The rapid expansion of Non-Terrestrial Networks for global IoT connectivity faces key challenges related to synchronization, latency, and energy efficiency under orbit-dependent satellite coverage. LoRaWAN Class B offers a potential solution by enabling deterministic downlink scheduling. However, its real-world performance remains largely unexplored, particularly regarding timing accuracy and energy consumption. This paper presents a comprehensive experimental evaluation of Class B synchronization accuracy and power consumption using a real testbed. Results show that absolute synchronization errors grow from 1.5 ms to 11.5 ms, while relative errors remain below 0.02%. Worst-case cumulative drift stays within the 30 ms reception window, reaching 23.6 ms. Current measurements indicate that idle consumption decreases from 16.80 mA to 6.40 mA, with Sleep mode reducing overall draw by approximately 62%. These findings provide an empirical characterization of Class B timing precision and energy efficiency, supporting the design of future hybrid terrestrial–Non-Terrestrial Networks architectures.

The maturity and commercial roll-out of 5 G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and ubiquitous coverage and wireless connectivity, 5G fully unlocks the potential of robot autonomy and boosts emerging robotic applications, particularly in the domain of autonomous mobile robots. Ensuring seamless, efficient, and reliable navigation and operation of robots within a 5 G network requires a clear understanding of the expected network quality in the deployment environment. However, obtaining real-time insights into network conditions, particularly in highly dynamic environments, presents a significant and practical challenge. In this paper, we present a novel framework for building a Network Digital Twin (NDT) using real-time data collected by robots. This framework provides a comprehensive solution for monitoring, controlling, and optimizing robotic operations in dynamic network environments. We develop a pipeline integrating robotic data into the NDT, demonstrating its evolution with real-world robotic traces. We evaluate its performances in radio-aware navigation use case, highlighting its potential to enhance energy efficiency and reliability for 5Genabled robotic operations.

Sensor infrastructures have become key enablers in collecting massive urban data. While representing an invaluable source of geographic information, high deployment costs of air quality stations relying on a chemical principle of operation leads to geographically imbalanced data. Typically, a minority of districts are equipped with such sensors or benefit from data collection campaigns, while others are data-poor. Moreover, the data available is highly heterogeneous, which adding on uncertainties. We present a methodology called ‘Smart Beestricts’ to improve the resolution of spatiotemporal data through Transfer Learning across districts of smart cities, based on: (i) aggregating spatiotemporal urban data into hexagonal grid representations, (ii) multi-variate clustering of the grid for discovering candidate regions for transfer, and (iii) transferring prediction models from data-rich sectors to data-poor ones. The transferred models are finally utilized to predict missing data across the city, thus improving spatiotemporal resolution. The methodology is validated in predicting missing air quality data in Madrid. The datasets generated are available through an open repository including meteorology, air pollution, and traffic data. Our methodology increased the spatial coverage of NO2 data from approximately 98 km2 to 253 km2, achieving coefficients of determination (R2) of up to 0.70 in the test regions.

Smart solutions increasingly involve the use of sensor data to represent the physical world in the digital world and apply intelligence to such representation. The main approach is a vertical end-to-end solution from sensors to intelligence and back to actuators. Recently, a new holistic approach, the so-called Digital Twin, has emerged. This goes beyond traditional smart solutions by replicating, with high-fidelity cross-domain aspects, a physical object into the digital world. The key differentiation is the inclusion of semantics information in the digital replica in a form of knowledge graph. Further, analytics continuously enrich Digital Twin’s information with predictions and insights. In this article, we adopt the Digital Twin approach for the creation of a digital replica of Espinardo’s campus at the University of Murcia (Spain). The starting point is the existing sensor network deployment and the infrastructure under development of Fog–Edge–Cloud computing based on a 5G private network. The smart campus Digital Twin is formed by the Digital Twins of 23 buildings for which different sets of data features have been thoroughly selected. We implement the concept of Digital Twin by merging sensor data with external open data sources, analytics models implemented, and information processed by these analytics. We report our experience showing the issues encountered handling the data and producing various analytics models for predicting energy consumption, building occupancy, room usage, solar energy, and anomaly detection. From our experience, we highlight some lessons learned and directions toward the full operational smart campus Digital Twin.

The recent proliferation of the Industrial Internet of Things has revealed the potential of Low-Power Wide-Area Networks as a complementary solution to cellular technologies. In this context, the LoRaWAN standard has already been consolidated as one of the most extended technologies in academia and industry for lightweight machine-type communications under negligible energy and cost. As LoRaWAN’s Aloha-like nature is known to hinder its reliability, especially under high-traffic and large-scale deployments, numerous time-slotted approaches have been presented as a means to schedule LoRa transmissions accordingly. However, the online allocation of resources based on application constraints has received scant attention in the literature, despite having proved to be significant in real-world deployments. To shed light on this question, this paper proposes a multi-agent approach to efficient resource allocation in multi-SF LoRaWAN networks, addressing architecture design, logic implementation and scalability-oriented evaluation. The integration of agents in the system resulted in network-size improvements of up to 21.6% and 66.7% (for nearby or scatter node distributions within the gateway, respectively). The work provides a set of learned lessons regarding slot-length computation and end-node allocation strategies enabling large-scale collision-free channel access in LoRaWAN networks.

In the building context, there is a growing requirement for numerous data models and ontologies to coexist as a means to cover different perspectives and communities. While there are some well-known efforts, such as the Brick schema, to create an overall usable ontology, interlinking available ontologies and data models is still a challenge that can provide significant benefits towards interoperability of Building Information Models. To shed light on this matter, we provide a review of some of the most important ontologies in the building context, which we then match against Brick as a means to provide an interlinked data model. For finding matches, we propose TrioNet, an interactive ontology matcher utilizing weak supervision and active learning, which we compare in terms of precision and recall with two well-known state-of-the-art ontology matchers: AgreementMakerLight (AML) and LogMap. TrioNet outperforms them in finding more verified matches with only a few domain expert annotations, making it an ideal tool for the creation of interlinked data models to improve interoperability. With this paper, we contribute the following datasets: (i) the overall Brick data model interlinked to five other ontologies; (ii) the discovered pairwise ontology alignments; and (iii) the manually-annotated matches used for evaluation.