Edge-powered dataspaces enhance time-sensitive applications, efficient bandwidth, security, and privacy, particularly for smart urban environment data spaces. In addition, designing optimized distributed edge-powered dataspaces that consider the shared data residing near the provider’s ease the management and control over the data. However, since distributed edge devices are heterogeneous, application deployment and management are typically challenging. Container virtualization is an important technology that addresses this problem, specifically on devices with limited resources. Scaling the number of nodes does not always guarantee adequate performance improvements. Therefore, it is crucial to study the effect of horizontal scalability on runtime, throughput, latency, and scaling the record and operation counts. In this study, the MongoDB database has been benchmarked using Yahoo! Cloud Serving Benchmark with a Docker Container of a single node and a Docker Swarm cluster of nine nodes scaled horizontally on Raspberry Pi. In addition, the study scales the record and operation counts to understand their effect in a similar environment.

In response to the escalating global conflicts, predictive models have become extremely important for peacekeeping initiatives. The proliferation of “Hybrid Threats,” including terrorism and unconventional warfare, necessitates innovative strategies for enhancing peace and security. This paper outlines a collaborative effort with the United Nations Global Service Center (UNGSC) to develop a predictive tool for domestic conflicts in Africa, using diverse open-source datasets. The study employs data engineering, visualization, and integration techniques to explore commonalities and discrepancies among the datasets. Notably, data inconsistencies emerge, underscoring the significance of verifying information sources. While horizontal and vertical data integration possibilities are identified, challenges related to data anomalies and miscommunication are highlighted. To build a reliable predictive model, rigorous data analysis, expert insights, and a multidimensional approach are indispensable, ultimately contributing to conflict prevention and sustainable peacekeeping.

Digital Twins (DTs) are the novel paradigm for the development of Cyber-Physical systems. The state of art presents several use cases in different domains, and one of the most complex examples is represented by the Urban Digital Twin (UDT), which aims to virtualize urban assets (e.g., buildings, mobility infrastructures, energy grids, waste management facilities, etc.) and build advanced analysis and prediction services upon a city’s digital representation. UDTs represent a formidable example of system of systems, as they are structured into a hierarchy of interconnected DT instances that process and share a huge amount of data subject to different access and usage policies.Regarding data management in complex distributed scenarios, Data Spaces are an emerging paradigm that aims at building a secure and privacy-preserving infrastructure to pool, access, share, process and use data. As a matter of fact, existing DTs solutions (not only in the urban domain) do not present a clear software architecture characterization and, moreover, they pay little attention to the data management aspects.To bridge this gap, in this paper we present the Digital Twin Space, an architectural proposal that aims to instantiate Data Spaces into DTs according to the guidelines set by relevant international projects. We use the UDT as a running example and we validate the model in the case of a smartPV panel, showing that the model matches the real cyber-physical system.