## 3 Problem Statement
### Calculating Data Freshness
These thresholds, while effective in scenarios with consistent data traffic, may lead to suboptimal performance in situations where traffic intensity varies significantly. The ANN mitigates this issue using real-time input features, such as request frequency and historical data, to make more informed caching decisions. As a result, the ANN exhibits better overall responsiveness to the inherent variability in IoT environments, particularly in cases with less frequent or irregular request patterns. Figures 2-5 present the simulation results in four different modes, as outlined in Table 3. Figure 2 illustrates the cache hit rate of the proposed method in the first simulation model, which includes 3000 requests over 180 s. As shown in Figure 2, the cache hit ratio of the proposed method is higher than that of the VLRU method. Figure 3 depicts the cache hit ratio of the caching methods in the second simulation model, where the proposed method again achieves a considerably higher cache hit ratio than the VLRU algorithm. The proposed caching method outperforms the VLRU method in terms of the cache hit ratio across different simulation models. The simulation results indicate a significant difference in cache hit rate between the proposed method and the VLRU method; the proposed method substantially improves cache hit rate performance. Figure 4: Cache hit ratio of the caching methods in Model 3. Figure 3: Cache hit ratio of the caching methods in Model 2. Figures 4 and 5 show the simulation results for Models 3 and 4. The third model's simulations include 3000 requests over 120 s. As shown in Figure 4, the cache hit rate of the proposed method is significantly higher than that of the VLRU method and is approximately equal to the values estimated by the ANN. Figure 5 depicts the cache hit ratio of the caching methods in the fourth simulation model, where the cache hit ratio of the proposed method is again a bit higher than that of the VLRU algorithm. The simulation results across all four models confirm the superiority of the proposed method in terms of cache hit rate, demonstrating a significant improvement in cache hit rate performance. Making cache decisions based on data freshness allows routers to cache data that are more likely to be re-requested by other consumers. Due to the high cache hit ratio and the lack of frequent reference to the data source, network delay has been reduced compared to the VLRU method. It should be noted that a large part of the network delay for the proposed method in the diagrams is related to the execution time of the router processes for cache decisions. Due to the implementation of this method in the simulation environment, the computational time for the simulated processing of routers is longer than in the natural environment. If implemented in the real environment, the network latency is significantly less than in the simulation results. The caching methods impose performance overhead on the system. The imposed performance overhead by the caching techniques should be evaluated as the other criteria. Network delay and traffic load are the leading performance overhead assessed in this study. Figures 6 and 7 show the network delay of the caching methods in the first simulation model with and without estimated values by the ANN. Similar experiments have been performed for the second model. Figures 8 and 9 show the results of the simulations in the second model. Figures 10 and 11 show the network delay imposed by the caching methods in the third simulation model with and without estimated results. The results of the simulations indicate that the proposed method has a lower network delay than the VLRU method. The results of the fourth model simulations have been shown in Figures 12 and 13. The results of the fourth model simulation are similar to those of the second simulation model. The imposed performance overhead in Simulation Model 2 is like the VLRU method (shown in Figures 8 and 9). Overall, the network delay imposed by the proposed method is lower than that of the VLRU method.