Optimization of drone base station locations and mobile charging drone routing for post-disaster communication
Bibliographic record
Abstract
In the aftermath of a disaster, traditional communication systems often become inaccessible, creating significant challenges for rescue teams and affected individuals. This research aims to design an innovative communication system to bridge this gap, ensuring efficient information transfer and establishing reliable communication channels between rescue teams and affected people. The focus is on using drones as communication tools to address this challenge. The study explores the use of drones as data collection and transmission platforms in disaster-stricken areas. By collecting information from individuals, including text messages and location data from different platforms, drones can efficiently transmit vital data to the communication backhaul. An optimization model is formulated to decide on the 3D location of drone base stations while maximizing coverage and service quality. In addition, mobile power drones are also required to supply the power for deployed base stations and data transfer; the model also decides on the routing of multiple power drones. To solve the problem efficiently, a clustering-based matheuristic is developed to determine the locations of the base stations. We show the solution performance of the model and the effectiveness of our heuristic algorithm in a case study using data from Sultanbeyli province in Türkiye. The heuristic algorithm solves all the case study instances, which consist of 700 nodes, 75–105 stationary drones, and 6–8 mobile drones, in less than an hour. The results show that with 105 stationary and 8 mobile drones, we can cover 82% of the users. Furthermore, we demonstrate how this coverage can be increased to 95% by implementing certain adjustments. The findings offer insights into the potential of using drone base stations in post-disaster scenarios, thereby empowering disaster management agencies with enhanced communication capabilities for improved coordination and response in the face of disaster.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".