Research on Application Strategy of Deep Learning of Internet of Things Based on Edge Computing Optimization Method

Abstract Due to the limitations of network performance IoT deep learning model, in order to optimize the performance of IoT network, this paper proposes an application strategy of IoT deep learning based on edge computing optimization method. Based on the multi-layered structure of learning, the edge calculation method is optimized, and the reduced intermediate data is uploaded at the edge node, thereby reducing the network traffic from the IoT device to the cloud server. On the basis of considering the limitation of edge node service capability in the edge computing process, the optimal strategy of task offload scheduling is formulated to improve the performance of the IoT deep learning model based on edge computing. The experimental results show that the IoT deep learning application strategy based on the edge computing optimization method can efficiently execute multiple deep learning tasks in the edge computing environment, which is superior to other algorithms.