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.

中文翻译：

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具有智能边缘计算的工作流辅助物联网范式


在本文中，我们提出了一种具有智能边缘计算（iEC）的工作流辅助物联网（WioT）范式，以自动执行具有依赖性的物联网应用程序。我们的设计主要是从两个角度来减少物联网系统的延迟。为了从应用程序的角度减少延迟，我们开发了WioT范式，以编程的方式编排各种物联网应用程序。为了从计算角度减少延迟，我们提出了一种新颖的 iEC 框架来在边缘网络执行延迟敏感的物联网任务。我们提出了深度强化学习算法，将边缘资源自适应地分配给动态请求，旨在实时为终端用户提供最佳的服务质量。此外，我们设计了一个软件平台来通过 iEC 实现所提出的 WioT。实验结果表明，与传统的基于云的物联网系统相比，采用iEC的WIoT可以显着降低服务延迟并提高网络吞吐量。