# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 4 Specific Applications and Performance Analysis  
DenseNet is a type of convolutional neural network that improves training by adding a DenseBlock module. The DenseBlock module concatenates the outputs of all previous layers to obtain the input for each layer. The architecture of DenseNet begins with a convolutional layer with a \(7\times 7\) kernel size and stride 2, followed by max pooling with a \(3\times 3\) kernel size and stride 2 [41]. The network then alternates between DenseBlock and transition layers. The last layer of the network is a global average pooling layer with a \(7\times 7\) kernel size [42], followed by a fully connected layer with 1000 units and a SoftMax classification layer. The basic structure of DenseNet is illustrated in Figure 10.  
As shown in the Algorithm 1,the transition layer in DenseNet is composed of three parts: a batch normalization layer [43], a \(1\times 1\) convolution layer, and a \(2\times 2\) average pooling layer. Its purpose is to reduce the number of feature maps. Unlike traditional convolutional neural networks, which have L connections for an L-layer network, DenseNet's DenseBlock module allows the output of the previous layer to be used as input for each subsequent  
Figure 9: The system has the ability of data processing and display. This is the flow chart of dataset data preprocessing and data visualization. layer [44]. This means that the input for each layer is a concatenation of the outputs of all previous layers, resulting in a narrower network and fewer parameters. This is one of the key advantages of DenseNet over traditional neural networks. We propose an innovative feature fusion network [45], based on previous research, and present its architecture in Figure 11. Our network extends the original model by including several feature fusion layers. These layers are designed to combine the deep and shallow information from the input multisensor data [46], in varying degrees, to expand the amount of feature information and enhance recognition accuracy. ```
Input: Data sequence Output: Data sequence after feature fusion
1 Put the feature matrix into the feature fusion network, set the number of convolution channels n and the number of convolution layers N in DenseBlock;
2 Convolution of input data tensorConvolution Layer: Kernel size [\(7\times 7\)];
3 Maximum Pooling: Core size [\(3\times 3\)];
4 DenseBlock: Alternating connection between DenseBlock and the transition layer;
5 Transition Layer: BN layer, [\(1\times 1\)] Convolution Layer and [\(2\times 2\)] Average pool layer;
6 Global Average Pool: [\(7\times 7\)];
7 Full Connections: 1000;
8 Classification Layer: SoftMax. ```  
**Algorithm 1**Steps of Feature Fusion Based on DenseNet. Figure 11: This paper introduces a new approach to feature fusion using a DenseNet-based network. The proposed network structure incorporates both shallow and deep information from the input multisensor data. Specifically, the feature fusion layer is designed to combine these two types of information in different proportions, allowing for a more flexible and effective feature representation. Figure 10: In the DenseBlock module, the output of all previous layers of this layer determines the input of each subsequent layer. This is the basic structure of DenseNet. To reduce redundancy in the feature fusion network, we have designed two structures for fusing features [47]. The first structure combines the outputs of two convolution layers, input layer 1 and input layer 2. Input layer 1 is a shallow convolution layer used for classification, while input layer 2 is a deeper layer in the network. To increase the dimension of features in input layer 2 [48], we use deconvolution layers. In the second feature fusion structure, we combine the previously fused features, controlling the size of the features through convolution.