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 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 | 0.000 |
| 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".