Enhancing Security in UAV-Assisted Image Data Collection for Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing utilization of unmanned aerial vehicles (UAVs) across diverse industries has led to increased interest in UAV-assisted data acquisition for the Internet of Things (IoT). The security of image data collected by UAVs during transmission within the IoT has become a critical concern. This article focuses on the security challenges associated with UAV-assisted image data collection in the IoT and presents a dedicated framework designed to enhance the security of this process. Given the high-resolution nature of UAV-captured images, traditional encryption methods face difficulties in directly and effectively encrypting such data. To address this issue, this article introduces an efficient chaotic image encryption algorithm integrated into the proposed protection framework. The algorithm features a novel 1-D chaotic system for generating effective chaotic sequences. For the scrambling phase, a chaotic four-spiral transformation method is employed, and the diffusion process utilizes the Fibonacci matrix. This strategic approach aims to minimize pixel correlation within the image, thereby bolstering the overall security of the encryption process. Experimental validation conducted on authentic UAV image data sets demonstrates the superior, practical, secure, and efficient characteristics of the proposed algorithm.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it