# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 5 Evaluation
### Evaluation of EdgeCNN
#### 5.1.2 Comparison of Delay on Two Different Architectures  
We build two system with diverse architectures for ECG diagnosis, one system uses a hybrid architecture in EdgeCNN while the other uses cloud platform. The experiments compare the two architectures in terms of delay. Delay is an important indicator that is used to evaluate the effectiveness of a system. We measure the length of time from data generation to the feedback of diagnostic results to the user under different request volumes. It contains both data transfer time and model prediction time. The predicted total time \(t\) is mainly composed of four parts: the data transmission delay \(t_{1}\), the data propagation delay \(t_{2}\), the time \(t_{3}\) from when the model receives the data to the final output of the prediction result and the time \(t_{4}\) when the prediction result is returned to the user. Therefore, time \(t\) can be described as follows:  
\[t=t_{1}+t_{2}+t_{3}+t_{4}. \tag{7}\]  
Since the propagation delay \(t_{2}\) is related and proportional to the network distance between the client and the server and \(t_{4}\) is very small, according to Algorithm 1, to avoid data congestion, \(t\) needs to meet the conditions \(t\leqslant(n/m)\), which must be considered from two aspects that affect \(t_{1}\) and \(t_{3}\):  
1. When the number of users gradually increases, the amount of data generated in the same time period will also increase. Whether the network bandwidth of the data processing point is sufficient to transmit such a large amount of data within a limited time will directly affect the sending delay \(t_{1}\). 2. The performance of the data processing point is limited. When the data that need to be processed at the same time reach the performance bottleneck of the data processing point, the next part of the data will be queued for processing, which will increase the time \(t_{2}\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Attributes** & **EdgeCNN Model** & **2017 CinC Model** \\ \hline \(F_{1}\) & 0.78 & 0.83 \\ Model size & 3.30 MB & 4.95 MB \\ Memory & 51 MB & 74 MN \\ Predict time & 310–330 ms & 430–490 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The differences between the two models in precision, storage space occupied, memory occupied in prediction and prediction time. Assuming that the bandwidth of the processing point is 1 Mbps, and \(N\) is used to represent the number of data segments generated in the same time period, the transmission time \(t_{1}\) of these data transmission in the network can be calculated by Equation (8):  
\[t_{1}=\frac{N\times(n\times 4)/1024}{(a\times 1024)/8}. \tag{8}\]  
If the hardware performance is sufficient to handle the current concurrency, \(t_{2}\) will be constant, and then obviously the time \(t\) will be inversely proportional to the bandwidth \(a\). The larger is the bandwidth, the smaller is the sending delay \(t_{1}\). The overall performance of data processing points will vary with changes in hardware, and quantitative measurements cannot be made. However, as the amount of data that need to be processed at the same time increases, the average number of requests processed by each thread in the processing point will inevitably increase. We can measure its impact on time \(t\) under the condition that the requests processed by a single thread gradually increase. As shown by the experimental results in Figure 11, as the request volume of each thread gradually increases, the cloud faces bottlenecks in performance and network I/O. As a result, there is a gradual growth in latency. The delay is about 393 ms when the number of concurrent requests to be processed by each thread of the cloud platform is less than 7. The delay reaches at least 384.7 ms when the number of simultaneous requests processed by the thread is 7 or 8. Because of the request rate and the fastest rate handled by a single thread of the cloud platform, which are close at the moment, the thread does not need to be awakened frequently, and the cloud platform has the highest processing efficiency.