The growing scale of smart city sensing devices and infrastructure entails a wide variety of available sensing information that can provide valuable insights into user mobility and traffic congestion. This information can be used to optimize service delivery through the development of mobility-aware services. 5G systems and their associated technologies provide an ideal environment with capabilities to efficiently support edge computing and bring the processing and storage resources closer to the end users, which results in a latency and backhaul usage reduction. This article proposes the integration of edge computing in 5G operator network and a mobility/road-side infrastructure with edge orchestration to provide mobility-aware services to the end-users on demand. With this approach, a service instantiation can be translated into resource allocation both on the 5G platform through multi-slicing and the edge infrastructure. Resource management is then optimized for the users on the move by continuously allocating the necessary virtual network slices, processing, and storage resources in the appropriate locations for the user to consume its services while maintaining the appropriate QoS levels and optimized resource distribution in the edge platform. This approach is evaluated in a real mobile 5G network with emulated Radio Access Network (RAN) resources through two use cases based on infotainment and emergency services. The results show that the approach is efficient in using mobility, service requirements, and platform’s resources information to enable a proactive resource reservation both in the 5G base stations and edge computing nodes throughout the path traversed by the users. • Smart City sensing and mobility information can be used to optimize service delivery. • The integration of mobility, MEC and 5G enable the instantiation of emergency services. • Allocation of the network and computing resources, maintaining the required QoS levels. • Results successfully demonstrate proactive resource reservation in both 5G and MEC.

This paper proposes an urban and mobility-based Digital Twin that provides the representation of an urban scenario with both real and simulated mobility data of vehicles, 2-wheelers and people. The platform integrates real-time data from the Aveiro Tech City Living Lab (ATCLL) with 2D and 3D visualizations using SUMO and CARLA, respectively, enabling detailed traffic analysis and management. Key functionalities include the synchronization of real-world sensor data with simulated environments, providing accurate and dynamic traffic visualizations. The platform also supports the change and blocking of intersections, roundabouts and lanes, being able to test scenarios when the road conditions change, anticipating the impact of those changes in the urban mobility. The platform results show that they can help decision makers to optimize the traffic flow and anticipate changes in the roads, providing information on the travel times, CO2 emissions and congestion in the roads.

The demand for more secure, available, reliable, and fast networks emerges in a more interconnected society. In this context, 5G networks aim to transform how we communicate and interact. However, studies using 5G data are sparse since there are only a few number of publicly available 5G datasets (especially about commercial 5G network metrics with real users). In this work, we analyze the data of a commercial 5G deployment with real users, and propose forecasting techniques to help understand the trends and to manage 5G networks. We propose the creation of a metric to measure the traffic load. We forecast the metric using several machine learning models, and we choose LightGBM as the best approach. We observe that this approach obtains results with a good accuracy, and better than other machine learning approaches, but its performance decreases if the patterns contain unexpected events. Taking advantage of the lower accuracy in the performance, this is used to detect changes in the patterns and manage the network in real-time, supporting network resource elasticity by generating alarms and automating the scaling during these unpredictable fluctuations. Moreover, we introduce mobility data and integrate it with the previously traffic load metric, understanding its correlation and the prediction of 5G metrics through the use of the mobility data. We show again that LightGBM is the best model in predicting both types of 5G handovers, intra- and inter-gNB handovers, using the mobility information through Radars in the several roads, and lanes, near the 5G cells. • Development of a data exploration pipeline for 5G network metrics and mobility data. • 5G network data anonymization using PCA. • Traffic load metric research. • Machine Learning for network traffic prediction. • Correlation of traffic load and mobility data.

Abstract The effective use of time series data is crucial in business decision-making. Temporal data reveals temporal trends and patterns, enabling decision-makers to make informed decisions and prevent potential problems. However, missing values in time series data can interfere with the analysis and lead to inaccurate conclusions. Thus, our work proposes a Focalize K-NN method that leverages time series properties to perform missing data imputation. This approach shows the benefits of taking advantage of correlated features and temporal lags to improve the performance of the traditional K-NN imputer. A similar approach could be employed in other methods. We tested this approach with two datasets, various parameter and feature combinations, and observed that it is beneficial in scenarios with disjoint missing patterns. Our findings demonstrate the effectiveness of Focalize K-NN for imputing missing values in time series data. The more noticeable benefits of our methods occur when there is a high percentage of missing data. However, as the amount of missing data increases, so does the error.

Mobile networks have proved to be an attractive solution to support emergency services, due to their inherent mobility, which requires flexible and yet efficient communication. However, the management of resources in slices in a 5G network is still made static and with no mobility information and prediction. This paper proposes the integration of a 5G network platform and a city mobility network, which allows 5G network resources to be allocated dynamically and in advance according to the users mobility and their needs. This mobility and network integration is important for different types of services, from infotainment to emergency. The prediction of the mobility and location through the mobility network provides the knowledge for a 5G cellular network to dynamically reserve the required network slicing resources. This approach has been tested in real road scenarios and vehicles covered by 5G. The results show that the solution guarantees the required resources, reserved in a proactive and predictive approach, both to optimize the resources of one base station, and to optimize resources between base stations, offering the required quality to the services.

Multimedia services are rapidly growing in interest with the increase of available bandwidth on core and mobile technologies. There is a business opportunity for both adaptability and personalization of services centralized in users and their surrounding environment. Context Awareness requires the network to support mechanisms that are able to get information from the user situation as location, activity, presence, velocity, preferences, surrounding noise, etc., and triggering session modification and network control mechanisms. Context awareness provides a new trend in pervasiveness since it allows a full, but unconscious interaction between users, operators and service providers. This paper presents the results, from simulation and demonstrator implementation, of an architecture that enables context-aware and personalized multiparty multimedia content delivery, independently of the underlying access and transport technologies.

The evolution of third generation cellular network focuses on the provision of enriched multimedia services and the support of QoS (quality of service) guarantees. The IMS (IP multimedia subsystem) is specified as subsystem providing resource, admission and charging control. Enabling GPRS (general packet radio service) and UMTS (universal mobile telecommunications system) to support multicast and broadcast transmissions, 3GPP (third generation partnership project) has recently standardised the MBMS (multimedia broadcast multicast services) framework. Up to now, IMS and MBMS are separated subsystems sharing no common interfaces in order to utilise each other. However, 3GPP is working currently on release 7 to integrate IMS and MBMS. In this paper we present an efficient integration of IMS and MBMS which supports several phases of unicast, multicast and broadcast transmissions. Furthermore, an integrated solution framework is introduced. Several end-to-end signalling procedures are finally discussed.

Common radio resource management strategies are devoted to achieve an efficient usage of the pool of radio resources available in a heterogeneous radio access network context. This paper describes the functionalities associated with the common vision of radio access technologies, the different possibilities in the functional model split and the corresponding implementation considerations.