Online UAV Scheduling Towards Throughput QoS Guarantee for Dynamic IoVs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ensuring network QoS for Internet of vehicles (IoVs) is crucial for safe and intelligent transportation system, while the vehicle density variation seems invincible for stationary base station (BS) networks. In this paper, we study IoV's downlink throughput guarantee, in which, in addition to the cellular BS resource, UAVs (equipped with WiFi interfaces) can be dynamically sent out to provide additional wireless connections. To cope with the dynamic IoV density, we propose an Online UAV Scheduling scheme, referred to as OUS, to online schedule and manage UAVs to guarantee seamless connections with reliable throughput performance. In OUS, we first use the complementary cumulative distribution function (CCDF) of IoV throughput to calculate the likelihood of a channel resource shortage. If a shortage condition is imminent and then minimal UAVs will be sent out to their optimal hovering positions. In particular, we revealed the marginal effect for the optimal hovering position acquisition, i.e., the further the UAV is away from the BS, the larger throughput gain can be achieved by the system. We conduct extensive simulations to evaluate the performance of our OUS scheme, and results demonstrate that it can well react to the throughput QoS demand by intelligently sending out minimal UAVs, and its hovering position acquisition method can fully utilize the efficacy of UAVs.
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.
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 it