Software-Defined Networking (SDN) enables flexible and programmable control over network behavior through the deployment of multiple control applications. However, when these applications operate simultaneously, each pursuing different and potentially conflicting objectives, unexpected interactions may arise, leading to policy violations, performance degradation, or inefficient resource usage. This paper presents a Digital Twin (DT)-based framework for the early detection of such application-level conflicts. The proposed framework is lightweight, modular, and designed to be seamlessly integrated into real SDN controllers. It includes multiple DT models capturing different network aspects, including end-to-end delay, link congestion, reliability, and carbon emissions. A case study in a smart factory scenario demonstrates the framework’s ability to identify conflicts arising from coexisting applications with heterogeneous goals. The solution is validated through both simulation and proof-of-concept implementation tested in an emulated environment using Mininet. The performance evaluation shows that three out of four DT models achieve a precision above 90%, while the minimum recall across all models exceeds 84%. Moreover, the proof of concept confirms that what-if analyses can be executed in a few milliseconds, enabling timely and proactive conflict detection. These results demonstrate that the framework can accurately detect conflicts and deliver feedback fast enough to support timely network adaptation.

UAV-assisted networks provide a rapid and flexible solution for deploying communication infrastructures in environments with limited or no conventional access, such as rural areas or disaster-stricken regions. However, the inherent constraints of UAVs, particularly their limited flight time, necessitate frequent node replacements, which disrupt microservices hosted on the UAVs. This paper introduces a model for live migration of stateful microservices in UAV-assisted networks, leveraging pre-copy mechanisms and containerized environments to minimize service downtime and ensure state preservation. The proposed architecture incorporates a Digital Twin for real-time monitoring, a Swarm Management Function for orchestrating UAV replacements, and a Central Image Server for managing storage layers. By combining these elements, the model achieves seamless handovers, reducing disruptions and enabling reliable deployment of state-sensitive applications. Potential use cases, including real-time data analysis and edge computing, underscore the advantages of this approach. Future work will focus on evaluating performance metrics such as service downtime, network overhead, and energy consumption to further optimize this solution.

As the climate crisis intensifies, the ICT sector faces increasing pressure to reduce its environmental impact. Computer networks, which form the backbone of modern digital services, contribute significantly to global energy consumption and carbon emissions, especially when powered by fossil-based electricity in geographically diverse regions. While renewable energy adoption is growing, its availability remains uneven and insufficient to fully decarbonize large-scale infrastructures. Consequently, reducing the energy consumption of network devices, particularly in high-carbon-intensity areas, becomes a critical short-term objective. A key strategy is using Software Defined Networks (SDN), which enable better device management, to adapt routing and network management to consider both traffic demands and the carbon footprint associated with energy use. In this paper, we propose ECOWN (Efficient Carbon Optimization for Wired Networks), an ILP-based framework that minimizes the carbon emissions of wired networks. ECOWN jointly optimizes routing and link activation by leveraging realworld data on traffic patterns, carbon intensity, and router power models. Through simulations over the Abilene topology, ECOWN achieves up to 59 % of reduction in carbon emissions compared to traditional shortest-path routing schemes.

The research agenda on programmable data planes has been primarily focused on high-end networking devices, driven by technical requirements derived from operations & management needs of large scale datacenters and cloud providers. In this paper, we argue in favor of a yet incipient but equally paramount and challenging topic in this agenda: research on programmable low-end devices, like Low-power wide-area network (LPWAN). One main motivation is “unlocking” LPWAN, enabling one to freely redefine how they parse and process packets by means of Domain-specific languages such as P4. In addition to reducing capital expenditure by allowing interoperability between devices from multiple vendors, programmability would open LPWAN to an entire novel class of use cases, like providing inclusive internet access to technologically marginalized populations (such as rural communities). To contribute to this emerging research agenda, we propose a conceptual architecture and demonstrate the technical feasibility of a Programmable LPWAN by means of a proof-of-concept prototype, built using off-the-shelf hardware. More importantly, we present and discuss valuable lessons towards the design of such devices, maintaining their popular characteristics (like low power, low cost, long rage) yet freely (re)programmable for a broader class of novel use cases.

We present a prototype that could open the doors to mitigate digital exclusion in technologically underserved pop-ulations. By converging software-defined networks (SDN) with programmable data planes (PDP), our approach enables one to customize low-cost hardware to fit the network behavior. We integrate low-power wide-area networks (LPWANs), known for their scalability and efficiency, with PDPs to propose a flexible solution for changing requirements, such as distance and terrain configurations, validated through real-world test scenarios.

The classification of Congestion Control Algorithms (CCAs) is vital in current networks, where increasing traffic demands and dynamic conditions challenge their stability and performance. CCAs play a fundamental role in managing congestion, balancing throughput, minimizing latency, and reducing packet loss to ensure reliable data transmission across diverse scenarios. Despite their importance, accurately identifying the CCA in use remains a challenging task; critical for optimizing resource allocation and enhancing Quality of Service (QoS). This paper presents a framework that leverages P4-programmable switches for real-time extraction of key traffic metrics, including queuing delay, interarrival time, queue depth, RTT, and sending rate. These metrics are analyzed using a Random Forest classifier to predict the CCA in use with high accuracy. Extensive experiments in a controlled network environment, featuring a bottleneck link and flows utilizing CCAs such as Cubic, Reno, BBR, and Vegas, validate the effectiveness of our approach.

The rapid evolution of 5G and 6G technologies, coupled with growing environmental concerns, underscores the critical need for energy-efficient computer networks. To this end, a key challenge would be to minimize energy consumption by dynamically adjusting the number of active network devices based on the traffic demand and matrix. This requires an efficient mapping of the traffic matrix, which represents the amount of data traffic exchanged between different nodes over a given period, onto the network to ensure a minimal number of active while meeting performance requirements. Traditional approaches, based on Integer Linear Programming and heuristic algorithms, face significant limitations in scalability and computational efficiency, particularly for large-scale networks. To address these challenges, this work proposes a Machine Learning (ML)-based algorithm that leverages clustering techniques to identify near-optimal mappings of traffic matrices in Software-Defined Networks. Simulations on realistic network topologies demonstrate that our solution achieves substantial energy savings, up to 53 %, outperforms heuristic methods in execution time by orders of magnitude, and delivers near-optimal performance. These results highlight the potential of ML-driven approaches to enable scalable and energy-efficient network management.

This paper presents empirical results from the use of multi-UAV ad hoc networks that also perform edge computing through the deployment and execution of microservices requested by user equipment on the ground. For this purpose, the most important aspects on how the UAV-based mesh network is configured and how user equipment on the ground accesses it are detailed. Four different tests are provided, all conducted with affordable user-grade hardware, explained and analyzed. Results demonstrate how the use of mesh-connected swarms of UAVs is a viable solution for providing connectivity and edge computing capabilities to large areas of terrain where there is no traditional network infrastructure. Given a suitable distance between UAVs, useful throughput values of at least 10 Mbps are observed, reaching up to 25 Mbps in specific situations. In addition to that, it is also observed that the communication channel between UAVs is unstable and that special attention must be paid to the distance between them.

The Internet of Things enhances the quality of life by automating tasks and streamlining human-device interactions. However, manual device management remains time-consuming, especially in multiple or new environments that demand new settings and interactions. Learning systems aid in automating task management, but their learning times hinder personalization and struggle when the system has to interact with multiple IoT environments, impacting user experience. This paper aims to optimize knowledge sharing for IoT environments, proposing a framework that utilizes recommender systems to find optimal and reusable configurations among IoT environments and users. To that end, this work leverages teacher-student relationships in Knowledge Distillation, facilitating knowledge sharing and enhancing knowledge reuse in learning models. In addition, real-time processing eliminates training time. This approach achieves a remarkable 93.15% accuracy.

Next-generation applications, marked by their critical nature, need to cope with stringent Quality of Service (QoS) requirements, such as low response time and high throughput. Moreover, the increasing number of devices connected to the Internet and the need to provide a consistent network infrastructure to serve the applications requested by users, open the tradeoff of jointly considering the QoS improvement for such applications and the reduction in the energy consumption of the infrastructure. To address this challenge, this paper proposes ELTO (Energy-Load Trade-Off), a system designed for the joint optimization of energy efficiency and traffic load balancing during the transition from IP networks to Software-Defined Networks (SDN). Leveraging SDN and Network Function Virtualization (NFV) paradigms, ELTO introduces an Integer Linear Programming multi-objective formulation, and a Genetic Algorithm heuristic to tackle the optimization problem in large-scale scenarios. ELTO encompasses a holistic approach to network configuration, including network equipment status and routing, to strike a balance between network traffic load balancing and energy efficiency. Results over realistic topologies show the effectiveness of the proposed solution, outperforming other state-of-the-art approaches, being able to switch off nearly half of the links in the network while also reducing the Maximum Link Utilization.

The Software-Defined Networking (SDN) paradigm disaggregates the data plane, embodied by switches that only forward data, from the control plane, embodied by SDN controllers that communicate with said switches. SDN also proposes a third, application layer, which implements various functions such as firewalls or service discovery by communicating with the controllers through the northbound interface. However, while state-of-the-art works propose the deployment of multiple, distributed SDN controllers, the software architecture of these controllers is still monolithic, requiring not only the controller runtime but also all the network-level applications to be deployed across all SDN controller hardware. On the other hand, state-of-the-art SDN controllers such as MSN allow treating network-level applications as microservices, which comes with the challenge of orchestrating the microservices across the network. In this paper, we present Grex, a framework to orchestrate network-level applications across microservice-based SDN controllers. We test and validate the optimization model of Grex by performing experiments in a realistic network testbed using the MSN controller.

In recent years, Software-Defined Networking (SDN) research literature has proposed the integration of multiple SDN controllers into the same network, improving the scalability and reliability of the network. However, while this evolution has focused on control plane hardware, the architecture of SDN controller software is still monolithic, and its communication with the application plane through the northbound interface is done by the integration of the network-level applications' codebase with the controller software. The proposal of SDN Mi-croservices Architectures (SDN MSAs) is aimed at transforming the application plane, from a monolithic architecture to a set of independently deployable modules named SDN microservices. However, the promising paradigm of SDN MSAs also increases the complexity of network management, as these microservices must be placed through the SDN controllers. This placement is especially complex due to its NP-hard nature. In this work, we present Genetic Algorithm for SDN MSA (GASM), an evolutionary computation-based heuristic to solve this issue in tractable times. Experimental results show that GASM represents an average speed-up of 846.33 × compared to optimal solvers.

The Internet has become the driving wheel of social interactions as well as economic growth, but its access is vastly unequal. Noticeably, such rural dwellers experience a digital service access gap, due to the inadequate broadband infrastructure available to them when compared with their urban counterparts. This paper is aimed at overcoming the gap between rural communities with no reliable Internet service by deploying the service through the use of Unmanned Aerial Vehicles (UAVs). By decomposing the Internet of Things (IoT) applications into elements (namely microservices) and deploying them through UAVs, latency is shortened, distributing workload among UAVs while limiting microservice deployment to optimize request execution, making the quality service improvement, essential for such applications. Simulations revealed the effectiveness of our approach, as it indicated the lowest latency when a UAV connected to several users in scenarios where there was no Internet connectivity.

Rural areas broadband access suffers from the limited investment of network operators, due to the forecasted return on investment. As a consequence, digital services such as eHealth, remote education, or smart agriculture cannot be offered to the rural population. In this context, research on Unmanned Aerial Vehicles (UAV) networks has emerged, which aims to solve the coverage problem by relying on small cells mounted on UAVs to provide coverage. From the network Quality of Service (QoS) point of view, i.e., the performance offered to users according to certain parameters such as delay, reliability, and throughput provisioning can be identified as one of the weak points. For the problem of maximizing throughput, the main solution is to group several UAVs in the same area. However, as the offered throughput increases, the power consumption will also increase. In this context, this paper proposes a genetic algorithm to solve the problem of jointly maximizing the offered throughput in rural scenarios where users request microservice-based IoT applications while minimizing the energy consumption of the swarm of UAVs. The algorithm is defined and evaluated in realistic scenarios, demonstrating its effectiveness on increasing the throughput while decreasing the number of UAVs that are required.

As of today, TCP remains the de-facto transport protocol in the Internet. However, TCP may incur high delays, especially when retransmitting lost packets as they have to be retransmitted only by the source and after a timeout that is roughly equal to a round trip time. To reduce such delay, recent work [1]-[3] proposed to deploy a special network function, called Transport Assistant (TA), that is able to detect and retransmit lost TCP packets from inside the network rather than the source, and thereby, reduces retransmission delays. Unfortunately, there is no study so far on the impact of the placement of the TA on its performance benefits in terms of packet delivery delayIn this paper, we focus on the TA placement problem. We discuss the trade-offs and parameters to be considered to select the best placement for the TA. We mathematically model the TCP packet delivery delay, i.e., the time needed to deliver TCP packets when the TA is deployed, using different parameters like the location of the TA, the loss probabilities and the propagation delays of the network links. Thanks to this model, we study the impact of these parameters on the performance and efficiency of the TA.

The flexibility and programmability of Software-Defined Networks (SDN) has allowed the research community to propose new Traffic Engineering (TE) techniques to improve their performance. Although the installation of heuristic or optimal solutions in the SDN controller allows to obtain good results in network performance, these are based on historical data that may not be updated to actual traffic variations. Moreover, the research community is exploiting the strength of Deep Reinforcement Learning (DRL) and its capability to learn and adapt to the complexities inherent in networks to propose enhanced routing solutions. However, the nature of DRL can cause a performance degradation during the learning process due to the application of exploration when determining the best policy. For this reason, in this work we propose a Multi-Agent DRL (MADRL) based solution that is able to reduce the Maximum Link Utilization (MLU) of SDN networks only considering the local information of the nodes. Each node has a DRL agent and is able to decide the best routing decision for each flow so that the MLU is minimized. The performance evaluation shows that our approach outperforms the classical shortest path rule based on Dijkstra in 8%.

Opportunistic Mobile Ad Hoc Networks (MANETs) offer versatile solutions in contexts where the Internet is unavailable. These networks facilitate the transmission between endpoints using a store-carry-forward strategy, thereby allowing information to be stored during periods of disconnection. Consequently, selecting the next hop in the routing process becomes a significant challenge for nodes, particularly because of its impact on Quality of Service (QoS). Therefore, routing strategies are crucial in opportunistic MANETs; however, their deployment and evaluation in real scenarios can be challenging. In response to this context, this paper introduces a monitoring software-driven tool designed to evaluate the QoS of routing algorithms in physical opportunistic MANETs. The implementation and its components are detailed, along with a case study and the outcomes provided by an implementation of the proposed solution. The results demonstrate the effectiveness of the implementation in enabling the analysis of routing protocols in real scenarios, highlighting significant differences with simulation results: mobility patterns in simulations tend to be inaccurate and overly optimistic, leading to a higher delivery probability and lower latency than what is observed in the real testbed.

Latency is a critical aspect for a broad spectrum of applications that relies on the internet, such as, voice over IP (VoIP) or teleconferencing, and the lack of ultra-fast and highly reliable communications is prominent in rural areas even in mature economies. Our proposal focuses on optimizing the deployment of microservice-oriented architectures (MSA) in computing and routing enabled unmanned aerial vehicles (UAVs). For that matter, an information system which gathers all the information of the flying ad hoc network (FANET) is developed. From there, we propose multiple approaches, based on integer linear programming (ILP) and heuristics, to tackle the minimization of end-to-end latency by deploying multiple instances of microservices in the UAVs that are close to the users that make use of them. Extensive experiments based on network emulation prove the performance of our ILP formulation of the problem and address the optimality gap between the ILP-based approach and the heuristics ones, which are highly scalable and usable in real-time for large-scale scenarios.

The Software-Defined Networking (SDN) paradigm enables network administrators to manage the behavior of the network thanks to a centralized control plane. By programming network-level applications, it is possible to determine how traffic flows must be handled programmatically. In the same manner that computing applications have evolved from monolithic to microservice-based architectures, network-level applications are expected to evolve into microservice-based SDN controllers, implementing each application as a replicable and individually deployable component of the SDN controller. In such a scenario, the Quality of Service (QoS) experienced by traffic flows depends on how these microservices are placed and deployed through the network topology. In this work, we provide a system to optimize the QoS of the traffic in microservice-based SDN networks by optimally placing and replicating the network-level microservices. Experimental results show the effectiveness of the proposed solution over a real network topology with varying traffic loads.

The Internet of Things has brought digitalization to intensive domains through the automation of their real-world processes. However, the criticality of these processes is reflected in high Quality of Service (QoS) requirements for the application to work properly. Moreover, business-level QoS, such as the operational cost, are also key to the feasibility of these applications. This QoS depends on three, closely-related dimensions: the application software, the computing devices and the communication network, which provide high flexibility to obtain different performances at different costs. Thus, to achieve optimal QoS in these scenarios, the application, computing and networking dimensions must be optimized, considering their crucial interplay in a joint effort. Furthermore, this solution must allow multi-objective optimization, finding the optimal trade-off between operational cost and application performance. In this paper, we present Multi-Objective SDN Fog Optimization (MO-SFO), a holistic framework that allows for the optimization of both the response time and the deployment cost. MO-SFO is evaluated over an emulated smart city case study, showing the cost and performance trade-off achieved in different topologies.

In recent years, the number of devices connected to the Internet (and hence the data traffic) has significantly increased. The adoption of the Internet of Things paradigm, the use of the MicroServices Architecture for applications and the possibility of deploying such applications at different layers (fog, edge, cloud), makes the selection of an appropriate deployment a critical task for network operators and developers. In this paper, an emulation framework is proposed to allow them make a decision for the network, computing and application deployment in the cloud continuum, while satisfying the required Quality of Service. The framework is compatible both for IP and SDN network paradigms and is extensible to different types of scenarios thanks to its approach based on Docker containers. The evaluation over a realistic network scenario shows that it is extensible to any scenario and deployment required by the research community working on the cloud continuum.

During the last years, applications requirements have been changing and the Information and Communication Technology (ICT) sector had to evolve and explore new solutions to approach the requirements of applications of the future such as telepresence, augmented reality, metaverse, holoportation, etc. One of the aspects that could serve as a basis to satisfy the stringent QoS requirements of the applications of the future is the reduction of the latency caused by TCP retransmissions. Through the proactive location of a novel network function, namely Transport Assistant (TA), the delay caused by TCP retransmissions is reduced, thus improving the network QoS and satisfying the QoS required by the applications. In this paper, a Machine Learning solution based on Logistic Regression (LR) is proposed to predict which is the part of the network that is prone to negatively impact the network performance. Through experiments based on the training of historical data, the LR-based solution is able to predict the correct location of the TA with a precision average of 95% and accuracy of 90%. Such good results in the prediction help to make better decisions and therefore to save time and resources, improving the network management.

The irruption of the Internet of Things (IoT) has attracted the interest of both the industry and academia for their application in intensive domains, such as healthcare. The strict Quality of Service (QoS) requirements of the next generation of intensive IoT applications require the QoS to be optimized considering the interplay of three key dimensions: 1) computing; 2) networking; and 3) application. This optimization requirement motivates the use of paradigms that provide virtualization, flexibility, and programmability to IoT applications. In the computing dimension, paradigms, such as edge or fog computing, software-defined networks in the networking dimension, along with micro-services architectures for the application dimension, are suitable for QoS-strict IoT scenarios. In this work, we present a framework, named Next-gen IoT Optimization (NIoTO), that considers these three dimensions and their interplay to place microservices and networking resources over an infrastructure, optimizing the deployment in terms of average response time and deployment cost. The evaluation of NIoTO in a healthcare case study reveals a response time speed up of up to 5.11 and a reduction in cost of up to 9% with respect to other state-of-the-art techniques.

The main goal of the Web of Things (WoT) is to improve people’s quality of life by automating tasks and simplifying human–device interactions with ubiquitous systems. However, the management of devices still has to be done manually, which wastes a lot of time as their number increases. Thus, the expected benefits are not achieved. This management overhead is even greater when users change environments, new devices are added, or existing devices are modified. All this requires time-consuming customization of configurations and interactions. To facilitate this, learning systems help manage automation tasks. However, these require extensive learning times to achieve customization and cannot manage multiple environments so new approaches are needed to manage multiple environments dynamically. This work focuses on knowledge distillation and teacher–student relationships to transfer knowledge between IoT environments in a model-agnostic manner, allowing users to share their knowledge each time they encounter a new environment. This work allowed us to eliminate training times and achieve an average accuracy of 94.70%, making model automation effective from the acquisition in proactive WoT multi-environments.

Abstract There are contexts where communication with TCP/IP protocol is not possible due to the lack of infrastructure or a reliable and continuous data transmission. In this cases, alternatives such as Opportunistic Networks (OPPNets) are valid. Such challenging conditions are common in rural areas and are a significant obstacle for the deployment of eHealth technologies for older adults. Considering this context, the present work introduces Interest-based System for Communication in Isolated Areas (ISCA), an OPPNet architecture for remote monitoring and emergency detection in ageing people who live alone. For this, the energetic requirements are considered, providing efficient and sustainable operation. The proposal makes use of a routing algorithm based on interests which enables asynchronous communication among entities. ISCA is evaluated over a realistic scenario and compared with similar state-of-the-art solutions. Experimental results show that ISCA notably improves the delivery probability with an enhancement of 52.25% in comparison to the second best alternative and provides a suitable average latency. Moreover, it also achieves better performance in terms of overhead and hops number compared to the other studied protocols

The Internet is the most powerful engine for social and economic growth, and it needs to be accessible to everybody. However, the possibilities for people living in rural areas to use digital services through broadband access are reduced compared to the inhabitants of urban zones. In this paper, digital services are brought closer to people living in rural areas where there is lack of Internet connectivity by means of an Unmanned Aerial Vehicle (UAV) based network. Decomposing loT applications into microservices and deploying them into UAV s allow to reduce their latency and hence to improve the associated quality of service, which is strict for this type of applications. Simulation results show the effectiveness of the proposed solution, comparing the latency experienced by the applications in case there is lack of Internet connectivity with the situation in which one of the UAV s is able to reach a ground base station.

A TCAM (Ternary Content-Addressable Memory) is a type of memory used in the flow tables of Software Defined Networking (SDN) nodes. Although these memories are very fast, their size is limited. This has an impact on the number of rules that can be installed, and an inefficient rule management can lead to a degradation of the network quality of service. In this work, an heuristic algorithm named Active Traffic First (ATF) is proposed to efficiently manage the content of the flow tables of the SDN nodes in order to maximize the Global Service Time (GST) of the active flows in the network. The idea behind ATF is adopted by deleting flows that are not being used in case a new flow aims to be served and there is no space available. Experimental results show that ATF outperforms other state-of-the-art solutions by improving GST and reducing re-installations.

Deploying a real-world software defined network (SDN) requires instantaneous link traffic information. This has necessitated for the need of accurate real-time data analytics and traffic matrix (TM) prediction methods. So far, several frameworks have been developed to enable analysis and generation of valuable information from huge volumes of partial and noisy data. But, owing to the linear nature of network design, generally typified by manual control plane forwarding design, current frameworks are incapable of performing accurate traffic prediction over datasets in modern non-recurrent large-sized networks. To address this issue, deep learning (DL) methods have recently been proposed as a possible solution. But, deciding the most appropriate DL models to be employed for accurate TM prediction is still a challenge. This paper proposes an improved DL framework that utilizes different dimensionality feature reduction techniques to perform short-term TM prediction in SDN networks. The two dimensionality reduction techniques required to perform feature reduction for the DL model are correlation component analysis (CCA) and principal component analysis (PCA). Investigational results show that the proposed method can achieve more accurate forecast of link traffic in comparison to the traditional baseline machine learning frameworks.

Ternary content-addressable (TCAM) memories of Software-Defined Networking (SDN) nodes are very fast and allow parallel lookups to be performed in a very short period of time. However, they have both a high energy consumption and cost, which make their size limited. This limitation has an impact on the number of rules that can be installed in the flow tables of the SDN nodes, and an inefficient rule management can lead to a degradation of the QoS (Quality of Service) of the network. In this work, a solution based on Deep Reinforcement Learning (DRL) is proposed to tackle the rule placement problem of SDN flow tables. The main idea is to remove the rules that are intended to be less used in order to make room for new rules that are prone to be used often. In this way, the goal of increasing the number of flows that can be handled in the network, and therefore improving the network QoS is achieved. Simulation results show that the proposed solution obtains better results than its static idle timeout counterpart. In particular, DRL is able to handle 7.36% more satisfied flows on average compared to the case of setting a static idle timeout of 1 s., which indeed requires a high number of rules re-installations. This is therefore a promising result that demonstrates the good performance of the proposed DRL solution.

The advent of the Internet of Things (IoT) paradigm to intensive domains, such as industry, is a key enabler for the automation of critical, real-world processes. The strict Quality-of-Service (QoS) requirements of these domains make low-latency computing paradigms, such as fog computing, very attractive for meeting these requirements. Moreover, the requirements of scalability and flexibility in the underlying network communications motivate the use of software-defined networking (SDN) in the infrastructure. To enable these fog-SDN environments, fog nodes (FNs) that have both computing and SDN capabilities can be deployed, thus easing the deployment of fog in SDN networks. However, the exact placement of these FNs is key to the latency of the hosts that make use of them, and thus, must be carefully assessed to meet the stringent QoS requirements of critical, time-strict IoT applications. This article focuses on this FN placement problem by formalizing it and solving it through both optimal and approximated methods, including comparisons with state-of-the-art benchmarks. In particular, we analyze the performance of each of these methods in terms of latency and execution time in both SDN Internet topologies and Industrial IoT infrastructures. Our proposed heuristic provides placements with near-optimal latencies, with smaller optimality gaps than the benchmark, and computes them in tractable times.

The Internet of Things (IoT) is more present in our daily lives than ever before, turning everyday physical objects into smart devices. However, these devices often need excessive human interaction before reaching their best performance, making them time-consuming and reducing their usability. Nowadays, Artificial Intelligence (AI) techniques are being used to process data and to find ways to automate different behaviours. However, achieving learning models capable of handling any situation is a challenging task, worsened by time training restrictions. This paper proposes a Federated Learning solution to manage different IoT environments and provide accurate predictions, based on the user’s preferences. To improve the coexistence between devices and users, this approach makes use of other users’ previous behaviours in similar environments, and proposes predictions for newcomers to the federation. Also, for existing participants, it provides a closer personalization, immediate availability and prevents most manual interactions. The approach has been tested with synthetic and real data and identifies the actions to be performed with 94% accuracy on regular users.

The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined Networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear Programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.

Leveraging the Internet of Things (IoT) in intensive domains, such as in the Industrial Internet of Things (IIoT) or Internet of Medical Things (IoMT), provides automation and sensing solutions for complex environments through the interconnection of different sensors and actuators. However, these scenarios usually demand to meet stringent Quality of Service (QoS) requirements to work properly. Fog computing, a paradigm that brings computation and storage closer to the edge, and Software-Defined Networking (SDN), a networking paradigm that enables for network scalability and flexibility, can be combined. To do so, fog nodes that integrate both, computation resources and SDN capabilities, are leveraged to meet these stringent needs. Clearly, the placement of such fog nodes plays a key role in the achieved QoS. In this paper, an optimal fog node placement formulation is evaluated in an emulated fog and SDN environment. Results show that an optimal fog node placement can achieve a reduction of up to 59% in the network latency with a minimal jitter compared with other well-known placement methods.

The Internet-of-Things (IoT) paradigm offers applications the potential of automating real-world processes. Applying IoT to intensive domains comes with strict Quality-of-Service (QoS) requirements, such as very short response times. To achieve these goals, the first option is to distribute the computational workload throughout the infrastructure (edge, fog, cloud). In addition, integration of the infrastructure with enablers, such as software-defined networks (SDNs) can further improve the QoS experience, thanks to the global network view of the SDN controller and the execution of optimization algorithms. Therefore, the best placement for both the computation elements and the SDN controllers must be identified to achieve the best QoS. While it is possible to optimize the computing and networking dimensions separately, this results in a suboptimal solution. Thus, it is crucial to solve the problem in a single effort. In this work, the influence of both dimensions on the response time is analyzed in fog computing environments powered by SDNs. DADO, a framework to identify the optimal deployment for distributed applications is proposed and implemented through the application of mixed-integer linear programming. An evaluation of an IIoT case study shows that our proposed framework achieves scalable deployments over topologies of different sizes and growing user bases. In fact, the achieved response times are up to 37.89% lower than those of alternative solutions and up to 15.42% shorter than those of state-of-the-art benchmarks.

Service Function Chaining (SFC) is an enabling technology to provide end-to-end service differentiation according to specific user requirements. Although emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are perfect enablers for SFC, hardware limitation of Ternary-Content Addressable Memories (TCAMs) can be an obstacle when handling a large variability of SFC requests, derived from the increasing number of users, and the heterogeneity of applications and Quality of Service (QoS) requirements. This article introduces and investigates the problem of TCAM size limitation on the classification procedure of SFC requests in SDN-based SFC environments. To overcome this limitation, the classification of incoming SFC requests is proposed to be offloaded to transient nodes when the occupation of the ingress node flow table is close to its maximum. An Integer Linear Programming (ILP) formulation is provided to formalize the Chain Request Classification Offloading (CRCO) problem, that consists in maximizing the number of SFC requests that can be served. Furthermore, a heuristic algorithm is presented to solve the CRCO problem in feasible time. The performance evaluation carried out over two real topologies, shows that the proposed offloading strategy can greatly increase the number of accepted requests without significantly affecting the network QoS.

Many rural areas along Spain do not have access to the Internet. Despite the huge spread of technology that has taken place during recent years, some rural districts and isolated villages have a lack of proper communication infrastructures. Moreover, these areas and the connected regions are notably experiencing a technological gap. As a consequence of this, the implementation of technological health solutions becomes impracticable in these zones where demographic conditions are especially particular. Thus, inhabitants over 65 suppose a large portion of such population, and many elderly people live alone at their homes. These circumstances also impact on local businesses which are widely related to the agricultural and livestock industry. Taking into account this situation, this paper proposes a solution based on an opportunistic network algorithm which enables the deployment of technological communication solutions for both elderly healthcare and livestock industrial activities in rural areas. This way, two applications are proposed: a presence detection platform for elderly people who live alone and an analytic performance measurement system for livestock. The algorithm is evaluated considering several simulations under multiple conditions, comparing the delivery probability, latency, and overhead outcomes with other well‐known opportunistic routing algorithms. As a result, the proposed solution quadruples the delivery probability of Prophet, which presents the best results among the benchmark solutions and greatly reduces the overhead regarding other solutions such as Epidemic or Prophet. This way, the proposed approach provides a reliable mechanism for the data transmission in these scenarios.

The Industrial Internet of Things (IIoT) provides automation solutions for industrial processes through the interconnection of different sensors, actuators and robotic devices to the Internet, enabling for the automation of manufacturing processes through Factory Automation. However, IIoT processes are often critical, and require very high Quality of Service (QoS) to work properly, as well as network scalability and flexibility. Fog computing, a paradigm that brings computation and storage devices closer to the edge of the network to enhance QoS, as well as Software-Defined Networking (SDN), which enables for network scalability and flexibility, can be integrated into IIoT architectures in the form of fog nodes that integrate both, computation resources and SDN capabilities, to meet these needs. However, the QoS of the IIoT system depends on the placement of these fog nodes, creating a need to obtain placements that optimize QoS in order to meet the requirements by minimizing the latency between the fog nodes and the IIoT devices that consume their services. In this paper, this fog node placement problem is formalized and solved by means of Mixed Integer Programming. We also show relevant experimental results of our formulation and analyze its performance.

Nowadays, cloud computing has become a key paradigm in distributed applications thanks to the rise of low-power Internet-connected devices as commonplace. However, stringent Quality of Service (QoS) requirements are complicated to achieve when a pure cloud computing paradigm is applied, due to the physical distance between end devices and cloud servers. This motivated the appearance of fog computing, a paradigm that adds computation and storage resources, named fog nodes, closer to the end devices in order to reduce response time and latency. However, the placement of fog nodes, as well as the relative placement of the end devices each fog node serves, can affect the QoS obtained. This can be crucial to those services that have stringent QoS requirements. In this work, we analyze the effects that different placements of fog nodes have on QoS and present the problem of placing fog nodes to obtain an optimal QoS, with a focus on the Industrial Internet of Things domain because of its strict QoS requirements. We conclude that an optimized placement of the fog nodes can minimize latency to support the QoS requirements of IIoT applications.

With the increasing tendency on data rates in forthcoming communication networks, availability is a crucial aspect to guarantee Quality of Service (QoS) requirements. The possibility of predicting the lifetime of networking hardware can be a key to improve the overall network QoS. This paper proposes a generic Machine Learning (ML) based framework that learns how to mimic the mathematical model behind the lifetime of network line cards. Results show that a good precision (85%) and recall (close to 100%) on the estimation can be achieved regardless the type of line cards the network is composed of.

The advent of the Software Defined Networking (SDN) paradigm represents a great opportunity for the definition of new network management solutions. In this work, we focus on the definition and implementation of a novel technique to solve the Traffic Matrix Assessment (TMA) problem from the perspective of an Internet Service Provider. Since the migration from legacy IP networks to fully-deployed SDN ones needs to be incremental due to budget and technical constraints, this paper proposes a mixed measurement and estimation scalable solution for hybrid IP/SDN networks to accurately solve the TMA problem by exploiting the availability of flow rule counters in SDN switches. The performance evaluation shows that our error-tolerant solution is able to assess the TM with a negligible estimation error by only measuring a small percentage of traffic flows, overcoming other state-of-the-art algorithms proposed in the literature. Moreover, the performance analysis of the proposed implementation using the OpenDaylight controller over an emulated network environment, shows that a trade-off between the quality of the assessed TM and its impact on the network in terms of control messages can be found by properly tuning the number of measured flows.

Nowadays, there is a number of applications that only run on mobile devices. Mobility is a feature required by an increasing number of current applications. Mobile applications are often required to work even in a scenario with frequent disconnections. These environments with intermittent connectivity compose the so-called Intermittently Connected Mobile Networks (ICMN), where long disconnection intervals and opportunistic connections are assumed. Although the main challenge in such networks is the fact of designing efficient routing protocols to deliver messages from a source to a destination, there is no research focused on providing a generic framework to validate the existing routing schemes proposed by researchers. In this paper, we study the possibility of creating a networking technology for volatile network environments where messages can participate in forwarding decisions as they progress across the network.

Nowadays, wireless technologies are continuously in expansion and make more comfortable people life in many ways. Technologies like Wi-Fi, Bluetooth or IrDA are well known to everyone. This is a consequence of the benefits that they offer to the society: global Internet access, files sharing without wires, etc. But, there is a problem related to the effects of these technologies: the interferences they cause to each other. These interferences have negative effects to the performance of wireless networks.