Realistic emulation of wireless communication is essential for the development and validation of protocols in lowpower networks. While tools like Wmediumd provide accurate medium emulation for IEEE 802.11, no equivalent exists for IEEE 802.15.4, an important standard underlying protocols such as ZigBee, Thread, and 6LoWPAN. This paper presents an extension of Wmediumd to support IEEE 802.15.4, enabling probabilistic modeling of wireless effects such as interference, attenuation, and packet loss. By integrating with the existing mac802154_hwsim kernel module, our approach allows for accurate, reproducible experimentation in virtual environments. We demonstrate the emulator’s effectiveness through scenarios involving range-based communication and unstable links, highlighting its potential for advancing research and testing in IoT and low-power wireless networks.

This demonstration showcases the benefits of P4 programmability to improve the efficiency and adaptability of IEEE 802.15.4-based LoWPANs using the RPL protocol. By leveraging P4, the platform enables dynamic packet processing and real-time telemetry, essential for optimizing routing decisions and enhancing network reliability in mission-critical environments. To validate these capabilities, we present an emulation platform that integrates P4-programmable BMv2 switches with IEEE 802.15.4 LoWPAN and RPL, creating a realistic and flexible environment for simulating wireless sensor networks in aviation scenarios. The platform supports dynamic sensor management, in-band telemetry, and fine-grained control over network behavior, enabling the study of routing dynamics, bottlenecks, and load-balancing strategies. Preliminary results confirm the effectiveness of this approach in replicating low-power wireless communication, highlighting its potential as a powerful and costeffective testbed for next-generation IoT and aviation systems.

The Message Queuing Telemetry Transport (MQTT) protocol is a cornerstone of IoT communications. It relies on service brokers to enable reliable data delivery between devices and clients. In modern deployments, MQTT brokers are frequently hosted in virtualized environments to support scalability, flexibility, and resource efficiency. However, virtualization enlarges the attack surface, posing challenges to service reliability and security. This paper investigates the use of Virtual Machine (VM) migration as a Moving Target Defense (MTD) to enhance security in MQTT-based IoT services. While VM migration is an established technique in network and service management for workload balancing and fault tolerance, its impact, when used as a proactive security mechanism in MQTT deployments, remains unexplored. This work shows a comprehensive performance evaluation of VM migration under both normal and active attack scenarios. The results demonstrate that the security benefits of VM migration come with minimal performance degradation, characterized by a modest effect size (Cohen D measure<0.5), thus ensuring service continuity and operational stability. However, it comes with a cost of increased performance oscillation (i.e., higher incidences of peaks in the response time). This paper also introduces an interactive, web-based tool that enables pre-deployment MTD simulation. This work offers insights into integrating security-aware VM migration within IoT service management.

Immersive Media Services (IMS) such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are advancing with the development of Beyond $\mathbf{5 G}$ (B5G) and 6G networks. These services integrate three-dimensional elements into user environments through Head Mounted Displays (HMDs) and require stringent latency to ensure real-time interactivity and prevent cyber-sickness. However, traditional IMS architectures often face challenges due to their monolithic designs, which do not efficiently handle user mobility, network fluctuations, or network failures. This paper introduces REACH, a robust orchestration mechanism based on Genetic Algorithm (GA) with resilience support, improving reliability and adaptability in IMS deployments. REACH optimizes microservice chaining (MSC) by considering factors such as microservice sharing probabilities, the spatial distribution of services, and the computational capabilities of edge nodes to enhance network and computational resource efficiency. Our evaluations indicate that REACH substantially increases the instantiation acceptance ratio by up to 52.5% during failure events, significantly improving service continuity and quality in dynamic network environments.

The service model is constantly changing on the Internet, and the most prominent model in recent years is the use of cloud computing. The more connected world has generated new needs that must be met by the core of the network in order for the Internet to function. Communication at high speeds, always available and efficient is vital if demand is to be met. The characteristics of this traffic have prompted the development of new mechanisms to deal with the increasing rate of data transmitted over the network. Innovations at earlier layers of the network are emerging, and the proposal for flexible spectrum links is proving to be very promising for meeting the heterogeneous requests coming in. In this context, this paper proposes a routing algorithm for the SDM-DC-EONs network model. The proposed solution presents results that surpass models in the literature by up to two orders of magnitude when it comes to establishing transmissions in the network, represented by the Bandwidth Blocking Ratio.

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.

Advanced networking technologies, such as 5G and 6G, enable and enhance eHealth solutions. However, these technologies also present challenges, particularly in relation to data breaches and security threats that can undermine eHealth solutions. The protection of sensitive user data from unauthorized access and cyberattacks is essential to maintain user trust and ensure data integrity. As wearable sensors continuously collect and transmit critical health information, the risks associated with data breaches, unauthorized access, and potential misuse become increasingly apparent. It is crucial to improve user safety and security when using wearables, especially since significant personal information is stored on these devices. User identification is fundamental; It allows wearable devices to protect confidential data and prevent unauthorized users from accessing it. This paper proposes a generic Federated Learning (FL) architecture to process PPG and ECG data to extract representative features to understand user behavior and facilitate continuous identification. Our main results demonstrate that it is possible to identify users based on their biometric behavior using ECG and PPG data, achieving up to 80% accuracy with 11 rounds and maintaining stability with just one epoch. In contrast, when using only one type of data, the same approach yields unstable results, with accuracy fluctuating between 60% and 20% over the same 11 rounds.

The Internet of Things (IoT) has transformed modern networks by interconnecting diverse devices across smart homes, industrial automation, and environmental monitoring. However, IoT networks face challenges due to constrained devices and unreliable communication links, requiring efficient routing protocols and adaptive architectures. The Routing Protocol for Low-Power and Lossy Networks (RPL), while the standard for IoT, struggles with real-time monitoring and dynamic topologies. Evaluating RPL proposals in realistic environments is essential, and network emulation offers a practical middle ground between full-scale deployment and simulations. To address these challenges, this paper introduces P4LoWPAN, an innovative IoT network emulation platform that integrates P4 with RPL, leveraging In-band Network Telemetry (INT) for real-time monitoring and a programmable data plane. Built on Mininet-WPAN and powered by Docker-based sensor nodes, P4LoWPAN provides a scalable and flexible environment for IoT network emulation. It enables dynamic routing, topology visualization, and detailed telemetry, making it a valuable tool for researchers optimizing IoT networks. Case studies demonstrate P4LoWPAN’s capabilities, showcasing its potential to support more efficient, adaptive, and programmable IoT data planes.

Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However, traditional sector selection schemes involve extensive searches for the highest signal strength sector, introducing extra latency and communication overhead. This paper introduces a dynamic layer-wise and clustering-based federated learning (FL) algorithm for beam sector selection in autonomous vehicle networks called enhanced Dynamic Adaptive FL (eDAFL). The algorithm detects and selects the most important layers of a machine learning model for aggregation in FL process, significantly reducing network overhead and failure risks. eDAFL also consider an intra-cluster and inter-cluster approach to reduce overfitting and increase the abstraction level. We evaluate eDAFL on a real-world multi-modal dataset, demonstrating improved model accuracy by approximately 6.76% compared to existing methods, while reducing inference time by 84.04% and model size up to 52.20%.

The rise of mobile devices and growing concerns about model privacy have posed significant challenges in distributed artificial intelligence, especially due to the heterogeneity of devices, leading to model generalization and resource management issues. Federated Learning (FL), a method where machine learning models are trained collaboratively by sharing only local parameters with an aggregation server, faces challenges in model convergence, optimization, and communication overhead due to this heterogeneity. This paper introduces FedSCCS, an FL-based framework designed for such heterogeneous settings. FedSCCS clusters devices based on the similarity of their models, allowing for efficient model aggregation and improved resource utilization. Our evaluation, set against established benchmarks, shows that FedSCCS achieves superior accuracy compared to existing methods, indicating a promising direction for scalable and tailored FL solutions.

Federated Learning (FL) has emerged as a privacy-preserving powerful tool in decentralized Machine Learning (ML) environments. However, real-world scenarios often face bandwidth limitations that can be overwhelmed when all clients simultaneously perform training and communicate with the server in a federated system. Consequently, selection mechanisms are critical for identifying optimal subsets of clients to participate in the federation. Traditional selection methods, however, typically do not allow clients the autonomy to decide whether or not to contribute to the federation. Therefore, this paper proposes LetsFed, a client selection framework that respects client independence throughout the training process. The LetsFed framework differentiates between participating and non-participating clients, employing targeted selection mechanisms to address system challenges effectively. Empirical results demonstrate that LestFed can outperform, in dynamic client participation environments, literature solutions by up to 40%, reducing unnecessary data transmission by as much as 29%, while also enhancing the efficacy of the selection process.

Federated Learning (FL) allows distributed devices, known as clients, to train Machine Learning (ML) models collaboratively without sharing sensitive data. A characteristic of FL for mobile and IoT environments is system heterogeneity among clients, which can vary from low-end devices with constrained communication and computing resources to powerful devices with high-speed network access and dedicated GPUs. As the server must wait for all the clients to communicate their updates, slow clients (a.k.a. stragglers) will significantly increase the training time. To tackle this problem, we propose FedPulse, a Partial Training (PT) based mechanism to mitigate the effect of stragglers in FL. The idea is to reduce the training time by dynamically allocating smaller submodels to resource-constrained clients. Experimental results on famous classification datasets show that the proposed solution outperforms other submodel allocation mechanisms and reduces the training time by up to 58% with an accuracy loss of less than 1% when compared to FedAvg.

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.

Smart grids present risks when exchanging valuable data between their systems; theft or alteration of this data could violate consumer privacy. Mainly, the non-technical losses (NTL) occur by illegal connections, meter problems (installation delays or wrong readings), dirty, defective, or mismatched meters, very low estimates of adequate consumption, faulty connections, and missing customers. According to a recent study, utilities lose ${\$}$89.3 billion annually through NTL. We present an energy fraud characterization study based on Information Theory Quantifiers (ITQ) to mitigate this challenge. First, we convert the user’s energy consumption time series into a Bandt-Pompe (BP) probability distribution function using a sliding window. The second step is to extract the ITQ used by the technology. We then apply each metric to the Probability Density Function (PDF) and map the layers to characterize their behavior. Our results show that users with normal and abnormal energy consumption can be distinguished using only Information Theory Quantifiers by considering the range of values for each metric.

The effective use of exercise prescriptions for the re-habilitation of patients with cardiovascular diseases significantly improves the quality of life and reduces mortality rates. In this sense, the development of strategies to increase adherence and consequently, the time of physical activity of the population as a whole. Several recent technological advances, such as Internet of Things and sensors, represent an improvement in the monitoring and prescription of physical activity. Such technologies can be integrated to provide biofeedback on the prescribed physical activities, which can be accessed by smartphones. In this paper, we introduce a serious game that integrates Gamification and biofeedback techniques to be used by patients with Physical activities prescription to prevent and rehabilitate cardiovascular diseases. Specifically, the game has differentiated and integrated approaches through (i) the use of applications with a Gamification system to increase user adherence; (ii) the use of biofeedback to monitor prescribed physical activities. The game has a carefully crafted visual identity, icons, and avatars, considering a system of colors, typography, shapes, sounds, and images that seek to frame the general public.

Multi-User Augmented Reality (MUAR) is gaining popularity and enabling interactions collaboratively with mobile users in the virtual 3D world. In this way, decomposing MUAR into elements with specialized ordered Service Functions (SFs) to form a SF chain with decoupled source dynamically allows the SF distribution into multiple edge computing servers and executes SFs of MUAR services in parallel while improving its quality level. However, orchestrating distributed SFs at the network edges and at multiple mobile clients while minimizing latency and meeting resource-hungry requirements is still a research challenge. This paper proposes a mobility-aware SF chaining orchestration scheme for MUAR services called MSF. MSF improves the usage of processing, storage, and networking resources and reduces the latency of MUAR services by optimizing SF chaining in real-time and reinstantiating services into optimal routes. The results show that MSF outperforms state-of-the-art approaches regarding MUAR session acceptance ratio, CPU and bandwidth utilization, and latency in different mobile scenarios.

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

In recent years, global communication has undergone intense transformations, especially in its use. The post-pandemic world shows new everyday situations where the connection needs specific characteristics, such as low latency, higher reliability, and higher bandwidth, among other things. The Space Division Multiplexing Elastic Optical Networks provide the scenario that contemplates these demanded situations but exposes the need for mechanisms capable of managing such a network architecture. This article evaluates four routing algorithms for Elastic Optical Networks with Space Division Multiplexing and some of its most discussed problems, such as Fragmentation and Crosstalk. The proposed algorithms prevent the formation of network bottlenecks and reduce the resources used. Also, it is possible to compare the impact of using different routing schemes in SDM-EONs.

Immersive entertainment based on Multi-User Virtual Reality (MUVR) is gaining popularity to enable in-game interactions with multiple users. However, computing and network-intensive utilization of resources require changes in cloud-based and traditional monolithic (single machine) deployments towards distributed edge computing architecture to enable high-quality interaction. Decomposing MUVR into elements with specialized Service Functions (SF) in order and organizing it into a Service Function Chaining (SFC) requests orchestration enables MUVR to reuse frame processing between users and improves MUVR scalability. However, orchestrating distributed edge computing resources and multiple destination SFCs while minimizing delay and meeting resource requirements is a challenging task. This article proposes a multi-criteria SFC orchestration scheme for MUVR services, called MuSFiCO. MuSFiCO maps edge computing resources and instantiates SFCs on distributed servers based on delays threshold, CPU and memory resources, and bandwidth. We developed a constrained-based heuristic to minimize delay and compare it with the baseline monolithic deployment and cloud-based SFC algorithms. Results demonstrate the efficiency of MuSFiCO compared to other approaches in terms of latency, CPU, memory, bandwidth utilization, as well as orchestration decision-time.

Wearables today play a key role in E-Health computing, with investments expected to exceed $70 billion by 2024. With the massive use of apps on wearable devices, it is crucial to improve safety when using wearables, considering that important information about user information is stored on these devices. We present SOMEONE ensemble learning, a set machine learning algorithm for body recognition of wearable devices, which operates on the basis of both PhotoPlethysmoGram (PPG) and ElectroCardioGram (ECG) signals. We consider an individual's PPG and ECG signals, where algorithms process these signals stored on the wearable device to identify the user. The SOMEONE algorithm achieves better results on metrics such as F1 score, accuracy, false acceptance rate (FAR) and false rejection rate (FRR) for human recognition in MIMIC dataset of ECG signals and CapnoBase dataset of PPG signal.

Virtual Reality (VR) content is gaining popularity and allowing users to immerse themselves in a new world over the Internet. However, the high-demand for resources and the low latency requirements of VR services require changes in the current 5G networks to deliver VR with quality assurance. Microservices present a suitable model for deploying services at different levels of a 5G fog computing architecture for managing traffic and providing Quality of Experience (QoE) guarantees to VR clients. However, finding the most suitable fog node to allocate microservices for VR clients in QoE-aware 5G scenarios is a difficult task. This article proposes a QoE VR-based mechanism for allocating microservice dynamically in 5G architectures, called Fog4VR. Fog4VR determines the optimal fog node to allocate the VR microservice based on delay, migration time, and resource utilization rate. This article also presents the INFORMER, an integer linear programming model aiming to find the optimal global solution for microservice allocation. Results obtained with INFORMER serve as a baseline to evaluate Fog4VR in different scenarios using a simulation environment. Results demonstrate the efficiency of Fog4VR compared to existing mechanisms in terms of cost, migration time, fairness index, and QoE.

In modern 5G and Beyond (B5G) networks, the number of users and devices consuming highly-demanding services in terms of latency and throughput. Due to their high dynamicity and fine-grainess, such services must be supported by a joint management and integration effort between technologies such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs), and novel radio and energy transfer techniques. The notion of Flying Edge Computing (FEC) arises as a prominent solution to provide a deeper level of integration and capabilities to UAV networks in collaboration with traditional edge computing and B5G infrastructure. FEC constitutes a highly elastic computation layer in modern networks, which can quickly adapt to surges in demand. This paper dives into FEC’s main opportunities and motivations in modern scenarios and presents some of the important design aspects of FEC. Experimental results show that the coupling of traditional MEC with FEC can deliver significantly better Quality of Service (QoS), improve service availability, and user satisfaction. Furthermore, FEC can adapt to user mobility patterns more efficiently, delivering contents and services.

Floods are the most frequent type of natural disaster, and it is crucial to search and track the objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders in determining correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects for UAVs is challenging because it is necessary to change the UAVs’ topology frequently to organize them according to the target object’s new location and the rescue team’s position. It is also mandatory to assure the distribution of real-time video flows with a high-quality level and a minimal delay in mobile environments and save scarce energy power. This paper proposes a swarm-based and mobility prediction algorithm for UAVs, called SUAV, to efficiently orchestrate UAVs to track mobile objects in flooding scenarios while saving energy and delivering real-time videos with Quality of Service (QoS) and Quality of Experience (QoE) assurance to first responders. Simulation results present the impact and the benefits of our proposal in delivering videos with QoE and QoS support compared to state-of-the-art approaches.

In this paper, we propose a novel privacy-preserving identification-permission system (entitled Smart Human Identification System) to provide human identification, privacy, and security for users of wearables devices. To perform this function, we divided the system into six algorithms. We used PPG and ECG signals from two public datasets (MIMIC and CapnoBase). The proposed system creates a human ID and then compares it to other individuals and tests its accuracy to evaluate the quality of using biosignals for direct human identification in wearable devices. The experimental results indicate that the proposed system presented accuracy for the PPG signals from MIMIC and CapnoBase equal to 96.875% and 99.15%, respectively. For the ECG signals from MIMIC and CapnoBase, the accuracy obtained was 96.6% and 90.66%, respectively.

Intelligent Transportation Systems (ITSs) will be part of our daily lives, where new services are bringing novel challenges for smart cities. The ITS services rely on vehicular clouds (VC) to aggregate tasks from other vehicles to provide cloud services closest to the vehicular users. However, the resource and task allocation processes in dynamic and mobile environments are still open issues. This paper proposes a task optimization mechanism based on the meta-heuristic algorithm of the Grey Wolf Optimizer, called TOVEC. It aims to improve the usage of the available resources in a VC and maximizing task allocation. Simulation results showed that the TOVEC increases the number of tasks served by up to 34.2%, maximizes the use of resources by up to 21.5%, and improves the allocation reward by up to 24.7% compared to Greedy and Dynamic Programming (DP) methods.

In modern networks, edge computing will be responsible for processing and learning from the critical network- and user-generated data, such as wireless link usage, mobility information, application requests, and many others. The presence of Artificial Intelligence-based (AI) applications at the edge of the network will enable the network to predict necessary user behavior and its impact on network infrastructure, such as base station overloading. One of the main strategies for offloading users and base stations is to deploy UAV base stations, or flying base stations, which can dynamically provide service and connectivity. In this article, we introduce a framework for distributed learning over Multi-access Edge Computing (MEC), which manages data applications in a fully distributed setting across edge servers, thus reducing the cost of collecting user information in a centralized server. We couple the proposed distributed learning with a novel similarity metric for user trajectories, which can aggregate neural network models with similar costs as other model aggregation techniques. However, the aggregation technique can achieve much higher accuracy. Furthermore, we apply the proposed distributed learning scheme to manage and deploy flying base stations to areas that experience high demand or poor user connectivity, thus optimizing connectivity in terms of user satisfaction, delay, and network throughput.

Long Range Wide Area Network (LoRaWAN) enables flexible long-range communication with low power consumption and low-cost design perspectives. However, the adoption of this technology brings new challenges due to the densification of IoT devices, which causes signal interference and affects the QoS directly. On the other hand, the flexibility in the LoRaWAN transmission configurations allows higher management in the use of end-device parameters, which allows better resource utilization and improves network scalability. This paper proposes an adaptive solution to handle the define best LoRaWAN parameter settings to reduce the channel utilization and, consequently, maximize the number of packets delivered. Additionally, to validate our method, we formulated mixed-Integer linear programming and results compared to those given by the heuristics. Results provided by the heuristic are close to those provided by the MILP.

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a significant role in the safety of the vehicles and passengers. The extensive amount of sensors in each car will generate vast amounts of data that cannot be processed promptly by onboard units. Edge and fog computing are emerging solutions for remote data processing for autonomous vehicles, offering higher computing power, as well as the low latency required by autonomous driving. However, due to the highly distributed nature of fog and edge computing servers, CAV mobility may pose a challenge to keep services close to end-users and maintaining QoS. In this paper, we propose MOSAIC, service migration, and resource management algorithm for intra-tier and inter-tier communication in edge and fog computing. The proposed solution performs proactive migration of services based on mobility information, server resources, QoS, and network conditions. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

The growing popularity of heterogeneous applications on the Internet, added to new information and communication technologies, has driven the exhaustion of the physical limitations of the Internet backbone. To overcome these limitations, emerged the Space-Division Multiplexing Elastic optical networks is a promising solution to cope with the expected depletion of the capacity of single-core networks. This paper proposes an algorithm for routing, modulation, spectrum, and core allocation (RMSCA) problem. The proposed solution maps the links, slots, and cores on edge efficiently, improving resource allocation. Results show that the proposed algorithm decreases the blocking ratio by up to three orders of magnitude when compared with other RMSCA algorithms in the literature.

Wearable devices in e-Health provide easy usage access as well as an information return to the user. In general, such devices possess a range of sensors that capture several information from both the environment and the user. The most popular information collected by smartwatches and bracelets is about the measurement of heartbeats, steps, oxygenation, and photoplethysmogram (PPG) and electrocardiogram (ECG) signals. These wearable devices rely on mobile devices for user authentication. If the user needs validation, he will resort to traditional methods on other equipment that possesses recognition sensors such as iris, face, or fingerprints. In this paper, we introduce a model for double authentication based on PPG and ECG signals for promoting another layer of security to the user, ensuring data security, and avoid weak dependence on a single biosignal for validation. The proposed model has a algorithm with two zones, namely the Algorithm for PPG and ECG Signals and Error Rate zones. The experimental results indicate that the proposed model presented up to 99.98% of accuracy.

Wearable devices/sensors and wireless networking play a pivotal role in enabling e-Health environments demanding biometry monitoring outside hospital facilities. In this regard, the Long-Range Wide-Area Network (LoRaWAN) technology is considered the most adopted wide area network since it promises ubiquitous connectivity in outdoor e-Health applications while keeping network structures and simple management, recently gained interest from the research and industrial community. However, the coexistence of high-dense wireless sensors brings several issues to LoRaWAN, such as high interference and channel congestion. In this paper, we introduce assessments on the LoRaWAN in an e-Health scenario by modeling wireless sensor traffic and implementing it on a network simulator. Simulation results suggest that while LoRaWAN can be extremely useful, by providing communication to thousands of simultaneous users, but its MAC layer structure and design limits significantly. We also introduce future research directions driven by the simulation results and LoRaWAN characteristics, for the goal to improve LoRaWAN performance in an e-Health scenario.

Video-on-Demand (VoD) services create a demand for content orchestrator mechanisms to support Quality of Experience (QoE). Fog computing brings benefits for enhancing the QoE for VoD services by caching the content closer to the user in a multi-tier fog architecture, considering their available resources to improve QoE. In this context, it is mandatory to consider network, fog node, and user metrics to choose an appropriate fog node to distribute videos with QoE support properly. In this article, we introduce a content orchestrator mechanism, called of Fog4Video, which chooses an appropriate fog node to download video content. The mechanism considers the available bandwidth, delay, and cost, besides the QoE metrics for VoD, namely number of stalls and stalls duration, to deploy VoD services in the opportune fog node. Decision-making acknowledges periodical reports of QoE from the clients to assess the video streaming from each fog node. These values serve as inputs for a real-time Analytic Hierarchy Process method to compute the influence factor for each parameter and compute the QoE improvement potential of the fog node. Fog4Video is executed in fog nodes organized in multiple tiers, having different characteristics to provide VoD services. Simulation results demonstrate that Fog4Video transmits adapted videos with 30% higher QoE and reduced monetary cost up to 24% than other content request mechanisms.

Future mobile networks will enable the massive deployment of mobile multimedia applications anytime and anywhere. In this context, mobility management schemes, such as handover and proactive multimedia service migration, will be essential to improve network performance. In this article, we propose a proactive mobility management approach based on group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction algorithm by combining the Long-Short Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model training procedure. We further develop a group user trajectory predictor to reduce prediction calculation overheads of users with similar movement patterns. To validate the impact of the proposed mobility management approach, we present a virtual reality (VR) service migration scheme built on the top of the proactive handover mechanism that benefits from trajectory predictions. Experiment results validate our predictor's outstanding accuracy and its impacts on enhancing handover and service migration performance to provide quality of service assurance.

eHealth applications become popular with the increasing incidence of cancer and postoperative rehabilitation, that require continuous remote monitoring of patients. Given the huge diversity of eHealth applications, their proper and non- invasive identification assist in attaining important requirements as low latency and reliability. But, their identification is not trivial once they have similar characteristics to common applications. Also, the time taken to identify an eHealth application is crucial, however usually it is not addressed as relevant. This paper presents MOTIF, a method for identifying eHealth applications from side-channel information extracted from network traffic. It is non-invasive and does not inspect packet payload, employing machine learning algorithms for the particularities of healthcare scenarios. Results show MOTIF feasibility and point out an accuracy higher than 90% in less than 30 seconds.

As IoT (Internet of Things) has expanded year over year enabling the presence of sensing in almost everywhere. This leads to increase the concerns about authentication and security. In this scenario, the academy is engaged with alternatives to automated recognition of individuals and provide proof of liveness. In this context, the application of physiological features such as ECG (electrocardiography), PPG (photoplethysmography), and EMG (electromyogram) is a promising approach for continuous authentication. Specifically, ECG has been used by many researchers as biometric identification, since it has features that are unique to an individual, such as, statistical, morphological, and wavelet features. In this paper, we proposed a particular feature selection, using only fiducial points related to amplitude and time that can be found directly from the signal acquired, without any kind of complex processing. We also investigate some of the most used machine learning algorithms for user identification. Evaluation results show the potential of the proposed solution, which has reached accuracy higher than 98.2% in the continuous authentication and identification scenario, which seems to be a feasible approach to increase security in many critical applications and services.

Wearable devices increasing popularity provide convenient alternatives to healthcare services outside hospital premises. Wearables provide enhancements for automatic tools to assist physicians during patient diagnosis, treatment, and many other situations with limited costs and computing resources. In this context, in-device processing using machine learning algorithms can accelerate syndromes monitoring such as epilepsy detection and minimize risks of privacy disclosure due to extended data transmission to cloud servers. In this paper, we investigate the performance of five machine learning algorithms, i.e., Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Neural Network (NN), in terms of accuracy to diagnose a syndrome and the computational cost to embed it in a wearable device. We tested the algorithms in the classification of an Electroencephalography (EEG) sampled dataset available at the UCI machine learning repository. From the results, we concluded that SVM and RF have good accuracy in identifying epileptic seizures from the EEG dataset. Additionally, only RF fulfills the low computational cost required to embed such applications in-device.

With the growth of large urban centers, some problems arise and solutions must be sought to contain them. In this context, traffic congestion is one such problem, where road infrastructure does not follow the high growth in the number of vehicles. Traffic Management Systems (TMS) arise to mitigate traffic-related problems, with automatic detection of slow roadways and vehicle rerouting to avoid such routes. Such applications are supported by the Vehicular Ad hoc NETworks (VANETs), where traffic information are disseminated between the vehicles or central server, and thus the better decisions about traffic management can be made. However, the data dissemination in VANETs is a challenging task, due to the short-range communication and high node mobility. Thus, this paper introduces a protocol for Data Dissemination based on Metrics of Complex Networks, called CRONOS. It provides data dissemination of traffic data with low overhead and high coverage. Simulation results show that CRONOS reduced the number of transmissions by 95%, the congestion time by 48.95%, and travel time by 18.11% for a TMS application.

The Internet became the primary medium for content sharing, playing a central role in our modern lives. This new paradigm changed the behavior of the user and the characteristics of the applications, such as multimedia content, real time interaction, among others. Thus, the Internet Service Providers (ISPs) need to evolve, adding new technologies and management strategies to their network infrastructures, resulting in the Future Internet Service Providers (FISPs). Within this context, this paper presents the key technical trends and challenges for the management of FISPs, considering the requirements of user's applications and the technologies to be standardized. Additionally, we introduce an architectural framework, called Framework for Integrated Management of Future Internet Service Providers (FIM), to expand the key technologies and to address the challenges in both access and edge networks, and to integrate the management of the FISP. A case study is presented to show the suitability of the proposed framework to enhance the management capacity of FISPs.

Vehicular Ad Hoc Networks (VANETs) allow users, services, and vehicles to share information and will change our life experience with new autonomous driving applications. Multimedia will be one of the core services in VANETs and are becoming a reality in smart environments, ranging from safety and security traffic warnings to live entertainment and advertisement videos. However, VANETs have a dynamic network topology with short contact time, which leads to communication flaws and delays, increasing packet loss, and decreasing the Quality of Experience (QoE) of transmitted videos. To cope with this, neighbor vehicles moving on the same direction and wishing to cooperate should form a platoon, where platoon members act as a relay node to forward video packets in autonomous VANETs. In this article, we introduce a game theory approach for platoon‐based driving (GT4P) for video dissemination services in urban and highway VANET scenarios. GT4P encourages the cooperation between neighbor vehicles by offering reward (e.g., money or coupon) for vehicles participating in the platoon. In this sense, GT4P establishes a platoon by taking into account vehicle direction, speed, distance, link quality, and travel path, which reduces the impact of vehicle mobility on the video transmission. Simulation results confirm the efficiency of GT4P for ensuring video transmissions with high QoE support compared to existing platoon‐based driving protocols.

Software-Defined Networking (SDN) has turned the Data Center Network (DCN) environment into a more flexible one by decoupling control plane from data plane, allowing an innovative and easily extensible network management solutions. Nowadays, OpenFlow is the most successful protocol for SDN. However, SDN based on OpenFlow protocol presents performance issues on forwarding table increasing and packet match cost. Our proposal named Tag-and-Forward (TF) is a data plane that reduces the number of flow table required in the Fat-Tree software-defined DCNs to optimize forwarding. The results noticebly outperformed RTT and packet transmission rate when compared to usual OpenFlow data plane.

Vehicular Ad-hoc NETworks (VANETs) are composed of moving vehicles with the ability to process, store, and communicate via wireless medium. VANETs promise a wide scope of services, such as, safety and security, traffic efficiency, and others. For instance, a VANET application can detect, control and reduce traffic congestion based on data that describes traffic patterns. However, disseminating data in VANET is a challenging task, due to its particular characteristics, i.e., heterogeneous density, short-range communication, and node mobility. Since, existing protocols for data dissemination do not effectively address the high overhead, in this paper, we proposed a Data Dissemination protocol Based on Centrality (DDBC) for urban scenarios. The simulation results show that DDBC protocol offers good efficiency in terms of delays and overhead, while achieve network coverage around 90%.

Even after decades, the Internet does not provide Quality of Service (QoS) guarantee for the users. In order to soften such situation, clients negotiate Service Level Agreements (SLAs) with their Internet Service Providers (ISPs), where reliability and available bandwidth are key requirements for Internet services. On the other hand, the ISPs aim to decrease the energy consumption to deliver their services and provide a green environment. The mixing of Software Defined Networks (SDNs) and Virtual Networks (VNs), called Virtual Software Defined Network (VSDN), arises as a realizable option to deal with both client and provider perspectives. Within this context, this paper proposes two algorithms to allocate VSDNs combining reliability, bandwidth, and energy efficiency, considering the current network state to adjust the importance of each of these aspects during the allocation process. The results show the effectiveness of the proposed algorithms in allocating reliable VSDNs, while improving the energy efficiency and bandwidth usage of the ISP.

The popularity of personal devices has been creating a new location-based content era, where users produce, share and access content anytime and anywhere. Mobile devices in the same area can cooperate to each other by using a Device-to-Device (D2D) communication and maximize the usage of network resources and allow the creation of new applications and experiences. In this paper, we investigate the dynamics of physical proximity of peers in a long-term study. We used one year of collected data from social media for an analysis of spatiotemporal features of how users encounter each other and the opportunities for content offloading in cellular networks. The results provided insights about the regularity of encounters, the role of routine and other spatial characteristics.

In recent years, there is a growing research interest in smart city applications based on opportunistic communications. The opportunistic networks in these scenarios are composed of disconnections, network partitions, high delay and strong influence of human mobility. To cope with these challenges, social-inspired approaches have been proposed considering the structure of networks and personal user features, however, limited studies have explored temporal variations of features and influence of exogenous variables, disregarding adaptive forwarding policies recommended for dynamic scenarios. In this paper, we address these challenges while investigating both, the temperature and the season calendar, as environmental features able to model the behavior of users' mobility and peer contacts. The results showed distinct social and spatiotemporal features characterized by thermal conditions able to affect the network performance. We also identified critical points of temperature able to provide early signals about the network changes. Finally, our results indicate that environmental data are crucial information towards the design of the next generation opportunistic mobile networks.

Traffic congestion causes drivers' frustration and costs billions of dollars annually in lost time and fuel consumption. In order to overcome such issues, this paper presents a mechanism for Intelligent Transport Systems named FOX (Fast Offset XPath), which aims to detect and manage traffic congestion in Vehicular Ad hoc Networks. FOX is implemented in a FOG computing environment, taking advantage of the aspects inherent to this platform, such as scalability, low latency, the importance of geographical location and network conditions. The focus is to reduce the time to process, reroute and notify vehicles. Simulation results show that the proposed mechanism can reduce the average trip time, CO2emissions and fuel consumption. In particular, the average trip time was decreased approximately in 32%, the average fuel consumption in 14% and the stop time in 59%.

As an important component of Smart Cities, transportation system plays a critical role to address the sustainability and mobility of the society. One key concern is that the number of vehicles continuously increases faster than the available infrastructure, as well as the traffic congestion is a difficult issue to deal with. Several solutions to Intelligent Transportation Systems (ITS) have been proposed to identify congestion and re-route the vehicles afterwards. In this direction, this work introduces SPARTAN, a fully distributed ITS solution, which notifies drivers about congested areas through Vehicle-to-Vehicle communication and employs a real-time decision making mechanism used to reroute vehicles to avoid the congested areas. Simulation results show the effectiveness of SPARTAN in calculating new routes and disseminating them to vehicles that approaching a congestion area. As a consequence, SPARTAN reduces the travel time and the congestion time in urban scenarios when compared to existing approaches.

It is a consensus that the Internet suffers from architectural limitations, including resilience, scalability, and manageability, among others. Therefore, companies access the Internet by establishing a service level agreement (SLA) in the attempt to ensure quality of service (QoS) for users. To address the current limitations of the Internet, researchers have recently proposed the edge as a service (EaaS) paradigm as a suitable solution to improve the access capacity of edge networks. EaaS uses network virtualization and software-defined networks to expand flexibility and manageability of access to edge network resources. Moreover, to maintain QoS assurance for users, EaaS addresses network events (such as traffic overload, failures, etc.) that can potentially affect QoS. Within this context, this paper proposes a software-defined management of EaaS environment, called SDM-EaaS. The proposal enhances the QoS for the end user, while improving the utilization of network resources in dynamic scenarios allowing for unpredictable changes in traffic demands, network infrastructure availability, and customer characteristics. Experiments based on emulation as well as real testbed demonstrate the effectiveness of the SDM-EaaS strategy.

The Internet is now present in our personal and professional life, where reliability and green communications are still open issues to be addressed. Usually, clients establish Service Level Agreements (SLAs) with their Internet Service Providers (ISPs), including parameters of reliability and bandwidth. On the other hand, the ISPs aim to maximize their profit by establishing as many SLAs as possible and decreasing the energy consumption to provide their services. In this context, this paper presents an algorithm for Virtual Software Defined Network (VSDN) allocation that considers both reliability and energy efficiency. The algorithm deploys the VSDN aiming to comply with the parameters defined in SLAs, as well as to improve the energy efficiency of the ISP. Experiments using a real network topology show the effectiveness of the algorithm to deploy the VSDN with a performance 15% better than existing algorithms.

Nowadays, the multimedia content accessed by users represents 55% of the Internet traffic and it is forecast to expand to 92% by 2020. This fact increased the resource demand in access networks across the world, since multimedia traffic has high network requirements. To deal with this scenario, Internet Service Providers (ISPs) tend to apply the Edge as a Service (EaaS) approach. EaaS aims to use Network Virtualization and Network Programmability to expand the flexibility of access to edge network resources. In this context, this paper presents the Bitrate-Aware Virtual Resources Adjustment (BEAVER) mechanism, which increases the Quality of Experience (QoE) support for multimedia clients in EaaS environments. Experiments using real traffic demonstrate the effectiveness of the proposed mechanism to ensure the user's QoE.

The Smart Grid (SG) is one of the most ultimate topics within the energy communities. It refers to the modernized electric grid, including innovative Information and Communication Technologies (ICTs) to support enhanced power management, protection, security, reliability, and improved costumer choice. To that, SG systems depends on reliable and scalable networking solution to allow efficient communications amongst applications, as power monitoring, protection, demand response and advanced metering, and Intelligent Electronic Devices (IEDs) that provide SG system measurements. However, the deployment of SG applications is complex and very challenging, given the missing of standards and convergent technologies to enable the jointly operation of the underlying heterogeneous systems. SG applications must implement different interfaces to intercommunicate with IEDs in their platforms, process-bus communication protocol, network technologies, and other, limiting the system scalability and time response. We propose the Session Aware Multicast based Architecture (SAMbA) to decouple the intercommunication amongst SG applications and multiple heterogeneous IEDs. SAMbA also leverages multihoming control to keep best connected sessions, and adopts multicast-based channels to achieve optimized communications, while hiding network aspects, as naming and addressing, of IEDs. Simulation results from scenarios under Advanced Metering Infrastructure (AMI) application traffic show that the SAMbA architecture outperforms the typical communication scheme in terms of network performance aspects.

Real-time video dissemination over Vehicular Ad hoc Networks (VANETs) is fundamental for many services, e.g., emergency video delivery, road-side video surveillance, and advertisement broadcasting. These applications deal with several challenges due to strict video quality level requirements and highly dynamic topologies. To handle these challenges, geographic receiver-based beacon-less approaches have been proposed as a suitable solution for forwarding video flows in VANETs. In general, the routing decisions are performed only based on network, link, and/or node characteristics, such as link quality and vehicle's location. However, in real situations, due to different requirements and hierarchical structures of multimedia applications, these existent routing decisions are not satisfactory to select the best relay nodes and build up reliable backbones to delivery video content with reduced delay and high Quality of Experience (QoE). This paper introduces the QOe-Driven and LInk-qualiTy rEceiver-based (QOALITE) protocol to allow live video dissemination with QoE assurance in Vehicle-to-Vehicle (V2V) scenarios. QOALITE considers video and QoE-awareness, coupled with location and link quality attributes for relay selection. Simulation results show the benefits of QOALITE when compared to existing work, while achieving multimedia transmission with QoE support and robustness in highway scenarios.

To access the Internet, companies define a Service Level Agreement (SLA) with Internet Service Providers (ISPs). Nevertheless, the current Internet does not assure Quality of Service (QoS), what points toward the concept of network virtualization and software defined network (SDN) to support the Future Internet. Moreover, the initial parameters defined in SLAs may not withstand traffic changes, resulting in a lower quality experienced by users and a waste of resources for ISPs. Within this context, this paper proposes an architecture to deal with resource usage variations. The proposal aims to enhance the QoS provided by the ISPs, while improving the usage of network resources in dynamic scenarios. Experiments using real traffic demonstrate the effectiveness of the proposed architecture.

Cloud computing is a technological service that has become a trend. This paradigm adds many benefits to storage of personal health records (PHR) and electronic health records (EHR), such as availability and on-demand provisioning. It also facilitates sharing of health records among doctors, family members, friends and clinicians in general. However, this technology increases the risk of leaking sensitive health data. Aiming at mitigating this problem, we present here a new architecture that takes advantage of identity federation for secure storage and sharing of PHR and EHR in the cloud environment.

Online video transmissions over wireless networks are rising in popularity and have already become part of our daily life. In the meantime, it is necessary to address a number of challenges ranging from the scarce resources, time-varying, and high error rates, to the fluctuating bandwidth, unveiling the need for an adaptive mechanism to ensure a good video transmission. Adaptive Forward Error Correction (FEC) techniques with Quality of Experience (QoE) assurance are appropriate to deliver QoE-aware video data to wireless users in dynamic and high error rates networks. This paper proposes an adaptive Video-aware FEC and Fuzzy Logic-based mechanism to shield realtime video transmissions against packet loss in wireless networks, improving both user experience and the usage of resources. The benefits and drawbacks of the proposed mechanism compared with exiting work are demonstrated through simulations and assessed with QoE metrics.

Video transmission over wireless networks has shown a great increase in recent years and it is becoming part of our daily life. Meanwhile, several difficulties can impair the success of the transmission, such as limited network resources, high error rates and fluctuating signal strength that may lead to variable bandwidth. Therefore there is the need for adaptive mechanisms that can provide a good video transmission. Adaptive Forward Error Correction (FEC) techniques which assure Quality of Experience (QoE) are a convenient means of delivering video data to wireless users in dynamic and error prone networks, while taking into account the content of the transmitted data. This paper proposes an adaptive content-aware and Random Neural Network (RNN) based mechanism to provide protection of real-time video streams against packet loss in wireless networks, improving user experience and optimising network resources. The benefits of the proposed mechanism are demonstrated through simulations and assessed with QoE metrics.

Opportunistic routing (OR) employs a list of candidates to improve reliability of wireless transmission. However, list-based OR features restrict the freedom of opportunism, since only the listed nodes can compete for packet forwarding. Additionally, the list is statically generated based on a single metric prior to data transmission, which is not appropriate for mobile ad-hoc networks. This paper provides a thorough performance evaluation of a new protocol - Context-aware Opportunistic Routing (COR). The contributions of COR are threefold. First, it uses various types of context information simultaneously such as link quality, geographic progress, and residual energy of nodes to make routing decisions. Second, it allows all qualified nodes to participate in packet forwarding. Third, it exploits the relative mobility of nodes to further improve performance. Simulation results show that COR can provide efficient routing in mobile environments, and it outperforms existing solutions that solely rely on a single metric by nearly 20-40 %.

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user’s satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a non-intrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks’ impairments, videos’ characteristics, and users’ perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user’s perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

The eHealth technology will bring many benefits for patients, healthcare professionals, and institutions, especially in developing countries, such as Brazil. The eHealth solutions will enable faster, safer, and better healthcare services through the integration of Information and Communication Technologies (ICTs) with eHealth issue. This paper gives an overview of the Context-Aware Mobile Approach (CAMA) on going research activities for eHealth environments. CAMA seeks to improve the Brazilian primary healthcare system through context-aware eHealth facilities, expecting to change the way that healthcare services are applied, managed, and maintained to achieve efficiency, safety, and reliability for both patients and hospital staffs.

Envisioning to meet the expectations arising from the Future Internet, and specifically to provide efficient Quality of Service (QoS) support for multimedia multi-user sessions, the attempts by the research community have resulted in mechanisms such as Multi-User Aggregated Resource Allocation (MARA). The MARA mechanism adopts an over-provisioning centric strategy to control surplus class-based bandwidth reservations and aggregate IP multicast trees, which allows to significantly improve network performance through a signaling-constrained approach. The results of MARA have been promising, mainly because it drastically reduces signaling and processing overhead. Although MARA has emerged as a promising QoS control mechanism, it was designed exclusively for single-ingress scenarios, thus restricting it's applicability in today's and future networks that mostly adopt multi-ingress capabilities. Therefore, this paper proposes the Multi-User Aggregated Resource Allocation - Multi Ingress (MARA-MI), whose main extension can be found in a new architecture that is able to correlate aggregate IP multicast trees and to assist the adaptation of the over-reservation patterns in multi-ingress scenarios without the possibility of QoS violations and waste of resources occurrence. The overcoming of the main limitations of previous MARA, confirmed by results, qualifies MARA-MI as a promising tool for todays and future IP-based network systems.

Several concepts behind opportunistic networks come from the studies on Delay Tolerant Networks (DTNs). The DTN architecture is suitable for a wide variety of heterogeneous networks and provides long-term storing and forward information switching to overcome communication disruptions. In most DTN solutions the focus of research on opportunistic networks has been on routing, bundle forwarding and message delivery validation. However current solutions do not have mechanisms to predict the amount of bundles, which can be transmitted during a DTN contact. In places where opportunistic connections are unstable, such as Amazon forest, it is critical to predict the amount of information that can be transmitted as a way to optimize bundles size, according to the duration of a contact and node speed. Information about the Contact Volume (CV), which is the amount of data that can be transferred during a time interval, allows the adoption of key strategies for optimizing message dispatch to improve the delivery rate and reduce the delivery delay in DTNs. Based on this observation, we present a set of experiments that validate an Adaptive Contact Volume Predicting Model in DTNs required to handle messages that are dispatched during an efficient contact. Our experiments consist of measurements using smartphones to define the amount of data transferred between two nodes, and the radio frequency signal strength measurements. Our results indicate clearly that efficient CV predictions are achieved by the proposed adaptive CV model.

Opportunistic routing (OR) takes advantage of the broadcast nature and spatial diversity of wireless transmission to improve the performance of wireless ad-hoc networks. Instead of using a predetermined path to send packets, OR postpones the choice of the next-hop to the receiver side, and lets the multiple receivers of a packet to coordinate and decide which one will be the forwarder. Existing OR protocols choose the next-hop forwarder based on a predefined candidate list, which is calculated using single network metrics. In this paper, we propose TLG - Topology and Link quality-aware Geographical opportunistic routing protocol. TLG uses multiple network metrics such as network topology, link quality, and geographic location to implement the coordination mechanism of OR. We compare TLG with well-known existing solutions and simulation results show that TLG outperforms others in terms of both QoS and QoE metrics.

As Wireless Mesh Networks (WMNs) are being increasingly deployed, there is an increasing demand for new quality assessment mechanisms that allow service operators to evaluate and optimize the utilization of network resources, while ensuring a good quality level on multimedia applications as perceived by end-users. However, existing real-time assessment schemes for WMNs are not capable of capturing the actual quality of received multimedia content with regard to user perception. Therefore, it is not possible to assure the user experience of content services. To address this problem, this paper introduces the Hybrid Quality of Experience (HyQoE) Prediction, which is a quality estimator specially designed to assess real-time multimedia applications. HyQoE is designed based on the framework of the widely used Pseudo-Subjective Quality Assessment (PSQA) Tool which exploits Random Neural Network (RNN). Crucial extension work has been implemented to achieve our objectives. A performance evaluation verifies the effectiveness and advantages of HyQoE in predicting users' perception of multimedia content in WMNs over existing subjective and hybrid methods.

Data-sets derived from subjective experiments are often exploited to construct objective quality models using parametric statistics such as MOS and multiple regression. In this paper, using data type and normality tests, we verify that non-normally distributed user opinion scores in nominal or ordinal responses should not be analysed using parametric statistics. The paper introduces a number of non-parametric statistics for valid model building and parameter estimation based on user opinion scores. A set of modelling results are also presented to demonstrate the effectiveness of non-parametric statistics.

The success of mobile and ubiquitous computing, coupled with the increasing demand for applications with high Quality of Service (QoS) and Quality of Experience (QoE) requirements, has brought great challenges to the future access networks. Thus, wireless mesh networks distinguish due to its flexibility, redundancy, low-cost and broadband capacity. However, aspects as scalability, availability and reliability, are still challenging. Following the limitations of existing solutions, this paper proposes the Multi-Service Resource Allocation in Wireless Mesh Networks (MIRA-WMN) for provisioning resources of wireless mesh networks compliant with IEEE 802.11e/s standard. The MIRA-WMN proposes a single solution to integrate QoS and connectivity resource control to efficiently support multi-user sessions. The MIRA-WMN was evaluated by simulations, which demonstrated its benefits in the data and control plane, as well as user's perception.

For smart applications, nodes in wireless multimedia sensor networks (MWSNs) have to take decisions based on sensed scalar physical measurements. A routing protocol must provide the multimedia delivery with quality level support and be energy-efficient for large-scale networks. With this goal in mind, this paper proposes a smart Multi-hop hierarchical routing protocol for Efficient VIdeo communication (MEVI). MEVI combines an opportunistic scheme to create clusters, a cross-layer solution to select routes based on network conditions, and a smart solution to trigger multimedia transmission according to sensed data. Simulations were conducted to show the benefits of MEVI compared with the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. This paper includes an analysis of the signaling overhead, energy-efficiency, and video quality.

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.

Delay and Disruption Tolerant networks (DTNs) are an alternative means of providing Internet access to remote communities, where there is a lack of a telecommunications infrastructure. Communication in this scenario is carried out through opportunistic encounters between nodes, called contacts, in which messages are forwarded to the destination. Predicting information about a contact, at the time of its occurrence, allows strategies to be adopted for optimizing message dispatch to improve the delivery rate and reduce the delivery delay in DTNs. This paper outlines a model to predict the amount of data that can be transferred, during a DTN encounter, based on real experiments conducted in the Amazon region. The results show that the proposed model has the advantage of including environmental conditions, such as soil and vegetation in the Contact Volume prediction, plus parameters like Radio Frequency power, the wireless networking standard, and the type and height of the radio antennas.

As Wireless Mesh Networks (WMNs) have been increasingly deployed, where users can share, create and access videos with different characteristics, the need for new quality estimator mechanisms has become important because operators want to control the quality of video delivery and optimize their network resources, while increasing the user satisfaction. However, the development of in-service Quality of Experience (QoE) estimation schemes for Internet videos (e.g., real-time streaming and gaming) with different complexities, motions, Group of Picture (GoP) sizes and contents remains a significant challenge and is crucial for the success of wireless multimedia systems. To address this challenge, we propose a real-time quality estimator approach, HyQoE, for real-time multimedia applications. The performance evaluation in a WMN scenario demonstrates the high accuracy of HyQoE in estimating the Mean Opinion Score (MOS). Moreover, the results highlight the lack of performance of the well-known objective methods and the Pseudo-Subjective Quality Assessment (PSQA) approach.

The Future Internet will arise from the convergence of new network concepts and combine technologies, services, media and content. It will offer flexibility and diversity with scalable content and services that are accessible through a wide range of interfaces and devices. However, the biggest challenge now is how to enable and test the proposed approaches so that they can be validated without sacrificing the current production infrastructure. The OpenFlow protocol allows production networking environments such as campus networks, metropolitan networks or R&D networks, to be used as experimental infrastructure hosting, future Internet architectures, software and protocols, in parallel with the production traffic. During rollout, there is a practical problem that arises with Legacy networks that do not support OpenFlow and need to be replaced/upgraded or refined by means of costly network re-engineering. This paper proposes a new OpenFlow architecture with new components, capable of managing Legacy-non-OpenFlow elements by offering a new solution that facilitates the management of Legacy technologies and allows them to be employed in FI experimentation environment and increase the number of experiment with the Legacy Network Environment using OpenFlow control.

Wireless Multimedia Sensor Networks (WMSNs) play an important role in pervasive and ubiquitous systems. The multimedia content in such networks has the potential of enhancing the level of information collected, enlarging the range of coverage, and enabling multi-view support. For WMSN applications, the multi-tier network architecture has proven to be more beneficial than a single-tier in terms of energy-efficiency, scalability, functionality and reliability. In this context, a multimedia intrusion detection application appears as a promising application of multi-tier WMSNs, where the lower tier can detect the intruder using scalar sensors, and the higher tier camera nodes will be woken up to send real time video sequences from the detected area. The transmission of multimedia content requires a certain quality level from the user perspective, while energy consumption and network overhead should be minimized. Among the existing mechanisms for improving video transmissions, Forward Error Correction (FEC) can be regarded as a suitable solution to improve video quality level from the user point-of-view. In this work, we propose a Quality of Experience (QoE)-aware FEC mechanism for WMSNs, which creates redundant packets based on impact of the frame on the user experience. According to the simulation results, our proposed mechanism achieved similar video quality level compared with standard FEC, while reducing the transmission of redundant packets, which will bring many benefits in a resource-constrained system.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the lifestyle of users.The multimedia era is allowing users to create, distribute, and access content in ubiquitous way and cost-effectively, while providers explore new ways to increase their revenues.The efficient delivery of real-time multimedia services over emerging diverse and heterogeneous systems is a challenging research goal.The interoperability of applications, transport and network protocols as well as the demand for improved Quality of Experience (QoE) create new challenges and opportunities for further research on novel communication protocols, architectures, and methods to efficiently support current and future multimedia networking systems.In 2010, the Third International Future Multimedia Networking (FMN 2010) workshop was organized in Kraków, Poland and achieved a success attracting numerous submissions from various countries.FMN 2010 produced a high quality peer reviewed technical programme with an acceptance rate at around 24% and addressed important aspects of future multimedia systems with a focus on QoE.Authors of selected papers from FMN 2010 were invited to submit extended versions of their papers in order to be considered for inclusion in this Special issue on Future Multimedia.

The proliferation of multimedia content and Wireless Mesh Networks (WMNs) are changing the Internet facilities and life style of fixed and mobile users. For the success of the next generation wireless networks, novel multimedia approaches with quality level assurance are required to allow the distribution of video-streaming, video conference, gaming, multimedia social networking, green multimedia content and other entrainment applications for thousand of users in ubiquitous wireless (mesh) systems. In this context, new routing schemes are needed to provide end-to-end Quality of Service (QoS) and Quality of Experience (QoE) support for delay/loss/jitter-sensitive multimedia applications in WMNs. The well-known OLSR (Optimized Link State Routing) protocol with ETX (Expected Transmission Count) metric bring many benefits for the path selection process, but present a drawback in the queue availability management and reduce the system performance. Therefore, multimedia-related packets will suffer with loss/delay/jitter and the system overall performance will decrease. This paper proposes the Queue-based OLSR ETX (QoETX) approach to overcome the limitations of OLSR-ETX regarding queue availability by using a cross-layer scheme and supporting QoS and QoE assurance. QoETX optimizes network and user-based parameters by coordinating queue availability, QoS and fuzzy issues in the routing decision process as a way to allocate the best paths for multimedia applications. In order to present the benefits of the proposed solution compared with existing routing schemes, namely OLSR-ETX, OLSR-FLC, OLSR-MD and HWMP (IEEE 802.11s standard), regarding QoS (block probability and throughput) and QoE (PSNR, SSIM, VQM and MOS) parameters, performance evaluations were carried by using the Network Simulator (NS-2.34).

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the life style of users.The multimedia era is allowing users to create, distribute, and access content in ubiquitous way and cost-effectively, while providers explore new ways to increase their revenues.The efficient delivery of real-time multimedia services over emerging diverse and heterogeneous systems is a challenging research goal.The interoperability of applications, transport and network protocols as well as the demand for improved Quality of Service (QoS), Quality of Experience (QoE), and seamless mobility create new challenges and opportunities for further research on novel communication protocols, architectures, and methods to efficiently support current and future multimedia networking systems.In 2009, the Second International Future Multimedia Networking (FMN 2009) workshop was organized in Coimbra, Portugal and achieved exceptional success attracting 64 submissions from 33 countries.FMN 2009 produced a high quality peer reviewed technical programme with an acceptance rate at around 25% and addressed important aspects of future multimedia systems with a focus on Autonomic Content Networks.Authors of selected papers from FMN 2009 were invited to submit extended versions of their papers in order to be considered for inclusion in this Special issue on Future Multimedia.These papers were peer-reviewed again and then further revised by the authors.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the life style of users. User demands for multimedia access anywhere, anytime from any device are creating new challenges for research communities from both academia and industry. It is expected that video-based services alone will account for 50 percent of all consumer network traffic in 2012 and we will continue to witness the explosive growth in users sharing multimedia content over the Internet. In this context, new network, application, and user-based approaches must be created to deal with such complex multimedia systems. This paper presents some of the recent advances in multimedia networking focusing primarily on areas that have been receiving attention recently and are expected to continue to generate further interests in coming years. These areas include Quality of Experience (QoE) and various related standardization issues, Content Distribution Networks (CDNs), multimedia communications, mobile Multimedia. This paper also briefly highlights some of the major challenges that still need to be addressed to enable the support and delivery of multimedia services anywhere, anytime over highly heterogeneous infrastructures and user terminal devices.

Real-time multimedia sessions are now present in our daily live experience, and will be among the most important applications in future generation networks. The distribution of those sessions, towards fixed and mobile users, with Quality of Experience (QoE) assurance is important to attract and keep customers, while increasing the profits of providers. This paper proposes the Quality Level Control for Multi-user Session (QUALITIS) approach to assure the quality level of sessions shared by multiple users (multi-users) in future generation networks. QUALITIS aims to keep sessions with an acceptable quality level and to avoid session blocking. The QUALITIS control is achieved by coordinating Quality of Service (QoS) mapping, QoS adaptation, resource allocation and mobility mechanisms. Performance evaluation was carried out based on simulation experiments and verified the QUALITIS impact on the user's expectation and on the performance of the network.

The great demand for real-time multimedia sessions encompassing groups of users (multi-user), associated with the limitations of the current Internet in providing quality assurance, has raised challenges for defining the best mechanisms to deploy the next generation of networks (NGN). There is a consensus that an efficient and scalable provisioning of network resources is crucial for the success of the NGN, mainly in what concerns access networks. Previous solutions for the control of multi-user sessions rely mostly on uncoordinated actions to allocate per-flow bandwidth and multicast trees. This paper introduces a multiuser aggregated resource allocation mechanism (MARA) that coordinates the control of class-based bandwidth and multicast resources in a scalable manner. In comparison with previous work, MARA significantly reduces signaling, state and processing overhead. The performance benefits of MARA are analyzed though simulations, which successfully demonstrated the significant optimization in the network performance.

It is expected that the next generation of networks will provide group communication services to mobile users, independently of the underlying Quality of Service (QoS) model, access and connectivity technologies, which may be different in each domain along the communication path. However, current mobility management schemes have limitations in terms of end-to-end QoS support and connectivity control for sessions crossing heterogeneous networks. This paper presents the Multi-user Session Control (MUSC) mechanism that controls the mobility of multiuser sessions while providing QoS mapping, QoS adaptation and connectivity support in heterogeneous environments with mobile receivers and static senders. Performance evaluation shows the MUSC efficiency to setup sessions inside and between access-networks, by analysing the packet delay and the percentage of packet losses during handovers.

It is expected that next generation of networks will handle new types of services which are destined to large audiences and have different QoS requirements. From the transport point of view, multicast is the most suitable technology for the required group communication services, since it avoids packets duplication and saves network resources. However, QoS-aware multicast content deliver raises several problems, such as the control of QoS trees in environments with asymmetric routing. This paper presents Multi-service Resource Allocation (MIRA), a multicast-aware resource reservation protocol for class-based networks that consider routing asymmetries. MIRA controls the resources of network classes associated to multicast sessions considering the QoS characteristics of the latter and network conditions of the available classes in the source-to-receivers path. A detailed description of the functionalities of MIRA and a conceptual analysis against RSVP and RMD-QoSM are presented. Finally, the session setup time, and the signalling and state overheard in comparison with RSVP are analyzed based on simulations.

Multicast mobility management is a challenging and unsolved task for the successful deployment of multi-user real-time multimedia services in next generation mobile networks. Therefore, a solution that constrains losses during handover and limits the service degradation perceived by users is needed. Moreover, the losses should be constrained taking in consideration the impact that different packets have in the quality of the multimedia session. In this paper, the seamless mobility of users for media distribution services (SEMUD) proposal takes in account this fact when constraining packet losses that result from the handover process. This leads to an augmented capability to provide seamless mobility for multi-user services. This ability is evaluated by comparing the quality of a received video sequence with and without taking into consideration the different importance that packets have in the quality of the sequence. The results attest for the quality improvement of the video sequence when the packets are handled taking in consideration their importance.

Next generation IP networks are envisioned to be heterogeneous, to provide a wide variety of services, and to support mobility of users with distinct requirements. Moreover, mobile communications are expected to expand from telephony to publish-subscribe services, such as real-time multimedia. With this goal, the University of Coimbra is working with DoCoMo Euro-Labs on a unifying control architecture for multimedia publish-subscribe services over an IP-based mobile system, where our proposal aims to control the quality of service and mobility across heterogeneous networks with no perceived service degradation to the users. This article highlights the requirements of a control architecture for publish-subscribe services, and presents our proposal, called QoS Architecture for Multi-user Mobile Multimedia (Q3M). The main components and functionality of the Q3M architecture are analysed from the user and network perspectives, and some simulation results concerning the analysis of the session setup times are presented.