Design and Dimensioning of a UAV Set Covering in High-Traffic IoT–Fog Environments
Bibliographic record
Abstract
The fog-computing paradigm provides low-latency processing and storage between Internet of Things (IoT) applications and cloud data centers. Normal IoT activity may produce infrequent yet substantial spikes in user traffic, which would require a large static fog infrastructure to service. Instead, we consider the viability of having a smaller static fog infrastructure, and supplementing additional traffic spikes with fog-enabled uncrewed aerial vehicles (fog-UAVs). This article formulates the optimal design and dimensioning of fog-UAVs and fog-UAV charging/deployment stations as a probabilistic location set covering problem (PLSCP). We model the fog-UAV PLSCP as a mixed-integer linear program (MILP), from which we derive several relaxed models, including one based on the Benders decomposition technique. Finally, we simulate our models over a set of city-wide IoT hotspots with various traffic percentile thresholds, and evaluate our results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".