Distributing microservices across the Computing Continuum reduces latency and preserves data locality but introduces management complexity on heterogeneous, resource-constrained edge nodes. Traditional reactive orchestration triggers only after saturation occurs. Under bursty or high-density workloads, this latency leads to service degradation, instability, and inefficient energy usage. To address this, the Adaptive Resource-Aware Predictive Orchestrator (ARAPO) couples per-service local forecasting with calibrated node-level aggregation. It employs a dual-threshold policy based on predicted and observed load to trigger migrations. It maps CPU forecasts to power for energy-aware placement without external instrumentation. ARAPO is evaluated in a realistic hospital reference scenario against a reactive-only baseline. Results demonstrate that the system anticipates saturation and prevents control plane congestion. It significantly improves stability in oscillating workloads. Overload time drops from 28.4% to 4.5%. Consequently, energy usage during overload falls to 14.9% of the reactive baseline. Node-level forecasting achieves R 2 up to 0.86. The power model tracks consumption with a mean absolute error as low as 0 . 40 W . This validates its suitability as a lightweight, energy-efficient controller.

Practical training in electrocardiogram (ECG) interpretation remains uneven, particularly in resource-limited settings, despite the central role of ECGs in cardiovascular diagnosis. This work evaluates whether an ECG-focused digital twin that integrates interactive simulation and deep learning guidance can achieve educationally valid realism, improve recognition of patterns and abnormalities through interactivity, and enhance accuracy and learner motivation via predictive feedback. We present ECGTwinMentor, a cross-platform system that synthesizes parameterized ECG waveforms, enables fine-grained control of physiologic variables, and delivers immediate predictive feedback for formative assessment. The diagnostic model supports low-latency inference on modest hardware. Validation with healthcare experts and medical students showed positive evaluations for realism, usability, and integration potential. Experts reported average ratings between 3.5 and 4.5 out of 5, while students rated usability between 4.6 and 4.8 and motivation and realism at 5.0, with most items scoring at least 4. These findings support the conclusion that an interactive, predictive digital twin can narrow the gap between theory and practice in ECG interpretation, offering an accessible, scalable, and reproducible approach to ECG education.

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.

Federated Learning has emerged as a promising approach for maintaining data privacy across distributed environments, enabling training on a diverse range of devices from high-performance servers to low-power gadgets. Despite its potential, managing numerous data sources can strain these devices, particularly those with limited capabilities, leading to increased latency. This is especially critical in virtual reality, where real-time responsiveness is crucial due to the need for constant data connectivity. Historically, virtual reality systems have relied on tethered computer setups, restricting their flexibility and the benefits of wireless technology. However, recent advancements have enhanced the computational power of VR devices, allowing them to perform certain tasks independently. This work explores the feasibility of training a neural network on VR devices, using a federated learning approach, to develop a collaborative model aggregated and stored in the cloud. The goal is to assess the computational demands and explore the potential and constraints of leveraging VR devices for artificial intelligence applications.

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 rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this article, we present the Genetic Algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability.

The urgency in the adoption of the Computing Continuum paradigm, which allows computing services to be de-ployed closer to users, requires a plethora of powerful, distributed devices that host such services. In this context, user-owned devices, such as phones or gaming devices, massively distributed by nature and experiencing a continuous growth in computing resources, are naturally fit to host services, and thus, their incorporation to the Continuum to provide the urgently needed infrastructural support is inevitable. In this future, two key challenges must be addressed: automating service orchestration across a massive number of devices, and ensuring device owners maintain agency over the circumstances and conditions under which services can be hosted on their devices and consumed by other users. As a first step towards this future, we present Atmos, an automated service orchestration platform for integrating user-owned devices in the Computing Continuum. Atmos enforces user-defined policies, automatically adjusting the placement and replication of services across devices. The evaluation of Atmos shows that it enforces all user policies, compared to state-of-the-art service orchestration systems, which violate up to 90.3% of user policies, with minimal impact on the experienced QoS.

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 advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications’ stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS. While continuous reasoning techniques have enabled the creation of tools for these scenarios, they can have some trouble finding a feasible adaptation for abrupt and sharp changes, requiring non-adaptive techniques in those cases. Furthermore, for scenarios with smoother changes, it would be desirable to have faster algorithms to perform this placement. To explore the trade-off of effectiveness and execution times of different methods while ensuring that an application placement is found, we propose Multi-Layered Continuous Reasoning (MLCR) as an autonomic framework to adapt application placements through multiple continuous reasoning-based methods. We also present an MLCR prototype based on three methods: Faustum, MigDADO, and ConDADO. An evaluation in a realistic use case shows that MLCR is faster than traditional methods for application placement and maintains an acceptable QoS.

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.

Smart cities have deployed a myriad of devices to sense the status of the city and its citizens in order to reconfigure different elements to improve the quality of life. However, the analysis and reconfiguration of these elements on the fly can lead to unforeseen problems. The digital twin paradigm has risen as a promising technology to analyze and test these re-configurations before their execution. These digital twins are usually centralized in the cloud. However, the emulation of highly distributed systems can lead to scalability, response time, and security and privacy problems. In this paper, we propose a hierarchical and distributed architecture for digital twins, deployed over the Cloud-to-Thing Continuum. The proposal is illustrated by means of a case study about public transportation in smart cities.

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 pervasive presence of the Internet results in an increasing number of people and systems interacting with each other. However, since each system only captures its own representation of reality, the information of the involved actors is scattered and even duplicated. This scattering generates problems by itself since only a small fragmented vision of the actors’ reality is available for each system. At the same time, the duplication of information in different systems can lead to inconsistencies that impede the correct integration of different systems. In this paper, we present an architecture based on the use of an upper blockchain in charge of federating a set of lower-level blockchains. This way, actor-centered data integration is achieved, offering a single, global vision of the dispersed information of each actor. This solution, called FedBlocks, relies on the blockchains’ federation concept where all interconnected systems are communicated and can share authorized data of their actors, providing a benefit for these systems and for the actors themselves. The proposed implementation has been successfully tested integrating 5689 records from 50 different institutions, belonging to 1156 actors.

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.

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 exaggerated use of smartphones and growing informatization of the environment allows modeling people’s behavior as a process, namely, a social workflow, where both individual actions and interactions with other people are captured. This modelling includes actions that are part of an individual’s routine, as well as less frequent events. Although infrequent actions may provide relevant information, it is routine behaviors that characterize users. However, the extraction of this knowledge is not simple. Current process mining techniques face problems when analyzing large amounts of traces generated by many users. When very different behavioral patterns are integrated, the resulting social workflow does not clearly depict their behavior, either individually or as a group. Proposals based on frequent pattern mining aim to distinguish traces that characterize frequent behaviors from the rest. However, tools that allow grouping/filtering of users with a common behavior pattern are needed beforehand, to analyze each of these groups separately. This study presents the so-called federated process mining and an associated tool, SOWCompact, based on this concept. Its potential is validated through the case study called activities of daily living (ADL). Using federated process mining, along with current process mining techniques, more compact processes using only the social workflow’s most relevant information are obtained, while allowing (event enabling) the analysis of these social workflows.

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 increasing capabilities of mobile devices have led to the emergence of new paradigms exploiting them. These paradigms foster the onload and distribution of functionalities on mobile devices, allowing the development of distributed mobile applications. This distribution reduces the latency and the data traffic overhead and improves privacy. As in any other mobile application, their success largely depends on the quality of service (QoS) they offer. Nevertheless, the evaluation of distributed mobile applications is particularly complex due to the number, heterogeneity, and interactions between the devices involved. Current techniques allow developers to assess the quality of a single device, but they are not designed for highly heterogeneous, distributed, and collaborative environments. This paper presents a framework called Perses, which allows the creation of virtual scenarios with multiple heterogeneous mobile devices to launch end-to-end tests to evaluate not only each device but also the interactions among them. The framework was evaluated against a real deployment, showing that the behavior and the quality attributes measured are similar to those of the real deployment, allowing developers to evaluate these applications before launching them. Finally, Perses was integrated into a DevOps methodology to automate its execution and further facilitate its adoption by software companies.

Abstract The development that quantum computing technologies are achieving is beginning to attract the interest of companies that could potentially be users of quantum software. Thus, it is perfectly feasible that during the next few years hybrid systems will start to appear integrating both the classical software systems of companies and new quantum ones providing solutions to problems that still remain unmanageable today. A natural way to support such integration is Service-Oriented Computing. While conceptually the invocation of a quantum software service is similar to that of a classical one, technically there are many differences and technological limitations, which refer to platform independence, decoupling, scalability, etc. To highlight these differences and the difficulties to develop quality quantum services, this paper takes a well-known problem to which a quantum solution can be provided, integer factorization, making use of the Amazon Braket quantum service platform. The exercise of trying to provide the factorization as a quantum service is carried out following the best practices, design patterns and standards existing in the implementation of classical services. This case study is used to highlight the rough edges and limitations that arise in the integration of classical-quantum hybrid systems using service-oriented computing. The conclusion of the study allows us to point out directions in which to focus research efforts in order to achieve effective quantum service-oriented computing.

Opportunistic data sharing allows users to receive real-time, dynamic data directly from peers. These systems not only allow large-scale cooperative sensing but they also empower users to fully control what information is sensed, stored, and shared, enhancing an individual's control over their own potentially private data. While there exist context-aware frameworks that allow individual users to define when and what shared information peers can consume, these approaches have limited expressiveness and do not allow data owners to modulate the granularity of the information released depending on a particular peer or situation. In addition, these frameworks do not consider the consuming peers' privacy, i.e., how much information they have to provide to get access to some desired data. In this paper, we present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule in order to precisely define who has access to what data when and at what level of detail. Our evaluation shows that PADEC is more expressive than other access control mechanisms and protects the provider’s privacy up to 90% more.

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 current trend of smart environments is leading towards a world where everything is considered as a service. Internet-connected smart devices make these environments largely manageable and controllable through services. In these environments, not only devices offer services, but lately, people through their smartphones can also offer services such as personal information provided by the name, the preferences, or the location, promoting the offer of almost anything as a service. However, this high supply of services makes it more difficult for IoT systems to identify which services to use to solve a particular need. This paper proposes a solution to characterize services homogeneously and a service selection mechanism is outlined considering the properties of the services and the context in which they are found. With this proposal, services are defined commonly to facilitate a smart selection by IoT applications.

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.

Summary During the last decade, the mobile application market has grown steadily thanks to the massive use of smartphones and the emergence of cloud computing for offloading computation tasks and improving the quality of experience. With the more recent deployment of Internet of Things (IoT) devices, this cloud‐based architectural design and the corresponding communication flow has been maintained. Nevertheless, the increasing amount of information exchanged, the stringent requirements of many IoT applications, and the need for these applications to adapt their behavior in real time to the user's context set these architectural assumptions a challenge. Paradigms such as mobile, mist, and edge computing have recently been proposed to exploit the computational and storage capabilities of current smartphones and IoT devices in order to onload some tasks onto them, reducing the overhead on both the cloud and the network. Currently, the application of these paradigms requires much attention from skilled developers to create ad hoc systems, as there lack standards and tools facilitating their use. This communication introduces Human Microservices as a framework facilitating the deployment of APIs on companion devices in order to provide personal and updated information that can be consumed by other entities. The framework improves the integration of humans in the IoT loop and facilitates the deployment of computation units in devices closer to end users, enhancing system response time by reducing the stress on cloud and network infrastructure. The proposed framework is based on existing standards in order to improve software quality and shorten the learning curve.

The rate of world population aging is increasing. This situation directly affects all countries socially and economically, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detects anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multidevice environments where sensor and smartphone data are considered to automate environments with elderly people or to learn from them. Also, most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable, and transparent way for them.

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.

This artifact is a guideline for the generation of APIs through the APIGEND (API Generator for End Devices) tool. This tool is an extension of the OpenAPI Generator [1] . It originally allows developers to create both the client and server side through an OpenAPI Specification with a ServerCentric style in different languages. The extension developed also allows one to generate APIs for end devices, specifically for Android devices and ESP32 Microcontrollers, making the application of the Edge [2] and Mobile-Centric [3] paradigms easier.

Smartphones have become the perfect companion devices. They have myriad sensors for gathering the context of their owners in order to adapt the behaviour of different applications to the device's situation. This information can also be of great help in enabling the development of social applications that, otherwise, would require a costly and intractable deployment of sensors. Mobile Crowd Sensing systems highly reduce this cost, but realizing this vision using traditional centralized networking primitives requires a constant stream of the sensed data to the cloud in order to store and process it, which in turn leads to the individuals about whom the data is sensed losing control over the privacy of the data. In this paper, we propose an architecture for a device-to-device Mobile Crowd Sensing system and we deepen on a new privacy model that allows users to define access control policies based on their context and the consumer's context.

We introduce our solution developed for data privacy, and specifically for cognitive security that can be enforced and guaranteed using blockchain technology in SAAL (Smart Ambient Assisted Living) environments. Personal clinical and demographic information segments to various levels that assures that it can only be rebuilt at the interested and authorized parties and no profiling can be extracted from the blockchain itself. Using our proposal the access to a patient's clinical process resists tampering and ransomware attacks that have recently plagued the HIS (Hospital Information Systems) in various countries. The core of the blockchain model assures non-repudiation possible by any of the involved information producers thus maintaining ledger fidelity of the enclosed historical process information. One important side effect of this data infrastructure is that it can be accessed in open form, for research purposes for instance, since no individual re-identification or group profiling is possible by any means.

In 2012, the startup Gloin began to develop a new social mobile application, called BeeFun, allowing users to communicate with each other based on their contextual information, i.e., their history, location, preferences, etc. For two years the company invested a great amount of resources to develop the idea, to specify the business plan and to implement the product. In the last phase, seeking funding to launch the product, Gloin was finalist of the 2013 LaunchPad Denmark Business Plan Cup. The finalists won the access to an acceleration program to get funding and launch their products in Denmark. In this process, some flaws in the idea and in the business plan that endangered the project success were detected. In this paper, we detail the process of developing BeeFun, the major failings, the lessons learnt and how Gloin pivoted to develop a new product called nimBees. A product that is currently getting the attention of medium and large enterprises. In addition, some of the barriers that the promoters of the idea, as researchers, had to overcome are also detailed.