RSMR: A Reliable and Sustainable Multi-Path Routing Scheme for Vehicle Electronics in Edge Computing Networks
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
Consumer electronic devices used to support data communication are integral components of vehicular networks. However, due to factors such as the limited reliability and failure of electronic devices, vehicle communication data may fail to be uploaded and downloaded in a timely manner, potentially leading to serious traffic accidents. With the emergence of edge computing technology, computing tasks are distributed from traditional centralized cloud computing to the network edge, thereby enabling faster response to the processing demands of vehicle data. However, even though edge computing offers faster data processing capabilities, the issue of effective routing of data within vehicular edge computing (VEC) networks remains to be addressed. Therefore, this paper proposes a two-phase multi-path routing scheme for VEC networks. In the route decision phase, the scheme introduces an integrated adaptive function, that plans the route reasonably by considering the transmission latency, energy balance and communication quality. On this basis, different routing requirements (e.g., maximizing network lifetime or transmission reliability) can be achieved by setting the weights of the proposed function. In the route maintenance phase, the scheme implements real-time multi-path adjustment based on the route maintenance mechanism to support data routing. The simulation results show that the proposed scheme has significant advantages over three baseline schemes in terms of routing reliability and energy balance. In addition, we explore the impacts of the weights and initial network configuration on the routing performance. The obtained results can provide guidance for planning reliable and sustainable routes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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