# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Numerical Results
#### 4.3.4 Effects of Network Size and Nodes Density  
We finalize the exposure of our mechanisms by comparing the performance of the considered dynamic compute service in two extreme cases: large mobile mesh featuring 49 nodes and small static mesh consisting of just 25 (\(5\times 5\)) and 9 (\(3\times 3\)) nodes in Figure 14 for a range of input system parameters. Please note that in both cases, the node's density is kept constant. As one may observe by analyzing the presented data, the amount of satisfied Interests barely reaches 0.3 for a large mobile mesh within 200 ms. This behavior is regulated by two factors: (i) high interference conditions and (ii) events of nodes' disconnections from the network. Although there are no measures to combat the latter effect, one may utilize spatial frequency reuse in the underlying wireless technology to improve the  
Figure 13: Loss and delay performance measures in mobile mesh.  
performance of the system. In the same interval, is \(5\times 5\) and \(3\times 3\) meshes, all the Interests are successfully served.  
As one may observe, the performance of the dynamic compute service heavily depends on the network size. To investigate this effect, deeply revealing and isolating the sources of losses, we complement the previous results by assessing the impact of network density and frequency of requests. To this end, Figure 15 presents satisfied Interest fraction and mean delay for various node velocities in mobile mesh for different initial spacing between nodes specifying the compartment size and thus directly affecting node density. Analyzing the presented results, one may observe that the interplay between node density and velocity is characterized by a complex behavior. In particular, higher velocity leads to worse performance for the lowest considered density. The rationale is that for this density, higher velocity frequently leads to situations where nodes lose entirely network connectivity. Thus, the increase in the network density leads to better performance for higher velocities and worse performance for lower speeds, i.e., for initial internode spacing of 40 m, the satisfied Interest fractions for different velocities even out. This is explained by the second phenomenon affecting system performance--interference. Indeed, by increasing the network density, the amount of interference in the system increases, reducing the satisfied Interest fraction.  
Figure 16 illustrates the satisfied Interest fraction and mean delay for 10 Interest/s frequency, different initial internode spacing, and different nodes' velocities. By directly comparing the results of Figure 16 and those from Figure 15 illustrating the same metrics for 1 Interest/s, one may observe that the increase in the traffic load logically worsens the system performance for all considered values of nodes velocities and density. The effect of the increased interference impacts the considered deployments differently. In particular, it affects denser deployments with high node velocities, which are much heavier compared to sparser deployments with lower node velocities.  
Figure 14: Loss and delay performance measures in mobile mesh.  
Figure 16: Loss and delay metrics for 10 Interest/s frequency.  
Figure 15: Loss and delay metrics for different network densities.