Optimizing Edge Resources in Intelligent Railway Construction: A Two-Level Game Approach
Bibliographic record
Abstract
With the development of digitization, railways have also entered the era of intelligent construction. Intelligent construction can effectively improve the safety and efficiency of railway construction, but it has very high demands for communication and computing resources, especially in remote and uninhabited areas without public network coverage. Providing real-time and reliable computing services for intelligent railway construction in uninhabited areas is a considerable challenge. In this paper, we design an intelligent railway construction system based on edge computing, where unmanned aerial vehicles (UAV) are deployed in the construction scenario to provide spectrum and computing resources for intelligent construction devices. In order to improve the real-time performance of computing in the intelligent railway construction system, we formulate the communication and computing resources optimization process as a two level game model. In the low-level game, we address the inherent personal selfishness exhibited by construction devices. To enhance spectral efficiency, we propose a non-cooperative potential game among these devices. In the high-level game, a single UAV server faces challenges in promptly handling all incoming computation tasks. To address this issue, we present a cooperative game model aimed at optimizing load balancing among UAV servers. Our proposed algorithms are effectively evaluated in terms of spectral efficiency and average task delivery time, as evidenced by the performance assessments.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".