IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management
Bibliographic record
Abstract
In this paper, we design and implement a distributed Internet of Things (IoT) framework called IoT-guard, for an intelligent, resource-efficient, and real-time security management system. The system, consisting of edge-fog computational layers, will aid in crime prevention and predict crime events in a smart home environment (SHE). The IoT-guard will detect and confirm crime events in real-time, using Artificial Intelligence (AI) and an event-driven approach to send crime data to protective services and police units enabling immediate action while conserving resources, such as energy, bandwidth (BW), and memory and Central Processing Unit (CPU) usage. In this study, we implement an IoT-guard laboratory testbed prototype and perform evaluations on its efficiency for real-time security application. The outcomes show better performance by the proposed system in terms of resource efficiency, agility, and scalability over the traditional IoT surveillance systems and state-of-the-art (SoA) approaches.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".