Table 5 Automation efficiency and impact analysis of various IoT-enabled smart learning methods. The table summarizes time saved and error rates, showing SLICED offers the greatest automation efficiency and lowest errors among all compared frameworks.
From: SLICED: A secure and adaptive cloud–iot framework for low-latency e-learning environments
Method | Automation efficiency (time saved/error rate) | Relevant/impact |
|---|---|---|
SL-IoT | 80 s saved/15% error rate | Basic automation with limited optimization, resulting in errors. |
LMS-IoT | 85 s saved/10% error rate | Some automation, and errors still occur, reducing system efficiency. |
PEF-En | 90 s saved/18% error rate | Automation improves and at the cost of higher error rates. |
R-BEC | 75 s saved/20% error rate | Poor automation features, high error rate reduces overall efficiency. |
H-Ed-AI | 95 s saved/12% error rate | Increased automation, fewer errors and still dependent on manual input. |
Cc-ELn | 70 s saved/22% error rate | Limited automation with higher error margins. |
Azure IoT | 105 s saved/7% error rate | Efficient automation, good scalability; modest error under high concurrency. |
Google Cloud IoT | 98 s saved/9% error rate | High concurrency management, scalable autoscaling, moderate error rate. |
SLICED | 120 s saved/5% error rate | Highly efficient automation, minimizing errors and maximizing efficiency. |
