VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT

TL;DR

This paper proposes an AI-native VHetNets architecture that integrates AI and network management to enable efficient anomaly detection in ubiquitous IoT, addressing resource constraints and decentralization issues.

Contribution

It introduces a novel framework combining VHetNets and AI for anomaly detection in IoT, with a focus on distributed AI training and intelligent network management functionalities.

Findings

The framework effectively detects anomalies in IoT environments.

Distributed AI training in VHetNets enhances anomaly detection accuracy.

The case study demonstrates improved efficiency and effectiveness of the proposed approach.

Abstract

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.