Multi-UAV networks for disaster monitoring: challenges and opportunities from a network perspective
Bibliographic record
Abstract
Disasters, whether natural or man-made, demand rapid and comprehensive responses. Unmanned aerial vehicles (UAVs), or drones, have become essential in disaster scenarios, serving as crucial communication relays in areas with compromised infrastructure. They establish temporary networks, aiding coordination among emergency responders and facilitating timely assistance to survivors. Recent advancements in sensing technology have transformed emergency response by combining the collaborative power of these networks with real-time data processing. However, challenges remain to consider these networks for disaster monitoring applications, particularly in deployment strategies, data processing, routing, and security. Extensive research is crucial to refine ad hoc networking solutions, enhancing the agility and effectiveness of these systems. This article explores various aspects, including network architecture, formation strategies, communication protocols, and security concerns in multi-UAV networks for disaster monitoring. It also examines the potential of enabling technologies like edge computing and artificial intelligence to bolster network performance and security. Further, the article provides a detailed overview of the key challenges and open issues, outlining various research prospects in the evolving field of multi-UAV networks for disaster response.
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 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 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".