Jin Wenquan, Xu Rongxu, Lim Sunhwan, Park Dong-Hwan, Park Chanwon, Kim Dohyeun
Big Data Research Center, Jeju National University, Jeju 63243, Korea.
Department of Computer Engineering, Jeju National University, Jeju 63243, Korea.
Sensors (Basel). 2021 Jan 18;21(2):630. doi: 10.3390/s21020630.
Computation offloading enables intensive computational tasks in edge computing to be separated into multiple computing resources of the server to overcome hardware limitations. Deep learning derives the inference approach based on the learning approach with a volume of data using a sufficient computing resource. However, deploying the domain-specific inference approaches to edge computing provides intelligent services close to the edge of the networks. In this paper, we propose intelligent edge computing by providing a dynamic inference approach for building environment control. The dynamic inference approach is provided based on the rules engine that is deployed on the edge gateway to select an inference function by the triggered rule. The edge gateway is deployed in the entry of a network edge and provides comprehensive functions, including device management, device proxy, client service, intelligent service and rules engine. The functions are provided by microservices provider modules that enable flexibility, extensibility and light weight for offloading domain-specific solutions to the edge gateway. Additionally, the intelligent services can be updated through offloading the microservices provider module with the inference models. Then, using the rules engine, the edge gateway operates an intelligent scenario based on the deployed rule profile by requesting the inference model of the intelligent service provider. The inference models are derived by training the building user data with the deep learning model using the edge server, which provides a high-performance computing resource. The intelligent service provider includes inference models and provides intelligent functions in the edge gateway using a constrained hardware resource based on microservices. Moreover, for bridging the Internet of Things (IoT) device network to the Internet, the gateway provides device management and proxy to enable device access to web clients.
计算卸载使边缘计算中的密集型计算任务能够被分离到服务器的多个计算资源中，以克服硬件限制。深度学习利用大量数据和充足的计算资源，基于学习方法得出推理方法。然而，将特定领域的推理方法部署到边缘计算中可提供靠近网络边缘的智能服务。在本文中，我们通过为建筑环境控制提供动态推理方法来提出智能边缘计算。动态推理方法基于部署在边缘网关的规则引擎提供，该规则引擎通过触发的规则选择推理函数。边缘网关部署在网络边缘的入口处，并提供包括设备管理、设备代理、客户端服务、智能服务和规则引擎在内的综合功能。这些功能由微服务提供模块提供，这些模块为将特定领域的解决方案卸载到边缘网关提供了灵活性、可扩展性和轻量级特性。此外，智能服务可以通过卸载带有推理模型的微服务提供模块来更新。然后，边缘网关使用规则引擎，通过请求智能服务提供商的推理模型，基于部署的规则配置文件运行智能场景。推理模型是通过使用边缘服务器的深度学习模型训练建筑用户数据得出的，边缘服务器提供高性能计算资源。智能服务提供商包括推理模型，并基于微服务在边缘网关中使用受限的硬件资源提供智能功能。此外，为了将物联网设备网络连接到互联网，网关提供设备管理和代理，以实现设备对网络客户端的访问。
