This paper presents a centralized management architecture model for designing workgroup-based Internet of Things (IoT) and Internet of Everything (IoE) networks. The architecture establishes the organization of an object according to its functions and capacities in layers. From its model, it is derived the design of the algorithms that give the network operation. These algorithms include the multi-protocol communication and interconnectivity algorithm, the routing algorithm, the resource sharing algorithm, and the grouping algorithm, all controlled by Artificial Intelligence (AI). The grouping algorithm consists of creating collaborative workgroups based on Machine Learning (ML) techniques that use the objects’ features to allocating these within a workgroup that attends a type of service and within an architecture layer according to its capabilities. The model was tested with a simulation that shows the Machine-to-Machine (M2M) interaction between the devices involved in providing a service to a user within a Smart Home. This simulation uses an AI hosted within an IoT-Gateway to collect data on the features that define a connected object's functions and services. The extraction of the features is done using the Discovery of Functions and Services Protocol (DFSP) transported through an IoT-Protocol. With this information, the AI assigns a layer and a workgroup to a new object when it enters the network. The result of these tests can be used to know which ML technique has better accuracy.

本文提出了一种集中式管理架构模型，用于设计基于工作组的物联网（IoT）和万物联网（IoE）网络。该架构根据对象的功能和层次来建立对象的组织。从其模型中，可以得出给出网络操作的算法的设计。这些算法包括多协议通信和互连算法，路由算法，资源共享算法和分组算法，它们均由人工智能（AI）控制。分组算法由基于机器学习（ML）技术的协作工作组组成，该技术使用对象的功能根据服务的功能在服务类型的工作组中和体系结构层中分配对象的功能。该模型通过仿真进行了测试，该仿真显示了在智能家居中向用户提供服务的设备之间的机器对机器（M2M）交互。该仿真使用IoT网关中托管的AI收集有关定义连接对象的功能和服务的功能的数据。使用通过物联网协议传输的功能和服务发现协议（DFSP）完成功能的提取。有了这些信息，当AI进入网络时，它会为新对象分配一个层和一个工作组。