IoDAPM: A Reinforcement Learning Approach for Dynamic Assignment of Protection Mechanisms in IoD
Bibliographic record
Abstract
Location Privacy Protection Mechanisms (LPPMs) have been designed to enhance privacy in the Internet of Drones (IoD), however, they present suitable privacy levels only in specific network conditions. Also, they can lead to a lack of Quality of Service (QoS) if applied in unfavorable conditions. Thus, the dynamic assignment of the most suitable LPPM, given the IoD conditions, is a significant challenge. Reinforcement Learning (RL) represents a useful concept to handle this problem, given its exploratory characteristics and being able to enhance the knowledge about network dynamics. In this study, we propose IoDAPM, an RL-based approach for the Dynamic Assignment of Protection Mechanisms in IoD. Through simulations, we extensively trained the RL-based model, exploring the possible IoD network conditions. With this model, we carried out a comparative evaluation of existing LPPMs. The results highlighted that IoDAPM outperforms the compared mechanisms considering the QoS, providing enhanced performance regarding location privacy, energy efficiency, and flight delay.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".