This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agents that accompany users, Follow-Me AI negotiates data management based on user consent, aligns environmental controls as well as user communication and computes resources available in the environment with user preferences, and predicts user behavior to proactively adjust the smart environment. The manuscript illustrates this concept with a detailed example of Follow-Me AI in a smart campus setting, detailing the interactions with the building's management system for optimal comfort and efficiency. Finally, this article looks into the challenges and opportunities related to Follow-Me AI.

In today's digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models, highlighting the necessity for robust data communication infrastructures. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims to delve into a comprehensive analysis of traditional and modern message brokers, offering a comparative study of prevalent platforms. Our study considers numerous criteria including, but not limited to, open-source availability, integrated monitoring tools, message prioritization mechanisms, capabilities for parallel processing, reliability, distribution and clustering functionalities, authentication processes, data persistence strategies, fault tolerance, and scalability. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, recognizing that these limitations are crucial in understanding their real-world applicability. We then leverage these insights to propose a sophisticated message broker framework -- one designed with the adaptability and robustness necessary to meet the evolving requisites of GenAI applications. Finally, this study examines the enhancement of message broker mechanisms specifically for GenAI contexts, emphasizing the criticality of developing a versatile message broker framework. Such a framework would be poised for quick adaptation, catering to the dynamic and growing demands of GenAI in the foreseeable future. Through this dual-pronged approach, we intend to contribute a foundational compendium that can guide future innovations and infrastructural advancements in the realm of GenAI data communication.

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines Distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating Distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations.

These applications have high, ever-growing requirements in terms of security, reliability and performance. Currently, the development of these applications is heavily dependent on cloud, the abundant resources of which are a necessity for the computationally intensive Artificial Intelligence (AI) methods. However, cloud-native processing requires transmitting data between the end users and the cloud, which increases the latency, burdens the core network and raises privacy concerns. Hence, several computing paradigms, such as edge and fog computing, Multi-access Edge Computing (MEC) and cloudlets (Ren et al. (2020)), have emerged to bring the computing and storage resources from the cloud to the edge, closer to the end users. Even though these paradigms have differences in their architectural considerations and driving forces, they all have the same essence: placing and using computational resources between the end user and the distant cloud in order to reduce latency and energy consumption, as well as increase security and privacy by keeping the application data local. Bringing the intelligent applications onto the edge between the end users and the cloud is not a simple task. Traditional AI is inherently centralized and resource consuming, while the edge is inherently distributed and limited in resources. Further, the edge nodes are highly heterogeneous in terms of their capabilities, while the edge environment as a whole is characterized by intermittent connectivity, distributed and non-IID data, as well as geographically distributed, opportunistic computing resources (Kokkonen et al. (2022)). Research on developing and adapting AI methods to the edge environment has been coined as AI on Edge (Lovén et al. (2019); Deng et al. (2020)), which is an active research area with an ample amount of research (Deng et al. (2020); Xu et al. (2021); Park et al. (2021)).

Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.