# Distributed Dynamic Cluster-Head Selection and Clustering for Massive IoT Access in 5G Networks
## 4 Numerical Results
### Parameters  
The simulation scenario is a circular area with a radius of 1000 meter. The IoT devices are distributed evenly in the network. The data size of each device is generated according to the periodic escalation model in the GERN business. Random access parameters are derived from literature [30; 31]. The path loss model mainly considers the large-scale fading and uses the Friis propagation loss model [32]. Table 3 shows other simulation parameters [33; 34; 35; 36]. In addition to the proposed algorithm, this paper also contrasts the simulation performance between the direct transmission scheme without algorithm and the K-means algorithm scheme with different proportion of cluster. At the same time, five performance metrics [37; 38; 39; 40] of the system consider the collision probability of random access; the average transmitted data amount per unit RB, the average number of RBs transmitted per unit time, the access success probability and system \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \hline \multicolumn{2}{c}{**Traffic distribution parameter**} \\ \hline Cell radius & 1000 m \\ Number of devices & Variation (5000–30000) \\ Number of aggregation devices & Variation (depends on the algorithm) \\ Group arrival rate of the device & 1/10 \\ Grouping model & Periodic reporting of business models \\ \hline \hline \multicolumn{2}{c}{**PHY parameter**} \\ \hline Working frequency & 900 MHz \\ Transmit power of the base station & 30 dBm \\ Transmit power of the device & 23 dBm \\ The power density of thermal noise & \(-121.45\ \mathrm{d}\mathrm{B}\mathrm{m}/\mathrm{Hz}\) \\ Target bit error rate of AMC & \(5\times 10^{-4}\) \\ \hline \hline \multicolumn{2}{c}{**MAC parameter**} \\ \hline Number of preambles & 54 \\ Backoff time & 20 subframe \\ Maximum number of retransmissions & 10 \\ \hline \multicolumn{2}{c}{**System parameter**} \\ \hline System bandwidth & 1.4 MHz \\ Simulation time & \(10\times 10^{3}\) subframe (10 s) \\ \hline \multicolumn{2}{c}{**Algorithm parameter**} \\ \hline The maximum number of devices that trigger & 27 \\ the algorithm & 300 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of main simulation parameters. As for the direct transmission scheme, the collision probability increases with the increase in the number of IoT devices. Especially when the number of devices is between 10,000 and 14,000, the probability of collision and the average number of using RB per unit time increase exponentially, and the average number of using RB per unit time saturate when the number of devices reaches 14000. This results in a sharp decrease in the access success probability and the average amount of data carried by unit RB. After that, with the increase of device number, the increasing trend of collision probability tends to be smooth, the average number of RBs used per unit of time decreases, so that the access success probability decreases and the average number of carrying data of unit RB tends to flatten. Consequently, this shows that the energy efficiency of the system decreases with the increase of device number. In Figure 7, as the number of devices increases, the throughput of the system is first increased and then reduced when the number of devices is 12,000. This is because it was the optimal number of devices in the phase when the average number of RBs used per unit of time was sharply increased, and the average amount of data carried by unit RB was significantly reduced. For the general K-means algorithm scheme [20, 27], different proportion cluster header devices show different performance. From Figure 3, we can see that when the number of devices does not exceed 10,000, the collision probability of the K-means algorithm clustered by any proportion is higher than that of the direct transmission scheme. However, from Figure 5, the average number of using RB is more significant in unit time, which results in a relatively low amount of the access success probability and the average number of carrying data of unit RB.