Opportunistic UAV Deployment for Intelligent On-Demand IoV Service Management
Bibliographic record
Abstract
Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from Internet of Things (IoT) devices, such as radars, sensors, and actuators. Consequently, overwhelming the existing infrastructure, namely cloud, and Road Side Units (RSU), reduces the quality of service (QoS) experienced by vehicular users. Accordingly, this paper contributes in proposing a new architecture for using Unmanned Ariel Vehicles (UAVs) and On-Boarding Units (OBUs) working in collaboration to achieve a significantly improved QoS. The proposed framework offers an end-to-end solution for master election, cluster management and recovery, vehicle selection, service placement, and accurate localization of vehicles. A QoS improvement is possible through an efficient cluster formation and placement solution that assigns lightweight services, as containers, to OBUs and UAVs while meeting various objectives. The efficiency of the proposed scheme originates from the use of the evolutionary Memetic Algorithm that 1) respects the mobility and energy constraints of UAVs and OBUs, 2) meets the user demands, and 3) uses machine learning for the accurate localization of vehicles. Our experiments using the Mininet-WiFi and SUMO simulators show at least 30% improvement in terms of QoS compared to a state-of-the-art solution.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".