## 4 Experimental Results and Analysis
### WVE-UDI Algorithm Ablation Experiments
(3) The results of the ablation comparison experiments in the presence of three unknown IoT devices in an open environment are shown in Figure 16.  
As can be seen in Figure 16, in the open environment where the three unknown IoT devices coexist, the WVE-UDI algorithm performs relatively well overall and is able to achieve high recognition results, as follows: (1) By comparing the Agglomerative algorithm and the WVE-UDI algorithm, it is found that the recognition effect is significantly improved by using the clustering integration, and although the recognition accuracy of the recognition accuracy of Belkin Wemo Switch devices decreased by 8.3 percentage points, the accuracy of Canary Camera devices increased from 72% to 76%, the accuracy of Insteon Camera devices especially increased from 0% to 100%, and the overall performance was effectively improved; (2) By comparing the Birch algorithm and the WVE-UDI algorithm, it is found that the Birch algorithm not only has a very low recognition accuracy of 10% for the Canary Camera device, but also still fails to recognize the Insteon Camera device, which once again shows that the clustering pattern relied on by the Birch algorithm does not match the data characteristics of the camera as a class of device exactly, resulting in its limited recognition capability. However, with the integration of the WVE-UDI algorithm, the recognition accuracies of the Canary Camera device and the Insteon Camera device are improved by 66 percentage points and 100 percentage points, respectively; (3) By comparing the GaussianMixture algorithm with the WVE-UDI algorithm, it is found that the recognition accuracies of the three types of IoT devices are enhanced, with the Belkin Wemo Switch device accuracy increased by 0.1 percentage points and Canary Camera device accuracy increased by 4 percentage points, and the WVE-UDI algorithm maintains the same level of recognition as the Insteon Camera device since the GaussianMixture algorithm has already achieved 100% accuracy; (4) A global comparison of the three single clustering algorithms and the WVE-UDI algorithm reveals that the Belkin Wemo Switch device's recognition accuracy decreased by 8.3 percentage points, the Canary Camera device improved by 66 percentage points, and the Insteon Camera device's accuracy improved by 100 percentage points; although the Belkin Wemo Switch device dropped slightly, the remaining IoT devices saw a substantial improvement in recognition accuracy. This small sacrifice for a significant improvement in the recognition rate of the other IoT devices is clearly worthwhile. This shows that the WVE-UDI algorithm is able to effectively integrate the clustering capabilities of multiple single clustering algorithms to improve the overall recognition effect. In order to further analyze the specifics of the WVE-UDI algorithm integration in detail, the recognition confusion matrix of the WVE-UDI algorithm in the coexistence environment of the three unknown IoT devices is plotted, as shown in Figure 17, where each square denotes the recognition accuracy for each IoT device, and a darker color denotes a higher recognition accuracy. Figure 16: Comparison results of recognition accuracy of three unknown IoT devices. As can be seen in Figure 17, the WVE-UDI algorithm is able to achieve 100% accuracy for the Insteon Camera device, with no misidentification as other IoT devices, and the best recognition effect; for the Canary Camera device, there is a 10% misidentification rate to judge it as the Belkin Wemo Switch device, while there is a 14% misidentification rate to judge it as Insteon Camera device.