufacturer partner, we have conducted real implementation and experiments. The obtained results show the effectiveness of our approach in term of data workload reduction at the processing infrastructure. The mean improvement, besides the fact that it is dependent of the target application, was in general about 10 times less in comparison to native MQTT protocol.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it