# A Node Density Control Learning Method for the Internet of Things
## 6 Experimental Results  
To verify the comprehensive effectiveness of density control learning method of the wireless sensor mobile node under an IoT environment, an experiment is required as given in [18; 19; 20; 21; 22]. In this experiment, the experimental analysis was carried out in Matlab environment. The experimental environment included an IS-3210M CPU, with RAM of 16 GB and Matlab R2010a software. The sensor network nodes with the number of coils of 50 are placed in the square detection area with 100 m edge length, and the radius of the sensor network nodes is set to be 10 m, and the detection area is divided. The transmission broadband between nodes is set to 2 Mbps. in the divided area, 200 nodes are randomly deployed. In this experiment, two kinds of sensor nodes based on CC2430 (represented by A and B) are used to form the sensor network. The monitoring module is responsible for collecting the data. The sensor node is shown in Figure 1.  
Our proposed method was compared with the traditional methods given in [5; 6; 7; 8]. In detail, which included an optimized coverage control method for joint sensing wireless sensor [5], a method for grouping control of WSN monitoring nodes based on evolutionary game theory [6], a control method of multi-source fault-tolerant topology node method under wireless sensor actuator network [7] and a control method of three-dimensional WSN topology based on node degree estimation [8]. Experimental results were as follows. The comparative experiment of the control completion time(s) was compared based on our proposed method and References [5; 6; 7; 8]. Experimental results are shown in Figures 2. Figure 2 show that when the number of samples increased, the corresponding network node density control completion time of the five methods increased. When the number of samples increased from 0 to 600, our proposed node density control completion time fluctuated within the range of 17 s-30 s and the fluctuation range was relatively stable. The optimal coverage control method for joint-sensing wireless sensors fluctuated within the range of 22 s-50 s and the fluctuation range showed a slight upward trend. Based on evolutionary game theory, the node control method of WSN monitoring node was controlled to fluctuate within the range of 32 s-58 s and the fluctuation range was increasing. However, the sample number was between 400 and 500 and then continued to rise. Figure 1: Wireless Sensor Node Schematic. A multi-source fault-tolerant topology node control method in a wireless sensor actuator network had a node density control completion time ranging from about 45 s-72 s, and the fluctuation range was increasing. The 3D WSN topology control method based on node degree estimation had a node density control completion time fluctuating within the range of 52 s-90 s. The fluctuation range increased and fluctuated. Experimental results showed that our proposed method had the shortest completion time of node density control, which was better than the other four methods and has higher control efficiency. The power consumption (Joules) comparison experiment is performed in the method proposed by our proposed method and methods in References [5; 6; 7; 8]. Experimental results are shown in Table 1. In Table 1, Method 1 represented the proposed method; Method 2 represented an optimized coverage control method for a joint-aware wireless sensor; Method 3 represented a clustering control method for WSN monitoring nodes based on evolutionary game theory; Method 4 represented a multi-source fault-tolerant topology node control method in a wireless sensor actuator network; Method 5 represented a three-dimensional WSN topology control method based on node degree estimation. Table 1 showed that when the number of samples increased, the corresponding network node control power consumption of the five methods increased. Among them, the overall power consumption of this method is less, the maximum power consumption is not more than 25 Joules, and the energy consumption of other methods is up to 70-86 Joules.