# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 4 Simulation Results and Analysis  
In this section, the proposed cache placement strategy based on energy consumption optimization (ESCPS) is simulated. And MATLAB tool is used to numerically calculate the model of ESCPS policy and to compare it with the other two buffer strategies (LCD (Leave Copy Down) [6], Random [7]). We analyze and evaluate the comparison results of energy savings, caching benefit, and delivery success rate when the number of the user \(m\), distribution content size \(c_{k}\), and node distribution range _area_ are changed. The parameters in simulations are given in Table 1.  
Here, the number of users in the network is \(m\), and it changes in 100-1000 (number). The energy consumption of the node change is \(P_{c}\). That is the energy that the user needs to consume in the process  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Value** \\ \hline \(m\) & Number of users & 100–1000 \\ \(P_{c}\) & Extra energy & 0.1–1 \\ \(\tau\) & Distribution rate & 5 \\ \(\alpha_{k}\) & Content size & 10–100 \\ \(g\) & Noise power & 3 \\ \(f\) & Noise ratio & 1 \\ \(G_{R}\) & Node receiving power & 3 \\ _area_ & Node distribution range & 1–20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
of finding the cache node and the value is a random number of 0.1\(\sim\)1 (J). The average rate of content distribution in the network is \(\tau\) and its value is 5 bps. The average value of the node distribution in the network is _area_ and the distance is 1 to 20 (m). The total number of simulations is 10,000.  
The two strategies used for comparison in this paper are: (1) Down replication reservation policy LCD--based on the classical full cache LCE (Leave Copy Everywhere) policy, the redundancy of the strategy is greatly reduced, and the content is cached only to the next hop of the hit node. (2) Random caching strategy Random--the network nodes are selected by random equal probability. In these three strategies, the energy consumed by the replacement of the cache node also has a certain effect on energy savings besides the parameters of \(m\), \(c\), and _area_ change. The effect of node energy consumption (\(P_{c}\)) on the energy savings of the three strategies is shown in Figure 2.  
From Figure 2, we can see that there is a common characteristic--when the value of \(P_{c}\) is small, the energy consumption that can be saved is large, and when the value of \(P_{c}\) is large, the energy consumption that can be saved is small for three strategies. The reason is that \(P_{c}\) is the main factor in the cost of energy consumption. The larger the value is, the more energy is consumed when the node changes. Among them, LCD is the most affected by \(P_{c}\), because each time the next node that selects the hit node is used as the cache placement node, the first method is oversimplified, and the number of node changes cannot be made in conjunction with the network conditions and other factors. In this way, the cost energy consumption is almost linearly increased when \(P_{c}\) is continuously increased. Ultimately, the saved energy is less than the cost of energy, and the saved energy is negative. It shows that the placement node of this policy selection is not only saving but also consuming more energy than other nodes. For Random, it is a random selection of equal probability. It is slightly less affected by \(P_{c}\) than LCD, but it is not targeted when selecting the best cache placement node, so the effect of saving energy is not as good as ESCPS. In these three strategies, it can be clearly seen that ESCPS has the best energy-saving effect. In the continuous increase of \(P_{c}\), the decrease is the smallest and the energy consumption is always higher than the other two strategies. The choice is very selective, making it almost unaffected by the power consumption of node changes.