Research Article
Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
Table 5
Comparison with previous research.
| Research | Deployed algorithm | Performance measurements | Results |
| [39] | Multiagent deep reinforcement learning (MADRL) | Caching reward | 21% | Cache hit rate | Highest | Traffic load | 43% | Multiagent actor-critic (MAAC) | Caching reward | 56% | Cache hit rate | Higher | Traffic load | 45% | Deep reinforcement learning (DRL) | Caching reward | 43% | Cache hit rate | Lower | Traffic load | 36% | Least recently used (LRU) | Caching reward | 34% | Cache hit rate | Lowest | Traffic load | 12% |
| [40] | Personalized edge caching system (PECS) | Deep packet inspection | Top-down analysis (network level) and bottom-up analysis (user level) |
| [41] | One-dimensional convolutional neural network (ODCNN) Self-Organizing Map (SOM) | Accuracy rate | 99.8% |
| [42] | Support vector machine | Accuracy | 0.984 | Precision | 0.984 | Recall | 0.983 | F1 score | 0.981 | Logistic regression | Accuracy | 0.983 | Precision | 0.982 | Recall | 0.983 | F1 score | 0.983 | K-nearest neighbors | Accuracy | 0.984 | Precision | 0.983 | Recall | 0.984 | F1 score | 0.984 | Isolation forest | Accuracy | 0.870 | Precision | 0.969 | Recall | 0.973 | F1 score | 0.919 |
| Proposed | Self oranizing map (SOM) | Quantization error | 0.000024 | Topographic error | 0.092 | QE + TE | 0.0000235 | Two-level spin quantum phenomenon (TLSQP) | Basic states | 0 | OV = 85.4% | PV = 85.2% | 1 | OV = 14.6% | PV = 14.8% |
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