Enabling Massive IoT Toward 6G: A Comprehensive Survey

【Author】 Guo, Fengxian; Yu, F. Richard; Zhang, Heli; Li, Xi; Ji, Hong; Leung, Victor C. M.

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as augmented/virtual reality online games, autonomous driving, and smart everything, which are massive in number, data intensive, computation intensive, and delay sensitive. Due to the mismatch between the fifth generation (5G) and the requirements of such massive IoT-enabled applications, there is a need for technological advancements and evolutions for wireless communications and networking toward the sixth-generation (6G) networks. 6G is expected to deliver extended 5G capabilities at a very high level, such as Tbps data rate, sub-ms latency, cm-level localization, and so on, which will play a significant role in supporting massive IoT devices to operate seamlessly with highly diverse service requirements. Motivated by the aforementioned facts, in this article, we present a comprehensive survey on 6G-enabled massive IoT. First, we present the drivers and requirements by summarizing the emerging IoT-enabled applications and the corresponding requirements, along with the limitations of 5G. Second, visions of 6G are provided in terms of core technical requirements, use cases, and trends. Third, a new network architecture provided by 6G to enable massive IoT is introduced, i.e., space-air-ground-underwater/sea networks enhanced by edge computing. Fourth, some breakthrough technologies, such as machine learning and blockchain, in 6G are introduced, where the motivations, applications, and open issues of these technologies for massive IoT are summarized. Finally, a use case of fully autonomous driving is presented to show 6G supports massive IoT.

【Keywords】6G mobile communication; 5G mobile communication; Internet of Things; Machine learning; Blockchain; Security; Wireless communication; 6G; blockchain; Internet of Things (IoT); machine learning; space-air-ground-underwater networks

【发表时间】2021 44774

【收录时间】2022-01-02

【文献类型】

【主题类别】

--