  Vertical heterogenous networks (VHetNets) and artificial intelligence (AI)
play critical roles in 6G and beyond networks. This article presents an
AI-native VHetNets architecture to enable the synergy of VHetNets and AI,
thereby supporting varieties of AI services while facilitating automatic and
intelligent network management. Anomaly detection in Internet of Things (IoT)
is a major AI service required by many fields, including intrusion detection,
state monitoring, device-activity analysis, security supervision and so on.
Conventional anomaly detection technologies mainly consider the anomaly
detection as a standalone service that is independent of any other network
management functionalities, which cannot be used directly in ubiquitous IoT due
to the resource constrained end nodes and decentralized data distribution. In
this article, we develop an AI-native VHetNets-enabled framework to provide the
anomaly detection service for ubiquitous IoT, whose implementation is assisted
by intelligent network management functionalities. We first discuss the
possibilities of VHetNets used for distributed AI model training to provide
anomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. After
that, we study the application of AI approaches in helping provide automatic
and intelligent network management functionalities for VHetNets, i.e., AI for
VHetNets, whose aim is to facilitate the efficient implementation of anomaly
detection service. Finally, a case study is presented to demonstrate the
efficiency and effectiveness of the proposed AI-native VHetNets-enabled anomaly
detection framework.
