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Review
. 2022 Aug 1;22(15):5750.
doi: 10.3390/s22155750.

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Leila Ismail  1   2 , Rajkumar Buyya  3
Affiliations
Review

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Leila Ismail et al. Sensors (Basel). .

Abstract

The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations' development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications' systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

Keywords: Artificial Intelligence (AI); Deep Learning; Internet of Things (IoT); Machine Learning; Sixth Generation (6G) wireless communication; beyond 5G; blockchain; metaheuristics algorithms; smart city.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A view of smart city digital ecosystem.
Figure 2
Figure 2
Evolution of wireless communication technology from 1G to 6G.
Figure 3
Figure 3
Artificial Intelligence (AI)-enabled 6G networks.
Figure 4
Figure 4
Deep Learning (DL)-based channel estimation.
Figure 5
Figure 5
Convolutional Neural Network (CNN)-based modulation recognition in networking.
Figure 6
Figure 6
Deep Learning (DL)-based network traffic classification for Internet of Things (IoT) applications.
Figure 7
Figure 7
Deep Learning (DL)-based time series prediction of network traffic data.
Figure 8
Figure 8
Deep Reinforcement Learning (DRL)-based data caching in the Internet of Things (IoT) environment.
Figure 9
Figure 9
Deep Reinforcement Learning (DRL)-based intelligent routing in the Internet of Things (IoT) environment.
Figure 10
Figure 10
Deep Learning (DL)-based mobility management for Internet of Things (IoT) devices and users.
Figure 11
Figure 11
Deep Learning (DL)-based intrusion detection in Internet of Things (IoT) environments.
Figure 12
Figure 12
Deep Learning (DL)-based network traffic anomaly detection.
Figure 13
Figure 13
Machine Learning (ML)- and Deep Learning (DL)-based botnet detection for Internet of Things (IoT).
Figure 14
Figure 14
Taxonomy of smart city applications in 6G based on underlying technologies.
Figure 15
Figure 15
Growth of Internet of Things (IoT) and non-IoT devices from 2010 to 2025.
Figure 16
Figure 16
Types of communications on the Internet of Vehicles (IoV).
Figure 17
Figure 17
Blockchain-enabled Integrated Internet of Vehicles (IoV)-Edge-Cloud environment.
Figure 18
Figure 18
AI-enabled smart city applications in self-learning 6G networks.
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