Foundation Forecasting in IoE Networks: When Generative AI Meets Programmable Edge Nodes

Artificial intelligence (AI)-native edge networks are promising solutions to seamlessly integrate AI into modern network architectures, promoting intelligent and hyper-flexible behavior in self-adaptation and reconfiguration of hybrid networks. AI-native edge nodes have to promptly react to any change in network conditions and to be linked to Internet of Everything (IoE) deployed in etherogeneous communication domains. This paper deals with a next-generation programmable edge node capable of being employed in different domains in a unified and flexible manner. With the aim to achieve a low re-configuration overhead in such herogeneous contexts, this paper proposes the integration of a generative-AI module within an edge node exploiting foundation models for efficient general-purpose time-series prediction, without involving overhead and costs due to models trained from scratch and overcoming data scarcity. This permits to manage IoE networks deployed in both homogeneous and heterogeneous domains, i.e., aqua, ground and air, autonomously, without the need for adjustments from the outside. As foundation models, we focused on Chronos and TimesFM, in both the zero-shot and fine-tuning learning paradigms. Finally, performance results in terms of prediction accuracy, training, and inference time are provided and compared with those achieved by the state-of-the-art recurrent neural networks trained from scratch and baseline alternatives. The obtained results corroborate the potential of foundation models as key enablers of native AI networks, achieving good accuracy despite the absence of a training phase (i.e., in zero-shot mode) or with limited training (fine-tuning), w.r.t. alternatives.