Edge computing health model using P2P-based deep neural networks

Currently, significant amounts of IoT data are being collected as big data for deep neural network learning algorithms that are used to extract meaningful information from big data and applied to various areas. However, a neural network’s over-fitting problem and rising computational costs associated with high levels of time complexity are obstacles to deep learning computations. Because of these problems, response delays are prevalent in big data learning processes and data extraction processes that use deep neural networks, which exponentially increase the cost of data extraction. Therefore, the amount of computation must be controlled so that data processing models can be used practically, and various dedicated devices can be used to process big data, including methods such as distributed processing methods. In general, a P2P method is a typical distributed processing method. P2P is based on traffic distribution and cooperation, and edge computing is a method that increases efficiency by locally processing large volumes of data produced and only transmitting essential information. If P2P method-based edge computing techniques are used, an effective parallel processing model can be constructed, reducing the computing requirements of a central server and the load of a network. In this paper, we propose an edge computing health model using P2P-based deep neural networks. The proposed method is used to process health big data in edge computing. To construct the model, multiple edge nodes are required, and the edge node modules are deployed at locations where health data are produced and directly connected to the deep neural network model. At the edge nodes, a modularized deep neural network is constructed, thereby implementing a parallel big data processing system. In addition, a single server is required for gathering the results of a neural network model, and because the server only collects evaluation results and provides them to the users, the response time delay is improved. In this paper, to evaluate an improved response time, a regular server model and an edge computing health model are constructed separately, and positive numerical values are extracted from the experiments. The extraction results confirm that a combination of parallel processing models and deep neural network techniques can be used to distribute and process computing operations of big data size, ensuring an effective system for reducing response time delay.