Security in the Industrial Internet of Drones
Bibliographic record
Abstract
The Industrial Internet of Things (IIoT) has played a key role in enabling an efficient and interconnected industry through real-time communication and processing systems, thereby building on the principles of Industry 4.0. Nowadays, industrial systems are in the process of transitioning towards Industry 5.0, where humans will once again take center stage in decision-making, supported by Artificial Intelligence (AI)-based methods. In this context, drones have emerged as a feasible device for enhancing environmental sensing tasks, reducing operational costs, alleviating communication bottlenecks, and cooperating with humans through the use of Virtual Reality (VR) and Augmented Reality (AR) platforms. Therefore, Internet of Drones (IoD) network paradigm has been adopted in the industry, giving rise to the Industrial Internet of Drones (IIoD). Given these aspects, there have been changes in the privacy and security requirements for this network environment, which demands a thorough analysis of these modifications, including the challenges that arise and possible solutions to overcome them. Consequently, this study analyzes the privacy and security issues related to IIoD. Namely, we highlight the elements of IIoD which require protection, the threats and the countermeasures. We also present how these aspects differ from the general IoD environment. Lastly, we discuss the challenges regarding IIoD security and privacy, leveraging the future directions to address Industry 5.0 aspects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".