The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban applications through Urban Foundation Models. However, base models face limitations, as they only contain the knowledge available at the time of training, and updating them is both time-consuming and costly. Retrieval Augmented Generation (RAG) has emerged in the literature as the preferred approach for injecting contextual information into Foundation Models. It prevails over techniques such as fine-tuning, which are less effective in dynamic, real-time scenarios like those found in urban environments. However, traditional RAG architectures, based on semantic databases, knowledge graphs, structured data, or AI-powered web searches, do not fully meet the demands of urban contexts. Urban environments are complex systems characterized by large volumes of interconnected data, frequent updates, real-time processing requirements, security needs, and strong links to the physical world. This work proposes a real-time spatial RAG architecture that defines the necessary components for the effective integration of generative AI into cities, leveraging temporal and spatial filtering capabilities through linked data. The proposed architecture is implemented using FIWARE, an ecosystem of software components to develop smart city solutions and digital twins. The design and implementation are demonstrated through the use case of a tourism assistant in the city of Madrid. The use case serves to validate the correct integration of Foundation Models through the proposed RAG architecture.

The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through wireless networks. New research on edge computing and tiny machine learning (tinyML) explores the execution of AI models on low-performance devices to address these issues. However, there are not many studies proposing agnostic architectures that manage the entire lifecycle of intelligent cyberphysical systems. This article extends a previous architecture based on FIWARE software components to implement the machine learning operations flow, enabling the management of the entire tinyML lifecycle in cyberphysical systems. We also provide a use case to showcase how to implement the FIWARE architecture through a complete example of a smart traffic system. We conclude that the FIWARE ecosystem constitutes a real reference option for developing tinyML and edge computing in cyberphysical systems.

Context Management technology is not novel itself, and ICT companies are already looking at this area and spending effort for a long time trying to find a technically feasible solution, appealing marketing usage and solve all the possible issues with its privacy and security concerns. However, after many years of technology scouting and academic scrutiny within this still innovating area, there is no unique best practice or reference standardization solving all the technological difficulties within this field. The context information available in the real world from many potential sources should be handled in a near real-time way, efficiently processed by many devices and be interoperable among different actors dealing with the context. Therefore not only a comprehensive context management framework shall be in the place but also efficient context representation formalism should be employed in order to represent the context data suitably for an autonomous Machine-to-Machine processing, with all the data maintained within that representation and with all the mechanisms or artifacts needed for a secure and privacy safeguarding sensitive data handling. This all compose a set of requirements to be respected in the context information data representation, which are listed and solved by the solution described within with paper.