MQTT Protocol-Based Detection and Classification in UAV-Assisted Smart Farming for IoT Intrusion Detection System
Bibliographic record
Abstract
The rapid growth of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) presents challenges in energy efficiency, communication reliability, and network security in applications like precision agriculture and smart cities. To address these challenges, we propose a MQTT Protocol-Based Detection and Classification in UAV-Assisted Smart Farming for IoT Intrusion Detection System (MDCUSI). An advanced energy framework optimized for UAV energy consumption of convolutional neural networks (CNNs) combined with Long Short-Term Memory (LSTM) networks for enhanced intrusion detection, and an FT-Transformer-based Intrusion Detection System for heterogeneous IoT data analysis. The Transformer component improves accuracy by leveraging attention mechanisms to identify correlations in data features. The evaluation analysis we used to calculate accuracy, CNN model loss, CNN model accuracy, remaining energy, communication delay, and energy consumption of the MDCUSI model.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".