UAV-Employed Intelligent Approach to Identify Injured Soldier on Blockchain-Integrated Internet of Battlefield Things
Bibliographic record
Abstract
This study proposes an intelligent approach to identifying an injured soldier on blockchain-integrated Internet-of-Battlefield Things (IoBT) employing unmanned aerial vehicles (UAVs). The intelligent approach combines a unique deep learning (DL) model with a smartwatch-based heart-rate (HR) data collection technique. Different activation functions (i.e., MISH and Leaky rectified linear unit) are used in the proposed DL model to enhance the identification task by extracting the in-depth features from the images. Furthermore, a smart-watch-based HR data analyzing technique is introduced to confirm the injury of a soldier. However, due to the UAV’s low battery capacity, the identification task is offloaded to the neighboring edge computing server to improve system performance. Moreover, to restrict the access of registered IoT devices (e.g., UAV, smartwatch, etc.) and protect the sensitive data leakage on IoBT, a blockchain-integrated access control (ACL) mechanism is utilized. Detailed experimental results are provided for the proposed DL model that outperforms existing DL models. Besides, implementing a smartwatch-based HR data analysis technique for the soldiers improves the outcome of the proposed DL model. To provide a fine-grained data protection mechanism in the proposed system, a private blockchain-based ACL management policy is constructed utilizing hyperledger, and various assessment metrics have been scrutinized.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".