Figure 1:
Proposed Architecture
Figure 2:
Reinforcement learning framework
Figure 3:
DELM Framework
Table 1:
Parameters used for implementation
| S.No | Simulation Parameters | Specifications |
|---|---|---|
| 1 | Number of Nodes Deployed | 90 |
| 2 | Number of Fog Gateways | 04 |
| 3 | Initial Energy per BAN Node | 0.002 Joules |
| 4 | Distance Variation Between BAN Nodes | 4-12 meters |
| 5 | Equipped Transceivers | 5G |
| 6 | Uplink Bandwidth | 250 Mbps |
| 7 | Downlink Bandwidth | 125 Mbps |
| 8 | RAM in Fog Gateways | 3GB |
| 9 | Number of Recorded Attributes | 08 |
Table 2:
Performance measures employed in the evaluation
| SL.NO | Performance Measures | Expression |
|---|---|---|
| 1 | Accuracy | |
| 2 | Recall | |
| 3 | Specificity | |
| 4 | Precision | |
| 5 | F1-Score |
Table 3:
Comparison of Performance Metrics for different Models in IoT-Fog Environments
| Algorithm | Accuracy (%) | Sensitivity (%) | Specificity (%) | Precision (%) | F1-Score (%) |
|---|---|---|---|---|---|
| RF-SVM | 89.4 | 88.9 | 87.8 | 88.2 | 88.5 |
| CNN-LSTM | 91.7 | 91.2 | 90.1 | 90.6 | 91.0 |
| Hybrid Fuzzy-Q-Learning | 93.5 | 93.1 | 92.3 | 92.7 | 93.0 |
| DRL-GRU | 95.8 | 94.9 | 94.5 | 94.7 | 95.2 |
| Proposed Model | 98.6 | 99.1 | 98.8 | 98.9 | 99.0 |
Figure 4:
Performance Evaluation of Various Models in IoT-Fog Environments
Figure 5:
Latency Performance Comparison of Different Learning-Based Fog-BAN Networks with Varying Node Counts