# Method for Handling Massive IoT Traffic in 5G Networks
## 10 Results and Analysis  
In terms of \(k_{no\_mu x}\) and \(k_{mux}\), comparison of analytical and simulation results for various traffic load scenarios are performed. The evaluation highlights the case of relaying without multiplexing and the impact of relaying with multiplexing plus timer mechanism. The performance of the multiplexing scheme is evaluated for scenarios with 100, 200,..., 900 up to 1000 IoT devices connected to the RN. These scenarios offer traffic load of less than 0.1. The reason for evaluating performance only in low load scenarios is that the expiry of timer rarely occurs in high load scenarios. The multiplexing process would almost always start before the timer expiry. The maximum load in the simulated scenarios is 0.07 for 1000 IoT devices in the RN coverage area. Results are depicted using spider web charts. Such a chart is used to illustrate outcomes of multiple load scenarios in a single figure. Each chart axis represents one of 10 scenarios (i.e., 100, 200,..., 1000 users). Each axis signifies a scale of 1 to 5 (Figure 7) for resource block utilization and 1 to 50 (Figure 8) for multiplexing gain percentage. Figure 7 illustrates two aspects. First, the comparison of results for relaying with as well as without multiplexing depicts that the utilization of frequency resources is considerably improved. In the no multiplexing case, the average resource usage \(k\) is low. This shows that the total of multiplexed packets sent to BS from RN would be quite large. Therefore, the overhead data would grow significantly. The results for multiplexing with timer scenarios show that since packets are multiplexed into larger blocks, overheads needed for transmission blocks are significantly reduced. The second aspect is that the analytical results and simulation results agree with each other quite well. This conformity between analytical and simulation results indicate the validity of models. Simulation models are developed on the basis of artificial Random Number Generators (RNGs) for generation of random sequences of random values. Hence, simulation runs with various seeds for RNGs are performed to get results from varying random sequences. The 95% confidence intervals for simulation results are provided here in Table 4.  
Figure 8: Comparison of multiplexing gain in analytical and simulation models10. Relaying Delay. Figure 7: Analytical and simulation comparison of \(k\) with/without multiplexing (blue curve overrides black curve). The achieved multiplexing gain \(G\) for all the traffic load scenarios is shown in Figure 8. Significant gains are achieved in all scenarios. Thus, it can be concluded that the framework provides better spectral utilization when IoT data traffic is sent over the network. It can also be noticed that the results for both simulation and analytical model agree with each other in Figure 8.  
The M2M data traffic aggregation and multiplexing scheme introduces relaying delay to the uplink data packets. The evaluation of relaying delay is achieved by considering 3 GPP MCS index 26, which implies relatively good channel conditions. The simulations are performed for two categories of environment settings. In one category, the simulations are performed without relaying and in the second category, simulations runs are carried out with RN for aggregation and multiplexing. The simulation parameters in addition to Table 3 parameters are provided here in Table 5. The simulations are performed for 15 scenarios, where the traffic load in each scenario varies from 1000 IoT devices up to 15,000 IoT devices. In the first scenario, the number of M2M devices is 1000. In each successive scenario, the IoT devices are increased by 1000. The load of data traffic, which is the ratio of arrival rate and service rate, is given in Table 6 for each scenario with various number of M2M devices.