# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 7 Evaluation
Several simulations were run based on two parameters: the number of nodes and the jamming rate. The number of nodes represents the network size, which is denoted as \(n\). MAC networks can have a large number of nodes; however, it is typical for them to have hundreds or fewer nodes [10,12,14]. Therefore, the value \(n\) is defined in the range [10-100], with a step size of 10. The jamming rate is chosen from the range [0.0-1.0], with a step size of 0.05. These simulations were run for the two injection models of the LBIM and RIM. The three broadcasting algorithms were compared based on two performance measures: the throughput and the average packet latency. The throughput of a broadcasting algorithm is  
Figure 7: The standard deviation of the average packet latency for the three broadcast algorithms for 100 simulation runs and \(n\in\)[10–100]. defined as the number of successfully transmitted packets divided by the total number of injected packets during the whole execution. Packet latency is computed as the number of rounds a packet waits in a queue before it is successfully transmitted. For each configuration, the performance results were computed by averaging the results of 100 simulations. The simulation was individually run for each broadcast algorithm using the two injection models (the LBIM and the RIM). The throughput and the average packet latency results were recorded and then used to generate charts to compare the performances of the three broadcast algorithms. Using the simulation, we compared the performances of the three broadcast algorithms for all jamming rates in the range [0.0-1.0], with a step of 0.05. As seen in Figures 9 and 10, the KTFW has a higher throughput than the BEB algorithm. For \(n=10\), the BEB throughput is 0.95 while the KTFW achieves the perfect throughput of 1. Moreover, as the number of nodes increases, the superiority of the KTFW over the BEB in terms of throughput increases. As the figures show, the BEB throughput decreases by around 0.05 for every increment in the number of nodes. This degradation in the throughput is due to the inherited randomness of the BEB in which a node chooses a random backoff time before retransmitting a packet. The probability of collision increases in the channel as the number of nodes grows, which in turn affects the throughput. On the other hand, the KTFW's throughput is not affected for \(n\geq 20\). The difference in the throughput becomes as large as 0.5 when \(n=100\) and \(j=0\), and it diminishes as the jamming rate approaches 1 (see Figure 10). In general, the results show that the throughput of the KTFW is not affected in both small and large network sizes. In contrast, the throughput of the BEB degrades as the network grows larger due to how the algorithm behaves. As more nodes contend for access to the channel, they typically wait for longer durations because a packet can experience multiple collisions before being successfully transmitted. In the KTFW, the throughput is not affected because all nodes follow the same process to transmit all injected packets even when the number of nodes increases. These packets are processed in segments that consist of at most \(k\) active nodes. As there is no random access to the channel, the KTFW is able to maintain the same throughput for a larger number of nodes. Figure 8: Comparing the average packet latency for different \(k\) values of the KTFW algorithm and all \(n\in\) [10–100]. The throughput results for the QB algorithm with no jamming are the same for both types of injection models and is equal to 1. For any \(j\geq 0\), the throughput value becomes 0 (see Figure 9). The main reason for this huge degradation in throughput performance is the reliance of the QB on control information for managing access to the channel. As the algorithm is adaptive, additional information is attached to packets, which are used to control access to the channel based on queue paradigm.