# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 4 Performance Evaluation
### Simulation Setup  
To produce our results, we upgraded ndnSIM with the compute service framework introduced in the previous work [23]. On top of that, we implemented in ndnSIM modifications described in Section 3. The main simulation parameters are summarized in Table 2. (There is something wrong with this sentence: Marcin) We specifically note that for static mesh environment, we position nodes on a grid separated by internode distance, \(L\). The considered region is thus a square with an area given by \(N(L-1)^{2}\) m\({}^{2}\), where \(N\) is the  
Figure 8: Example of the forwarding time window dynamics.  
number of nodes. In a mobile mesh environment, network nodes are initially positioned on a grid and then start to move according to a random direction model (RDM, [48]).  
The simulator was configured as follows:  
* **Simulation environment.** The considered scenario assumes mobile wireless nodes deployed in an area of limited size (free space environment).
* **Wireless channel.** Connectivity between nodes is enabled by IEEE 802.11n (5 GHz) wireless technology, with a range propagation loss model with the cutoff at 100 m and a Minster-HT rate adaptation algorithm.
* **Mobility model.** At the beginning of the simulation, wireless nodes were organized in a grid with a distance between nodes of 90 m. After the simulation started, wireless nodes started moving following the Random Direction Mobility (RDM) model.
* **Computing application.** In this simulation campaign, we assumed that there is a limited number of computing services available in the network. This assumption is motivated by a common IoT operation where devices (e.g., wearables) react to an event (e.g., weather notifications, traffic information) by utilizing standard processing algorithms (different types of software). This assumption justifies the overlapping of services requested by mobile users, supposing that users may request the same processing operations over the same data. More specifically, we considered 100 different software types that can be used to process data. Recognizing the varying popularity levels among software options, we extended our study beyond the uniform choice model, typically referred to as the Constant Bit Rate (CBR) traffic model, to incorporate the Zipf-Mandelbrot law for content popularity assessment. Furthermore, we imposed a constraint on the timeliness of computing results, setting their freshness threshold at one second. This approach implies that any data-processing outcomes  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Runs for each experiment & 100 \\ \hline Number of wireless nodes, \(N\) & \(3^{2},5^{2},7^{2}\) grids \\ \hline Mobility model & Random direction model [48] \\ \hline Wireless technology & IEEE 802.11n \\ \hline Initial internode distance, \(L\) & 95 m \\ \hline Frequency band & 5 GHz ISM band \\ \hline Propagation model & FSPL \\ \hline Coverage range & 100 m \\ \hline RTS/CTS operation & Disabled \\ \hline Automatic frequency selection & Disabled \\ \hline Velocity of wireless nodes, \(V\) m/s & 0, 3, 6, 9 \\ \hline NDN application type & CBR, Zipf \\ \hline Cache type & LRU \\ \hline Cache size, compute results & 20 units \\ \hline Interest frequency & 1 Interest/s \\ \hline Results freshness & 1000 ms \\ \hline Compute time & 1 ms \\ \hline Server selection time & 6 ms \\ \hline Number of consumers & \{1, 3\} nodes \\ \hline Number of producers & \{1, 3\} nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Default parameters for simulation campaign are deemed obsolete after one second and are subsequently purged from the cache, provided caching is active. Detailed insights into the computing service methodology utilized in this analysis are elaborated in [PERSON] (2020) [23].