SDOR Analysis for Task Offloading in Smart Farm Secure UAV-Assisted Communication Systems
Bibliographic record
Abstract
Due to the critical need for ultra-high reliability, low latency, and high energy efficiency in IoT devices for smart farming, in this work, we introduce the secrecy delay outage rate (SDOR) as a novel benchmark for evaluating the upper performance limits of secure UAV-assisted communication systems. This benchmark effectively measures the timeliness and confidentiality of data transfers in secure unmanned aerial vehicle (UAV)-assisted uplink communication systems. To facilitate the SDOR analysis, we consider height-dependent probabilistic Line-of-Sight (LoS) channels subject to Rician fading effects and employ a computing model strategy for task offloading. Additionally, we implement a UAV scheduling strategy to enhance the channel capacity for legitimate users. We derive the closed-form expression of the SDOR and conduct an asymptotic analysis in high signal-to-noise ratio (SNR) regimes for deeper insights. Through analytical and numerical results, we examine the impact of environmental conditions, transmit power, task offload ratio, and the volume of transmitted data on the overall system performance. This work not only advances the theoretical framework of SDOR analysis for task offloading in smart farming applications but also paves the way for future research in secure UAV-assisted communication networks.
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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 | 0.001 | 0.003 |
| 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".