Dynamic VNF Orchestration for UAV-Aided Border Surveillance
Bibliographic record
Abstract
The integration of unmanned aerial vehicles (UAVs) and satellite technologies into modern border surveillance offers significant potential, eliminating the deployment limitations of terrestrial networks in remote and dynamic environments. These technologies ensure comprehensive coverage, and provide continuous connectivity in all areas with dynamic deployment. However, the integration presents optimization challenges in terms of resource management, communication, and energy efficiency. In this work, we propose a novel hierarchical optimization framework to address these challenges, modeling a terrestrial-non-terrestrial border surveillance (TNTBS) system in which telecom functions and surveillance services (e.g., synthetic aperture radar) are containerized and dynamically deployed into UAVs and satellites. We deal with battery, backhaul, coverage and surveillance service constraints to optimize the operational cost. To overcome the computational intractability of the large-scale problem of TNTBS, which is modeled as a mixed-integer linear programming (MILP) problem, we introduce a Stackelberg game-based approach that enables scalable and distributed decision making with multiple agents and each agent can leverage a local solver or learning model to optimize its local objective. Our extensive simulations demonstrate that our solution achieves near-optimal performance, while ensuring real-time operation and computational efficiency.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".