The rapid advances in the Internet of Things (IoT) have promoted a revolution
in communication technology and offered various customer services. Artificial
intelligence (AI) techniques have been exploited to facilitate IoT operations
and maximize their potential in modern application scenarios. In particular,
the convergence of IoT and AI has led to a new networking paradigm called
Intelligent IoT (IIoT), which has the potential to significantly transform
businesses and industrial domains. This paper presents a comprehensive survey
of IIoT by investigating its significant applications in mobile networks, as
well as its associated security and privacy issues. Specifically, we explore
and discuss the roles of IIoT in a wide range of key application domains, from
smart healthcare and smart cities to smart transportation and smart industries.
Through such extensive discussions, we investigate important security issues in
IIoT networks, where network attacks, confidentiality, integrity, and intrusion
are analyzed, along with a discussion of potential countermeasures. Privacy
issues in IIoT networks were also surveyed and discussed, including data,
location, and model privacy leakage. Finally, we outline several key challenges
and highlight potential research directions in this important area.

本论文综合调查了智能物联网（IIoT）在移动网络中的重要应用以及与之相关的安全和隐私问题，并提出了若干关键挑战和潜在研究方向。

智能物联网综述：应用、安全、隐私与未来发展方向

A Survey on Intelligent Internet of Things: Applications, Security,  Privacy, and Future Directions

Intelligent Internet-of-Things (IoT) will be transformative with the
advancement of artificial intelligence and high-dimensional data analysis,
shifting from "connected things" to "connected intelligence". This shall
unleash the full potential of intelligent IoT in a plethora of exciting
applications, such as self-driving cars, unmanned aerial vehicles, healthcare,
robotics, and supply chain finance. These applications drive the need of
developing revolutionary computation, communication and artificial intelligence
technologies that can make low-latency decisions with massive real-time data.
To this end, federated machine learning, as a disruptive technology, is emerged
to distill intelligence from the data at network edge, while guaranteeing
device privacy and data security. However, the limited communication bandwidth
is a key bottleneck of model aggregation for federated machine learning over
radio channels. In this article, we shall develop an over-the-air computation
based communication-efficient federated machine learning framework for
intelligent IoT networks via exploiting the waveform superposition property of
a multi-access channel. Reconfigurable intelligent surface is further leveraged
to reduce the model aggregation error via enhancing the signal strength by
reconfiguring the wireless propagation environments.

文章介绍了一种基于超信道叠加和可重构智能表面的、通信高效的联邦机器学习框架，用于智能物联网网络的无线计算，以减少模型聚合期间的通信瓶颈和误差。