# rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
## 4 Results  
This section will introduce the experimental results about the system entropy routing method and mainly focuses on the power consumption. All the simulations in this paper are carried out in MATLAB 2020, and the random functions of the software are used to realize the scatter process described, the generation of white noise, and the generation of small-scale fading. The measurement of the network power consumption is described as follows: After the routing algorithm searches the path of the network, each sensor reports its data to the sinknode. Through a large number of simulations for the same routing scheme and in the same topology, the transmitting power is obtained under the premise of satisfying a certain successful connection rate. Obviously, the smaller the power is, the more reasonable the routing is and the lower the power consumption is. The required successful connection rate in this experiment is 0.9 (like the targeted outage probability in [14]). Once the routing algorithm has determined the network connection, all that remains is to generate a large number of small-scale random fading numbers to test the successful connection rate of the current network. By appropriately increasing or decreasing the transmitting power of the sensor, the average connectivity probability of the network converges to the required successful connection rate 0.9. The final successful connection rate \(P_{f}\) error tolerated by the experiment is 0.0001, i.e., the final successful connection rate satisfies \(\left|P_{f}-0.9\right|\leq 0.0001\). In addition, the connection rate for each transmitting power comes from the average result of 10,000 independent Rayleigh fast fading \(h\). The receiving sensitivity of a sensor is 2 dB. The noise power here is \(-104\) dBm (thermal noise within 1 MHz bandwidth). In the experiment, the number of cluster heads is assumed to account for 50%. The network consists of 16 nodes and 1 sinknode. In addition, four slots are preset for LEACH and RED\_LEACH, which ensures the fairness. The millimeter-wave frequency band in the 5G communication scenario is discussed, and the carrier frequency is \(f_{c}=30\) GHz. Therefore, the following path loss model is used [15]:  
\[PL=\left\{ \begin{array}{ll}32.4+21\mathrm{log}_{10}(\mathrm{distance[m]})+20 \mathrm{log}_{10}(f_{c}[\mathrm{GHz}])\,&\mathrm{LOS}\\ 22.4+35.3\mathrm{log}_{10}(\mathrm{distance[m]})+21.3\mathrm{log}_{10}(f_{c}[ \mathrm{GHz}]),&\mathrm{NLOS}\end{array} \right.,\ \ \mathrm{distance}>10\ \mathrm{m} \tag{7}\]  
Among them, the LOS and NLOS are included. Considering that the rSEM algorithm is designed to adapt to the future smart factory, the production line with a complex structure and composition will inevitably block electromagnetic waves. In this case, the LOS and NLOS must coexist. In this experiment, the LOS and NLOS characteristics of each wireless channel are established when the topology is formed, and the ratio of the LOS to the NLOS is preset to be \(9:1\) under the feature of a relatively close distance. The LOS/NLOS characteristic of each connection is generated by the Bernoulli distribution of a 0.9 success probability. In the first experiment, the scatter method of the sensor nodes is to randomly scatter 16 nodes in the 2D range of 80 m \(\times\) 80 m, and the sinknode is located in the position of (40 m, 40 m), as shown in Figure 2. Considering the use of the large-scale transmission model of the 3 GPP above, the pairwise distance of the 16 sensor nodes should be maintained at more than 10 m. If the single scatter point fails to meet this condition, the scatter point will be re-scattered until it is satisfied. The second experiment will simulate a scenario of a two-floor factory building, aiming to simulate a scenario with more dense nodes. The 10m interval between nodes is also maintained from the perspective of 3D.