# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 4 Experimental Evaluation
### Results and Discussion  
The proposed framework significantly improves network performance while reducing the traffic load directed at the source node. In contrast to DCS, the TCS employs tags that require separate computations and fail to accommodate popular content when it regains its popularity. Although the central caching node (CC) excels due to its strategic location, the limited number of such nodes results in inadequate content availability, characterized by a diminished cache-hit ratio, increased network stretch, and extended retrieval latency. The proposed framework effectively leverages caching nodes to enhance the cache-hit ratio and minimize the network stretch. In the case of DCS, each node is equipped with \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Description** & **Parameter Value** \\ \hline Simulation time & 48 h \\ Cache Size (elements) & 500, 1000 \\ Content size & 10 MB \\ Catalog Size (elements) & 10\({}^{6}\) \\ Content Categories & File \\ Topology & Tree, Abilene, \& GEANT \\ Zipf distribution (content popularity) & 0.8 \& 1.2 \\ Replacement Policy & LRU \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental setup simulation parameters. a table that calculates interests to identify the most frequently requested data across the network, taking into account data names, access frequency, and a specified threshold value. This threshold value helps us to recognize the most frequently requested content that needs to be cached at the edge node, considering the cache's service to multiple devices. The results from these devices are consolidated by a central caching node. The main challenge lies in selecting an optimal threshold to maximize the cache-hit rate. The threshold adaptation algorithm outlined in [37] is utilized to enable the caching node to learn and implement an optimal threshold strategy. Figures 3-5 depict graphical comparisons of cache-hit ratios, latency, and stretch for CC, TCS, and the proposed framework across various network topologies, including Tree, Abilene, and GEANT. The proposed model is rigorously tested and validated under different cache size and content popularity configurations, specifically, 500 and 0.8, 1000 and 0.8, 500 and 1.2, 1000 and 1.2. In DCS, the selection of optimal caching nodes, along with the essential role played by edge nodes in maintaining data freshness, works to minimize the distance between the source node and the end nodes. Figure 3 shows the results of the cache hit ratio for different numbers of cache sizes and popularity. Results' analysis of Figure 3a shows that the proposed framework gained 105.5%, 109%, and 21.42% improvement in terms of cache-hit ratio over CC executed on Tree, Abilene, and GEANT network topology, respectively, for cache size of 500 and popularity of 0.8. The improvement of DCS over TCS with the same parameters and configuration is 72%, 76%, and 11.47%, executed on Tree, Abilene, and GEANT network topologies, respectively. Figure 3b shows that the proposed framework achieved 132.4%, 122.8%, and 55.3% improvement in terms of the cache-hit ratio over CC executed on Tree, Abilene, and GEANT network topology, respectively, on the cache size of 1000 and popularity of 0.8. The improvement of the proposed model over TCS with the same parameters and configuration is 59.2%, 65.9%, and 17.7% in the case of Tree, Abilene, and GEANT network topologies, respectively. Figure 3: Comparative analysis of cache-hit ratio with cache sizes and popularity of (**a**) 500 and 0.8, (**b**) 1000 and 0.8, (**c**) 500 and 1.2, and (**d**) 1000 and 1.2. Figure 3c shows the comparative results of the cache-hit ratio of DCS with CC and TCS on the cache size of 500 and content popularity of 1.2. The results analysis shows that  
Figure 4: Comparative analysis of latency with cache sizes and popularity of (**a**) 500 and 0.8, (**b**) 1000 and 0.8, (**c**) 500 and 1.2, and (**d**) 1000 and 1.2.