A Walkthrough of Blockchain-Based Internet of Drones Architectures
Bibliographic record
Abstract
The open and unreliable environment of drones could make data transfer and authentication challenging. Given the proliferation of drone applications, the Internet of Drones (IoD) represents a promising phenomenon to enhance flight reliability and security. IoD has recently acquired pace due to its exceptional flexibility in numerous challenging circumstances. Furthermore, the incorporation of drones holds the potential to enhance many network systems’ performance metrics, such as throughput, scalability, connection, and latency. Regardless of its diverse domain, IoD is susceptible to malicious attacks owing to the wireless medium’s inherent unreliability. Drone communication can be made secure, reliable, and affordable by utilizing blockchain (BC) concepts that can be used to develop security mechanisms for addressing IoD’s shortcomings. This article reviews emerging BC-powered schemes, focusing on their application in drone communication, authentication, and security. Our study explores various drone applications and intricacies associated with BC-based drone technology. By leveraging BC concepts, security mechanisms can mitigate IoD’s shortcomings. Unlike previous works, this survey offers a detailed examination of BC applications in drone technology. The research includes a thorough investigation into current IoD challenges and proposes insightful recommendations to fortify its security framework. Additionally, we conduct experiments on the runtime of consensus algorithms, providing a detailed comparison along with various security models. An analysis of recent work shows diverse approaches in BC for IoD, with a comparative study aimed at mitigating challenges. An overview of machine learning in IoD along with insightful research recommendations are also given that provide ways to improve IoD’s security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".