## 1 Introduction  
With the prominence of connected smart cities and the recent emergence of a smart city's mobile applications and their building blocks architecture in terms of Internet of Things (IoT) [1], Artificial Intelligence (AI) [2], federated and distributed learning [3], big data analytics [4], blockchain [5], and edge-cloud computing [6], the implementation of a new generation of networks has been prompted. While optimization strategies at the application level along with a fast network, such as the currently in-deployment 5G networks, play an important role, this is not enough for AI-based distributed, dynamic, contextual, and secure smart city applications enabled by emergent technologies [7]. As shown in Figure 1, these applications include, but are not limited to, autonomous driving, accident prevention and traffic management enabled by the Internet of Vehicles (IoV), remote patient monitoring, medical drug supply chain management, the prognosis/diagnosis of diseases empowered by the Internet of Medical Things (loMT), industry automation and surveillance using the Internet of Robotic Things (IoRT), building maintenance and package delivery enabled by the Internet of Drones (IoD), system maintenance and pollution control enabled by the Industrial Internet of Things (IIoT), interactive gaming and aerospace navigation using Holographic Communication (HC), immersive training and guided repair enabled by Extended Reality (XR), intelligent transportation systems and smart connected healthcare using blockchain, and data analytics empowered by edge-cloud computing. These applications and their rigorous support for meeting requirements in terms of the quality of services (QoS) and dependability to satisfy the Service-Level-Agreement (SLA) for the end users [8; 9; 10; 11] have been a driving force for the evolution of networks. The energy consumption of the smart cities' digital ecosystem serving these applications is a major issue causing environmental threats and increasing electricity bills, requiring immediate sustainable remedies [12]. Estimates show that cloud data centers, considered the backbone of smart cities, will be responsible for 4.5% of the total global energy consumption by 2025 [13]. The average electricity cost for powering a data center could be as high as 53 million per year [14]. Furthermore, it is predicted that by 2040, Information and Communications Technology (ICT) will be responsible for 14% of global carbon emissions [15]. Consequently, the underlying communication networks should focus on deploying efficient, dependable, and secure applications in smart city applications while considering the critical requirements of privacy, energy efficiency, high data rates, and ultra-low latencies for those applications. Therefore, industries and academia have started to look beyond 5G networks and design the upcoming 6G. In particular, 6G network designers propose the use of AI in its underlying protocols and operations for optimal performance and energy efficiency. The vibe over AI and its tremendous potential for intelligent applications and network systems, in combination with IoT smart city applications, have been a great motivation in developing AI-IoT-based solutions. These solutions require huge communication and computation resources, giving rise to latency, energy consumption, network congestion, and privacy leakage. Figure 1: A view of smart city digital ecosystem. The current research on this topic either focuses on the requirements of AI applications in a smart city ecosystem that can benefit from the underlying 6G networks or on the self-learning 6G networks for agility, flexibility, and energy efficiency. To our knowledge, no work adopts a holistic perspective considering the underlying 6G networks, middleware, and technology-enabled applications for an intelligent and integrated smart city digital ecosystem. The main contributions of our paper are as follows. 1.