# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 4 Results and Discussions
### Satellite IoT Edge Intelligent Computing Architecture Performance
In general, distributed training has a significant effect compared to single-node centralized training. As the number of nodes increases, the training time tends to be flat. When the number of nodes exceeds a certain amount, the training time will not be significantly reduced. In the satellite Internet of Things environment, the increase of distributed nodes will increase the communication delay. In the reasoning process, the input data is an image of 224 * 224 * 3, and each task is described as processing an image. The amount of computation required to process an image is the value of \"Flops (One pass)\" in Table 3. In reality, the pixels of the picture produced by the satellite sensor will exceed the image pixels input by the inference process. Therefore, the image of the satellite is divided into several pictures as input data for reasoning. In general, satellite nodes can make inferences locally or share data to nearby satellites for distributed reasoning. In the process of reasoning, we will simulate the reasoning time of a reasoning task and multiple inference tasks under different inference models. First, in the case of a single node, the completion time of different task amounts is as shown in Figure 9.  
Figure 8: The relationship between the number of training nodes and the training duration. As can be seen from Figure 9, in the case of single node inference, when the amount of inference task increases, that is, the number of images to be inferred increases, the inference time of a single picture remains substantially unchanged. The time it takes to complete the reasoning is at the millisecond level. Lightweight neural networks (such as MobileNet and ShuffleNet) have lower inference delays. However, the reasoning delay of the larger neural network model, such as VGG16, is slightly higher. Next, we will simulate the average inference delay of a single picture of different neural networks in the case of multi-node distributed reasoning, as shown in Figure 10.  
Figure 10 shows the effect of the number of different satellite nodes on the inference time of each picture when the calculated amount is 10000. We can find an interesting phenomenon. When the number of satellite nodes participating in reasoning increases, the delay of single-picture inference in some neural network models is decreasing (such as VGG16). However, in some neural network models, the delay of single picture inference is increasing (such as MobileNet and ShuffleNet). Since there are thousands of kilometers of link distance between satellite nodes, and the inter-satellite link bandwidth is not very high, the communication propagation delay and transmission delay between satellites become non-negligible. Especially in the case of small tasks (such as the reasoning of MobileNet and ShuffleNet), the time for satellite single node to make inference is small. If distributed processing is still performed, it will undoubtedly increase the communication delay. For neural networks with  
Figure 10: The relationship between the number of nodes and the reasoning time of each picture. Figure 9: The relationship between the calculated amount and the reasoning time of each picture. large computational complexity (such as VGG16), distributed reasoning is helpful to speed up the completion of reasoning. In summary, we can find that lightweight neural networks like MobileNet and ShuffleNet are more suitable for satellite IoT scenarios. In the satellite IoT edge intelligent computing architecture, the training phase of this kind of neural network can be combined with multi-satellite for online distributed training based on the pretraining model, while the inference phase is directly performed locally.