Edge–Cloud Collaborative Deep Learning Framework for Real-Time IoT Analytics

Abstract

The Internet of Things (IoT) ecosystem has expanded and produced massive volumes of real-time streaming data, that must be processed quickly and in an efficient way. The latency problem, bandwidth congestion and bandwidth limitations in the traditional cloud-centric processing architecture are significant challenges, particularly in latency-sensitive systems such as smart healthcare, industrial automation and intelligent transportation systems. While edge computing reduces the amount of data that needs to be sent, edge devices are typically more limited in resources, which means they cannot run complex deep learning models. To address these limitations, this paper proposes a new Edge–Cloud Collaborative Deep Learning (ECC-DL) Framework to orchestrate different aspects of inference and learning between the edge nodes and cloud server. The lightweight convolutional neural networks (CNNs) are deployed at the edge nodes to extract features and make initial inferences, and the heavier end models are deployed at the cloud end for global analysis and regular retraining. To optimize the partitioning of the workload between the resources, an adaptive workload partitioning algorithm is designed that considers latency, bandwidth and computational resources. By experimental evaluation, latency is reduced by 40% and bandwidth consumption is reduced by 30% and prediction accuracy is improved over the centralized approach.