Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

TLDR

Deep learning can accurately extract information from raw sensor data in complex IoT environments, and its multilayer structure makes it suitable for edge computing. This work introduces deep learning for IoT into edge computing environments. We propose a novel offloading strategy that optimizes the execution of multiple deep‑learning tasks on resource‑constrained edge nodes and evaluate its performance. Evaluation shows that our strategy outperforms existing optimization solutions for IoT deep‑learning applications.

Abstract

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.