In this research, we propose automating network management through data-driven intelligence, with a particular focus on anomalies and network traffic during specific events or periods. We analyze a large dataset collected by Orange mobile network operator in France with the goal of forecasting mobile demand for different classes of services. To model the underlying network infrastructure, we introduce a model for the underlying network based on a hierarchy of virtualization layers and slices. Building on this model, we propose algorithms to optimize the resources allocated to network slices and traffic distribution within the operator’s network. Network performance is evaluated as the fraction of time the mobile traffic is within the capacity of the network. Our results demonstrate that dynamic reallocation of resources among slices, and dynamic load balancing (traffic shaping) between nodes notably improves network performance. These results provide insights into critical aspects related to future 5G network management.

This paper makes a compelling case for the adoption of the recently proposed Data Mesh architecture within IoT-Edge-Cloud Continuum scenarios, particularly in the context of Intelligent Transportation Systems and Data-driven Mobility Services. Unlike centralized cloud-based approaches, based on data warehouses/lakes connected with ETL (Extract, Transform, and Load) pipelines, Data Mesh promotes a decentralized data ownership model which brings several advantages in addressing open challenges in IoT-Edge-Cloud Continuum scenarios. First, we present an overview of the Data Mesh concepts, and how they advance the state of the art in data management architectures. Secondly, we discuss how their adoption might ease the development of IoT -Edge-Cloud applications in terms of: (i) hiding the heterogeneity of the IoT Layer, (ii) mitigating latency by enabling full domain migrations, and (iii) promoting the adoption of AI techniques, such as MLOps and Federated Learning at the edge of the net-work. Finally, we provide practical guidelines for implementing such an architecture to enhance the safety of pedestrians and vulnerable users, based on our experience with the Modena Automotive Smart Area.

Local Energy Communities are becoming key actors in the panorama of sustainable development. One of the biggest challenges for such communities is to become self-efficient, thanks to an efficient management of the balancing between produced and consumed energy. In order to achieve this goal, it is necessary to design and implement forecasting models that can provide accurate estimates to be subsequently used by optimization and planning algorithms. In this work, we show how neural networks, and in particular long short-term memory networks, can be used to this aim, highlighting an interesting trade-off between the computational requirements and the forecasting accuracy induced by learning different models for clusters of users.

Vehiclesproduce a large amount of data which, together with the possibility offered by 5G, creates new opportunities for the development of applications and services. In this context, the 5GMETA Platform aims to establish a vehicle data monetisation environment taking advantage of the 5G-enabled low latency and faster communication between data sources. Data providers will easily make vehicles’ data available according to current technical standards; Data Consumers will quickly and securely access high-quality data to design and develop new products and services. In both cases, data monetisation will create opportunities for companies’ growth and for an improved quality of service for end users of mobility. The paper presents the business model for the market uptake of the 5GMETA Platform.

In the maritime sector, 5G enables innovative applications and services to improve efficiency, security, safety and optimise operations. In this context, it is interesting to identify the 5G-enabled applications that foster technological development in the maritime sector. We deploy the survey’s results performed in the context of the European project 5G-LOGINNOV and combine them with Twitter data. Thanks to words frequency and sentiment analysis, we found that in the maritime sector, there are several 5G technological enablers related to real-time information transfer. Furthermore, 5G-enabled applications for cybersecurity, are the most promising technologies.

Since the global request for freight transportation is increasing as a consequence of the increasing requirements of the modern economy, logistics processes need to be optimized through the application of innovative technologies, to ensure a high level of quality, flexibility, and effectiveness in logistics operations. The adoption of innovative technologies allows the creation and development of new products and services, able to optimize the existing logistics processes and create value. In particular, one of the most promising technology for logistics applications is the 5G communication network that allows, together with companion technologies such as the Internet of Things, Artificial Intelligence, and the Cloud, the collection, integration, and sharing of a large amount of data from different sources. However, to ensure the market adoption of innovative products and services, the different actors and stakeholders of the logistics chain must be involved from the early stages of the development. This allows them to keep into account their actual needs in the development process of the business models and for the future exploitation of the solutions. This paper analyzes the process of development of collaborative business models in the context of 5G-LOGINNOV, a project aimed at the development of 5G-based solutions to optimize the logistics operations in ports and retro-ports.

This work aims to evaluate a set of Critical Success Factors (CSF) that are important for port operations optimization. Furthermore, a set of 5G enabled applications is evaluated based on their importance for two typologies of companies located in the port of Hamburg, Athens and Luka Koper. More specifically, the importance of CSFs and 5G enabled applications and services is assessed based on the point of views of respondents working for technological companies and companies involved in the port’s operations, using Multi Criteria Analysis. Finally, the relationship between the CSFs and 5G applications and services is considered based on the χ2test of hypothesis. Then, the possibility to promote 5G applications and services as CSF for port operations optimization which will in turn increase port competitiveness, is discussed.

The estimation of OD flows from mobile phone and GPS positioning data is an important application that can naturally support urban and transport studies. In this work, we first present an approach to generate OD matrices from mobile phone positioning and GPS data, and scale them with traffic counts. Then we compare these matrices, that we consider as an estimate of the potential demand for public transport, with matrices describing the actual routes of public transportation services, that represent the supply. Finally, we present a data driven approach to identifywhereandwhenthe demand for transport is not satisfied. We run experiments with different mobility datasets and the actual public transportation routes in a mid-sized Italian city. In this scenario, our approach allows to detect similar areas of unmatched demand with both such datasets. In particular, two case studies show that the proposed methodology is able to identify two existing bus lines that were recently introduced by the public transport company and local government. Finally, we show an upper bound for the reduced impact of$CO_{2}$emissions, if the unmet demand for transport is entirely shifted to public transport.

Road traffic safety is one of the major challenges for the future of smart cities and transportation networks. Despite several solutions exist to reduce the number of fatalities and severe accidents happening daily in our roads, this reduction is smaller than expected and new methods and intelligent systems are needed. The emergency Call is an initiative of the European Commission aimed at providing rapid assistance to motorists thanks to the implementation of a unique emergency number. In this work, we study the problem of classifying the severity of accidents involving Powered Two Wheelers, by exploiting machine learning systems based on features that could be reasonably collected at the moment of the accident. An extended study on the set of features allows to identify the most important factors that enable to distinguish accident severity. The system we develop achieves around 90% of precision and recall on a large, publicly available corpus, using only a set of eleven features.