# rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
## 4 Results
The height of the two-story factory is 15 m, and the size of each floor is 60 m \(\times\) 60 m. The scatter method of the sensor nodes is as follows: Firstly, sprinkle eight nodes randomly in the 60 m \(\times\) 60 m first floor of the factory, and then sprinkle eight nodes randomly in the 60 m \(\times\) 60 m second floor of the factory. The sinknode is located at (30 m, 30 m, 7.5 m), as shown in Figure 3.  
Figures 4 and 5 show the average power consumption statistics of the 2D and 3D scenarios under 100 groups of different topologies, assuming that there is no signaling conflict between the sensors during the experiment, that is, ALOHA. The ordinate represents the sensor transmit power required under the network connectivity rate of 0.9. As can be seen from the two figures, the oscillation trend of the three curves is consistent. In other words, the topology determines the power consumption level to some extent. For example, there are \"isolated nodes\" in the sense of distance, which exist in isolation from most node groups. The participation of isolated nodes will inevitably lead to a higher power consumption. Table 1 shows the statistical results of the power consumption and delay in the 2D and 3D simulation scenarios. Obviously, the 3D denser industrial scene requires a lower average power consumption and fewer hops for the sensor to reach the sinknode. The following conclusions can be drawn from the experimental results: The power consumption of LEACH or RED_LEACH cannot be in the same order of magnitude as the other two algorithms, and the rSEM outperforms RED_LEACH in both power consumption and delay. After the statistics of the experimental results, the power consumption of the rSEM algorithm is in the same order of magnitude as that of Dijkstra; this indicates that the system entropy measure can optimize the selection of the cluster heads and cluster members by evaluating the topology. The system entropy measure ensures that the power consumption performance of the rSEM can approximate the optimal routing algorithm under the limit of two hops. Meanwhile, compared with the Dijkstra algorithm in the 2D and 3D scenarios, the delay of the rSEM is reduced by 29.8% and 18.4%, respectively. In addition, the rSEM only needs 5% more delay than LEACH to achieve the above performance. Therefore, the  
Figure 3: Schematic diagram of cluster structure iWSNs in 3D factory. Figure 2: Schematic diagram of cluster structure iWSNs in 2D factory. rSEM is the most suitable routing algorithm for the time-delay and power consumption dual-sensitive networks among the four methods simulated in this paper. The results of the power consumption and delay show that the rSEM algorithm with the network topology as a variable can comprehensively optimize the network performance, reduce the power consumption, and improve the network efficiency. From the experimental results, the rSEM routing algorithm is more suitable for the future \"power and delay sensitive networks\". \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**2D**} & \multicolumn{2}{c}{**3D**} \\ \cline{2-5}  & **Power (dBm)** & **Delay (hops)** & **Power (dBm)** & **Delay (hops)** \\ \hline Dijkstra & \(-10.9498\) & \(2.1332\) & \(-12.2281\) & \(1.8245\) \\ \hline rSEM & \(-10.0275\) & \(1.4968\) & \(-11.8600\) & \(1.4886\) \\ \hline RED\_LEACH & \(-6.5284\) & \(1.5878\) & \(-8.9027\) & \(1.5278\) \\ \hline LEACH & \(-4.9473\) & \(1.4362\) & \(-7.3625\) & \(1.4141\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of power consumption and delay performance among rSEM, LEACH, and Dijkstra. Figure 4: Simulation results in 2D factory scenario. Figure 5: Simulation results in 3D factory scenario.