Sensor numerical prediction based on long-term and short-term memory neural network

Abstract: Many sensor nodes are scattered in the sensor network,which are used in all aspects of life due to their small size, low power consumption, and multiple functions. With the advent of the Internet of Things, more small sensor devices will appear in our lives. The research of deep learning neural networks is generally based on large and medium-sized devices such as servers and computers, and it is rarely heard about the research of neural networks based on small Internet of Things devices. In this study, the Internet of Things devices are divided into three types: large, medium, and small in terms of device size, running speed, and computing power. More vividly, I classify the laptop as a medium- sized device, the device with more computing power than the laptop, like server, as a large-size IoT(Internet of Things) device, and the IoT mobile device that is smaller than it as a small IoT device. The purpose of this paper is to explore the feasibility, usefulness, and effectiveness of long-short-term memory neural network model value prediction research based on small IoT devices. In the control experiment of small and medium-sized Internet of Things devices, the following results are obtained: the error curves of the training set and verification set of small and medium-sized devices have the same downward trend, and similar accuracy and errors. But in terms of time consumption, small equipment is about 12 times that of medium-sized equipment. Therefore, it can be concluded that the LSTM(long-and-short-term memory neural networks) model value prediction research based on small IoT devices is feasible, and the results are useful and effective. One of the main problems encountered when the LSTM model is extended to small devices is time-consuming.