Collective Perception Messages (CPMs), defined by European Telecommunications Standards Institute (ETSI), enable vehicles and roadside infrastructure to exchange information about detected objects, enhancing situational awareness in cooperative environments. However, as the size of CPMs increases — particularly in dense traffic scenarios — the wireless channel can become saturated, leading to delays in transmission and reduced packet delivery ratios. This paper starts by assessing how the number of objects included per CPM impacts communication performance, highlighting the necessity for effective object selection strategies during periods of congestion. To address this issue, we propose a lightweight, real-time object prioritization algorithm based on deviations from the predicted path. Our method estimates each object’s expected state based on its last transmission, and prioritizes those whose current state deviates most from this prediction, as these are likely to be more informative. The evaluation uses a real-world dataset and demonstrates that our strategy significantly improves predictive accuracy by at least 7%. Moreover, the algorithm does not increase CPU or memory usage, demonstrating similar resource consumption compared to the method described in the Collective Perception Service (CPS) standard, making it well-suited for embedded platforms. These results confirm that Prediction–Deviation selection can enhance the efficiency and informativeness of CPMs, especially when the message size must be constrained due to network congestion.

The emergence of 6G networks introduces new challenges, including the planning of ultra-dense networks that can be optimized through Network Digital Twins (NDTs). In smart cities, NDTs are especially valuable, as they integrate network and mobility data to enable adaptive and intelligent management. This work proposes a NDT designed to analyze the impact of urban mobility on 5G networks. The NDT environment integrates real and simulated urban mobility with emulated 5G base stations, emulating real vehicles in the road accessing 5G network services while on the move. The proposed system leverages metric collection for beyond-5G networks with real-time and historical capabilities, to support the envisioned simulations. The system combines real-world data collection with large-scale network emulation using open-source projects, such as SUMO framework, UERANSIM, and Kubernetes to simulate multiple V2X connections between vehicles and 5G base stations. The proposed NDT bridges urban mobility and network behavior, providing a foundation for planning and emulating beyond-5G network management solutions in mobile and dynamic environments. Several scenarios have been tested, such as random, a marathon with road blocks, and lane closure. The results show a high aggregation of vehicles and data traffic in one of the 5G antennas when blocking roundabouts on fast roads in comparison to the random scenario; moreover, the closure of a double entry lane to the roundabout at the location of the 5G gNodeB translates to much lower peak utilization and a better balance of antenna resources during the running scenarios.

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.

The accelerated deployment of smart cities has led to a proliferation of diverse services operating simultaneously within urban environments. However, this growth presents the challenge of scalability issues, due to the varied resources used per service and the difficulty in analyzing wide-ranging network flows, since most traffic is not classified by their service. We present a path toward a smart city sensitive to criticality by: (1) showcasing contributions to evolve an infrastructure with the internet-of-things (IoT) and edge computing to accommodate traffic differentiation and service criticality; and (2) proposing a distributed network protocol, Rank, that is capable of dynamically allocating various types of resources along paths between nodes in the network infrastructure, so that new services could be added on demand. Our results show that it is possible to transform a smart city into a criticality-sensitive one by adopting a traffic classification with little to no impact on packet forwarding and time synchronization through hierarchical elements with respect to a GNSS source, where Rank conveniently solves the dynamics of variable traffic with low overhead.

In recent years, the adoption of containerization solutions has grown significantly. Simultaneously, there has been a rise in applications with stricter latency and jitter requirements, such as tactile internet, autonomous vehicles, and immersive AR/VR. Time-Sensitive Networking (TSN) emerges as a promising solution for these latency-critical applications, offering tools specifically designed to manage such conditions and support mixed-criticality scenarios involving both hard and soft deadlines. This paper explores techniques and solutions for running latencysensitive applications in containerized environments. It focuses on how high-priority traffic can coexist with best-effort traffic using TSN mechanisms, and how containerized applications can leverage hardware-based techniques to achieve precise transmission timing. The results demonstrate that, with TSN mechanisms alone on the network side, high-priority traffic can coexist with interfering traffic while maintaining accurate transmission scheduling and the target bandwidth. However, hardware-based techniques such as Launchtime proved crucial in achieving the most precise transmission schedule-evident in a $\mathbf{4 2}$-fold reduction in standard deviation. This improvement is also reflected in the P99 metric, where 99% of transmission delays remained below 0.501 ms. Finally, the paper presents mitigation strategies to address challenges introduced by container isolation, which can cause unforeseen conflicts between transmission schedules and add latency to communications.

The growing need for healthier and more ecofriendly city transport has empowered the enforcement of electric and smart bikes, which integrate sensors and communication technologies to improve monitoring and safety. This paper presents a data management architecture designed to enhance the efficiency and adaptability of 2-wheeler monitoring and safety systems. The architecture integrates a data stream service and a persistence system, using fuzzy logic to dynamically prioritize and manage data transmission. The data stream service ensures the delivery of critical messages, such as diagnostics, malfunction and safety alerts, while the persistence system stores data batches and schedules their transmission based on factors such as priority, deadlines, and expiration status. Experimental evaluations show that fuzzy logic-based prioritization significantly reduces message waiting times, outperforming traditional approaches. These results demonstrate the effectiveness of the proposed solution in ensuring reliable and timely data management under varying conditions.

The advancement of smart city services requires innovative approaches to manage urban transportation and enhance tourist mobility through sensor-based IoT data models for efficient processing. This work proposes an integrated tourist recommendation system within a multimodal travel orchestration platform, named Multipres. Designed to support sustainable tourism, Multipres optimizes urban infrastructure, including buses, bicycles, and pedestrian paths, while reducing resource strain. The platform leverages AI-based recommendation engines, integrates IoT mobility sensors, bike-sharing stations and vehicles’ parking platforms, and external applications, such as Waze. Results demonstrate that Multipres provides recommendations according to the users’ needs with updated information through the GenAI integration, in a multimodal approach with real-time data from the city sensing and external platforms, both for mobility and traffic events, and taking into account environmentally indicators.

The growing need for healthier and more ecofriendly city transport has made electric and smart bikes more popular, but this also brings safety concerns to cyclists. This paper proposes an Advanced Driver Assistance System (ADAS) solution for smart bikes to improve cyclist safety, throughout a set of cooperative perception services that communicate using Vehicle to Everything (V2X) to a city network. The services presented collect physical data from the $\mathbf{2}$-wheelers and surrounding environment: detect rear obstacles using Light Detection and Ranging (LiDAR); use computer vision on the front to identify pedestrians and vehicles; and use inertial sensors to detect the road conditions. These services are designed with constraints on power consumption while meeting real-time detection requirements. Experiments in real scenarios show that: (1) the LiDARbased rear detection system achieved an F1-score of 0.98; (2) the road quality assessment system distinguished smooth roads from potholes with an F1-score of 0.75; and (3) the pedestrian detection service achieved $85 \%$ accuracy. This work provides innovative and practical solutions to improve road safety and collaborative perception in smart bicycles.

Federated learning (FL) enables decentralized model training, allowing data privacy by processing locally on client devices. However, challenges such as high communication costs, especially in heterogeneous environments, hinder the efficient scaling of FL systems. This paper introduces OPALA, an adaptive pruning optimal learning approach designed to improve personalized Federated Learning (pFL) efficiency and accuracy. OPALA combines dynamic pruning with adaptive aggregation, adjusting model complexity based on the client’s computational capabilities and data characteristics. In this sense, OPALA significantly reduces the processing time of clients to train their models based on the computational capabilities of each client, providing higher system efficiency and responsiveness to real-time applications. The results show that OPALA significantly improves training time and data transmission by up to $53 \%$ without sacrificing the model accuracy, outperforming traditional methods. This approach offers a promising solution for efficient, scalable FL in environments with non-IID data and heterogeneous client resources.

The rapid expansion of vehicular networks is expected to cause significant congestion in communication channels, potentially compromising the reliability of safety and traffic management systems. To mitigate congestion, the European Telecommunications Standards Institute (ETSI) developed the Decentralized Congestion Control (DCC) mechanism, which dynamically adjusts transmission parameters based on real-time channel conditions. However, real-time DCC data may not always be available due to hardware limitations. This paper proposes a new approach to estimate the Channel Busy Ratio (CBR) in ITS-G5 vehicular networks through passive channel observation. Using real hardware, we compare the proposed estimator with measurements from the wireless card, showing a maximum error below 5.6%. We also analyze the impact of standard vehicular messages and IP traffic on network congestion. The estimator offers accurate, non-intrusive CBR measurements, supporting congestion control where direct measurement is impractical.

Heterogeneous networks integrate diverse devices and technologies, creating significant challenges for efficient traffic forwarding and network management. Smart cities are an example of such networks, relying on applications ranging from transportation to public safety, and demanding adaptive communication infrastructures, terrestrial and non-terrestrial, to ensure seamless connectivity and performance. Simultaneously, Software Defined Networking (SDN) has emerged and proved itself as a valid new approach to network management, enabling intelligent and centralized control. This paper presents an approach, denominated Service-Aware Multi-path BAlancing (SAMBA), to service-aware traffic forwarding to manage heterogeneous, multi-technology, multi-path environments, overcoming the limitations of previous, more technology-specific methods. This approach is evaluated against Shortest Path Forwarding (SPF) across various network technologies, including Ethernet, wireless networks (IEEE 802.11p and private 5G), and Non-Terrestrial Networks. The results demonstrate significant improvements in service throughput and latency while enhancing load distribution and preventing network link saturation.

Augmented Reality (AR) and edge computing can be used to enhance Vulnerable Road User (VRU) safety, leveraging a greater degree of interaction with the user and presenting real-time warning notifications, a use case that demands low latency for critical warnings. However, a new Multi-Access Edge Computing (MEC) system that distributes computational needs across edge nodes within the infrastructure is needed to meet these low-latency requirements. This paper proposes a microservice architecture designed to process data from various sources, including awareness and perception messages from road users and smart city sensors, through multiple communications technologies such as ITS-GS and 5G. This work employs peer-to-peer decentralized communications to perform seamless inte-gration and real-time processing, ensuring timely and relevant warnings for VRUs. Real-world road tests conducted in the Aveiro Tech City Living Lab in Aveiro, Portugal, and in commercial V2X equipment in the Autonomous Driving Test Field Baden-Wurttemberg in Karlsruhe, Germany, showed a maximum end-to-end latency of 110 ms, showcasing the system's capabilities in real-life demanding use cases and the system's interoperability.

The operation of hybrid networks (wireless and wired-based) is subject to a number of significant challenges, particularly in relation to the frequent changes in topology and high mobility that are inherent to them. This is especially the case in Vehicle to Everything (V2X) networks. This paper proposes a Software Defined Network (SDN)-based monitoring platform, designed for improving network visibility and responsiveness in dynamic environments. The proposed approach leverages Cooperative Intelligent Transportation System (C-ITS) messages to enable real-time tracking of vehicular mobility and wireless links quality, which enhances the SDN controller's ability to adapt to rapidly changing network conditions, thereby providing crucial data for informed decision-making. This broader view of the network provides the foundation for future seamless communication devices. The system's efficiency in maintaining up-to-date network awareness, optimizing handover processes, and ensuring communication stability across hybrid networks has been demonstrated in outdoor testing. The results show that incorporating the monitoring and control approaches within SDN environments markedly enhances the management and reliability of dynamic wireless networks.

Multi-Access Edge Computing (MEC) brings computational resources closer to end-users, enabling applications to deliver lower latencies and higher Quality of Service (QoS). However, as users roam across multiple domains, maintaining uninterrupted service and low latencies becomes difficult, especially when individual MEC systems experience resource overload. To address these limitations, MEC Federation (MECF) enables seamless interoperability among distinct domains, or MEC systems, regardless of their underlying specifics by using a common reference point. Subsequently, MECF facilitates application migration and resource sharing. In this paper, we analyze existing works and standards on MECF and propose a complete end-to-end framework for managing MECF between operators. This framework supports application migration, inter-MEC resource sharing, and the orchestration of these actions within individual MEC systems. Our evaluations demonstrate that the proposed framework supports efficient, secure, and scalable MECF operations, supporting the deployment of diverse use cases in multi-operator environments.

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.

Bandwidth estimation plays a crucial role in maintaining Quality of Service (QoS) in wireless networks. While active estimators can be more accurate, passive methods minimize the network overhead and avoid potential interference. To the best of our knowledge, there is no passive estimator for heterogeneous networks that preserves the network performance when adding a new communication flow, while calculating the estimated available bandwidth without synchronization, additional hardware, or non-local information.This paper introduces two passive bandwidth estimation approaches: one leverages only local interface measurements; the other also incorporates historical transmission rates from the network. The results demonstrate that the former enables a conservative estimation of available bandwidth, effectively preserving the performance of existing flows on all types of networks. More accurate estimations can be achieved with the latter, but only on homogeneous networks. We show that our approaches can match or outperform widely accepted available bandwidth estimation methods that rely on active probing.

Modern smart homes are becoming increasingly complex due to the growing number of connected wireless devices and the need for a fast, stable Internet connection. Tri-band Wi-Fi 6E addresses these demands by reducing network congestion and enhancing performance across the 2.4, 5 and 6 GHz bands. However, improper channel management can lead to frequent interference and network instability. This work introduces a Wi-Fi management solution that uses reinforcement learning to automatically optimize network configurations based on the observed interference levels. The Wi-Fi network is mapped to a virtual environment to simulate different scenarios with real data acquired from the network, allowing the system to learn and improve without disrupting the actual network. Practical tests were conducted in four residential network layouts, including star and daisy chain topologies. The solution effectively reduces the number of hops, improves the throughput in longer paths from the gateway to clients, adapts to unforeseen network dynamics in the presence of Ethernet clients and adjusts the configuration as clients move within the home. The results demonstrate improved network capacity and responsiveness compared to default configurations. In the best cases, clients achieved rates of up to 1 Gbps, round-trip times as low as 10 ms, and efficient airtime distribution across the three frequency bands. The proposed approach provides a robust and automated solution to address the challenges of domestic wireless networking and paves the way to more sophisticated virtualization solutions for end users at home.

Federated Learning (FL) is a promising approach for parameter normalisation in Machine Learning (ML) models, especially when data privacy and computing distribution are crucial. However, there are significant constraints in FL solutions, particularly concerning the handling of the mobility of participating nodes in the parameter aggregation processes, with a substantial impact on Vehicle to Everything (V2X) scenarios within the scope of smart cities. To address this challenge, we propose Mobile Federated Learning System (MobFedLS), a lightweight microservices-based framework capable of operating on various types of devices (mobile and non-mobile). MobFedLS features an interface to integrate ML models to cooperate in the FL process without intrusion between the parties. MobFedLS manages the entire federation process, from instantiating services on mobile nodes to the final parameter updates in the involved ML models and the release of resources used in all participating nodes. Additionally, MobFedLS handles node mobility and ensures the proper execution of federated processes, even with nodes entering and leaving at any stage of the aggregation process. To demonstrate the capabilities of MobFedLS, we use data collected through the city-scale infrastructure of Aveiro Tech City Living Lab (ATCLL), specifically the position of vehicles during their movement through the city. In the tests, we evaluate all phases of the aggregation process for mobile nodes. The results show that, even with intermittent connectivity to the city-infrastructure ATCLL, the MobFedLS system manages the node mobility and effectively handles node availability during the aggregation of ML model parameters.

Smart-city services are typically developed as closed systems within each city's vertical, communicating and interacting with cloud services while remaining isolated within each provider's domain. With the emergence of 5G private domains and the introduction of new M2M services focusing on autonomous systems, there is a shift from the cloud-based approach to a distributed edge computing paradigm, in acontinuumorchestration. However, an essential component is missing. Current orchestration tools, designed for cloud-based deployments, lack robust workload isolation, fail to meet timing constraints, and are not tailored to the resource-constrained nature of edge devices. Therefore, new orchestration methods are needed to support MEC environments. This paper addresses this gap. We propose an orchestration platform and its algorithms to facilitate the seamless orchestration of both cloud and edge-based services, encompassing both critical and non-critical services. This work extends the current Kubernetes orchestration platform to include a novel location-specific resource definition, a custom scheduler to accommodate real-time and legacy services, continuous service monitoring to detect sub-optimal states, and a refined load balancing mechanism that prioritizes the fastest response times.

Abstract Spontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.

Augmented Reality (AR) holds great potential for enhancing pedestrian’s urban experiences; however, its use in road traffic poses safety concerns due to potential distractions from interacting with AR interfaces. This paper investigates the effectiveness of AR applications for assisting pedestrians in crossing scenarios, against traditional crossing methods, by incorporating a collision warning system that uses an arrow to indicate the direction of a potential danger, and a virtual traffic light showing whether it is safe to cross. By leveraging Vehicle-to-Everything (V2X) communications within the living lab of Aveiro, Portugal, we conducted a user study to evaluate involved workloads, perceived safety and system usability in a realistic scenario. The findings from our study involving 20 participants reveal significant improvements in pedestrians’ perceived safety and a decrease in the perceived workload when using AR for pedestrian crossings, with both collision warning systems and virtual traffic lights demonstrating excellent usability.

The process of software updates for future autonomous vehicles on the road can be a critical process since they are constantly moving. Therefore, V2X services are susceptible to occasional connectivity interruptions, which can render traditional Over-the-Air (OTA) updates on the Software Defined Vehicle (SDV) concept impractical. By leveraging Mobile Edge Computing, using its Edge Nodes, with the assistance of the distributed protocol InterPlanetary File System (IPFS) and tools like Ansible, an OTA update system was designed to enable agile updates of V2X services under the Fifth Generation (5G) network and current Software Defined Network (SDN). The system enhances the network’s adaptability and minimizes service disruptions, reducing operational costs, and ensuring timely deployment of software patches and feature upgrades. This paper shows the proposed Vehicle to Everything (V2X) OTA updates solution and the tests performed in a real-world environment, the Aveiro Tech City Living Lab City (ATCLL) in Aveiro, Portugal, and shows how the vehicles gather the content blocks through the distributed edge road side units while the vehicles travel around in the different roads. We believe that such an approach can be set as an exemplary standard for modern Smart cities globally, in regard to the V2X service deployment.

In the last years there has been a strong effort in the development of Intelligent Transportation System (ITS)-based solutions, leading to an important change in the way that drivers and other road users become aware of the surroundings. The development of Cooperative-ITS, which utilises direct wireless short-range connections, is integrating cellular networks as well (4G and 5G), allowing the growing use of the road users smartphones to provide real-time information about Vulnerable Road Users (VRUs), like pedestrians and cyclists. Such increase is becoming a serious concern, since every VRU is likely to have one smartphone, which may lead to scalability and latency issues. This work presents an approach for a microservices-based application, targeting the always critical VRU safety use-case, in a multi-site scenario, using real road infrastructure Multi-Access Edge Computing (MEC) and mobile network provider cloud computing. The main novelty of this work is the multiple approaches on the deployment of the required microservices, and several scenarios that have been tested, and the investigation of the best approach to minimize the service-level latency of the safety application. The results show the potential of microservices distribution through the edge and cloud, with a strong impact on improving the efficiency of ITS. Depending on the services’ location, the latency of the VRU and vehicle’s notification is deeply affected, but using a federated scenario we are able to keep the VRU’s notification delay around the 200 ms, with better results being achieved if a closer mobile network provider cloud platform is used. The results present a noticeable advancement in the development of more scalable and operational solutions that work on improving ITS, with a focus on microservices and edge computing to minimize the delay of critical applications.

In this paper we surveyed the state-of-the-art on cross-domain authentication across multiple Wireless Ad Hoc Networks (WANETs) operational sectors, compared the different use cases and policies, and proposed a novel cross-domain authen-tication approach. The impact of the different operational sectors and their relation with the interoperability and authentication policies were assessed. The analysis shows that the particular characteristics of each operational sector do not influence the de-sign of the solutions. We categorised the interoperability policies found in the literature into 3 distinct groups and identified the relations between the interoperability and authentication policies. We describe and discuss a novel cross-domain authentication use case in WANETs requiring interoperability and authentication policies that differ from the ones present in the literature.

The growing use of Internet of Things devices and the increasing demand for high-speed, reliable, and secure wireless connectivity pose significant challenges for existing wireless networking solutions in modern smart homes. As such, there is an increasing urgency for the development of advanced and effective wireless technologies that can fulfill the new requirements for interconnected devices and services deployed in households. Tri-band Wi-Fi 6E equipment addresses these needs by reducing the network congestion and enhancing the performance across the 2.4 GHz, 5 GHz, and 6 GHz bands. However, improper band management can lead to frequent interference and network issues. As such, this work introduces a dynamic Wi-Fi link orchestration solution that follows a heuristic model. This approach aims to optimize the network layout and channel allocation based on device metrics, utilizing the EasyMesh specification for simplified network setup and management. The model was implemented and tested in a residential environment network, using EasyMesh and tri-band Wi-Fi 6E devices. Results show the effectiveness of the model in improving network capacity and adapting the links to the current traffic, outperforming the initial and base network configuration.

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.

Microservices offer advantages such as better fault isolation, smaller and faster deployments, scalability, and speeding up the development of new applications through the composition of services. However, its large-scale use and specific requirements increase the challenges of monitoring and management. To meet these challenges, we propose a monitoring and management system for microservices, containers and container clusters that autonomously predicts load variations and resource scarcity, which is capable of making new resources available in order to ensure the continuity of the process without interruptions. Our solution’s architecture allows for customizable service-specific metrics that are used by the load predictor to anticipate resource consumption peaks and proactively allocate them. In addition, our management system, by identifying/predicting low demand, frees up resources making the system more efficient. We evaluated our service management solution in the AWS environment, environment characterized by high mobility, dynamic topologies caused by disconnection, dropped packets and delay issues. Our results show that our solution improves the efficiency of escalation policies, and reduces response time by improving the QoS/QoE of the system.

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.

Abstract Considering that one of the goals of the future network generations is to provide ubiquitous communication in the most diverse scenarios to achieve high connection coverage, it is foreseen that the use of unmanned aerial vehicles as flying base stations (UAV-BSs) can potentially extend the network and communication range. UAVs as flying base station can bring the potential to assist user devices and vehicles by carrying communication resources that can accommodate clients that were not previously planned by the ground infrastructure design due to flash crowd events, sudden natural disasters, or any other event that let to an overloaded environment. Allocating UAVs as flying base station still poses significant challenges in their deployment and the effectiveness of information transmission through UAVs as flying base station in the context of wireless communication since it is necessary to deal with both wireless communication capability and interference in the presence of terrestrial infrastructures already present. Besides, it is essential to understand how communication resources affect network performance. This paper studies the feasibility of using UAVs as flying base station in the assistance of wireless communication in a scenario where there is a sudden demand for data transmission due to possible congestion of local infrastructure. We show how the number of communication resources provided by the UAV-BS, the interference caused by the presence of multiple next generation node Bs (gNBs), and the UAV as flying base station positioning affect the network performance. We also highlight the need for a better next generation node B (gNB) and UAVs placement criteria since the received signal power prevents the user equipments (UEs) from using most of the available resources.

Over the last years, there has been an enormous growth in internet and mobile users. In urban regions, the combination of these events has resulted in a massive flow of network traffic to the city infrastructure and cellular operators. Content Delivery Networks (CDNs) appear as a promising solution: by distributing the content through multiple nodes, they are capable to deliver content with low latency, over intermittent connections, leading to a reduction in the use of the 5G backhaul. This paper focuses on the proposal of a CDN for a smart city, deployed in a private cellular network, with CUPS and MEC, using the IPFS protocol. A comparison between the CDN and direct stream is performed, in controlled and real-world environments, to services already deployed on the Aveiro Tech City Living Lab (ATCLL). Lastly, the impact of IPFS distribution on the edge and core, in CUPS and MEC scenarios is assessed.

Over the years, WiFi became an essential technology. The success of the introduction of wireless devices with WiFi connectivity increased the demand for better WiFi networks. Such networks need better service and better coverage, either in mobile or residential environments. To solve this challenge, WiFi Alliance developed WiFi EasyMesh, a standard for WiFi networks that uses multiple access points that allow an easy setup and compatibility with WiFi certified devices. This work studies the performance of a mesh wireless network with different frequencies in use by the backhaul links (2.4 GHz and 5 GHz). The results can then be used to derive better backhaul steering algorithms to a better Quality-of-Service.

This work presents a LoRa channel access strategy that allows a single-channel high-speed moving LoRa gate-way to discover and receive sensing data from end devices installed in the railroad. This medium access is based on control messages to coordinate the communication between the mobile gateway and static sensors. The proposed approach is tested in a real mobile environment and compared with the well established LoRaWAN protocol. The results show that our solution outperforms LoRaWAN both in packet error rate and throughput, revealing the differences between a non-awareness solution such as LoRaWAN, and a solution where the end node only transmits when receiving a control message alerting that a LoRa gateway is nearby.

With the evolution of communication and sensing devices, in addition to connected and autonomous vehicles, real-time information about the status of the roads can become a reality. This paper presents a proposal of a Collective Perception Service (CPS), a system defined by the European Telecommunications Standards Institute (ETSI), which aims at enabling Intelligent Transport Systems (ITSs) to cooperate and exchange information about road users, obstacles on the road, and sensing information, through Collective Perception Messages (CPMs). The proposed system is able to join the information of the communication messages and the data from the different sensors, such as LiDAR, radars, and video cameras. The CPS is evaluated and tested in real-world scenarios in a connected city infrastructure. Results show that CPS allows Intelligent Transport System Stations (ITS-Ss) to cooperate and exchange information regarding perceived objects in a timely manner. A temporal analysis outlined the influence of the message size on the CPM dissemination process and the need for a mechanism that decides if specific information shall be sent in the CPM. Finally, the scalability tests showed the effect of network congestion, testing the system’s limits in the selected hardware.

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.

Vehicle-to-everything represent a major step in the evolution of Intelligent Transportation Systems (ITS), by allowing vehicles and the infrastructure to share information seamlessly. The new sensors introduced by the next generation of vehicles, that can be autonomous or partially assisted, generate huge volumes of data that, in some scenarios, may not be handled by the available resource-constrained devices. Therefore, edge and fog concepts have been applied to vehicular networks under Vehicle Edge Computing (VEC), to perform computational offloading, content caching, data management, flexible network management, security, and others. Technologies like virtualization and containerization are the cornerstones when addressing service deployment on edge and cloud environments. This work assesses different schemes of VEC service deployments to understand their use with the deterministic and reliable nature of some critical VEC services, such as an open-source implementation of the ETSI C-ITS protocol suite. Service prioritization and preemption are explored in order to achieve an upper-bound latency in native and containerized services deployment. The results show that the preemption leads to a slight increase in the average and median processing delay, but it also results in positive effects for real-time services; the prioritization of services is able to provide lower upper-bound latency, providing a performance without guarantees but with an expected delay.

The Federated Learning (FL) paradigm aims to provide performance advantages over centralized models, such as lower latency and communication overhead when doing most of the processing on the edge devices, better privacy as data does not travel over the network, easier handling in heterogeneous data sources and better scalability. However, the development of FL-based solutions is done through tools aimed for specialists as it always requires some programming. To cover this gap, we present an architecture for a lightweight container-based solution that offers a range of machine learning (ML) algorithms to build prediction engines for edge devices, which also includes the main options in algorithms/models for aggregation and refinement of models in the central server. The proposed framework allows the rapid build of containerized testbeds for the evaluation of ML and aggregation algorithms in the initial evaluation phase, and also later in the installation in real production infrastructures. We demonstrate the efficiency of our approach in estimating vehicle mobility in and out of the city of Aveiro, using real data collected by the communications and sensing infrastructure.

Modeling realistic millimeter-wave (mmWave) channels is crucial to the study of ultra-reliable communication in next-generation wireless networks. MmWave provides significant gains over sub-6GHz communication but has very stringent requirements on channel conditions, since slight variations in the channel may result in significant performance degradation of mmWave communication. In this work, we present an experimental mmWave testbed and the mathematical modeling of the channels using the measurements collected from an outdoor testbed that complies with IEEE 802.11ad. We show how the model fits the reality and demonstrate the impact of adaptive causal network coding in mmWave real and simulated networks.

Over time, mobile communication networks have seen significant evolution. In the 5G generation, the rollout of the network requires network densification, due to the new data-hungry applications and an exponential number of new devices. The idea of a smart city, a technologically advanced metropolitan area that gathers data to enhance the general quality of life of its residents, also contributes to the densification topic. To provide content for this increased number of end-users, Content Delivery Networks (CDNs) appear as a promising solution: by distributing the content through multiple end-nodes that are near the user, they are capable to deliver content with low latency, over intermittent connections, leading to a reduction in the use of the network backhaul. This work focuses on the architecture for 5G networks with Multi-Access Edge Computing (MEC) to be used in smart cities. By using User Plane Function (UPF) selection, the Control User Plane Separation (CUPS) concept has enabled the disaggregation of the Core Network (CN). Thus, a CDN was deployed in the private 5G network using the InterPlanetary File System (IPFS) protocol, to evaluate the proposed approach. The demo presented in this paper shows the integration of IPFS intelligence with the CN, more specifically in the desirable UPF, and it also approaches the idea of an Application Function (AF).

Mobile communication networks evolved staggeringly throughout the years. Current evolution (5G) needs a denser network, due to new vertical-based data-hungry applications and the increase of UEs, leading to additional costs in cellular deployment. To solve this issue and optimize the deployment of 5G, one unified network could be devised and shared by multiple operators to deploy their own networks. With a city network planned, ways to take full advantage of a neutral hosting architecture begin to be executed, which serve various services, like Vehicle-to-Everything (V2X) and Internet-of-Things (IoT). This is where topics such as Multi-Access Edge Computing (MEC) arise, by maximizing the exploitation of the city infrastructure to reduce latency and remove the computational effort from cloud servers to the edge. This thesis focuses on developing a neutral architecture for 5G networks, with Multi-acess Edge Computing (MEC), which can be deployed in a city. In the concrete case of this thesis, this architecture will be deployed in the city of Aveiro, taking advantage of its multiple edge nodes. The disaggregation of the Core Network (CN) is a focal point of this thesis, creating a way to have a gateway closer to the edge, thus enabling MEC technologies to improve end-user and service experience. A disaggregated architecture was proposed, and extended to the city infrastructure, to accurately simulate the city environment and to attest to the city services. The test scenarios involved the comparison of a service instantiated in the cloud and in the edge, mobility scenarios with a handover through the same network to attest user plane relocation, different user plane selection, and lastly, different flow level priority assessment, to higher priority services in a full bandwidth occupation scenario. The obtained results show that the deployment of the user plane in the edge brings significant improvements, both in common user traffic and service-oriented traffic; moreover, this outlines the capabilities of this core solution as well as its limitations providing a foundation for future works.

WiGig technologies, such as IEEE 802.11ad and later IEEE 802.11ay, provide multi-gigabit short-range communication at 60 GHz for bandwidth-intensive applications. However, this band suffers from high propagation losses that can only be compensated using highly directional antennas, making millimeter-wave (mmWave) links susceptible to blockage and errors. This high sensitivity to blockage leads to unstable and unreliable connections, since proprietary IEEE 802.11ad mechanisms, such as beamforming training, have high overhead, and can only be triggered when performance degradation is already detected, which compromises QoS and QoE even more. This article proposes a proactive machine learning framework that uses real-life data acquired in an outdoor setting to improve the reliability and resilience of a blockage-prone WiGig-based network. In particular, we propose a link quality classifier, which can differentiate between normal, long-term blockage and short-term operation with a test F1-score of 97%. Moreover, we introduce a novel deep learning forecasting model that can accurately capture the interactions between past multi-layer observations under different environments to produce accurate forecasts for 16 KPIs.

This article presents the deployment and experimentation architecture of the Aveiro Tech City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of Internet of Things (IoT) devices with communication, sensing, and computing capabilities. The communication infrastructure, built on fiber and millimeter-wave (mmWave) links, integrates a communication network with radio terminals [WiFi, ITS-G5, cellular vehicular-to-everything, 5G and LoRa(WAN)], multiprotocol, spread throughout 44 connected points of access in the city. Additionally, public transportation has also been equipped with communication and sensing units. All these points combine and interconnect a set of sensors, such as mobility (radars, light detection and rangings (LiDARs), and video cameras) and environmental sensors. Combining edge computing and cloud management to deploy the services and manage the platform, and a data platform to gather and process the data, the living lab supports a wide range of services and applications: IoT, intelligent transport systems (ITSs) and assisted driving, environmental monitoring, emergency and safety, and among others. This article describes the architecture, implementation, and deployment to make the overall platform to work and integrate researchers and citizens. Moreover, it showcases some examples of the performance metrics achieved in the city infrastructure, the data that can be collected, visualized, and used to build services and applications to the cities, and, finally, different use cases in the mobility and safety scenarios.

The usage of aerial drones has become more popular as they also become more accessible, both in economic and usability terms. Nowadays, these vehicles can present reduced dimensions and a good cost-benefit ratio, which makes it possible for several services and applications supported by aerial drone networks to emerge. Taking into account the enormous diversity of use cases, many of the existing solutions for autonomous control focus on specific scenarios. Generic mission planning platforms also exist, but most of them only allow missions consisting of linear waypoints to be traversed. These situations translate into a mission support that is not very flexible. This paper proposes a modular infrastructure that can be used in various scenarios, enabling the autonomous control and monitoring of a fleet of aerial drones in a mission context. The platform allows the communication with the flight controller so that it can collect telemetry data and send movement instructions to the drone, and to monitor this data and send the commands remotely, also enabling robust mission planning with multiple drones, and enabling the interaction with internal and external sensors. The real tests performed through the platform show that the planned missions are executed exactly as they are planned in the platform.

Wireless networking is expected to sustain the direct interaction between personal users’ devices, and to provide connectivity on large-scale resource-constrained devices. However, conventional networking protocols fail in large scale mobile wireless environments, due to node mobility, dynamic topologies, and intermittent connectivity. Information-Centric Networking (ICN) has been considered the most promising candidate to overcome the drawbacks of host-centric architectures where Named Data Networking (NDN) is one of the well-known and studied architectures within the ICN paradigm. The main objective of this work is to improve both content availability and network performance in mobile environments regarding the ICN paradigm. This is provided through a context-based approach for the caching admission policy providing in-network caching and content replication, facilitating the efficient and timely delivery of information. Content popularity, freshness, proximity, source mobility type and network density are some of the metrics considered in the caching decision. We conducted a comparative study between our proposal and the NDN caching strategy by using two different datasets with real mobility and connectivity traces, addressing intermittent communication. According to our results, we observed that using a multi-criteria context-based cache admission policy improves cache hits, cache evictions, and request satisfaction ratios in mobile environments, thus improving content delivery and network efficiency.

In recent years, the Internet of Things (IoT) has become a standard facet of modern communications, and information-centric networks have been pointed as an alternative to bypass the restrictions imposed by the traditional Internet protocol networks regarding the mobility of its network elements. However, the improvements imposed by this new paradigm fall short in large scale mobile wireless distributed environments inherent to IoT, due to high node mobility, dynamic topologies and intermittent connectivity. To tackle these issues, we present a named data network (NDN)-based publish–subscribe mechanism with support for both Consumer and Producer mobility. This approach handles the Producer mobility by combining the Data packets with infrastructure specific information, fixing the broken paths between the Producer and the Consumer; and the Consumer mobility by monitoring and anticipating mobile node trajectories while compelling the infrastructure to adjust to new paths. Simulation results, assuming a smart city use case and using real traces of vehicular mobility, have shown that the proposed solution far surpasses the native NDN workflow and traditional publish–subscribe solutions. With respect to the Producer mobility, the proposed solution delivers 79% of Data packets against 14% with the Native implementation, when using 25 mobile Producers; regarding the Consumer mobility, results have shown that our solution achieves almost the same Consumer satisfaction ratio as previous implementations but reducing substantially the network overhead related with the transmission of Interest packets.

The concept of a smart city comes with the need to support a data platform that can gather, process and export the data of millions of sensors, coming from different sources, with information in different formats, in a scalable approach for realtime and historical data visualization, processing, and actuation in the city. This paper proposes a Data Platform for the Aveiro Tech City Living Lab, to gather, process, visualize and actuate on mobility, environmental and network data. The architecture of the platform provides a real open platform that is accessible for third-parties to collect data and to experiment their own solutions, through a secure and open data platform at their disposal. The results with respect to the amount of data gathered and examples of data show how this platform can be used to develop new applications and use both real-time and historical data for future predictions and actuations in a smart city.

Accurate prediction of urban traffic flows brings enormous advantages to big cities. Therefore, many urban traffic flow predictors have been developed in recent years. Urban traffic flow predictors aim to identify complex mobility patterns and capture urban traffic flow characteristics from large-scale historical datasets. Afterward, trained models are used to predict the future traffic volume in terms of the number of moving objects (e.g., vehicles). Convolutional Neural Networks (CNN) and other deep learning approaches are brilliant choices because of their ability to learn Spatio-temporal dependencies. Nevertheless, the extensive set of hyper-parameters tends to make these neural networks overly complex and challenging to design. In this work, we introduce RL-CNN, a framework based on Reinforcement Learning whose objective is to autonomously discover highperformance CNN architectures for the given traffic prediction task without human intervention. We examine the proposed RL-CNN model as a traffic flow estimator on a real-world and large-scale vehicular network dataset. We observe improvements of 5% - 10% in the average traffic flow prediction accuracy over the state-of-art approaches.

Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn long-term dependencies. However, these neural networks only learn the temporal traffic information for each trajectory (moving object), failing to take advantage of spatial information shared by neighboring trajectories. This paper introduces MTL-LSTM (Multi-Task Learning-based LSTM) traffic flow estimator, which attempts to explore both temporal and spatial dependencies among adjacent trajectories. Specifically, we employ LSTM predictors with the MTL approach to explore traffic flow patterns across urban trajectories. To examine the proposed model, we predict traffic flow in Porto's city using a data set from buses and taxies. Our experiments show improvements of 10% to 15% over the state-of-the-art.

Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordE r tR affI c convoluTiOn R 1-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.

Low-power wide-area networks (LPWANs) are probably the most promising radio access technologies for Internet-of-Things (IoT) applications. Amongst these, one of the most auspicious solutions is LoRa, a versatile technology highly compatible with urban environments, enabling long-range communications. Most of the LoRa-based medium access protocols operate under the ALOHA rationale, whose performance is known to be fairly poor. This work targets the medium access in single-channel large-scale LoRa networks, proposing a new protocol, denoted as the LoRa mode adaptive protocol (LoRa-MAP), which manages to maintain the best possible connection between end nodes and the gateway, by adapting the LoRa's physical layer parameters and making use of control packets for its coordination without violating the duty-cycle constraints of both end nodes and gateway. An analysis on different medium access schemes is conducted, aiming to perceive how different parameters and network layouts influence the coordination process. An energy expenditure analysis is conducted comparing LoRa-MAP to simpler solutions to study the impact of additional transmission/listening periods. The simulation results have shown that the proposed solution increases the LoRa network scalability, deeming it a great candidate for IoT environments.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

Practicing engineers are expected to effectively work in teams and manage projects besides being capable of producing sound technical solutions. Project based learning allows students to improve their skills in issues such as team work and project management, and applying their knowledge in concrete situations. This paper describes an approach to Project Based Learning that has been recently introduced in a Master degree in Electronic and Telecommunications Engineering. In this course unit, students develop a project to solve a problem proposed by a company. A person from the company follows the project development, along with a faculty member. During the academic year 2018-19, students organized developed 14 projects, originating from 12 different companies. The outcome of this first edition is assessed using three data sources: academic data (assessment marks and approval rates); the University quality assurance enquiry; and a survey, developed specifically for “Industrial Project”. The results show that both students and supervisors agree that the course organization is adequate and capable of engaging students and participating companies.

Emergency services play an important role in the intelligent transportation systems based on mobile communication networks in smart cities. However, the characteristics of Vehicular Ad-hoc Networks (VANETs), such as high mobility, intermittent connectivity, scalability and constant changes in the network topology make this type of message dissemination a challenge. To improve emergency message dissemination between vehicles, we propose strategies that take advantage of the location, direction, speed, number of vehicles’ neighbors and characteristics of the region of the city, so that the message reaches all vehicles in the shortest time with the lowest network overhead. To show the effectiveness of our strategies, we deployed an emulation platform with real connectivity and mobility data. Our results show a delivery rate between 92% and 100% at various densities and vehicle speeds, for various zone sizes of relevance and range of the communication technology.

Vehicular Networks (VANETs) are a critical component of a Smart City environment. They extended the connectivity plane with support for a wide range of applications, from safety to entertainment. Such services, when deployed outside the vehicular network, may imply an additional delay, which can be critical. In addition, these services become inaccessible whenever the vehicles lose contact with the infrastructure.This paper proposes a practical solution that aims to minimize the impact of the services’ location and its inaccessibility in a VANET. The solution focuses on using Network Function Virtualization technologies to support the deployment of the services at the edge of a mobility-enabled multihomed VANET, thus allowing the services to be accessible in intermittent connectivity situations, as well as enabling lower delays for critical services. The results obtained show that the solution is capable of deploying services at the edge of the VANET with low delay and with a fast recovery when in handover and mobility scenarios.

For efficient network management, it is important to monitor and analyse the data, particularly big data applications based on time series, in terms of trends and correlations, to predict network problems and be able to react preventively. Machine learning techniques can help but, given the amount and complexity of the algorithms and metrics available, the use of these techniques is laborious and requires specialized knowledge. This paper proposes a framework for distributed real-time time series forecasting with the goal to make predictions for various dynamic systems simultaneously and provide straightforward horizontal scaling, increased modularity, high robustness and a simple interface for users. Moreover, the proposed framework also enables the creation of ensemble algorithms, combining the results of multiple predictors, without changes on each individual predictor component. To demonstrate the functionalities of our framework, we show how simultaneous predictions can be made about the number of Internet sessions, using a real data stream from users of the buses in the Porto city.

With the advent of IoT and wireless mesh networks, security risks inherent to these types of networks have grown in number, severity, and potential for harm. Most of the approaches currently available for anomaly detection in IoT networks perform frame and packet inspection, which may inadvertently reveal private behavioural patterns of its users.This paper proposes a privacy-focused framework for anomaly detection which analyses radio activity at the physical layer, measuring silence and activity periods, and extracts relevant features for training One-Class Classification (OCC) models.We train our models with data captured from interactions with an Amazon Echo with multiple devices generating background noise, thus simulating a business-like environment, and test them against a similar scenario with a tampered network node periodically uploading data to a local machine. Our data show that the best performing model is able to detect anomalies with a 99% precision rate.

LoRa technology has been attesting itself as one of the most prominent and widely adopted low power wide area technologies. Highly compatible with Internet of Things (IoT) applications and urban environments, this technology enables large range communications although with small bandwidths and duty cycle restrictions. In this work, we propose a distinct way of dealing with the Medium Access Control (MAC) in LoRa, through the use of control packets to enhance the technology performance in urban city scenarios, where a large number of nodes is expected. The proposed protocol is asynchronous and takes into account the energy expenditure. We compare this scheme with the standard (LoRaWAN) by considering different network densities and packet sizes, and through different LoRa collision models. Performance results, such as network throughput and fairness index, show that, depending on the ratio between data and control packet lengths, it is possible to greatly improve the bit rate and overall network performance, even if increasing the duty-cycle restriction time due to the addition of overhead.

One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Over-the-top (OTT) content is being delivered, as the Internet has now become the `de facto` path for video transmission, with video data representing over 75% of the entire network traffic, and the users' desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clients' QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrate-adaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.

The proliferation of multimedia devices and video content availability is producing an increase in video content consumption. Video is now the most significant slice of all Internet traffic produced, and it is still growing. However, Over-The-Top (OTT) video is the one significantly increasing its strength, causing infrastructural and business challenges to Telecommunication Operators (Telcos), which provide traditional video services. Telcos must improve their network infrastructure to sustain future demand for OTT video content while improving user perceived Quality-of-Experience (QoE). A meaningful way to accomplish that is to distribute hardware appliances compatible with the Software-Defined Networking (SDN) paradigm throughout the network, improving flexibility and capacity while introducing new ways of content delivery.Towards this objective, this paper proposes an SDN load balancing approach to distribute OTT video requests through multiple servers. The load balancing decision is performed at the edge of the network influenced by the client's QoE. Comparing to a traditional load balancing scheme, the proposed method outperforms the non-SDN approaches in scenarios in which the server-side infrastructure starts to become stressed.

Content distribution strategies for delayed-tolerance networks (DTNs), such as Local Rarest Bundle First (LRBF), reach high delivery rate in the vehicular ad-hoc networks (VANETs), in spite of their continuous node mobility and their wide geographical dispersion. However, to reach a high delivery rate, this strategy requires each node to periodically send control packets with information of which content bundles it contains and that are available to its neighbours, increasing the network overhead. To overcome this problem, we are proposing two content distribution approaches to minimize the size of the control packets. Our results show that our algorithms can reduce more than 90% the size of control packets, while decreasing the network overhead without changing the delivery rate and end-to-end delay.

The growing interest in vehicular networks (VANETs) is encouraging its deployment in new environments, for both safety applications and to provide Internet to its users. Vehicles can connect to others, or to the infrastructure, providing an Internet connection. The communication equipment placed inside the vehicles may have multiple network interfaces of diverse technologies, such as IEEE 802.11p/WAVE, IEEE 802.11a/g/n (Wi-Fi) and cellular. For this purpose, multihoming and network coding can be used to provide high bandwidth and reliability to these services, reducing packet losses due to poor wireless signal quality, therefore improving the final Quality-of-Service (QoS). This work evaluates the impact of a Systematic Network Coding (SNC) approach in multihoming when the coding is applied over traffic incoming from multiple technologies, or from a single technology, depending on the entity responsible for the encoding process. The multi-technology architecture proposed in this work will be evaluated using different coding configurations (generation and buffer sizes) and by analyzing its impact on the packet loss recovery ratio. The comparative results show that the multi-technology approach has a better delivery ratio when compared to the single-technology, despite the small impact on the packet delay.

With the Fifth-Generation (5G) mobile networks set to arrive within the next years, this new generation will transform the industry with a profound impact on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) will play key roles for the network operators as they prepare the migration to 5G, allowing them to quickly scale their networks. This paper presents a research work undertaken on this new paradigm of virtualized and programmable networks, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, focusing on detection and prediction of potential network and service anomalies. Towards this end, the performance management system performs aggregation, correlation and analysis of data gathered from the virtualized and programmable network elements. In particular, customized catalog-driven tools are developed, and the results show that they are able to successfully address these requirements. Current performance management platforms in production are designed for non-virtualized (non-NFV) and non-programmable (non-SDN) networks, and the knowledge gathered from this research brings some new understanding on how management platforms must evolve in order to be prepared for the upcoming next-generation mobile networks.

Video content makes up the most significant slice of all traffic generated by Internet Service Provider (ISP) networks and is the primary driver for future bandwidth increase. For internal ISP delivery, native IP-Multicast is the most efficient way to deliver video in a group-like manner but poses challenges such as additional configurations, security issues, and hardware compatibility along the whole network path. Unicast transmissions, on the other hand, do not scale well for large requests of video content but are the most straightforward way of delivering Over-the-Top (OTT) content. Content-Delivery Networks (CDNs) are the cornerstone for scalable OTT services that rely on unicast transmissions, but the significant growth of such services poses a threat to ISPs, at capacity and business levels. However, multicast transmissions may not fit future requirements due to the lack of scalability for OTT videos. To this end, this research work discusses two approaches for video content delivery and provides a bandwidth consumption forecasting model, evaluating the methods considered to support network operators infrastructure planning. Results from the forecasting model show that for an European operator scenario a unicast-type approach fits better, and for a North American operator scenario, the hybrid approach is considered the best proposal.

Cities are continuously growing in population, vehicles, infrastructures and intelligence. Using and deploying smart technologies in the cities infrastructure can improve the multiple existing areas of a city, such as mobility by improving the road network, infrastructure by improving the urban planning and population by contributing with better services. Porto city has an in-place infrastructure of xed and moving sensors in more than 400 buses and roadside units, with both GPS and mobility sensors in moving elements, and with environmental sensors in xed units. This infrastructure can provide valuable data that can extract information to better understand the city and, eventually, support actions to improve the city mobility, urban planning, and environment. This work has the objective of using the information generated by the sensors placed in the buses of Porto, and using it to analyze the road tra c information based on the mobility patterns of the buses. The data from the environmental sensors deployed in Porto is also provided and used to analyze the air quality of the city and its in uence by the tra c. The developed system provides a full stack integration of the information into a city dashboard that displays and correlates the data generated from the buses movement and the environment from the xed sensors, allowing di erent visualizations over the road tra c and the environment in the city, and decisions over the current status of the city. A good example is the relation of bus speed variation with possible anomalies on the road or tra c jams. Visualizing such information with a superior level of detail on the road tra c, more anomalies can be found, adding more value to a city manager when taking urban planning decisions to improve the city mobility in a smart way.

An important aspect in improving public transport efficiency is collecting information regarding traveler routes, usually represented as an OriginDestination (OD) matrix. Most public transportation systems implement fare collection systems that can provide the accurate origins of traveler routes but not accurate destinations. In this dissertation we look at WiFi, more specifically 802.11 data-link layer, as a candidate to provide OD matrix estimations. We present an algorithm capable of collecting information regarding Wi-Fi capable devices inside the bus complemented with positioning and time. A system is also presented to implement this concept using minimal requirements. An implementation of this system was deployed in a public bus to collect data for several weeks. This resulted on over 15000 traveler routes collected in 9 different days. This data was contextualized and mapped to an OD system in order to demonstrate how it can be used to generate OD matrix estimations.

A common concern among Smart Cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of Smart Cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed in- formation for future treatment. With these challenges in mind, we are proposing self-organizing opportunistic approaches that allow vehicles to disseminate information to each other even while on the move, over a multi-technology opportunistic communication platform for data gathering and data exchange with both static and moving elements. To demonstrate their characteristics, we perform real experiments to evaluate four distinguished routing strategies, Epidemic, Direct Contact, Controlled Replication with Neighborhood Classification - Contacts based and Controlled Replication with Neighborhood Classification - Mobility based over an opportunistic vehicular network.

This work presents a V2V video transmission system to help on a car-overtaking decision using the IEEE 802.11p/WAVE communication technology. The system consists on a ffmpeg based video encoder that encapsulates the data received from a camera placed in a front vehicle into HTTP Post packets, and forwards the packet to the rear vehicle through the On Board Units (OBUs) installed on both vehicles. The rear vehicle presents the images to the driver using a visualization device. The proposed system was evaluated through real vehicular experiments in two distinct scenarios: urban and highway. Performance studies were focused on delay measurements, re- transmission rate, signal strength and bandwidth consumption related with the traveling speeds and video quality. Results show that the communication delay is higher in the highway scenario, mainly due to the distance between vehicles and the different speeds. However, promising results regarding the maximum delay and the average number of retransmissions foresee important inputs for future services of assisted-driving, in general, and car- overtaking assistance, in particular.

The advances in wireless technologies and the automotive industry lead to the appearance of Vehicular Ad-hoc NETwork (VANET). Consequently, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g. videos, ads, touristic information) dissemination for vehicular network environments. The nodes of a VANET are equipped with On-Board Units (OBUs) to enable the communication with other vehicles and to the infrastructure through Road Side Units (RSUs). OBUs enable content dissemination by the mobile nodes while RSUs are static boards deployed along the road or/and strategic locations (e.g. intersections and high buildings). This paper studies and designs how data can be efficiently disseminated using real vehicular boards (RSUs and OBUs) equipped with Dedicated Short Range Communications (DSRC) interfaces in vehicular networks. It evaluates a content distribution strategy (Local Rarest Bundle First (LRBF)) through a laboratory testbed composed by real RSUs and OBUs equipped with IEEE 802.11p (WAVE) communication interfaces. The experimentation results, performed using real vehicular boards, show that the LRBF strategy provides a high delivery of content through the vehicles, with a limited amount of resources.

The next-generation 5G mobile networks are expected to bring a major shift on the management paradigm based on Network Function Virtualization (NFV) and Software-Defined Networking (SDN) compared with its precursor, 4G. Consequently, operators need to significantly change their network architectures, management mechanisms and business models to accommodate and address the 5G challenges. This paper contributes to advancing the operators' management capabilities in the monitoring and analytic domains, looking at the evolution of the existing performance management platform from a leading operator to a new SDN/NFV-enabled platform, in the context of the EU 5G project SELFNET. This work focuses on designing and prototyping the essential functionalities to perform real-time processing over network infrastructure data. This work devises a new Complex Event Processing (CEP) framework, a realtime framework for processing and aggregating big data using a dynamic rule-based approach. This CEP framework has been successfully implemented and deployed, with aggregation rules applied to a Self-Protection use case, which is able to provide in real-time information about detected botnets in the network. From a high-level perspective, this work brings some new understanding about the role of SDN/NFV network management tools for 5G network operators.

Smart cities aim to improve the citizens' quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on citywide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

Real-time monitoring of traffic density, road congestion, public transportation, and parking availability are key to realizing the vision of a smarter city and, with the advent of vehicular networking technologies, such as IEEE 802.11p and WAVE, this information can now be gathered directly from the vehicles in an urban area. To act as a backbone to the network of moving vehicles, collecting, aggregating, and disseminating their information, the use of parked cars has been proposed as an alternative to costly deployments of fixed roadside units. In this paper, we introduce novel mechanisms for parking vehicles to self-organize and form efficient vehicular support networks that provide widespread coverage to a city. These mechanisms are innovative in their ability to keep the network of parked cars under continuous optimization, in their multi-criteria decision process that can be focused on key network performance metrics, and in their ability to manage the battery usage of each car, rotating roadside unit roles between vehicles as required. We also present the first comprehensive study of the performance of such an approach, via realistic modeling of mobility, parking, and communication, through simulations, and an experimental verification of concepts that are key to self-organization. Our analysis brings strong evidence that parked cars can serve as an alternative to fixed roadside units, and organize to form networks that can support smarter transportation and mobility.

The Local Rarest Bundle First (LRBF) and Local Rarest Generation First (LRGF), content distribution strategies for delayed-tolerance networks (DTNs), reach high delivery rate in the vehicular ad-hoc networks (VANETs), where intermittent connectivity due to permanent network fragmentation and node mobility are main challenges. However, in order to achieve a high delivery rate, both strategies require each node to periodically transmit control packets with information about the availability of its resources to its neighbors. This large amount of control packets causes overhead on the network. To overcome this effect, we are proposing a content distribution strategy based on a Bloom Filter. The results showed that our strategy significantly reduced the size of control packets decreasing the network overhead. Besides that, the number of nodes that obtains the least disseminated packets is much smaller.

Low-Power Wide Area Networks (LPWANs) are prominent technologies in the field of the Internet of Things (IoT). Due to the long range capabilities and low energy consumption, Low-Power Wide Area Networks (LPWANs) are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The work in this paper presents a study on the LPWAN LoRa technology, by testing and evaluating its range, signal quality properties and its performance in delivering data. Three distinct scenarios are proposed and tested, one rural and two urban scenarios in Portugal. The maximum communication range achieved was 5660 meters for the rural scenario and around 2000 meters for the urban ones, and we concluded that there are three essential scenario characteristics that influence the achieved results: distance, elevation difference and obstacles in the signal path.

The Internet is increasingly considered an essential and integral part of people's lives. With its massification and the constant increase of the bandwidth offered to the consumers, services like Over-The-Top (OTT) have grown in recent years - e.g. YouTube and Netflix. OTT services refer to the delivery of audio, video and other data over the Internet without network operators' control. This trend leads to Quality-of-Experience (QoE) challenges in the presence of large delays or quality breaks in the visualization of a video stream. To overcome these issues, new approaches are needed to improve the content delivery reception to the users, and consequently, the services' QoE. This paper proposes a novel prefetching mechanism aiming to reduce the content delivery response, bringing the content as close as possible to consumers before being explicitly requested. This approach is based on the distribution of OTT contents in wireless networks, that can be integrated in network equipment: it is able to predict the type and quality of content that consumers connected to - or nearby - an access point may request, and cache it before being requested, thereby improving their quality perception of the service. Given the lack of information in the literature on management and control of proxy caches for embedded systems, the first step is to analyze and compare different proxy cache solutions - Nginx and Squid - and then evaluate two developed prefetching algorithms in mobile scenarios, given the characteristics of wireless networks. The results show that the proposed content-aware prefetching mechanism is capable of boosting the network's performance, from a technical perspective - higher hit-ratios, reduced data transfers and request latency - and from a user perspective, with significant improvements in the Mean Opinion Score (MOS) QoE metric.

The offers on cloud computing infrastructure have focused on providing computing and storage resources, and consider the network mostly as a required add-on and not truly as a resource itself. However, the need to have more robust solutions is becoming clearer. This paper argues that cloud infrastructure services will evolve and allow the definition of complete infrastructures that comprise both computing and network resources, referred to as Virtual Infrastructures (VI). The VI embedding problem is addressed through joint virtualization of computing and network resources. An Integer Linear Programming (ILP) formulation is presented and different embedding strategies are proposed: load balancing, combining the load balancing of physical resources with minimal bandwidth consumption; green, reducing the energy consumption of the infrastructure; dynamic, combining both load balancing and green strategies depending on the substrate state. To enable real-time optimization, a heuristic approach is also proposed.

Today, in the cloud computing context, the network is starting to arise as a true resource itself and not just as a required connectivity add-on. The ability to define network resources (e.g. routing/switching elements, bandwidth) in this context is still scarce, but there are clear evidences that this is a future reality. In this article, we consider that cloud infrastructure services will allow the definition of complete infrastructures, to which we refer as Virtual Infrastructures (VIs). These VIs comprise computing, storage and network resources. This article specifically tackles the VI embedding problem, which is known to be NP-hard. Having in mind this fact, different embedding strategies based on Integer Linear Programming (ILP) formulation are presented. The problem is addressed by proposing strategies that target load balancing and energy consumption of physical resources along with re-optimization strategies. A thorough evaluation of the different strategies is performed, studying the VI acceptance ratio, physical infrastructure energy consumption, and the impact of reconfigurations. Results show that there is a clear tradeoff between improving the VI acceptance ratio and reducing the physical infrastructure usage without allowing re-optimizations. We study in-depth different ways of combined strategies.

Efficient and cost effective data collection from smart city sensors through vehicular networks is crucial for many applications, such as travel comfort, safety and urban sensing. Static and mobile sensors data can be gathered through the vehicles that will be used as data mules and, while moving, they will be able to access road side units (RSUs), and then, send the data to a server in the cloud. Therefore, it is important to research how to use opportunistic vehicular networks to forward data packets through each other in a multi-hop fashion until they reach the destination. This paper proposes a novel data forwarding algorithm for urban vehicular networks taking into consideration the rank of each vehicle, which is based on the probability to reach a road side unit. The proposed forwarding algorithm is evaluated in the mOVERS emulator considering different forwarding decisions, such as, no restriction on broadcasting packets to neighboring On-Board Units (OBUs), restriction on broadcasting by the average rank of neighboring OBUs, and the number of hops between source and destination. Results show that, by restricting the broadcast messages in the proposed algorithm, we are able to reduce the network's overhead, therefore increasing the packet delivery ratio between the sensors and the server.

Low-Power Wide Area Networks (LPWANs) are a set of technologies that are growing in the field of the Internet of Things (IoT). Due to their long range capabilities and low energy consumption, LPWAN are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The integration of one of the LPWAN technologies, Long Range (LoRa), in a multi-technology data gathering platform is the key objective of this work. This objective is addressed with the following proposed approaches: (1) an organization based in clusters of sensor nodes; (2) a Medium Access Control (MAC) protocol that provides reliable communications through LoRa technology; and finally, (3) a connection manager that selects which wireless technologies should be used simultaneously to forward the sensing data, according to its classification. The evaluation experiments aim to understand which type of parameters can influence the performance of the overall proposed solution, as well as the advantages of a multi-technology approach in a data gathering platform. The results in a real scenario show that the proposed approach is able to achieve simultaneous multi-technology communication while delivering different types of data with reliability and low latency.

A comprehensive implementation of the envisioned traffic safety and efficiency applications of the IEEE 802.11p and Wireless Access for Vehicular Environments (WAVE) standards assume the premise of the use of Dedicated Short-Range Communications (DSRC) technology both as on-board units and as roadside units (RSUs). The high cost associated with RSUs, however, has so far prevented massive deployment of RSUs. Finding alternative solutions to this longstanding problem is therefore very important. In this paper, we propose a self-organizing network approach to using parked cars in urban areas as RSUs. This self-organizing network approach enables parked cars to create coverage maps based on received signal strength and make important decisions, such as if and when a parked car should serve as an RSU. Our results show the feasibility and cost-effectiveness of the proposed approach, which is able to provide excellent coverage using only a small fraction of the cars parked in a city.

Navy communications between mariners, boats and ships can be significantly demanding in terms of quality. Moreover, due to their intermittent connectivity in dynamic scenarios, they require the support of store and forward mechanisms to opportunistically use the available communications and transmit the information related to their operations. However, besides the fact that the communication can be intermittent, it needs to assure a minimum quality whenever communication is possible. This paper proposes an approach for delay tolerant routing based on the quality of the wireless links in navy communications. It measures the quality of the available connections and it uses this information to develop a quality function and a quality-based transitive rule. We propose a delay-tolerant quality-based routing protocol - Quality PRoPHET (Q-PRoPHET), that uses the quality function and transitive rule to determine the best routing decisions. This approach is tested in real indoor and outdoor scenarios that emulate the navy ones. Q-PRoPHET results in better performance than the original PRoPHET in terms of delivery rates, end-to-end delay and packets transmission, which are critical parameters for the communication in navy operations.

The exponential increase of mobile Internet traffic has been forcing the current mobile network architectures to evolve towards flatten network architectures with IP backhaul support. Hence, current centralized mobility management approaches need to be adapted to cope with flat network architectures. The distributed mobility management is a promising direction to cope with the flatten network architectures and distribution of content servers. Thus, the distributed IP mobility approaches are characterized and grouped into tunneling and host routing models. Both models are evaluated regarding performance and scalability through simulation in different scenarios. The outcome demonstrates that both models are valid for mobility management of future networks, depending on the users, the network and the sessions. Overall, the host routing provides a better data delivery performance than tunneling approaches, but a worst scalability. Regarding the tunneling approach, the AR anchoring has an opposite behavior to host routing, while the CN anchoring is the approach that provides the best tradeoff between performance and scalability. Moreover, the global mobility management can be easily supported by the coexistence of these different models, when tightly-coupled.

Since today's networks use traditional centralized management systems, the management became costly with the growth in the number of network equipments and available services. It became then clear that it was necessary to distribute the central management responsibilities throughout the network equipments.

The migration of popular Catch-up TV services to modern Over-The-Top (OTT) multimedia delivery infrastructures creates a wide set of scalability challenges which are commonly addressed using Content Delivery Networks (CDNs) relying on caching nodes close to users. The use of general-purpose caching nodes, tailored for generic web content, is far from optimal as it does not consider the particularities of Catch-up TV content, namely its dynamic popularity behavior, superstar effects, and relevance decay, as shown in existing scientific literature. Since caches are limited in size and are relatively small when compared to the whole catalog of available Catch-up TV content, which may contain tens of thousands of TV programs, it is crucial to make the most out of the available resources. To address these issues, this paper proposes a novel content-aware cache replacement algorithm, Most Popularly Used (MPU), capable of taking advantage of content demand forecasts built using machine learning models, to significantly outperform traditional cache replacement policies, such as Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO), and approach the optimal theoretical hit-ratio limits. MPU leverages millions of Catch-up TV request logs to validate its results under realistic conditions.

Summary form only given. Through a large-scale vehicular network in the city of Porto, denoted as CityVanet, that provides a low-cost and large range communication support all over the city, we are able to gather data from the vehicles, people and the sensors installed in the city and in the vehicles. The correlation of all this information from different sources and different types of sensors will be key to understand the city flows and dynamics, the impact in the environment of the city dynamics and mobility of people and vehicles, and how the change in these dynamics can also change the city environment. Moreover, it will enable the development of a myriad of applications from traffic management systems to tourism services. This talk will delve into the services and applications that build smart environments, and how the data gathered through moving elements in the city can provide all the information required for these applications.

The ability to predict the behavior of cars that are parked in an urban area can be very useful to the development of vehicular networks that leverage these parked cars. In this paper, we analyze the mobility patterns of people living in US cities who use cars as their primary means of transportation. We process and analyze survey data from the metropolitan areas of Atlanta, Chicago, and Knoxville, to extract statistics on the parking behaviors of individual cars.

Network is now a main part of the cloud resources, becoming more than a connectivity add-on. The ability to define network resources (e.g. routing/switching elements, bandwidth, delay) in this context is still scarce, but there are clear evidences that this is a future reality, as is the case of Network Functions Virtualization (NFV). In this paper we consider that cloud infrastructure services will allow the definition of complete infrastructures, to which we refer as Virtual Infrastructures (VIs). These VIs comprise both computing, storage and network resources. This paper specifically tackles the VI embedding problem in scenarios with multiple domains. These scenarios consider multiple Data Center (DC) locations inter-connected through an operator network. The embedding problem is known to be NP-hard, and therefore we present an embedding strategy based in Integer Linear Programming (ILP) formulation. The proposed strategy aims to balance the load among the different domains, and considers the location as a key constraint for virtual resources. Finally, a thorough evaluation of the formulation is performed, analyzing the VI acceptance ratio and the occupation of the physical infrastructure. The obtained results show that the location constraint has a high impact on both the acceptance ratio and occupation of physical resources.

With the emergence of OpenFlow, the first Software Defined Network (SDN) communication protocol standard, it is important to understand how current centralized network management platforms can be adapted to support this new protocol. This paper demonstrates how legacy centralized management systems, and their respective network devices, can be upgraded to provide Multi-Protocol Label Switching (MPLS) and Metro Ethernet Forum (MEF) services using the OpenFlow protocol. In a first step, this paper demonstrates how an OpenFlow controller can be implemented and interconnected with a proprietary centralized management system running in a Portuguese operator. In the second step, we will demonstrate how an OpenFlow agent application can be specifically designed and implemented for proprietary network equipments. The addition of the OpenFlow agent provides to the network equipment the capability to receive and translate OpenFlow configuration commands into native configuration commands, facilitating the control by generic OpenFlow controllers. The results of the tested scenarios, which comprise the establishment of MPLS tunnels with label switching, Ethernet virtual private lines and trees, show that the MEF services activation and deletion are performed in less than 10 msec.

Vehicular Ad-hoc NETworks (VANETs) have unique characteristics, such as the high mobility of nodes, which cause frequent losses of connectivity, turning technology and network selection into a complex problem. Moreover, due to the instability of networks and its fast change, this network selection requires information of the networks in advance, so that it can predict the quality of the networks to connect. This paper proposes a predictive connection manager that is able to choose in advance the best network and technology a vehicle can connect to. This approach will use the information of previous connections to a station or road side unit, such as the throughput and amount of transferred traffic, and combines this information with sensing information of the quality of available networks. With both previous and current information, it will choose the network that has been able to provide the best performance in previous connections and remains with a good quality. The results of the proposed approach, tested in a real vehicular network deployed in Portugal, show that key performance metrics are improved, such as connection range, throughput and the number of handovers, with a general improvement on connection stability.

Considering the growth of customers craving for complex services like IPTV, cloud storage, and online gaming, traditional networking is quickly becoming an obsolete technology. The new networking paradigm, Software Defined Networking (SDN), in combination with the recent communication protocol, OpenFlow, brings a new perspective to the operator and cloud provider's core networks by providing new autonomous tools. This paper proposes and evaluates a new SDN control mechanism that allows the definition of dedicated connectivity service within an operator network, making it possible to easily provide and support cloud services to the end user. It takes advantage of the combination of SDN and Cloud Computing, and further benefits the automation of both technologies, which empowers the operator to provide faster services to its users, such as virtual machine instantiation with automatic network creation. The evaluation of the proposed approach shows that the most time consuming phase is the installation of the OpenFlow rules in the network: in a network of 128 nodes, it takes around 250msec to both install and remove connections.

A large-scale urban sensing platform, composed of multiple Data Collection Units (DCUs) equipped with sensor hardware scattered accross the city, allows pervasive monitoring of environmental parameters. Gathering sensor data from a number of disparate locations at a backend server can be supported by delay-tolerant services provided by existing vehicular networks. Our real-world sensing platform takes advantage of an existing vehicular network with more than 400 vehicles equipped with On-Board Units (OBUs). A purposely-developed implementation of a delay tolerant service is installed in all elements involved in the communication flow, from DCUs to the backend server. In this demo, we showcase the full end-to-end data flow with the actual equipment being used in our real-world deployment. Data produced at a DCU is collected by an OBU installed in a vehicle and delivered to a Road-Side Unit (RSU), which then forwards the data to the backend server.

In this paper we propose VANESS, a naming service that supports user-to-user communication on mobile devices within a heterogeneous scenario comprising typical Internet Service providers and ad-hoc networks formed by vehicles and roadside units, VANETs. VANESS provides real-time mapping between user

In urban vehicular networks, Road-Side Units (RSUs) take a crucial role in improving the performance of the network, by working as content distribution points, relays for time-critical broadcasts, and points of central coordination. The high costs associated with the installation and maintenance of RSUs, however, keep these units from seeing widespread deployment. One approach to this problem is for cars to be used opportunistically as RSUs, and in urban areas, the presence of large numbers of parked cars make these entities promising candidates for establishing vehicular support networks. In this paper we introduce new methods for parked cars to self-organize and act as a support network to the existing urban vehicular network, alleviating the need for costly deployments of fixed road-side units. Our approach considers parked cars that can both complement existing fixed RSUs and take the role of RSUs themselves, improving the network's performance on multiple applications. We show that even a small number of parked cars can bring considerable improvements to the network, and that our proposed methods for self-organization create support networks of parked cars that can cover the urban area with an optimal numbers of vehicles.

The momentum around Software-defined Networking (SDN) is increasing. It has become clear that the network architecture needs to evolve to be able to provide current and next generation networking services, both from a performance and implementation point of view. However, most of the current SDN research has looked to specificities of SDN and few have looked from a full stack perspective. Although the former are essential, also the latter perspective needs to be taken into account. In this sense, our work proposes a complete and modular SDN framework targeted at connectivity services. This framework allows the creation and management of network connectivity services over an OpenFlow based network, with mechanisms of fault-management, as well as the optimal usage of the infrastructure. The performance results obtained from the SDN framework evaluation in a real environment show that the three different scenarios, service activation, link loss, and reaction to a new link, are dynamically supported with fast reaction to the network changes.

In sparse highway vehicular networks, the high probability for network disconnection at the initial stages of introducing the DSRC technology can be mitigated by the deployment of fixed infrastructure points known as Road Side Units (RSU). However, due to the cost associated with the deployment and maintenance of significant numbers of RSUs, it is highly unlikely that the majority of highways will be seeing RSU support in the near future. In this paper we study the impact of specific vehicular network parameters in the communication delays in infrastructure-less highway scenarios: first, the deceleration of vehicles, and consequently, a decrease in their separation from succeeding vehicles; and second, the transmission power of the IEEE 802.11p radio, which can be increased to achieve faster connectivity with the succeeding vehicle. Our results show that the connectivity of sparse vehicular networks can be improved substantially by varying these parameters.

The deployment of vehicular network testbeds encompasses a myriad of new challenges regarding resource management, execution of experiments on the nodes, and the collection of the resulting data, when compared to traditional testbeds. Vehicular nodes have very unpredictable mobility patterns, especially in cities, and use a specific communication protocol; therefore, the commonly used solutions for large scale testbeds are not directly applicable. This is the case of our vehicular network testbed developed in the framework of the DRIVE-IN and Future Cities! (DRIVE-IN and Future Cities!)1projects. This paper tackles the aforementioned issues by proposing the design and implementation of a testbed management system based on the cOntrol and Management Framework (OMF). The base functionalities of OMF were extended to integrate node control over cellular networks, as well as to guarantee that experiments are always safely deployed, minimizing the risk of node corruption and malfunctioning. The management platform is capable of uploading new software to the vehicular nodes, conduct experiments using the specific vehicular wireless (IEEE 802.11p) interface, collect the resulting data, as well as storing and managing information about all the nodes. The results obtained from real-world experiments show that the the proposed platform is able to manage a Vehicular Ad-hoc NETwork (VANET) testbed.

Conventional networks are deployed through hierarchical architectures, which in many cases deal with centralized mobility anchoring models to ensure IP session continuity and IP reachability to mobile devices. Current IP mobility management is based on a centralized and static mobility anchor, which is a single point of failure that can introduce long delays and high management signaling. IP mobility management needs to cope with the evolution of mobile network architectures towards flatten architectural models. Thus, novel distributed mobility models have been proposed to distribute the mobility anchors closer to the end-user.

Vehicular networks, which have been initially designed to support safety applications, are now also envisaged as a way to provide internet access to its users while in the vehicles. Due to the high mobility of the vehicles and of the network formed by the vehicles, a significant number of handovers of the vehicles between road side units will be in place. We have designed and implemented a seamless network handover mechanism that is able to move a network of vehicles between different infrastructure units, even in connections with a different number of vehicles to the infrastructure. This mobility approach is also integrated in a connection manager that provides seamless communication between vehicles and/or the infrastructure, electing the best technology and network to maintain the vehicle connected without breaking any active sessions of the users. To validate and evaluate the proposed handover approach, two real-world vehicular testbeds were developed, both combining two technologies: WAVE/IEEE 802.11p and IEEE 802.11g. The results of handovers performed on various road scenarios show that the proposed mobility approach provides transparent mobility for both vehicles and passengers, even in mobility between networks with a different number of vehicles to the infrastructure.

This paper proposes REINVENT, a software architectural solution to decouple the specificities of using vehicular network communication resources in Android smartphone applications. REINVENT provides a REST based message passing interface based on Android's content provider to abstract vehicular network communication services. Using REINVENT, Android applications can use transparently the communication resources avoiding network transport layer details. REINVENT was tested in both laboratory and real world scenarios in the road with WAVE/IEEE 802.11p technology, to assess its usefulness in two Android applications, (VNChat and iThere). The real experiments also assess how REINVENT supports a mobile application to log relevant parameters in typical vehicular networks testing in different network scenarios.

The IEEE Symposium on Computers and Communications started in 1995 in response to the growing interaction between the fields of computers and communications. IEEE ISCC takes place annually, in the beginning of the summer. Symposium locations include countries in the Mediterranean region, and in the Atlantic, such as Madeira this year.

In decision approaches applied to network control, it is common to use a function that encompasses a set of parameters weighted through empirically defined values. However, these weights are usually not optimized, and the error from the optimal value will be propagated to the decision function. This paper presents a method to determine the best input weight parameters according to a predefined payoff function using a genetic algorithm (GA). We have extended the GA with respect to its key elements, e.g. chromosomes coding scheme and fitness function. The method is quite general and suits any simulation-based optimization problem with multiple discrete input parameters, without requiring the whole system behavior to be expressed into a mathematical formula. The optimization method is tested in our approach for social-aware nodes' association in mobile networks, and the results show that it autonomously searches for the input weight values that lead to the best solution for the association decision, improving the results when compared to empirically defined weights.

The increase of mobile traffic and the new role of the users as active elements in the network require a new approach for network architectures to adopt more flatten models. However, current mobile IP approaches were developed assuming static and centralized models: a single entity is responsible for the mobile traffic, the signaling and the context information of a large amount of Mobile Nodes. We propose a new host-based mobility approach denoted as Dynamic Mobile IP Anchoring (DMIPA) that distributes mobility management functionalities among Access Routers (ARs) and MNs, and provides dynamic sessions anchoring. DMIPA is envisioned to support these novel flat network architectures, and it is able to provide global IP mobility to the users, independently of the access network, technology and Internet provider. Through an analytical model, we compare DMIPA against Mobile IPv6, which is the standard host-based mobility model for IPv6 networks. The evaluation shows that DMIPA achieves better results regarding the handover delay and the data delivery cost, while the signaling cost can also be reduced in networks with low loss probability.

It is a great pleasure to welcome you to the second edition of the IEEE Workshop on the Internet of Things: Smart Objects and Services.

Network Virtualization is claimed to be a key component of the Future Internet, providing the dynamic support of different networks with different paradigms and mechanisms in the same physical infrastructure. A major challenge in the dynamic provision of virtual networks is the efficient embedding of virtual resources into physical ones. Since this problem is known to be NP-hard, previous research focused on designing heuristic-based algorithms; most of them either do not consider a simultaneous embedding of virtual nodes and virtual links, or apply link-path formulation, leading to non-optimal solutions. This paper proposes an integer linear programming (ILP) formulation to solve the online virtual network embedding problem as a result of an objective function striving for the minimization of resource consumption and load balancing. To this end 3 different objective functions are proposed and evaluated. This approach applies multi-commodity flow constraint to accomplish a node-link formulation that optimizes the allocation of physical network resources. This proposal is evaluated against state of the art heuristics. The performance of the heuristics related to Virtual Network (VN) request acceptance ratio is, at least, 30% below the one of the Virtual Network Embedding Node-Link Formulation (VNE-NLF) method. From the three cost functions evaluated, the Weighted Shortest Distance Path (WSDP) is the one which embeds more VNs and also requires, on average, less physical resources per embedding.

We develop an optimization framework for Quality of Experience (QoE)-based routing in multi-service Wireless Mesh Networks (WMNs). The framework takes into account the heterogeneous requirements of different services delivered over a WMN, such that the overall end-user QoE is maximized under given resource constraints. We propose a novel QoE-aware double reinforcement learning strategy for dynamically computing the most efficient routes to deliver the flows of each service type. Comprehensive NS-2-based simulations demonstrate the substantial performance gains that our approach enables over conventional routing techniques such as AODV, with significant improvement over video quality.

The flexible infrastructure of Wireless Mesh Networks (WMNs) can be exploited to provide support to emerging communication paradigms. In this paper we consider an architecture of context-based virtual WMNs, where the context requirements and preferences of mesh clients are the driving force to dynamically create and configure, through network virtualization, a set of logical networks (each one specific to a set of context levels) on top of the physical infrastructure. This paper proposes a context-aware framework to match and select available Virtual Networks (VNs) for users, avoiding the constant creation of new VNs to serve single users. A control mechanism is presented to create, update, reconfigure, and terminate VNs on-demand. Several aspects of this control mechanism are analytically evaluated and compared against a simulation study, showing very similar results. Moreover, we evaluate the performance gains of our architecture when compared to other approaches in the field of WMNs. The obtained results show the small VN control signaling delays and overhead, the decrease of the number of nodes involved in a VN reconfiguration, and the increase of the average user throughput of our approach when compared to the others; they also show the gains of re-using available VNs avoiding the constant creation of new VNs.

Mobile users are increasingly demanding multimedia content created specifically or at least tailored to cater for their needs and preferences. The limitations of current Internet challenge a lot the design of such sessions, and require embedding innovations beyond standards to achieve rich networking. In this scope, the Context Casting (C-CAST) project designed a context-sensitive network architecture to support value-added multimedia sessions with Quality of Service (QoS) guaranteed towards multiple users simultaneously, and in their wireless technologies. However, the per-flow driven signalling approach of Legacy C-CAST exponentially increases with the number of flow admissions, which jeopardize the system performance. Thus, this paper proposes the Advanced C-CAST, which re-architects the Legacy C-CAST to support context-driven over-provisioning of per-class bandwidth and overlay trees. The evaluation of Advanced C-CAST demonstrated its benefits against Legacy C-CAST by significantly optimizing overall signalling load and latency experience.

This paper addresses the problematic of mobility management decomposition, as a first step towards the development of decentralized mobility management architectures. We propose and evaluate an IP mobility management solution based on the decoupling of data and control plane. We have carried out simulations, showing that the proposed approach can significantly reduce packet loss and reachability time for mobile nodes.

The access infrastructure to the Cloud is usually a major drawback that limits the uptake of Cloud services. Attention has turned to rethinking a new architectural deployment of the overall Cloud service delivery. We argue that it is not sufficient to integrate the cloud domain with the operator's network domain based on the current models. In this work we envision a full integration of the Cloud and the network, where cloud resources are no longer confined to a data center, but are spread throughout the network and owned by the network operator. In such an environment, challenges arise at different levels, such as at the resource management, where both cloud and network resources need to be managed in an integrated approach. We particularly address the resource allocation problem through joint virtualization of network and cloud resources, by proposing an algorithm to allocate cloud and network resources in an integrated way. This algorithm is evaluated through both simulation and experimental results in a real virtualization platform.

Autonomic and distributed management requires the cooperation between the network nodes and the periodical exchange of network information between them. To increase scalability of the information exchange, network nodes need to be associated and grouped through an efficient set of rules, to minimize the amount of control messages flowing in the network. This paper proposes a community-based association between nodes, where nodes in a community are connected through social-based metrics. The proposed social-based metrics are able to join the network nodes through their characteristics and probable interactions, reducing the disconnection between nodes and optimizing the information exchange between them. The obtained results show that the social-based metrics increase stability of the links and provide better resource aggregation, decreasing the required number of messages exchanged between nodes in a distributed management approach.

Opportunistic routing is being investigated to enable the proliferation of low-cost wireless applications. A recent trend is looking at social structures, inferred from the social nature of human mobility, to bring messages close to a destination. To have a better picture of social structures, social-based opportunistic routing solutions should consider the dynamism of users' behavior resulting from their daily routines. We address this challenge by presenting dLife, a routing algorithm able to capture the dynamics of the network represented by time-evolving social ties between pair of nodes. Experimental results based on synthetic mobility models and real human traces show that dLife has better delivery probability, latency, and cost than proposals based on social structures.

ABSTRACT Privacy is becoming a pressing requirement that spawns across different network layers and user interactions. Using multiple identifiers for the same user, pseudonyms, as means for preserving privacy, is becoming an important approach that has not seen proper discussion or validation. This paper presents formalization on pseudonymity specially considering its influence on the network stack. It presents a study on privacy requirements and its implementation cost, both at theoretical and practical levels, through the support of multiple network stack instantiations and network identifiers per user. We discuss how pseudonyms can be used to preserve users' privacy, and what key issues and requirements are needed, to use, control, and evaluate a pseudonym‐based solution at network level. The paper further presents an architecture that builds a pseudonym‐based solution supported throughout the network stack, describes its implementation in a real testbed, and depicts the results obtained related to both efficiency and costs of such a privacy approach. Finally, it discusses the characteristics of the architecture according to the identified privacy requirements, its impacts and constraints. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents a routing framework that embeds location and communication privacy into the routing mechanisms. It conceals endpoint identification by introducing waypoints, through encrypted routing hints, where each waypoint has knoweldge of the next hop, assuring network privacy over several waypoints. Based on IPv6 extension headers and Onion Routing techniques, the network waypoints comply with normal routing procedures, avoiding explicit tunneling or full packet encryption. By focusing on the network as a cooperative entity for privacy preservation, we propose a lightweight approach that can be easily deployed, establishing a good compromise between privacy and optimal routing.

In wireless networks a packet can be lost due to numerous reasons, such as congestion, medium related errors, routing and mobility. In these scenarios, the real congestion status of the network is crucial to develop accurate and efficient congestion control protocols. Some of the most known and recent protocols developed to provide faster and lighter congestion control are the eXplicit Control Protocol (XCP) and the Rate Control Protocol (RCP). These protocols have been proposed essentially to work in wired networks and environments; however, there are already new versions of XCP for wireless networks. Moreover, although these protocols estimate the available bandwidth of the links, this estimation is not accurate for wireless networks. This paper proposes new flow control protocols for wireless mesh networks, based in XCP and RCP, which we have designated as XCPWinf and RCP-Winf. These congestion control mechanisms are supported on a new method to estimate the available bandwidth and the path capacity over a wireless network path, denoted as rt-Winf, through a cross-layer approach. The estimation is performed in real-time and without the need to intrusively inject packets in the network. This is accomplished by resorting to the CSMA-CA scheme with RTS/CTS packets to determine each node's channel allocation. The results obtained, through the simulation of these protocols in different scenarios, show that rt-Winf is able to significantly increase the efficiency of congestion control mechanisms.

With the large number of access technologies and the emergence of user mobility, the need to "access the world", anywhere and anytime has gained a significant importance. It is essential to ensure the quality of the services when moving between access networks and technologies in a transparent manner. This paper presents a new architecture that enables seamless handovers in wireless heterogeneous environments. Towards this aim, the proposed architecture combines the MIPv6 mobility management protocol, the IEEE 802.21 Media Independent Handover (MIH) framework, and a mobility control entity responsible for controlling the handover decisions in a make-before-break approach. In order to validate and evaluate this new solution, a real testbed was developed, combining Wi-Fi, WiMAX and 3G technologies. The results obtained with this experimental platform cover the analysis of the handover processes, and important aspects as the handover performance, QoS and QoE of the services while handover is taking place. The results show the significant benefits of the proposed approach.

This paper presents a summary of work aimed at making current multihop routing in mobile ad hoc networks more sensitive to node mobility and ultimately improve routing performance in the face of node mobility. We discuss the various existing mobility tracking parameters, how they capture node mobility and also their shortfalls. We propose some mobility tracking schemes(heuristics) based on one of the mobility tracking parameters(Link Duration) and also show the performance of one of the proposed heuristics.

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.

As the interest on Network Virtualization continues to grow, so does the awareness of the many technical obstacles to transpose before the envisioned virtualized network environment may become a reality. A significant obstacle lies on the efficient assignment of virtual resources into physical ones. Performing the so-called mapping of a virtual network into a substrate network is a computationally intensive task, due to the dual optimization required for nodes and links placement. The purpose of this paper is to tackle this problem taking into consideration real-life scenarios of network operators, where the limitations imposed by the heterogeneity of the virtual and substrate networks must be accounted for. To that end, this paper proposes a heuristic algorithm for virtual resources mapping in the physical infrastructure that supports the heterogeneity of networks, in both links and nodes. The mapping heuristic was evaluated both through simulation and in a real experimental virtualization platform. Through the simulation results, it is shown that the mapping approach is able to embed a high percentage of the Virtual Network (VNet) requests respecting all links and node constraints. With respect to the experimental results, the proposed algorithm was shown to be fast, requiring a mapping time in the order of low tens of milliseconds, and linearly scalable with the increase in the number of existing VNets.

The growth of available broadband access technologies has brought an enormous challenge for operators wanting to ensure seamless mobility to its customers in heterogeneous environments. In this paper we present an enhanced Mobility Management entity focused on a real heterogeneous environments and based on IEEE802.21 standard. This entity is capable to perform terminal and handover management regardless the access technology used. This entity was deployed in a real heterogeneous environment with Wi-Fi, WiMAX and HSPA using an Android smartphone. In order to provide Layer 3 mobility support it was integrated with a modified version of MIPv6. The results show not only the impact of a centralized entity to manage vertical handovers, but also the performance of IEEE802.21 protocol in a real scenario with a real implementation.

The reliability of communication in vehicular networks depends mostly on the density of DSRC-enabled vehicles that form the network. In highway scenarios, and depending on the time of day, the probability of having a disconnected vehicular network can be very high, which hinders communication reliability. To improve communication in these scenarios, infrastructure points known as Road Side Units (RSU) may be used. RSUs, however, have an associated cost, and therefore the number of RSUs needs to be minimized while still providing a significant improvement on communications. In this paper we study the effect of including RSUs as relay nodes to improve communication in highway scenarios. We model the average time taken to propagate a packet to disconnected nodes (denoted as re-healing time) when considering both scenarios of connected and disconnected RSUs. We then compare the results of both these models and of a model with no RSUs. Results show significant improvements with RSU deployments, both connected and disconnected, particularly in multi-cluster communication scenarios.

In the future mobile Internet, a key challenge is how the content is delivered to the mobile user, especially when there are multiple networks belonging to distinct operators. Knowing the network services and conditions available at each one becomes crucial. In order to store information from several networks and operators, the new IEEE 802.21 standard specifies a Media Independent Information Service (MIIS) supporting various information elements (IEs) that provide network information within a geographical area, with the aim of optimising the handover process. Since next-generation wireless devices are expected to support multiple radio access networks, users will be able to maintain connections when switching from one network to another. This article addresses a general approach towards the deployment of a Hierarchical 802.21 Information Service management infrastructure in a heterogeneous mobile environment. Through an NS-2-based simulation, it is shown that the proposed scheme improves the user experience in terms of MIIS server access time and number of optimal handovers in a random mobility scenario.

This paper proposes an architecture to provide seamless reporting over heterogeneous networks while focusing on the End-to-End information exchange and Quality of Experience analysis. For this purpose, it is presented a reporting architecture based on IEEE 802.21, where the reports are seamless exchanged through new extended IEEE 802.21 messages. This approach, besides saving on overhead of service and end-users reporting, is able to seamless support reporting through heterogeneous technologies, in multiple layers, while integrating both actions of reporting and network reaction, through IEEE 802.21. This new framework balances the impact of the signaling over the network load, representing a good lightweight trade off when comparing to other solutions, while still being able to deploy new business cases.

The demanding requirements of new real-time and mobile multimedia group-based applications (e.g. IPTV and video-conferencing) motivates the research for novel and efficient network architectures, able to fulfil service requirements and increase user experience. Multicast, despite its scalability issues, is the most suitable network technology for the delivery of multiparty content. Context-awareness is taken as a key feature in Next Generation Networks, enabling a management framework with a more comprehensive knowledge of the network, application and user. The challenge is how to use the available knowledge towards service personalization and network efficiency. This paper presents and evaluates an architecture that aims to provide personalized session content delivery to multiple mobile users, integrating different access and transport technologies. The architecture relies on an end-to-end context-aware communication framework able to dynamically react and adapt to context changes, through session and network reconfiguration. Our focus is on the transport network part, addressing a new level of scalable multiparty content delivery through the concepts of abstract multiparty trees and overlay nodes. The results show that the concept of abstract trees reduces the network reconfiguration complexity while still guaranteeing end-to-end QoS.

The limited scope of Internet architecture forced network providers implementing centralized mechanisms (QoS, group communication, mobility, etc) to support attractive value added sessions. Besides ease to manage, performance aspects of such mechanisms, mainly in terms of scalability, robustness and availability, are shortcoming. In this sense, decentralization is promising, since complex networking operations may be provided throughout the nodes, increasing the network performance. However, current distributed solutions implement their own strategies for decentralization (e.g. synchronization, resilience, etc.), which contributes to increase Internet's complexity and damage overall performance. This paper introduces a cost-effective strategy for scalable decentralization of control operations in current Internet, called Self-Organizing Multiple Edge Nodes (SOMEN) mechanism. SOMEN enables multiple distributed network Control Decision Points -CDPs- (e.g. network borders) to jointly exploit control data inside a network with low signaling load. The effectiveness of SOMEN was proved through analytical modeling and simulation, which demonstrated significant minimization of control overhead.

Although mass entertainment markets, such as entertainment TV, were previously on the control of broadcast operators, the increased interest in supporting these multimedia services "anywhere and anyhow" is driving the interest of wireless operators. Therefore, the integration of broadcast technologies in the next generation networks is now a reality. However, since broadcast technologies are unidirectional, their use in mobile interactive communications requires the support of other technologies to enable the upstream communication through a return channel. This paper presents an experimental setup of the architecture developed in the framework of the IST-Daidalos project that is able to provide multimedia and interactive services through a heterogeneous network integrating DVB technology, and enabling seamless and media independent handovers with QoS support. The experimental testbed contains DVB, UMTS, Wi-Fi and Wi-Fi with WiMAX as backhaul technology, and supports seamless handovers with QoS between these technologies, integrating also handover of different return channels. The results show that the handover impact is more noticeable in handovers to DVB and UMTS technologies, when compared to Wi-Fi and WiMAX ones. Nevertheless, when the network is not overloaded, the handover timings do not exceed 200 msec.

A growing trend has emerged in network architecture research to switch focus from Quality of Service (QoS) to Quality of Experience (QoE) optimization. In this paper, we first present QoE models that characterize user satisfaction of video, audio, and data services over wireless networks. We then develop a novel packet scheduling algorithm for multi-hop wireless networks that jointly optimizes the delivery of multiple video, audio, and data flows according to the QoE metrics. We formulate a multidimensional optimization problem that minimizes the overall distortion across all flows for the given network resources on wireless links. Fairness constraints over the flows are also considered as part of the optimization. Our experimental results, obtained with the NS-2 IEEE 802.16 MESH-mode simulator, show that distortion-aware scheduling can significantly increase the perceived quality of different wireless services under bandwidth constraints. Additionally, improved fairness across the competing flows is demonstrated relative to conventional scheduling techniques.

TCP is the most widely used congestion protocol in the Internet. However, TCP has some limitations, even in the wired world, such as not providing high utilizations in high bandwidth-delay product networks, and introducing high load and overhead in the network. Due to these limitations, several congestion protocols have been proposed. Some of the most known and recent protocols developed to provide faster and lighter congestion control are eXplicit Control Protocol (XCP) and Rate Control Protocol (RCP). These protocols have been proposed essentially to work in wired networks and environments; however, there are already new versions of XCP for wireless networks. Since these protocols have a large acceptation on the research field, and simultaneously,Wireless mesh networks (WMN) are in undergoing rapid progress, it is important to evaluate how XCP and RCP behave in these networks, as compared to TCP. This paper presents an evaluation study of TCP, XCP and RCP in WMNs, studying different WMN scenarios. Surprisingly, the results show that TCP is more efficient in mesh scenarios, being more fair and stable than XCP and RCP. To obtain the available network capacity, both XCP and RCP need that all nodes in the network cooperate, which increases network overhead, and reduces performance and fairness. Moreover, their capacity evaluation is not accurate in wireless networks.

Personalization and group communications have always been conflicting approaches. From one side, the user wants to get the services with their specific characteristics. From the other side, group communications imply the support of the same service, with the same characteristics, by a group of users. To simultaneously enable personalization and profit from the resources improvements provided by group-based communications, these groups need to be defined based on the users requirements, services and network information, which we denote as “context”. Based on this context for group definition, it is possible to adapt the content to the groups of users or even re-group them according to common features. This paper addresses the network part of the problem: based on the context information, we define an intelligent network selection, both for the access and core, respectively to efficiently choose the attachment points and the multicast trees for groups of users. This selection approach will improve the efficiency of the architecture in delivering the multiparty services. The simulation results of the proposed approach show that performing a wise network selection based on context increases overall network efficiency while still providing a personalized multiparty content delivery.

Recent research efforts claim that dynamic per-class network resource over-provisioning is promising, since aggregated over-reservations allow to significantly reduce state, processing and signaling overheads. However, over-reservation must be carefully controlled to prevent waste of resources, which can be introduced by residual bandwidth (over-reserved but unused) of each Class of Service (CoS). Moreover, admission control can be compromised by inefficient over-reservation control mechanism, where excessive signaling can be placed under severe network conditions, such as congestion periods. In this scope, this paper proposes a novel Class-based resource Over-pRovisioning (COR) mechanism with novel techniques to: (a) over-provision CoSs by computing appropriate bandwidth over-allocation; (b) redistribute residual bandwidth among CoSs to avoid wasting resources. The performance evaluation shows the superiority of COR proposal over state-of-the-art solutions in optimizing the overall network performance, while preventing from bandwidth waste and unnecessary sessions blocking.

The flexibility and self-organization properties of Wireless Mesh Networks (WMNs), and the benefits arising from the split of a physical network in several context-aware Virtual Networks (VNs), are a two-fold force to deal with the huge heterogeneity of user requirements and network resources in a future Internet architecture. In this scope, we provide a novel context-aware architecture for WMNs, which supports user requirements on demand by the deployment of several adaptable VNs for different context-aware purposes. In this paper, we present an analytical model for the proposed architecture. We aim to provide and define the limits of our multi-VN approach, in terms of the number of VNs, attached mesh clients, and virtual nodes per VN, in order to accomplish feasible delays for different data or control communication requirements. Moreover, we describe and evaluate, based on a probabilistic approach, the delays involved in user connectivity establishment, which includes several management processes for the discovery, extension and update of one fitting VN for a user. Through a delay-based approach, our analytical results show the effectiveness of our architecture to deal with different communication requirements, and with distinct scenarios for user connectivity establishment.

The proliferation of the Internet and wireless access technologies introduced the constant mobile paradigm, where users want to be always connected making use of the best available networks and technologies. Although the IEEE 802.21 framework is able to improve the handover process independent from the access technology, it only supports an information server with static information storage. It is clear that in today's and future environments, the dynamicity of the scenarios is very large and that static information about the available networks is not sufficient. In this paper, we develop a new concept of a dynamic information server that is able to store, manage and deliver real-time dynamic information about the available access technologies, such as network topology, load and QoS, which can be used to help the preparation and decision of the handover process. Using this concept, the two different procedures in the handover preparation phase, candidate access technologies discover and resources availability check, can be performed only in one procedure, optimizing the handover process. The results provided through ns-2 simulations show the benefits of supporting such dynamic information server in a seamless mobility procedure optimized by the IEEE 802.21 framework.

The convergence of services and technologies has been driven by service and network providers with the aim to develop a unified infrastructure. From the services side, we have multimedia through unicast, multicast and broadcast services. From the technologies side, we have wired and wireless technologies, including unidirectional technologies such as Digital Video Broadcasting (DVB). These trends allied to the increasing mobility and QoS demands, introduce strong requirements to future telecommunication networks. This paper presents an innovative approach to handle multicast services in a heterogeneous networks environment, including broadcast technologies. The presented architecture aims at guaranteeing end-to-end QoS in mobile scenarios, efficiently handling the underlying network resources and integrating the emerging broadcast technologies. This architecture was developed in a real environment with mobility of multicast sessions through heterogeneous technologies, Wi-Fi and DVB, using also Wi-Fi as a return channel. The results show that the architecture is able to support the seamless mobility of users receiving multicast sessions, with low degradation on the running communications.

In the field of next generation networks, the user's request for ubiquitous access has become paramount in powering the development and integration of new access technologies, thus increasing the support of ad-hoc networks as extension of infrastructure operator networks. In this paper we introduce the integration of Ad-Hoc networks in the mobility architecture of the IST Daidalos II project, whose goal is to provide the seamless mobility of nodes between different access technologies. Here we will focus on the mobility architecture of ad-hoc networks and the movement of nodes, while still providing them with the infrastructured features. To help accomplish this goal, concepts like the IEEE 802.21 and NetLMM are used and extended to support and integrate mobility in mobile ad-hoc networks. We also present the testbed implementation of this architecture and the experimental results. We show that the majority of the time spent in the handover is consumed firstly, in the scanning of the wireless environment for possible destination networks, and secondly in the ad-hoc procedures. We also show that there is a network breakpoint for both losses and delay, beyond which they become too large for normal communications.

The Internet access paradigm is changing and a wide range of overlapping radio access technologies, such as WiMAX, Wi-Fi, 3GPP UMTS/LTE and DVB-H, will soon be available to most users and their multi-access devices. Therefore, novel mechanisms and protocols are needed to provide seamless mobility between different wireless access technologies. This paper presents novel handover procedures, based on media independent schemes, to address seamless mobility in heterogeneous environments. We propose an enhanced media independent handover framework (eMIHF), which extends IEEE 802.21 by allowing for efficient provisioning and activation of QoS resources in the target radio access technology during the handover preparation phase, and efficiently interacts with the mobility processes of different technologies employed. We evaluate eMIHF using the ns-2 simulator, and demonstrate that the proposed handover scheme provides seamless mobility, with low latency and no packet loss.

Although IEEE 802.16 has emerged as one of the major candidates for next generation networks, it is also clear that in the near future the combination of several technologies will be required. In this sense, the support of mobility in heterogeneous environments, addressing broadband wireless, is one of the main requirements in next generation networks. This paper evaluates an architecture based on the recently standardized IEEE 802.21 framework, integrating both mobility and quality of service (QoS) mechanisms, through an advanced mobility scenario using a real WiMAX testbed. This architecture supports seamless mobility in broadband wireless access (BWA) networks, integrates different technologies, such as WiMAX and Wi-Fi, and thus, it is suitable for next generation network environments. The results, obtained through real experimentation of the implemented architecture, show that it is able to provide QoS under dynamic scenarios, with fast integrated QoS and mobility signaling.

The anticipated deployment of IEEE 802.16-based wireless metropolitan area networks (WMANs) will usher a new era in broadband wireless communications. The adoption of the WiMAX technology for remote areas, for example, can address challenging scenarios in a cost-effective manner. While WiMAX Forum documents describe an architecture that inherently supports quality of service and mobility, several areas are left uncovered. We present an architecture which integrates WiMAX, quality of service and mobility management frameworks over heterogeneous networks, developing mechanisms for seamless handovers. Our approach takes into consideration the expected deployment of, on the one hand, the IEEE 802.21 (Media Independent Handover) proposed standard and, on the other, the IETFstandardized Next Steps in Signalling framework. The first contributions of this paper comprise a specification of the mechanisms for make-before-break vertical handovers taking quality of service signalling into account and the integration in a heterogeneous environment. The latter contribution is an empirical evaluation of the proposed architecture using a testbed demonstrator. We quantify the processing delays of the main components in our prototype implementation when a terminal hands over between different access technologies, demonstrating the potential of the proposed architecture.

Ubiquitous broadband Internet access is an important requirement to satisfy user demands and support a new set of real time services and applications. IEEE 802.16, as a broadband wireless access technology, can significantly contribute in this area. It is able to provide high throughput over large distances, with intrinsic QoS support, in line-of-Sight and Non- line-of-Sight environments. This paper discusses a network architecture, based on IEEE 802.16 d compliant equipments, which is able to support real-time services with QoS integration through dynamic configuration of the 802.16 channel, using the NSIS framework. Tests have been carried out to evaluate the QoS performance of the defined architecture, demonstrating that it is perfectly capable of handling and supporting real time services, using dynamic configuration of the 802.16 channel, without introducing an excessive cost in the network.

In the current mobile cellular networks, the terminal mobility support is based on layer-1 and homogenous layer-2 mechanisms. This mobility support is sufficient for a moderate number of mobile terminals. However, in the future it is expected that this number will significantly increase and that distinct layer-2 interfaces will be available and required for the paradigm of ldquoInternet Everywhererdquo of the next generation networks. This paper presents the implementation and the evaluation results of a novel approach for local mobility in next generation networks. This mobility approach, denoted as local-centric mobility system (LMS), was designed to provide scalability, fast and seamless handovers, and efficient exploitation of network resources and reliability. The LMS can provide handover timings smaller than 100 ms in wireless scenarios, negligible packet loss during handover, and reduced signalling overhead.

In this paper, a novel solution to dynamically control the resources of a WiMAX system is proposed. The presented solution is aligned with the NGN trends, as well as with the WiMAX Forum guidelines and architecture. The interaction with the WiMAX systems is performed through SNMP, supporting the standardized IEEE 802.16 f MIB. Furthermore, the paper defines an horizontal abstraction layer, hiding the WiMAX equipments specific functionalities from the network control plane, thus providing robustness and vendor independency. In order to verify the performance of the proposed framework, a WiMAX testbed has been installed, and a set of measurements have been made, showing that the proposed system ensures a quick resource allocation, even for a large number of simultaneous resource reservation requests.

The advances on wireless mesh networks are shifting the communications paradigm, where users can benefit from their cooperation to exchange information and make use of the spectrum space, forming wireless neighbourhood communities where resources are shared and services are distributed. This paper presents a modular cross-layer community management architecture, which considers that communities are formed and managed at different layers, from physical to application. It focus in more depth on one of its modules, implementing a community discovery mechanism designed to work in wireless mesh networks, and able to perform community advertisement and discovery with low overhead and delay, as compared to current approaches.

In densely covered areas and with multi-interface terminals, network selection is a complex problem, especially if it takes into account different criteria such as context and signal strength. We present an algorithm that allows a mobility decision manager to consider arbitrary criteria such as user history or preferences. Since a typical mobility module uses quantitative and well-known information (eg., radio signal strength or available resources), qualitative information must be fed into the mobility subsystem only after converting it to a suitable format. Our algorithm uses (up to) three stages to produce a decision: first, it discards unsuitable possibilities; second, it produces a set of possibilities considering resource availability; and third, the feasible possibilities are ranked according to contextual information.

This formulates a solution to integrate broadcast technologies under a localized mobility management protocol. It mainly deals with the technologies' mobility constraints and evaluates how it responds to the proposed requirements. The presented architecture is considered to be of special interest for deployment over proxy driven scenarios such as university campus and, in a near future, even cities or countries. The aim of this work is to deliver multimedia contents using any available uplink channel and DVB (digitial video broadcasting) as the primary downlink technology for high mobility scenarios and further validate the model via an experimental implementation. Finally we address a specific usage case where spontaneous and chaotic networks such as ad-hocs are not seen as a premise for the functional architecture, but rather as a last resource.

Empirical models are a technique that has been used in research for a long time, and that proved to be capable of describe pseudo-random phenomena. In this paper, we present PREFAIRS, a protocol used to deploy QoS, and that was designed using empirical models. With our empirical model we are able to determine the available capacity of the ad hoc network and the maximum bandwidth of new flows to guarantee a specified QoS. We show that a combination of our network model, PREFAIRS and a empirical model is able to enforce fairness, predictability and stability in ad hoc networks.

Roaming agreements in 3G and beyond 3G networks can greatly enhance the delivery of services to end users. The cost of service delivery to the user depends on the price charged on networks traversed by user traffic. In a QoS DiffServ environment, network resources may be sold in aggregate blocks at wholesale prices to competing ISPs. This can influence the cost of delivering services between two points using access networks owned by competing operators. Demand for network services by users varies over time and network capacities are finite; thus a fully loaded network would reject new service requests and an underutilized network will become less productive. In this paper we explore a service outsourcing scheme between competing operators that allows a custodian network operating at full capacity to outsource service provision to a candidate network. Outsourcing would be price-influenced enabling the home (custodian) operator to levy local predictable charges to the end users for services offered on the visited network. This will virtually expand the capacity of the custodian network and boost incomes for both operators.

User privacy is a growing requirement in the evolution of communication networks. In this sense, the concept of virtual personae, which corresponds to different identities of the same user, starts getting much attention. However, to provide privacy and non-linkage between these virtual users, a cross-layer approach to identity needs to be supported. This paper proposes a solution to preserve the application layer privacy models by applying the virtual personae concept throughout the network stack. It also proposes mechanisms for non-correlation between identities in 4G mobile environments, and addresses the benefits of the evolving multi-homing characteristics of 4G networks to enrich the non-linkage between identities support of our privacy solution.

This paper addresses the problem of inter-domain QoS routing. We propose a solution based on virtual-trunk type aggregates corresponding to service level agreements (SLA) for data transport between peering domains. We formally state the problem and formulate it in integer linear programming. As a practical solution, we propose an extension to the BGP routing protocol, QoSJNFO, conveying three QoS metrics (light load delay, assigned bandwidth and a congestion alarm), and a path selection algorithm using a combination of these metrics. We present simulation results of QoSJNFO, standard BGP, and BGP with the QoS_NLRI extension, and compare them with the optimal route set provided by the ILP formulation. The results show that our proposal yields better QoS parameters than standard BGP or BGP with the QoS_NLRI extension, since it is able to efficiently avoid congested paths, and that the impact of QoSJNFO in route stability is relatively low.

In the future, mobile terminals will be equipped with several interfaces to different technologies -such as UMTS, IEEE 802.11, DVB and WiMAX -and will be able to connect simultaneously to the most appropriate access networks. This paper presents a mobility and QoS architecture aiming at supporting intelligent handovers. We introduce the concept of Network-Assisted Mobile terminal Initiated HandOver (NAMIHO), where the handover decision is negotiated between the network and the user, taking into account inputs and decision algorithms running both on the terminal and network. We also present an instantiation of the architecture using IEEE 802.21 and concepts of local/global mobility. We further propose extensions to IEEE 802.21 standard.

Terminal mobility may be handled at different layers. Though the MIPv6 protocol is the strongest candidate for handling mobility in next generation networks, mobility management facilities are also provided by SIP, the most widely deployed and researched protocol for session control. When jointly used, this duplication of functions leads to inefficiencies in session setup signaling, particularly if coupled with end-to-end resource reservation for the media flows. This paper analyses these inefficiencies and proposes an integrated approach that minimizes the session setup delay. The gains of the proposal are demonstrated both by a delay analysis and by simulation results.

The growing demand of high bandwidth services and applications, and the increasing will of access to these services anywhere, is motivating the requirement to integrate the current Internet with the mobile networks. However, there will always be remote areas where Internet access will be difficult to achieve. The IEEE 802.16 is an attractive broadband wireless technology for these scenarios, non-withstanding its limitations for dynamic environments. This paper discusses a network architecture able to support real time services using 802.16 networks as a backhaul. We present the modules required to provide an integrated QoS approach over the 802.16 network, and address main implementation results in terms of QoS performance, and in terms of mobility with QoS support for converged networks comprising WiMax and WiFi technologies. We show that our architecture is able to provide end-to-end QoS over the concatenation of metro and local area networks, and that seamless mobility is achieved with high performance measures, thus being able to support real-time services.

This paper describes a solution for inter-domain QoS to be part of the framework for end-to-end QoS control. The solution is based on the use of virtualtrunk type aggregates, defined by means of Service Level Agreements, for data transport between peering domains. These aggregates are defined for several "well-known" traffic classes, supporting different types of applications. We define an extension to the BGP routing protocol to transport three different QoS metrics (light load delay, assigned bandwidth and a congestion alarm), and a path selection algorithm using a combination of these metrics. Simulation results demonstrate the use of our solution, showing that it yields much better QoS results than those achieved with standard BGP or BGP with the QoS_NLRI extension, since it is able to efficiently avoid congested paths, reducing delays and packet losses. Moreover, these results are achieved without significantly affecting route stability.

Abstract—This paper proposes an Ad-hoc QoS architecture, using as basis some concepts from SWAN, extending it to fulfill our requirements. It also proposes the modules required in the network elements and its interaction to provide optimized QoS in the ad-hoc network and integration between both networks. We also present the overall integration QoS signaling protocol and the differentiation mechanisms to address end-to-end QoS for real-time multimedia applications. Furthermore, the proposed solution addresses the use of multipath routing in order to provide load balancing and increased network reliability. Index Terms—Ad-hoc networks, infrastructure networks, integration, quality of service. D

The area of trustworthy charging in self-organized environments has been developed quite recently. This paper introduces a new secure charging-protocol, the polynomial-assisted charging protocol, for these environments. The protocol relies on polynomial composition for speeding the identification process in small groups. This protocol is able to provide strict guarantees of cooperative behavior in traffic forwarding, with minimal network overhead. Protocol performance is evaluated in multiple ad-hoc environments and results are compared with previously proposed work. The performance results show the merits of this protocol in multiple types of environments.

This paper presents a performance evaluation study of the scalable reservation-based QoS architecture. This architecture introduces a scalable per-flow signalling model, using techniques and algorithms developed to minimise the computational complexity, namely a label switching mechanism and an efficient timer implementation. The underlying architecture is based on DiffServ and resource reservation is performed for aggregates of flows at both core and access networks. The obtained results show that this architecture is able to provide QoS guarantees, irrespectively of the behavior of other flows in the same and in different classes, maximizing the network resource utilization. Based on the performance evaluation, we can state that this architecture is able to support service classes with strict and soft QoS guarantees in high-speed networks.