# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 4 Results and Discussions
### Configuring the Simulation Parameters  
Once the network has been modeled, the simulation parameters, such as latency and packet delivery ratio, must be configured. Latency is the time it takes for a packet to travel from the source to the destination. The packet delivery ratio is the percentage of packets successfully delivered to their intended destination. Fog simulation may include various parameters, such as the number and location of fog nodes, the type of applications or services running on the fog nodes, the communication and networking protocols used for data transmission, the processing and storage capabilities of the fog nodes, and environmental factors such as fog density, temperature, and humidity. The simulation may generate various output data, such as latency, throughput, energy consumption, and reliability measures, to evaluate the performance and effectiveness of the fog computing system. Figure 5 presents a visual representation of output of the fog simulation showing the fog nodes, the data flow between the nodes, and other relevant information related to the simulation. Figure 4: Keyword match and trust scores. Configuring the simulation parameters involves specifying the values for the various parameters used in the simulation. This step is crucial because it determines the conditions under which the simulation will be run and affects the results of the simulation. To configure the simulation parameters for a fog computing network, the following information is typically required. **Latency:** The value for latency, which represents the time it takes for a packet to travel from the source to the destination, can be specified. This value can be calculated using the equation T = d/s, where T is the latency, d is the distance between the source and destination, and s is the medium's light speed. **Packet delivery ratio:** The value for packet delivery ratio, which represents the percentage of packets successfully delivered to their intended destination, can be specified. This value can be calculated using the equation PDR = (number of successfully delivered packets)/(total number of packets sent). **Bandwidth:** The value for bandwidth, which represents the data transfer rate between nodes, can be specified. This value can be calculated using the equation B = R \(\times\) L, where B is the bandwidth, R is the data rate, and L is the length of the communication link. **Processing power:** The value for processing power, which represents the ability of a node to perform processing tasks, can be specified. This value can be calculated using the equation P = \(f\times V^{2}\), where P is the processing power, \(f\) is the frequency, and \(V\) is the voltage. **Workload:** The workload, which represents the processing tasks and data transfers performed in the network, can also be specified. This can include information such as processing tasks, data sizes, and arrival times. Once the simulation parameters have been specified, they can be used to run the simulation and evaluate the performance of the fog computing network. The specific values for the simulation parameters will depend on the particular requirements of the simulation and the metrics being assessed. Figure 6 shows the bandwidth, processing power, and workload over a series of simulation steps. The \(x\)-axis represents the simulation steps, while the \(y\)-axis shows the values for each of the three parameters. The bandwidth is shown in blue, the processing  
Figure 5: Fog simulation. power is shown in green, and the workload is shown in red. The plot reveals how the three parameters change over time in the simulation. The bandwidth values fluctuate over time and may indicate network conditions or communication protocol changes. The processing power values also change over time, showing the computational resources available to the system. The workload values represent the number of tasks or requests being processed by the system, which may increase or decrease over time depending on various factors, such as user demand or system load.