# Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
## 4 Experimental Evaluation
### Results and Discussion
Figure 5: Comparative analysis of stretch 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.  
the proposed model is 115.3%, 114.2%, and 17.9% better than CC executed on Tree, Abilene, and GEANT network topology, respectively. The improvement of the proposed model over TCS with the same parameters and configuration is 47.3%, 70.4%, and 6.75% tested on Tree, Abilene, and GEANT network topologies, respectively. Figure 3d presents the comparison of the cache-hit ratio of DCS with CC and TCS for a cache size of 1000 and popularity of 1.2. The results analysis shows 56.14%, 64.15%, and 29.4% improvement over CC in the case of Tree, Abilene, and GEANT network topology, respectively. The improvement of the proposed model over TCS with the same parameters and configuration is 28.98%, 22.5%, and 4.76% executed on Tree, Abilene, and GEANT network topologies, respectively. Figure 4 shows the comparative results in terms of the latency of DCS with CC and TCS for different cache sizes and content popularity. Figure 4a shows the results of models with 500 elements for cache size, and the content popularity was set to 0.8. Three different network topologies, i.e., Tree, Abilene, and GEANT were used in experiments for testing and validation. The results analysis shows that the proposed model consumes less time, i.e., a 29.31%, 30.35%, and 27.65% decrease in cache latency as compared to CC on Tree, Abilene, and GEANT topologies, respectively. Meanwhile, DCS is 21.15%, 20.4%, and 17.07% more efficient than TCS executed with the same configuration of parameters and topologies, respectively. Figure 4b shows the comparative results of DCS with CC, and TCS in terms of cache latency. For these experiments, 1000 elements of cache size and a 0.8 value of content popularity are considered and executed on Tree, Abilene, and GEANT network topologies. The results analysis shows that the proposed model is 28.8%, 35.41%, and 19.1% more efficient than CC in the case of Tree, Abilene, and GEANT topologies, respectively. DCS is 30.1%, 26.19%, and 5.0% efficient in cache latency, as compared to TCS executed on the same configuration of parameters and topologies. Figure 4c shows comparative results of DCS with CC, and TCS in terms of cache latency for the cache size of 500 and content popularity of 1.2. An analysis of the results shows that the proposed model is 26.1%, 27.6%, and 42.8% more efficient than CC in the case of Tree, Abilene, and GEANT topologies, respectively. DCS is 20.5%, 17.0%, and 28.2% efficient in cache latency as compared to TCS executed on the same configuration of parameters and topologies. Figure 4d shows the comparative results of DCS with CC, and TCS in terms of cache latency. For these experiments, 1000 elements of cache size and a 1.2 value of content popularity are considered and executed on Tree, Abilene, and GEANT network topologies. The analysis shows that the cache latency of the proposed model is 52.7%, 50.0%, and 51.2% better than CC and executed in Tree, Abilene, and GEANT topologies, respectively. DCS is 30.5%, 24.1%, and 16.6% more efficient than TCS executed on the same configuration of parameters and topologies. CC showed a higher latency rate because of the long distance between the source and the central position of the caching nodes. TCS increases the amount of similar content over the network with the help of tags and filters near the source location, due to which it does not select the central node for the caching; thus, latency increases, as shown in the results. The proposed model retrieves a copy of the popular data item first in the edge node, then increases the number of interest and sends it to the central position, which decreases the distance that leads to a decrease in the latency. Figure 5 depicts the comparative results of DCS with CC, as well as TCS in terms of stretch. Figure 5a shows the results of 500 elements of cache size and a 0.8 content popularity.