On Provision of Resilient Connectivity in Cognitive Unmanned Aerial Vehicles
Bibliographic record
Abstract
Mobile ad-hoc network (MANET) can be established in the areas/scenarios where the infrastructure networks are either out of service or no more available. MANETs have a lot of applications in sensor networks. Generally, a MANET deploys mobile ground nodes to set up a network. However, there can be some severe scenarios such as flood, battlefield, rescue operations, etc. where these ground nodes cannot be deployed. In such cases, a network of unmanned aerial vehicles (UAVs) can be a more viable option. Normally, UAVs operates on IEEE L-Band, IEEE S-Band or ISM band. These bands are already overcrowded, therefore, UAVs will face the problem of the spectrum scarcity. To resolve this issue cognitive radio (CR) is a most promising technology. Hence, in this work, we focus on CR based UAVs. As CR is based on opportunistic spectrum access, therefore, it is quite possible that all UAVs do not have one single channel available to communicate with each other. They need to form clusters for their communication depending on the availability of the channel. However, channel availability is intermittent because of opportunistic spectrum access. This may result in reforming of the cluster again and again. To avoid this frequent re-clustering and to maintain connectivity among the UAVs, in this paper, we present a resilient clustering technique with a concept of introducing a backup channel for each cluster. Simulation results show the significance of the proposed technique.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".