LI2: A New Learning-Based Approach to Timely Monitoring of Points-of-Interest With UAV
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
Unmanned aerial vehicles (UAVs) play a critical role in disaster response, swiftly gathering information from various points-of-interest (PoIs) across extensive areas. The freshness of this information is measured by the age of information (AoI), representing the time since the latest information acquisition of a specific PoI. However, devising AoI-minimizing routes for UAVs in obstructed post-disaster environments poses unique challenges that have yet to be fully overcome. Obstacles, like post-disaster barriers, can impede direct flight paths between PoIs, and limited battery life requires energy-conscious route planning. Additionally, existing solutions fail to universally minimize varying data freshness requirements. This research addresses the AoI-driven UAV travel problem, seeking to establish periodic routes that optimize AoI metrics while considering energy and general graph constraints. We develop a learning-based algorithm to enhance the current route iteratively, utilizing guidance from a deep reinforcement learning (DRL) agent and executing a series of operations to potentially decrease AoI while adhering to topological and energy constraints. The algorithm is validated on real post-disaster datasets, demonstrating significant improvements in various AoI metrics compared to other learning-based approaches. Furthermore, our algorithm outperforms approximation algorithms and can approach the global optimum when tailored to existing AoI-minimizing problems.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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