An Energy Saving Strategy for IoT Wristband Terminals Combined with Human Activity Laws

With the rapid development of 5G and Internet of Things (IoT), the objects connected by 5G have expanded from people to everything, and smaller and lighter IoT terminals are involved in people's production and life. The IoT wristband terminal is a kind of popular wearable devices with functions of monitoring human exercise intensity, sleep quality and daily health. The operating energy consumption of the heart rate sensor and global positioning system (GPS) in the IoT wristband terminal is the main source of energy consumption of the wristband terminal. The limited battery capacity leads to the problem of limited energy and short endurance of the IoT wristband terminal, which cannot realize long time human health monitoring. In order to achieve energy saving while ensuring the health monitoring functions and performance of the IoT wristband terminal, we research the user requirements and human daily activity laws, use the anomaly detection algorithm based on binary Gaussian distribution to fit the relationship between acceleration and heart rate in human activities and propose the energy-saving strategies for heart rate sensor, and then use the decision tree algorithm to obtain the human activity state classification model and the energy-saving strategies for GPS is then proposed. Simulation experiments show that the proposed energy-saving strategies can more intelligently adjust the sampling frequency and working mode of the heart rate sensor and GPS, reduce the power consumption of high-power sensors and improve the endurance of the IoT wristband terminal.