# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 5 Evaluation
### Evaluation of EdgeCNN
#### 5.1.3 Network I/O and High Availability  
Due to the low dependence on the cloud platform, the data generated by IoT does not need to be transmitted to the cloud platform in real time through the network. A portion of the data is only uploaded when the user authorizes the use of a third-party service. This makes the cloud platform not encounter too much dilemma on the network I/O.  
Figure 11: Comparison of the delays of the two system architectures.  
Moreover, for the traditional cloud-only architecture, it is difficult to ensure that the cloud platform does not have problems. Once the cloud platform fails, all users' services will be interrupted, which is catastrophic for the user experience and the system platform. The hybrid architecture with cloud and edge is a good way to circumvent this problem. Each edge node has autonomy. Even if the cloud platform fails, most users can still rely on the edge device to obtain services normally, and only special third-party services will be suspended. This ensures high availability of the entire architecture.  
The performance comparison of the two architectures, EdgeCNN architecture and traditional cloud based platform architecture, on different metrics are summarized in Table 3. First, because the processing unit is closer to the data source, the hybrid architecture achieves lower and more stable latency without data congestion due to the large user volume. Second, one of the important reasons for data congestion is the bottleneck of the network I/O. When large amounts of data flock to the cloud platform in real time, network I/O is under tremendous pressure, resulting in a decline in real-time processing power of the system. The hybrid architecture circumvents this problem by spreading the data across the various edge compute nodes. Third, the hybrid architecture uses edge computing nodes to diagnose, which greatly reduces the cost of operation and maintenance of cloud platforms. Fourth, the hybrid architecture can ensure that data are transmitted within the user-controllable local network, avoiding all data being exposed to the Internet, effectively protecting user privacy. Fifth, the hybrid architecture takes distributed edge nodes to process data. It will not paralyze the whole system due to the failure of the cloud platform. The high availability of the system is guaranteed.  
In addition to the related issues mentioned above, power consumption is also a key issue that edge devices need to consider. To this end, we measured the power consumption of the two models when running on the experimental equipment, as shown in Figure 12. We can see from the figure that it takes 325 min for the remaining power to drop from 100% to 70% without any processing tasks. After deploying the 2017 CinC model, the time required for the remaining power to drop to 70% was suddenly shortened by 75 min. It can be seen that this reduces the overall endurance of the device by approximately 250 min. EdgeCNN shortens the time required for the remaining battery to drop to 70% by 25 min. Compared with the former, the battery life has increased by 50 min, and the overall battery life can be increased by about 167 min. It can be seen that EdgeCNN has obvious advantages in energy efficiency.