A SAT-Guard-V-Satellite-First Edge-AI Framework for Real-Time Vehicle Theft Detection & Resilient Alerting
Bibliographic record
Abstract
Vehicle theft cases are biggest issues in worldwide and these cases are severe in ruler areas, where the mobile coverage areas are limited. SATGuard-V provide end-to-end vehicular security system that combines on-board edge AI, satellite IoT communication, and tamper-evident logging. The vehicle-mounted unit fuses CAN-bus telemetry, inertial (IMU) data, door/ignition status and GNSS signals. A lightweight anomaly detector (cascaded LSTM plus one-class auto-encoder) runs on this edge unit to identify theft patterns (unauthorized ignition, towing motion, flatbed transport, or GPS spoofing). Detected events trigger a prioritized cryptographically-signed alert payload sent via satellite (targeting Inmarsat’s ELERA/ISAT Data Pro L-band IoT network) to distributed servers. Those servers perform corroborative analysis and record incidents in a permissioned blockchain for auditing. The design explicitly copes with satellite link constraints and GNSS spoofing via multi-constellation cross-checks. The MATLAB-based simulation methodology used for real driving data (e.g. recorded GPS/telemetry logs), easy-to-use ML tools, and a software-modeled Inmarsat link. A Simulink model for analyzing and confirming the performance by measuring detection precision/recall/F1 and communication overhead will be used. Finally from results we can conclude that method significantly minimize the risk of false alarms and provide send alert of delivery received, even when terrestrial networks fail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".