A Workflow-Aided Internet of Things Paradigm with Intelligent Edge Computing
Yuwen Qian, Long Shi, Jun Li, Zhe Wang, Haibing Guan, Feng Shu, H. Vincent Poor
Abstract
In this article, we propose a workflow-aided Internet of things (WIoT) paradigm with intelligent edge computing (IEC) to automate the execution of IoT applications with dependencies. Our design primarily targets at reducing the latency of the IoT systems from two perspectives. To reduce the latency from an application perspective, we develop a WIoT paradigm to orchestrate various IoT applications in a programming way. To reduce the latency from a computation perspective, we propose a novel IEC framework to execute latency-sensitive IoT tasks at the edge network. We put forth a deep reinforcement learning algorithm to adaptively allocate the edge resources to the dynamic requests, aiming to provide the best quality of service for terminal users in real-time. Furthermore, we design a software platform to implement the proposed WIoT with IEC. Experimental results demonstrate that WIoT with IEC can significantly reduce the service latency and improve the network throughput, compared with the traditional cloud-based IoT systems.