# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 7 Evaluation  
In this section, we first describe the selected method for the evaluation, and then we present and discuss the results of the three broadcast algorithms. To compare our method with competing broadcast algorithms, we developed a network simulation tool to assess their performance under jamming attacks. Several network simulation tools can be used to assess the network's performance in various aspects [51; 52]. Many of these tools can accurately predict the performance by providing a high level of details regarding the network's technical aspects. However, they can fail in predicting the performance of future network protocol and technology changes. Furthermore, using a simulation tool that implements all specifications of the networks' protocols complicates the analysis of the networks' performance. Thus, the developed simulation tool provides the needed level of detail to evaluate the network's performance for the three broadcast algorithms. We developed a simulation tool that simulates a MAC network with a given number of nodes attached. The simulation models the behavior of multiple nodes communicating through a single shared channel. All communication aspects were implemented, from broadcasting packets to receiving feedback from the channel. The simulation operates with synchronous communication, in which time is divided into equal time slots, also known as rounds. Nodes could then transmit a packet on the channel in a single round or listen to its feedback. The simulation implements an abstraction of the MAC network without the various layers of the network protocols. This implementation allows us to investigate the effect of the broadcast algorithm on communication performance in isolation. Using the developed simulation, we analyzed and compared all algorithms under identical network scenarios for injection and jamming. A screenshot of the simulator is shown in Figure 6 where different parameters can be controlled including the number of nodes, the jamming rate and the injection model. Figure 6: Screenshot of the developed multiple access channel (MAC) network simulator. The traffic injection model was implemented based on the two types of injection models described in Section 5. The LBIM and RIM generate packets randomly based on \(\rho\) and \(\beta\). For all simulations, the parameters \(\rho\) and \(\beta\) were set to fixed values. In this work, we focus on high network traffic scenarios, where the broadcast algorithm becomes one of the main factors affecting MAC network performance. The injection rate \(\rho\) was set to 1 to evaluate the performance under relatively high network traffic. The simulation was run for different burstiness values in the range [1-100], and the result indicated no effect on the performance for the three broadcast algorithms. Therefore, the burstiness was set to 20 in this evaluation. In Figure 7, the standard deviation of the average packet latency is shown for 100 simulation runs. These results indicate that both the KTFW and QB algorithms have minor deviations from the average packet latency. In contrast, the BEB algorithm's standard deviation is in the range of [900-1500], which is due to randomization. These results are expected because the maximum window range is 1024 for the BEB algorithm. The performance of the KTFW was evaluated for different \(k\) values from the set \(\{4,8,16,32,64,128,256\}\) to find the optimal \(k\) value with the lowest average packet latency. The results in Figure 8 show the average packet latency of all \(k\) values under the LBIM. The result shows increasing average packet latency for all \(k\) values as the number of nodes increases. The average packet latency for \(k=4\) is the lowest among all \(k\) values, so the value \(k\) is set to 4 in this evaluation. The broadcast algorithms are evaluated on different network scenarios to cover most types of networks based on the size and the jamming power. They are evaluated for both small and large network sizes and networks with low and high jamming power.