Routing protocols strategies for flying Ad-Hoc network (FANET): Review, taxonomy, and open research issues
Bibliographic record
Abstract
A Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of applications, including smart ports, delivery of products, construction, monitoring of the environment and climate, and military surveillance. FANETs research is being driven by the potential for UAVs to be utilized in these regions. The purpose of this paper is to provide a comprehensive analysis of the most important FANET characteristics, mobility models, applications, and routing protocols. The present paper is an effort to provide a comprehensive description of the various routing techniques utilized by the most prevalent routing protocols in FANETs, including topology-based, position- based, hierarchical, swarm-based, and Delay Tolerant Networking (DTN) protocols. Reinforcement learning and deep reinforcement learning are both encompassed in a newly anticipated classification. In the meanwhile, this study primarily centres around the taxonomy for learning agents (single- agent, multi-agent) and learning models (model-based and free-model). In addition, the paper intends to shed light on identifying the applications of FANETs in various categories and identify research gaps and future opportunities in this field. In addition, it compares the results qualitatively to those of the previous surveys. Any future work on the FANET routing protocol could benefit from this paper as a reference and roadmap.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".