Evolution Toward Artificial Intelligence of Things Under 6G Ubiquitous-X

基于6G Ubiquitous-X的智能物联网演进

张平¹,许晓东²,秦晓琦¹,刘宜明¹,马楠¹,韩书君²

（1．北京邮电大学 网络与交换技术国家重点实验室,北京100876;

2．北京邮电大学 移动互联网安全技术国家工程实验室,北京100876）

中文说明：

随着蜂窝网络的演进,第六代移动通信系统（6G）通过为“人-机-物-灵”提供泛在的通信、计算、控制与意识能力,构建泛在智能化信息社会。“灵”作为6G用户的智能助手可以满足用户的智能需求,是6G实现从现实世界业务向虚拟世界体系业务延伸的关键部分。在6G的支撑下,物联网将迈入智能物联网（AIoT）时代,AIoT将具有智慧感知、智慧分析和智慧控制的能力。本文介绍了6G网络基础架构Ubiquitous-X的概念,及该架构下AIoT的定义与框架。指出了新型AIoT应用的业务需求所面临的技术挑战,包括大规模智能连接、高效计算、安全、隐私及身份验证、高扩展性和高效率等。进一步给出了实现AIoT的潜在使能技术,以支持跨终端的无缝服务体验。

关键词：智能物联网,6G,泛在性,人工智能,移动边缘计算,区块链,物理层安全

     

基于云原生的分布式物联网操作系统架构研究

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智能物联网AIoT研究综述

智能物联网（artificial intelligence of things,AIoT）是人工智能与物联网技术相融合的产物,这一新兴概念在智慧城市、智能家居、智慧制造、无人驾驶等领域得到了广泛应用。然而AIoT相关技术研究仍处于初级阶段, 存在大量问题和挑战。首先简述了AIoT技术产生的背景,明晰其定义和应用场景。以此为契机,构建了一个新型的面向智能信息处理的云边端融合AIoT架构。在给出AIoT研究体系的基础上, 详细探讨并比较了其各组成技术模块,包括AI融合IoT数据采集、复杂事件处理及协同、云边端融合研究、AI融合IoT安全及隐私保护和AI融合应用服务等方面的研究现状和解决方案。最后, 论述了AIoT未来的研究方向和发展趋势。     

AI-Enabled Metal-Polymer Plain Bearing Based on the Triboelectric Principle

With the rapid development of the Internet of Things and artificial intelligence (AI), the requirement for sensing technologies for smart bearings has increased dramatically. The general bearing sensors can only recognize the basic information from temperature or vibration, far from satisfying the self-diagnosis and self-maintenance. Recently, self-powered sensing technologies based on triboelectric nanogenerators have paved a new route for fabricating smart bearings. In this study, the triboelectric principle is applied to a commercial metal-polymer plain bearing (MPPB) bearing, which can achieve self-sensing, self-diagnosis, and self-maintenance. The geometrical structure of the triboelectric MPPB (T-MPPB) is designed to balance the output efficiency and external load, and the super durability and load capability are verified. Besides, the mechanism behind the output change trend under boundary and hydrostatic fluid lubrication is revealed for the first time. Furthermore, the deep learning algorithm can classify the lubrication states with highly accurate performance. The proposed T-MPPB has the potential to achieve self-maintenance with the lubricating pump according to the lubrication condition classified by the AI. This research not only establishes the feasibility of designing self-powered smart MPPB but also demonstrates a way for identifying lubrication states, thus achieving self-diagnosis and self-maintenance ability by self-powered sensors.