Industrial Vision Optimization Distributed Strategy based on Edge Intelligence Collaboration

In this paper, it is proposed that a novel strategy of based hierarchical data distribution and deep neural networks distribution over edge and end devices. In the Industrial Internet of Things environment, deep learning tasks such as smoke and fire classification based on convolutional neural network usually need to be performed on edge servers and end devices, which have limited computing resources, while edge servers have abundant computing resources. While being able to accommodate inference of a deep neural network (DNN) at the edge server, a distributed deep neural network (DDNN) also allows localized inference using a portion of the neural network at the end sensing devices. Therefore, this article proposed the distributed strategy can dynamically adjust network layers and data allocation proportion of end devices and edge servers according to different tasks to shorten the data processing time. A joint optimization problem is proposed to minimize the total delay, which is affected by the complexity of the DL model, the inference error rate, the computing power of the end devices and the edge servers. An analytical solution of a closed solution is derived and an optimal distributed data allocation and neural network allocation algorithm is proposed