Towards Softwarized Drone Networks
Bibliographic record
Abstract
Drones, or Unmanned Aerial Vehicles (UAVs), are considered essential tools in search and rescue, disaster relief, remote sensing, aerial surveillance and security. Drone-assisted communication networks are gaining considerable attention as a cost-effective and flexible network infrastructure offering new capabilities and opportunities. In addition to enabling connectivity for complex multi-drone tasks, drone networks can be deployed to facilitate connectivity in remote areas and extreme environments and supplement and extend the coverage of mobile networks in response to variable demands. However, utilizing such flexibility requires dealing with the inherent dynamics of drone networks, characterized by a high level of mobility and limited resources. Network softwarization using Software Defined Networking (SDN) and Network Functions Virtualization (NFV) enables flexible and adaptive control and reconfigurability in drone networks through centralized programmability and virtualized network functions. In this thesis, we investigate drone network softwarization by identifying potential gains from the flexibility offered by softwarization not investigated previously in the context of drone networks. To enable the utilization of SDN and NFV in drone networks, we propose and describe an architecture for softwarized drone networks. Furthermore, we address an important challenge relating to SDN control, a key element in SDN architectures, allowing the programmability of the network through an interface between logically centralized controllers and the programmable network nodes. To adapt to the network mobility and connectivity constraints, we propose schemes for deploying and assigning SDN controllers embedded in drones, allowing for continuous operation of control functions with changing network topology and possible unavailability of ground infrastructure. We also utilize the flexibility offered by NFV. New deployment and orchestration schemes are needed to efficiently deploy and manage drone networks defined by Virtual Network Functions (VNFs) implementing task and network functionalities. To this end, we describe the applicable use cases that benefit from this flexibility and propose schemes that efficiently deploy and manage NFV-based drone networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".