# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 6 Evaluation
### Results of the Simulation
#### 6.2.4 Caching Schemes  
The next biggest impact in terms of rank deviation can be determined for _caching schemes_. Here, the best values largely depend on the request type: For **REPETITIVE**, we can observe that the higher the caching probability, the better the rank of the corresponding caching scheme. An example of this is shown in Figure 5a. Since the repetitive type always requests five packets with the same name in succession, adding the previously received data packet to the CS is desirable. There is very little diversity in the network due to the repetitive nature of the requests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rank** & **Average Energy** & **Packet Size** & **Caching Strategy** & \(\dots\) \\ \hline
1 & 122.85575 & 128 & LRFU \\
2 & 122.8565 & 128 & FWF \\
3 & 122.85725 & 128 & LRU \\
3 & 122.85725 & 128 & RANDOM & \(\dots\) \\
5 & 122.85757 & 128 & FWF \\ \(\dots\) & \(\dots\) & \(\dots\) & \(\dots\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Part of an example table for ranks of the sorted list of variable parameters. The example is for connected stars, size 4 and REPETITIVE. Row 3 and 4 receive the same rank because the energy consumption is equal. Since rank 4 is skipped, the next assigned rank is 5.  
**INCREMENTAL** displays some perhaps surprising behavior: The best caching scheme here is probabilistic with 10% caching probability. The rank deviation for 10% is approx. 15 to 35, and the deviation becomes greater for bigger graph sizes. An exception is the connected-star topology: For smaller graphs (sizes 4, 8, and 16), the best caching scheme is CEE for almost all combinations. This best scheme shifts to probabilistic 50% for larger graphs (sizes 32 and 64). The reason for that is that INCREMENTAL enforces that \(3\cdot\mid\text{CS}\mid\) different names are requested consecutively. Thus, a large amount of data diversity in the network is very beneficial. By using a scheme with a relatively low probability, all the different elements distribute well across the nodes in the network. CONNECTED_STARS may be an outlier because the energy consumption of the P2P edges between the center nodes is not measured. So if more hops are necessary to access an element (due to lack of diversity in the case of CEE or probabilistic 50%), this is not punished by a higher consumption value.  
In the case of **SIMPLE_RANDOM**, probabilistic 50% and CEE are the best schemes with a rank deviation of approx. 40. Although the universe size used for SIMPLE_RANDOM and INCREMENTAL is the same (\(3\cdot\mid\text{CS}\mid\)), networks using SIMPLE_RANDOM evidently perform better if there is less data diversity because probabilistic 50% and CEE store more duplicate entries on adjacent nodes.  
The ranks for the caching scheme when using **LINEAR_RANDOM** depend on the topology: For complete and star graphs, CEE and probabilistic 50% are about equally favorable with a rank deviation of approx. 40 to 50. On the other hand, for grid (shown in Figure 4(b)) and connected stars, probabilistic 50% is overall superior to CEE. Here, the rank distance is up to 55 for graph size 64. In graphs with a small maximum distance, it is beneficial to always cache an element since LINEAR_RANDOM generates some elements much more frequently than others. Therefore, data diversity is not of interest in that case. However, most pairs in grid graphs and connected stars are further apart than one or two hops: In such graphs, it is reasonable that caching with a slightly lower probability, i.e., 50%, enables nodes to communicate on comparatively shorter paths.