A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network
Bibliographic record
Abstract
<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>
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.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".