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%.

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) has emerged to offer cloud computing services closer to vehicular users by combining vehicles and edge computing nodes into Vehicular Clouds (VCs). In this scenario, an intelligent task scheduler must decide which VC will run which tasks, considering contextual aspects like vehicular mobility and tasks’ requirements. This is important to minimize both processing time and monetary costs. However, such direct optimization can lead to unfairness in resource usage, easily leading to (as we will show) decreased performance. Towards this end, in this work, we propose FARID, a task scheduling mechanism that considers contextual aspects of its decision process and applies a probabilistic selection function on VCs to balance the processing load and increase the fairness in the use of vehicular resources. Compared to state-of-the-art solutions, FARID has a higher level of fairness and can schedule more tasks while minimizing monetary costs and system latency.

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.

In the context of the 5G e-health vertical, Network Slicing (NS) promotes mobile e-health (m-health) applications with high innovative facilities through a set of network resource components that can be extended through physical resource virtualization strategies and softwarization. The Cloud-Network Slicing (CNS) approach was recently introduced to offer services across multiple administrative and technological domains distributed across the federated cloud and network infrastructures. The CNS approach can improve m-health user's experience by allowing high content and service delivery flexibility through Multi-Access Edge Computing (MEC) capabilities within the Radio Access Networks (RAN) closer to the healthcare data source. In this scenario, characterized by the inevitability of handover between the various cells existing in the RAN, the infrastructure management system must be extended with improved capabilities to enable handover decisions to maintain the m-health UE experience during mobility events. This paper introduces a network-slicing mobility-aware control approach for paving 5G CNS-enabled systems with automated and proactive mobility control and management capabilities. Simulation results revealed that our proposal could provide m-health applications with service-level slicing-driven handover procedures while keeping connectivity constraints.

Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.

Due to the increased computational capacity of Connected and Autonomous Vehicles (CAVs) and worries about transferring private information, it is becoming more and more appealing to store data locally and move network computing to the edge. This trend also extends to Machine Learning (ML) where Federated learning (FL) has emerged as an attractive solution for preserving privacy. Today, to evaluate the implemented vehicular FL mechanisms for ML training, researchers often disregard the impact of CAV mobility, network topology dynamics, or communication patterns, all of which have a large impact on the final system performance. To address this, this work presents FLEXE, an Open Source extension to Veins that offers researchers a simulation environment to run FL experiments in realistic scenarios. FLEXE combines the popular Veins framework with the OpenCV library. Using the example of traffic sign recognition, we demonstrate how FLEXE can support investigations of FL techniques in a vehicular environment.

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.

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.

Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low-latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions.

Efficient content distribution over vehicular ad hoc networks (VANETs) is a challenging task due to highly topology changes caused by vehicle mobility. In this context, Vehicle Named-Data Networks (VNDN) architecture improves the performance and reliability in delivering content by providing content-centric network communication and caching capabilities. However, the success of VNDN architecture depends on mitigating the broadcast storm problem during the cache discovery process, where the network performance impairment occurs due to the waste of resources generated. In this paper, we propose a receiver-based cache discovery protocol based on degree-centrality for VNDN, called CLYMENE. The protocol paves the way for efficient content distribution by minimizing the broadcast storm problem. Simulation results show that CLYMENE enhances the cache discovery by 80.59% while allowing a content delivery rate of 39.49% and reducing the number of transmissions in the cache discovery process at 70.65% compared to existing protocols.

The automotive industry has been continuously investing in the modernization of the vehicles by the addition of more sensors and computational power. With this evolution, Intelligent Transportation Systems (ITS) make up a services framework that seeks to mitigate problems in the road sector. Many ITS services are facilitated by creating vehicular clouds (VCs) by using the communication capabilities of other vehicles to provide cloud services closer to vehicular applications. However, often the computational resources present in the vehicles are underutilized. For this reason, we propose in this work a mechanism that efficiently allocates computational tasks to be performed in VCs. Simulation results on a realistic mobility trace show that, with our mechanism, tasks are more allocated, the reward from allocating these tasks was higher, resource waste was minimized, and less CPU is used in the allocation processing. Also, the proposed mechanism is statistically close to a globally optimal solution.

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.

Virtual Machines (VMs) offer great flexibility, reusability, and portability to manage applications in physical resources of a Cloud data center or a mobile cloud. These VMs may suffer a migration from one datacenter to another or from one cloudlet to another, due to the characteristics of vehicular cloud, such as high mobility and the need to meet the demands of a particular service. In this way, establishing a VM migration policy in a vehicular cloud becomes a challeng e, since it has to deal with the unique challenges of vehicular networks, as well as to meet the demands of users' services without virtual machine migration, which impacts on the performance of such service. In this paper, we propose a decision policy based on multiple attributes to migrate VM in a vehicular cloud scenario. In this way, the proposed policy allows to decide more quickly if a VM migration should be carried out and to which cloudlet this machine should be migrated. Simulation results showed that the proposed policy reduced in 2% the amount of VM migration in the network, decreased in 3% the blockages of the migration requests, as well as a reduction in the inference time of approximately 5 ms.

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.

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.

Vehicular Ad-hoc NETworks (VANETs) promise a wide scope of multimedia services ranging from security and traffic announcements to entertainment and advertising videos. However, VANETs have a dynamic network topology with short contact time, decreasing the Quality of Experience (QoE) of transmitted videos due to frequent disconnections in the communication between neighbours vehicle. Those disconnections cause communication flaws and delays, increasing the packet loss during video transmissions. To cope with this, neighbor vehicles moving on the same direction and wishing to cooperate could form a platoon to disseminate live videos. In this paper, we introduce a platoon protocol based on game theory for video dissemination with QoE support, called P2V. The proposed protocol provides cooperation between neighbor vehicles to establish a platoon by taking into account vehicles direction, speed, and distance, where P2V provides a reward (money or coupon) for vehicles participating in the platoon. Simulation results confirm the efficiency of the P2V protocol to ensure video transmission with high QoE support compared to BLR and XLinGO protocols.

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%.

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.

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.

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.

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.

Link quality estimation is a fundamental building block for the design of several different mechanisms and protocols in wireless sensor networks. The accuracy of link quality estimation greatly impacts the efficiency of these protocols. Therefore, a thorough experimental evaluation of link quality estimators (LQEs) is mandatory. This motivated us to build a benchmarking testbed-RadiaLE, that automates LQEs evaluation by analyzing their statistical properties. Our testbed includes (i.) hardware components that represent the WSN under test and (ii.) a software tool for setting up and controlling the experiments and also for analyzing the collected data, allowing for LQEs evaluation. To demonstrate the usefulness of RadiaLE, we carried out a comparative performance study of a set of well-known LQEs.