Orchestrating microservices in Edge environments presents significant challenges due to the distributed and heterogeneous nature of the infrastructure, as well as the constraints of limited resources and variable connectivity. This paper addresses these issues by proposing a distributed framework for microservice orchestration based on a consensus algorithm. Our approach leverages a leader-follower consensus model, adapted to handle dynamic workloads and resource allocation efficiently. Through an extensive analysis of existing solutions, we identified the limitations of traditional centralized orchestration frameworks in Edge contexts, motivating the need for a decentralized methodology. The proposed framework introduces a dynamic leader election mechanism based on workload priorities and a distributed logging system for enhanced transparency and reliability. We validated our solution through experimental implementation on an Edge cluster composed of Raspberry Pi nodes, demonstrating its ability to adapt dynamically to variable workloads while ensuring consistency and fault tolerance. The results show that the framework effectively balances computational loads and meets the requirements of modern Edge computing applications.

Knowledge Graph (KG) processing faces critical infrastructure challenges in selecting optimal NoSQL database paradigms, as traditional performance evaluations rely on static benchmarks that fail to capture the complexity of real-world KG workloads. Although the big data field offers numerous comparative studies, in the KG context DBMS selection remains predominantly ad-hoc, leaving practitioners without systematic guidance for matching storage technologies to specific KG characteristics and query requirements. This paper presents a KG-specific benchmarking framework that employs connectivity density, scale, and introduces a graph-centric metric, namely Semantic Richness (SR), within a four-tier query methodology to reveal performance crossover points across Document-Oriented, Graph, and Multi-Model DBMSs. We conduct an empirical evaluation on the FAERS adverse event KG at three scales, comparing paradigms from simple filtering to deep traversal, and provide metric-driven, evidence-based guidelines for aligning NoSQL paradigm selection with graph size, connectivity, and semantic richness.

Multiomics is an emerging biological analysis approach in which the datasets come from multiple “omics”, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Nowadays, the convergence of Deep Learning and multiomics sciences presents an unprecedented opportunity to dissect the intricate interplay of biological processes. Specifically, multiomics data integration, propelled by Deep Learning methodologies, has revolutionised biological research, enabling a more holistic understanding of complex biological systems and disease mechanisms. This paper explores the current landscape of Deep Learning applications in multiomics, highlighting state-of-the-art techniques, emerging research areas, and the challenges that lie ahead. In particular, we delve into the application areas and computational methods that have been considered so far, offering guidance to researchers navigating this intricate field.

This exploratory study analyses parameters influencing a tele-rehabilitation program using the Virtual Reality Rehabilitation System (VRSS) HomeKit for patients with Parkinson’s disease (PD). Data from 10 patients with idiopathic PD who completed 20 upper limb motor exercise sessions were analysed to assess correlations between system-generated metrics and clinical outcomes. Patients were clinically assessed with the Fugl-Meyer Assessment (FMA) and the Unified Parkinson’s Disease Rating Scale (UPDRS). In the experiments, we focused on two macro categories of exercises performed by patients, i.e., reaching and catching, while analysing how correlations among patients’ age, clinician-provided Hoehn-Yahr (HY) stage, and VRRS-provided metrics (such as repetitions, mean duration, correct responses, and omission errors) influenced exercise score assessments and other potential outcomes. This study is propaedeutic for advanced ML-based analyses to model patient progress and predict outcomes throughout treatment, laying the foundation for patient-centric precision medicine and the development of tailored remote rehabilitation strategies.

In the last years, our cities become more and more crowded due to the increasing number of cars into old city planes. So, even small/medium cities experience a travel time comparable with the bigger ones. To improve mobility management in modern cities, specific simulation tools can be used to analyze the impact of different mobility plans on mobility and, therefore to find the most suitable solution for each city. However, these tools are often hard to be used by city traffic managers without advanced computer skills. In this article, we used a multiagent transport simulation (MATSim) to provide a simple tool that can be easily used by end-users to better plan mobility strategies for both private and public transportation. In particular, starting from the open data provided by the city of Messina, we have implemented a software tool able to process MATSim events. Moreover, we propose a metric to estimate the safety of roads for cyclists. From the experimental results provided by the proposed software, we are able to discover the most overloaded links and estimate the travel time distribution by hour of departure time.

Hospitals suffer from implementing Data Spaces due to the risks related to data security aspects. To ensure patients' data privacy, healthcare organizations can incorporate pseudonymization strategies into their data management practices, promoting collaboration and information sharing among several hospital departments and healthcare professionals. In this paper, we defined and implemented the intent-based mul-tilevel granular approach for HL7 FHIR JSON documents pseudonymization, by comparing it with non-granular encryption of the entire document. With this approach, we enhance patient confidentiality and facilitate efficient healthcare data sharing within the Intra-Hospital Data Space, facilitating enhanced flexibility and scalability in deploying and utilizing data management systems.

Remote patient monitoring is a form of telehealth that allows medical centres to monitor and manage their patients’ chronic conditions. Often, depending on the severity of the disease, patients can experience either temporary or permanent home hospitalization. Although the classical medical approach involves continuous monitoring of vital parameters through specialized medical devices, it does not allow observation of patient’s behaviours, which may provide additional information of interest to physicians. In this context, digital biomarkers represent the next frontiers towards precision medicine. In this paper, we explore the possible adoption of Audio Biomarkers for monitoring the behaviours of long-term home hospitalized patients. In particular, we trained and tested several Machine Learning (ML) models to recognise different sounds (i.e., sneezing, breathing, coughing, snoring, teeth brushing, and toilet flush). The results show that, even with a few audio samples, the considered models provide good performance.

This paper proposes a new solution for promoting safe driving and reducing accidents caused by drunk drivers. The scientific innovation of our work lies in the strong integration between virtual and physical environments, which allows users to experience strong emotions in simulated driving scenarios with alcohol impairments. To achieve this aim, we have implemented a Metaverse environment able to interact with multiple physical input and output devices. The virtual environment reproduces alcohol-related impairments, such as blurred vision, delayed response to commands and shaking in the event of an accident, also showing the tragic consequences of accidents during the experience. We have validated the developed solution by experimental measurement of both software performance and Quality of Experience (QoE) of volunteer users.

In the last few years, the healthcare industry has seen significant advances in medical image analysis, mainly driven by the substantial progress of Deep Learning (DL). Convolutional Neural Networks (CNNs) have been the reference model for image-processing tasks. Recently, however, the advent of Vision Transformers (ViTs) has challenged their dominance. In this work, we explore the potential of ViTs for pneumonia diagnosis, comparing their performance with CNNs using different learning approaches. Specifically, we assessed the behaviour of From-Scratch Learning (FSL) and Pre-Trained (PT) models, leveraging Transfer Learning (TL), to highlight their performance differences. Experiments are performed in a Microsoft Azure Cloud laboratory considering both centralised and distributed Federated Learning (FL) scenarios, proving that the latter helps to mitigate the potential biases contained in the dataset, achieving similar accuracies and reducing training times linearly with the number of clients.

In recent years, significant progress has been achieved in medical image analysis, mainly due to the substantial advances in deep learning methods. In the past decade, Convolutional Neural Network (CNN) was the best model for image classification, demonstrating remarkable success in various medical applications. However, the advent of Vision Transformers (ViTs) has challenged the dominance of CNN approaches. This study aims to explore the potential of ViTs in healthcare, comparing their performance with that of CNN models. The latter has traditionally excelled in image feature extraction through convolutional operations; on the other hand, ViTs, relying on self-attention mechanisms, exhibit unique capabilities in capturing long-range dependencies, enabling them to effectively capture complex patterns within images. In this study, after analyzing their architectures, we assessed the behaviour of from-scratch and pre-trained models, highlighting their differences in performance and providing light on the applicability of Transfer Learning (TL) approach in the healthcare scenario.

This work proposes a content-based approach for structured document obfuscation, which is based on Secret Share techniques implemented at the Edge of a computing system. The key innovation of our work is to increase the flexibility in data sharing, keeping data at the Edge, and improving data security, reliability, and availability. The proposed solution can also suit more complex scenarios that include continuum capabilities towards the Cloud, to benefit from high computing resources. We present the details of our work and the implementation. Then, we evaluate the performance at the Edge of the proposed approach, comparing it with its traditional implementation applied to the entire document. The goal is to analyze the differences in performance and security between the two approaches.

Military drones can be used for surveillance or spying on enemies. They, however, can be either destroyed or captured, therefore photos contained inside them can be lost or revealed to the attacker. A possible solution to solve such a problem is to adopt Secret Share (SS) techniques to split photos into several sections/chunks and distribute them among a fleet of drones. The advantages of using such a technique are two folds. Firstly, no single drone contains any photo in its entirety; thus even when a drone is captured, the attacker cannot discover any photos. Secondly, the storage requirements of drones can be simplified, and thus cheaper drones can be produced for such missions. In this scenario, a fleet of drones consists of t+r drones, where t (threshold) is the minimum number of drones required to reconstruct the photos, and r (redundancy) is the maximum number of lost drones the system can tolerate. The optimal configuration of t+r is a formidable task. This configuration is typically rigid and hard to modify in order to fit the requirements of specific missions. In this work, we addressed such an issue and proposed the adoption of a flexible Nested Secret Share (NSS) technique. In our experiments, we compared two of the major SS algorithms (Shamir's schema and the Redundant Residue Number System (RRNS)) with their Two-Level NSS (2NSS) variants to store/retrieve photos. Results showed that Redundant Residue Number System (RRNS) is more suitable for a drone fleet scenario.

Nowadays, thanks to the ever-increasing hardware capacity of Edge computing, the achievement of Ubiquitous Computing is no longer a utopia, even though it presents still several challenges. In this paper, we introduce the concept of the Cloud-Edge-Client Continuum, by extending the well-known Cloud-Edge Continuum paradigm with the addition of Clients as deployment nodes. Specifically, we propose both a system architecture and a piece of middleware that allows a web browser to be used seamlessly as a deployment Client node, introducing the concept of a Virtual Point of Deployment (VPod). Our solution allows to: a) leverage the computational capacity of a huge number of ready-to-use devices that do not require the installation of any dependencies; b) optimize the use of resources with clear benefits for end users, who can take advantage of their computing capacity to process sensitive data; c) reduce infrastructure costs. In addition, our proposal opens toward a multitude of scenarios, as the logical division that exists in the common client-server architecture is overcome, enabling the creation of a Cloud-Edge-Client Continuum environment.

Digital Twins (DTs) in Metaverse face many challenges such as the lack of optimized AI models to allow the interaction between the user and the virtual environment. In this paper, we propose an optimized model for human language processing based on Convolutional Neural Networks (CCNs) and we present an input processing strategy to meet the real-time requirements of smart applications that integrate DTs oriented to speech-based functionalities for user interaction and Metaverse. In our solution, CNNs are applied for the processing and classification of the human voice, while structured data and MFCC coefficients are used to train the neural networks and generate interference in the models. Similarly, the MFCC algorithm is provided to extract the unique characteristics that specify each generated audio file and to reduce the complexity of the neural network model in order to obtain better performance. Starting from an approach to the problem available in the literature, we have optimized a specific CNN model for Natural Language Processing (NLP) in order to increase effective results. The proposed model has demonstrated excellent performance and can be used as a basis for the implementation of software that allows the interaction of DTs with voice commands issued by a user.

Tele-rehabilitation has recently emerged as an effective solution for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering their reintegration into society, also pushing down clinical costs. Cloud computing in combination with Edge Computing, the Internet of Things (IoT), Big Data storage and analytics, and Artificial Intelligence (AI) are the main enablers for tele-rehabilitation. In this paper, we present the Italian founded PRIN 2022 project entitled “Tele-rehabilitation as a Service (TRaaS)”. It aims at creating a piece of reference intelligent Cloud/Edge framework architecture and a standard data model for the development of different kinds of new de-hospitalized tele-rehabilitation services. In particular, the rationale, study design, and methodology are discussed, also highlighting future research directions.

Nowadays, tele-rehabilitation has emerged as an effective approach for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering the reintegration of patients into society, also pushing down clinical costs. Cloud computing in combination with Edge Computing and Artificial Intelligence (AI) are the main enablers for tele-rehabilitation. In particular, Edge rehabilitation devices can act as smart digital biomarkers sending quantifiable physiological and behavioural patients' data to the Hospital Cloud. However, due to hardware limitations, it is not clear which Machine Learning (ML) models can be executed in cheap Edge devices. In this paper, we aim at answering this question. In particular, several ML-based pose estimation models (i.e., PoseNet, MoveNet and BlazePose) have been tested and assessed on the Edge, identifying the best one and demonstrating the feasibility of such an approach.

Tele-rehabilitation has recently emerged as an effective approach for providing assisted living, increasing clinical outcomes, positively enhancing patients' Quality of Life (QoL) and fostering their reintegration into society, also pushing down clinical costs. Nowadays, tele-rehabilitation has to face two main challenges: motor and cognitive rehabilitation. In this paper, we focus on the latter. Our idea is to monitor the patient's cognitive rehabilitation by analysing his/her facial expressions during motor rehabilitation exercises with the objective to understand if there is a correlation between motor and cognitive outcomes. Therefore, the aim of this preliminary study is to leverage the concept of Emotional Artificial Intelligence (AI) with a Facial Expression Recognition (FER) system which uses the face mesh generated by the MediaPipe suite of libraries to train a Machine Learning (ML) model in order to identify the facial expressions, according to the Ekman's model, contained inside images or video captured during motor rehabilitation exercises performed at home. In particular, different datasets, face features maps and ML models are tested providing an advancement in the state of the art.

Complex traffic dynamics can be modeled in real time through simulation models and methods which are attracting more and more research efforts. In particular, agent-based models based on agent behaviors with local plans or strategies can be useful for transportation study areas. These models can be used to solve real-world policy problems simulating certain regions or cities. In this paper, we implemented an agent-based transport model for analyzing traffic in the metropolitan city of Messina (Sicily, Italy). We created a scenario using (i) the Messina road network information from OpenStreetMap, (ii) public transport supply data of the Municipality of Messina from General Transit Feed Specification, and (iii) census data related to the six districts of Messina. Then, we made a preliminary analysis of the generated simulation output computing average travel time by agent trip mode, average activity duration and link volumes. Our scenario can be extended and adapted to solve specific problems related to the mobility of individuals in Messina.

Edge systems are increasingly popular for data collection and processing. Typically, due to their limited storage capacity, pieces of data are continuously exchanged with Cloud systems which store them in distributed DataBase Management System (DBMS). This scenario, known as Cloud/Edge Continuum, is critical from a data security point of view as it is exposed to many risks. Transparent Data Encryption (TDE) is proposed as a possible solution for encrypting database files. However, current solutions do not suit the Cloud/Edge continuum requirements. In this paper, we aim at fulfilling this gap by proposing a solution to encrypt the data locally at the Edge and transfer them to a distributed database over the Cloud. Our approach allows us to perform queries directly on encrypted data over the Cloud and to retrieve them on the Edge for decryption. Experiments performed on different NoSQL DBMS solutions demonstrate the feasibility of our approach.

This paper focuses on a new approach to conceiving “virtual sensors” operating in smart environments, which are abstracted components able to map different behaviours on the same Internet of Things (IoT)-based infrastructures according to the needs of the high-level applications. To realize “virtual sensors”, it is necessary to codify user requests in an automation process for the deployment at the Edge of the microservices (MSs) that satisfy such requests. We present a solution that implements all the necessary functionalities to bind the user application with the Edge device in charge to execute the “virtual sensors”. In particular, the solution we propose is based on the FIWARE NGSI-LD information model, which helps us to standardize the communication among the different entities involved in the process. Moreover, the paper describes the reference architecture we designed, provides the implementation details of our first prototype and reports the results of our evaluation experiments.

The European project URBANITE (Supporting the decision-making in URBAN transformation with the use of disruptive Technologies) aims to put in place a sustainable mobility with the support of disruptive and innovative technologies for the sector of urban mobility. Urban mobility and smart mobility contexts, but not only, now require more than ever the use of large amounts of historical data to carry out the necessary analyses for different use cases. A good management of time series data, able to use pagination concepts in an optimized way and providing the user with specifications functions, therefore become indispensable. This need emerged as a native implementation in MongoDB 5.0. With the release of this version, users have functionality to manage time series collections. This new solution has stimulated us to undertake a study on the methods of managing time series data and compare the solution proposed by MongoDB with our solution based on the advanced use of the bucket approach. The two solutions were tested in a real context and the results obtained are reported in the paper.

Several reasons have been given to justify the inaccessibility or poor connectivity of the internet and the limits of technological resources in rural areas, such as the high cost of physical equipment, facilities and their deployments, low income, low population density, the distance linked to the geographical distribution of certain agglomerations, and so on. Due to these multiple difficulties, in this paper, we focus on the concept of smart rural area, proposing a three-phase model based on the emerging Collaborative Edge Computing (CEC) paradigm. Our approach aims at looking for the best Edge nodes where to deploy microservices. The first phase, i.e., discovery is responsible to find out federated Edge nodes, the second phase, i.e., authentication, is responsible to verify the identity of discovered devices, in the end, the third phase, i.e., the selection is responsible to choose the best Edge node in which to deploy the microservice. In particular, discovery and authentication agents were developed considering Extensible Messaging and Presence Protocol (XMPP) and Security Assertion Making Language (SAML) respectively, whereas the selector agent was developed using a Gossip consensus algorithm.

In this paper, we present our recent improvements within the CapisciAMe project, an Italian initiative aimed at investigating the usage of deep learning strategies for automatic speech recognition in presence of speech disorders, such as dysarthria. Our research activity is focused on isolated word recognition by exploiting a convolutional neural network (CNN) architecture to predict the presence of a reduced number of speech commands within an atypical speech. Currently, by following speaker-dependent approaches, our speech models have been trained on a 21K speech dataset consisting of voice contributions, i.e., single speech data recordings, from 156 Italian users with neuromotor disabilities and dysarthria. Having a large number of repetitions (into the thousands) for each word on which to train our deep learning model is of crucial importance for our project. Nevertheless, people with impaired speech are generally weak in repetitive vocalization tasks, so producing a large number of speech samples for each work is a complex operation to be accomplished. To mitigate this difficulty, we investigate possible relationships between the number of samples for each word and the accuracy of automatic speech recognition. This study plays a critical role in our research, allowing us to minimize the amount of speech data samples required for each work from people with dysarthria to train the automatic speech recognition system.

Recently, both scientific and industrial communities have highlighted the importance to run Machine Learning (ML) applications on Edge computing closer to the end-user and to managed raw data, for many reasons including quality of service (QoS) and security. However, due to the limited computing, storage and network resources at the Edge, several ML algorithms have been re-designed to be deployed on Edge devices. In this paper, we want to explore in detail Edge Federation for supporting ML-based solutions. In particular, we present a new platform for the deployment and the management of complex services at the Edge. It provides an interface that allows us to arrange applications as a collection of interconnected lightweight loosely-coupled services (i.e., microservices) and enables their management across Federated Edge devices through the abstraction of the underlying clusters of physical devices. The proposed solution is validated by a use case related to video analysis in the morphological field.

Current speech recognition services are not suitable for people with speech disorders, which present difficulties in coordinating muscles and articulating words and sentences. In this case, a speaker-dependent approach is strongly required in order to address the specific vocal disarticulation. Several Deep learning approaches have been proposed in the literature to address this problem. However, they require many voice samples of people to properly work, and this is not practical. In this paper, we present an innovative Automatic Speech Recognition (ASR) system which is able to correct failures of deep learning based solution adopting Natural Language Processing (NLP) techniques. The proposed solution can perform both single word and whole sentence corrections by analyzing the speech context. We evaluated the solution in a home automation case study and proved the good accuracy of our model.

The innovation process for the management of a Water Distribution Network (WDN) in a Smart City starts from an efficient digital representation of the network itself. This paper presents a new visualization tool for WDN that overcomes current challenges and provides water companies with useful managing information. Existing visualization tools are self-contained systems that work independently from other visualization software and do not provide real-time analysis of the pipes and water flow status in the WDN. Using digital maps such as Google Maps it is possible to extend the traditional digital representation of the WDN based on EPANET software. Moreover, the WDN representation can be enriched with localized information (e.g. roads or buildings superimposed on the WDN), that is useful for planning maintenance and structural services. In presence of a WDN equipped with sensors and flowmeters, the proposed tool can be used for optimized visualization of the flow rate and the condition of the pipes in real-time. For these reasons, this tool can be a powerful instrument to help technicians quickly identify problems in the WDN. In this work, we used synthetic data generation techniques to obtain a data-set of values that updated over time. Finally, to evaluate the designed solution, we implemented the proposed visualization tool and performed some experiments to test its effectiveness.

Nowadays, the Internet of Things (IoT) is widely adopted. To push-down costs, a possible solution can be represented by the large-scale adoption of low-cost sensors that, unfortunately, present several issues including sensor ageing that may cause, over time, failures in IoT systems due to erroneous collected data. In our opinion, a possible solution is IoT rejuvenation, a proactive cost-effective technique that can contrast the inevitable ageing of IoT systems guaranteeing the accuracy of collected data over time. Specifically, the purpose of this paper is to experimentally demonstrate the seriousness of the IoT ageing issue. To achieve such a goal, we consider a smart city scenario including intelligent street pole lamps equipped with low-cost ultrasonic sensors able to switch on lights when a vehicle is detected. Moreover, we pave the way toward IoT rejuvenation by discussing the perspectives of a Function as a Service (FaaS) based approach acting at the Edge.

Nowadays, video surveillance is a very common practice in Smart Cities. There are public and private video surveillance systems, and very often different systems or single devices frame the same area. However, when a target needs to be identified or needs to be tracked in real-time, such solutions typically require human intervention to configure the devices in the best possible way (e.g., choosing the optimal cameras, setting up their focus, and so on). To address such a problem, in this paper, we define a new interrogation method based on a Federated Edge approach. This approach addresses the problem from the point of view of both camera hardware and shooting angle associated with it. According to the presented approach, it is possible to understand which the best camera to identify a target and possibly tracking it in a specific area is. A case study is defined in the context of urban mobility management.

To increase the flexibility and the dynamism of communication networks, Software Defined Networking (SDN) has emerged as a challenging approach to decouple control and data planes, using a logically centralized controller able to manage the underlying network resources. However, traditional network solutions can not be always used in SDN. In this paper, we deal with routing issues in the setup of dynamic SDN spanning Fog/Edge and IoT systems for supporting the new generation of applications. In particular, we present a modified version of Dijkstra's routing algorithm that can optimize complex routing metrics and uses MapReduce to speed up the configuration of routers in a software-defined network. The system can optimize the packet routing accordingly to different parameters including, e.g., hops, latency, and energy efficiency policies. To show the effective benefits of the proposed solution, we performed evaluations on the revised MapReduce version of the Dijkstra routing algorithm considering a highly scalable network topology with thousands of virtual nodes.

Recently, the Osmotic computing paradigm has emerged as a solution to enable the Cloud-Edge-Internet of Things (IoT) continuum. Specifically, it allows dealing with the transparent deployment of distributed services on a combination of Cloud and Edge (or simply Osmotic) nodes, guaranteeing data proximity to end users and IoT devices. In order to optimize applications, software components called Micro ELements (MELs) have to be properly deployed and moved between the Cloud the Edge and the IoT. In this paper, we focus on the MEL addressability problem, intended as the capacity to communicate with the same MEL without caring about its possible migration in different nodes of the same Osmotic Infrastructure. Specifically, we discuss an Osmotic Computing Enabled Domain Name System (OCE-DNS) integrated with the Osmotic Infrastructure, used to address the MELs and to hide their migrations through the use of a dynamic and low latency Resource Record (RR) database containing the real position of the MELs. Specifically, a system prototype developed using a CoreDNS server and an Etcd cluster is discussed and tested showing a good performance in terms of response time and scalability. In order to validate our work, we tested the OCE-DNS in an Osmotic smart city.

The usage of untrusted, non-certified or non-validated Internet of Things (IoT) devices can affect the entire system functioning, causing service interruption and invalidating information processing. These vulnerabilities are mainly due to the errors in providing services and the whole ecosystem's exposure to different cyber attacks. This paper aims to address these problems by proposing a new method to create a trusted distributed environment for implementing IoT services based on Edge computing. Our solution is based on the key idea that IoT devices are certified by a certification authority (CA) through an authorized broker. IoT devices will connect to the CA only if they have been previously authorized to the same. The CA will also allow single isolated IoT nodes to participate in one or more applications in a specific FIWARE-based domain. The entire certification process of IoT devices and the digital certificate issuance take place using a Mobile Edge Computing system (MEC) located near the IoT node. The MEC is the broker device that allows the interaction of the IoT device with the CA. We implemented the proposed solution and performed some experiments to test its effectiveness.

Smart environments are composed of an ever-increasing number of heterogeneous resources and devices for collecting and processing of a large amount of context data. These activities can be performed at the edge of the smart area, over a distributed and heterogeneous infrastructure, so to be close to the end-user and optimize response time. However, it is hard to define a data model able to support data exchange among different systems and between systems and users. This paper presents the key features of a smart environment and introduces the concept of virtual device, that is an abstracted component characterized by specific high-level functionalities. Then, the paper proposes a data model useful to represent and optimize the adoption of virtual device in smart environments. To better explain the data model features and benefits, we refer to a video surveillance use case, where a smart camera is able to provide the solid angle detection as a service.

Internet of Things has revolutionized the way services are distributed in smart environments, approaching the computation where data are generated. However, IoT devices have limited resources and reconfiguring them can be very difficult. In these cases, Edge computing represents a challenging solution supporting IoT with flexible management of resources. We investigate how pushing computation activities from Edge to IoT, changing the behavior of IoT nodes according to application or system requirements. We adopted the Multi-Hop-Over-The-Air update technology enabling the auto-configuration of IoT devices based on MicroController Units. Considering IoT nodes connected in a mesh network, we developed a distributed and collaborative ecosystem performing on-fly injection of code in IoT nodes, thus automatically deploying new services whenever necessary. We implemented a prototype of the proposed solution over a heterogeneous Edge-IoT mesh network and performed experiments with the purpose to study the update phase of end-devices while they execute Digital Signal Processing.

Sustainable mobility is a key objective for many Smart Cities. In this paper, we present an application for sustainable mobility that aims at encouraging citizens to use low-impact vehicles instead of private cars. Through a partnership between the University Messina and the Municipality of the Messina city (Italy), we developed MuoviME, a digital application to assign citizens electric bikes, free of charge for a limited period of time. The key issue we addressed in the development of such an application is security, both in terms of secure authentication of citizens that access the service and tracking of the whole assignment process, from the user's bicycle request to its restitution. To achieve this goal, we implemented a solution for the physical recognition of the user based on two-factor authentication (2FA) and Blockchain technology. This paper summarizes the secure by design approach, implementation details, and some experimental results on the service efficiency.

Nowadays, the increasing complexity of digital applications for social and business activities has required more and more advanced mechanisms to prove the identity of subjects like those based on the Two-Factor Authentication (2FA). Such an approach improves the typical authentication paradigm but it has still some weaknesses. Specifically, it has to deal with the disadvantages of a centralized architecture causing several security threats like denial of service (DoS) and man-in-the-middle (MITM). In fact, an attacker who succeeds in violating the central authentication server could be able to impersonate an authorized user or block the whole service. This work advances the state of art of 2FA solutions by proposing a decentralized Microservices and Blockchain Based One Time Password (MBB-OTP) protocol for security-enhanced authentication able to mitigate the aforementioned threats and to fit different application scenarios. Experiments prove the goodness of our MBB-OTP protocol considering both private and public Blockchain configurations.

Tele-biomedical laboratory is a medical laboratory where blood exams are performed either by patients themselves in their homes or by biomedical technicians in satellite clinical centres through the Internet of Things (IoT) biomedical devices interconnected with Hospital Edge/Cloud systems that allow results to be sent to doctors belonging to federated hospitals for validation and/or consultation. This paper aims at providing a clear picture of the current state of the art in the tele- biomedical laboratory, also highlighting current issues and future challenges. Specifically, we start motivating the need for tele-biomedical laboratories adopting IoT, Edge and Cloud technologies. After a classification of the main biomedical equipment (considering connected, not-connected, invasive, minimally invasive and noninvasive devices), we present different possible tele-biomedical laboratory scenarios. In the end, we will discuss the recent issues, current feasibility and future challenges.

Every year the healthcare sector suffers from incorrect therapies and an increasing number of patients analysis, which causes congestion in the hospitals and, potentially, worsening of patient's clinical conditions. Extending the concept of the Decision Support System already investigated by the authors, this work advances the state of the art of Reinforcement Learning (RL) via Markov Decision Process formulation, considering an agent acting in his environment motivated by the achievement of the maximum individual objective by appropriate incentives. Transparency, security and privacy of the model are guaranteed by the adoption of Blockchain to enhance the perception of safety around medical operators improving access to hospital services. Experiments focused on the Smart Contract execution time and resources usage have proved the goodness of the proposed model considering both private and public Blockchain configurations.

Social Networks, geotags, and Virtual Reality (VR) are parts of the everyday life of most of the people in the world. In particular, geotags represents a way to discover places through metadata added to media. At the same time, VR reproduces with high accuracy real places that we can navigate through a browser or a 3D visor. In this paper, we present GAVIN to fill the gap between social media geotagging and Virtual Indoor Navigation. GAVIN is a platform for the navigation of virtual environments able to exploit BIM Digital Twins and geolocated data coming from different sources and, in particular, from the social experience bounded to a real/virtual place. In the paper we provide design and implementation details on the GAVIN platform, and we present some performance evaluations.

Secret Share (SS) is becoming a very hot topic within the scientific community. It allows us to split a secret into fragments and to share them among parties in such a way that a subset can recompose the original information. SS techniques assure a high redundancy degree, but the security level is fixed. Therefore, if a minimum number of peers collude then attackers can recompose the secret easily. A possible approach to improve the security of SS is designing nest fragment sharing techniques. In this paper, we propose the Nested Secret Share (NSS) as a more reliable and scalable strategy. In particular, we discuss the security of NSS considering the number of recomposition attempts that an attacker has to perform to retrieve the secret and then we deeply analyse the impact of the redundancy and the number of peers on the secret management against the percentage of compromised nodes. Experiments were promising and showed that the redundancy degree of SS can be highly improved by NSS.

The papers in this special focus on enabling technologies for next generation telehealthcare applications. The use of Information and Communication Technology (ICT) for health and well-being is rapidly increasing in the majority of high-income countries. The interest about telehealthcare allows the provisioning of various kinds of health-related services and applications over the Internet. There are several benefits associated with tele-healthcare, including: the reduction of infection risk due to optimized patients access to clinical centers; optimized healthcare workflows; containment of hospital costs; increased patient safety; improves in the quality of life of both patients and their families. Common telehealthcare applications include tele-nursing, tele-rehabilitation, tele-dialog, tele-monitoring, tele-analysis, tele-pharmacy, tele-care, tele-psychiatry, tele-radiology, tele-pathology, teledermatology, tele-dentistry, tele-audiology, tele-ophthalmology, etc. In the past ten years, key enabling technologies (KETs) such as Internet of Things (IoT), tools for big data management and processing, Cloud/Edge/Fog computing, Artificial Intelligence (AI), Blockchain reached an advanced maturity, and therefore the potential for revolutionizing the whole tele-healthcare sector.

Verifiable Secret Share (VSS) is a branch of Secret Share (SS) that allow Verifiable Secret Share (VSS) is a branch of Secret Share (SS) that allows verifying the correctness of recomposed files. VSS techniques usually check the correctness of secrets at the end of the re-composition process, therefore, in case of errors, a lot of computational resources and time are wasted. In this paper, we propose an innovative VSS model that is able to verify the correctness of SS fragments before the ending of the recomposition task, thus increasing efficiency and response time. The basic idea behind the proposed approach is to make use of a Hash function to validate fragments, thus to decide if the recomposition task can be performed or not. The experimental result validates our model and proves its applicability in distributed storage systems.

Recent advancements in ICT have sped up the development of new services in healthcare. In this context, remote patient monitoring and rehabilitation activities can take place either in satellite hospital centers or directly in patients’ homes. Specifically, using a combination of Cloud/Edge computing, Internet of Things (IoT) and Machine Learning (ML) technologies, patients with motor disabilities can be remotely assisted avoiding stressful waiting times and overcoming geographical barriers. This is possible by applying the Tele-Rehabilitation as a Service (TRaaS) concept. The objective of this paper is twofold: i) studying how Machine Learning can improve the TRaaS, and ii) demonstrating how a NoSQL graph database approach can enhance the performance because it works directly at the database layer instead of at application one. In particular, the K-Nearest Neighbors (K-NN) algorithm is studied in order to identify the best therapy, i.e., rehabilitation training, for a new remote patient with motor impairment. Experiments compare two system prototypes, that are respectively based on Python and Neo4j, showing that the latter presents better performance in terms of processing time guaranteeing the same accuracy.

The emergency we are experiencing due to the coronavirus infection is changing the role of technologies in our daily life. In particular, movements of persons need to be monitored or driven for avoiding gathering of people, especially in small environments. In this paper, we present an efficient and cost-effective indoor navigation system for driving people inside large smart buildings. Our solution takes advantage of an emerging short-range wireless communication technology - IoT-based Bluetooth Low Energy (BLE), and exploits BLE Beacons across the environment to provide mobile users equipped with a smartphone hints on how to arrive at the destination. The main scientific contribution of our work is a new proximity-based navigation system that identifies the user position according to information sent by Beacons, processes the best path for indoor navigation at the edge computing infrastructure, and provides it to the user through the smartphone. We provide some experimental results to test the communication system considering both the Received Signal Strength Indicator (RSSI) and the Mean Opinion Score (MOS).

Osmotic Computing (OC) is an innovative computation paradigm that runs services on Cloud, Edge, and Internet of Things (IoT) resources based on their workload. Services are encapsulated in container images stored into a central repository on the Cloud. OC suffers from privacy and security issues, for example, hackers could attack the repository and download all container images. Furthermore, network latency could delay the deployment of services in Edge nodes. A possible solution to solve such problems is to employ Secret Share techniques to split the images of services into chunks and distribute them among Edge devices. This work aims at assessing the applicability of these techniques for OC employing the Redundant Residue Number System (RRNS) to split and store Micro-Elements (MELs). We made our analyses for different OC scenarios composed of 10, 100 and 1000 nodes running 1000 MELs each. Furthermore, we considered several degrees of redundancy from 0 to 7. From experimental analyses, we found that the reliability of the system increase with the increasing of the redundancy but the security decreases.

Several cases are reported every year where the prescribed therapy results incompatible with the patient’s medical history, leading to worsening of clinical condition or death. Some technologies and processes to prevent this misbehaviour already exist, but a concrete solution is not available in hospitals yet. This paper presents a Decision Support System (DSS) that can be easily integrated into a typical health workflow at hospitals and provides feedback on the possible prescription of drugs at a patient with specific diseases. The DSS is based on a Big Data analysis algorithm able to check drugs and diseases relationships and detect possible failures in drugs prescriptions. We developed a prototype of the proposed solution, implementing the DSS system and setting up the necessary Big Data management tools for the effective adoption of the DSS system. We performed some evaluations to assess the efficacy and the response time of the DSS algorithm.

Nowadays, a multitude of sensors are used to gather data in several fields from smart buildings, to industries, to cars, etc.. These sensor data are instrumental in making smart decisions. In order to send data to end users, these sensors are connected to the Internet of Things (IoT) devices. Usually, the intra-domain data transmission is secure, indeed sensors and consumers of data can belong to the same Virtual Private Network (VPN). Security problems can be raised in the inter-domain data transmission because the transmitting channel is not ciphered nor is the identity of devices certain. Therefore, in case of attack, for consumers of data is not possible to recognize real data gathered from devices from fake data sent by attackers. In order to address this challenge, in this paper we present a novel method to secure data acquired from sensors connected to IoT devices. In particular, utilizing a Public Key Infrastructure (PKI) and the ESP32 microcontroller, we can send data privately to each recipient. In order to validate the system, we performed specific analysis considering different levels of security (512, 1024, 2048 bits key length) and increasing number of connected sensors (0, 1, 5, 10, 20). In particular, we considered the time to set up the IoT device and to cipher packets. Experiments have shown that the time required for the setup increases with the increase of the key length. Considering the 512 and 1024 bits keys, the time required to cipher data coming from sensors increase with the increasing of sensors. Instead, for the 2048 bits key length the ciphering time is almost constant, this because packet size and key length are comparable.

In recent years, great attention has been given to new Internet of Things (IoT) technologies. The IoT concept is nowadays intrinsic to traditional products and services. With its rapid development, more and more small smart devices are connected over the Internet in order to monitor, collect and exchange data in real-time to provide smart IoT-as-a-Services (IoTaaS). A few years ago, IoT devices exclusively sent data to a centralized Cloud data center; today it is possible to perform "on board" processing tasks at the Edge of the network and subsequently share or use the obtained results closer to users. This paper, focusing on a smart grid scenario, investigates the possibility of creating an IoTaaS for smart metering, including a microservice for IoT devices capable of acquiring and processing electrical data using the Fast Fourier Transform (FFT) algorithm. In particular, we experimentally use the smart metering IoTaaS running on a Raspberry Pi 3 device to perform a harmonic analysis of a frequency signal of the domestic electrical grid in order to characterize the non-linear loads associated to the electronic devices (e.g., smart TV, computers, etc) with the purpose of monitoring their status and preventing possible malfunctions and faults.

The digitization of health records has massively increased Health Information Exchange (HIE) activities among different practitioners, but it has lagged behind Electronic Health/Medical Records (EHRs/EMRs) adoption for numerous reasons, including confidentiality, interoperability, integrity, and privacy-related concerns. In this paper, we present a Blockchainas-a-Service based solution for HIE (BaaS-HIE). In particular, our design work involves the use of a private Blockchain and smart contracts as access control manager to medical records. In order to maintain high level of performance for applications, thus that such applications could be economically viable, all of the health data are encrypted and stored into a decentralised InterPlanetary File System (IPFS) and the hash of the assets URI is stored in blockchain. Our experimental results demonstrate the feasibility of the proposed approach in offering a decentralized and fine-grained accessibility mechanism for the patient and the doctor in a given healthcare system.

The rapid development of Cloud, Edge, Fog Computing and Internet of Things (IoT) technologies has played a key role in the Industry 4.0 evolution. In this context, the Osmotic Computing paradigm, theorized in 2016 as integration between a centralized Cloud layer and Edge and/or IoT layers, has further emphasized the Industry 4.0 objectives including productivity and Quality of Services (QoS). This emerging paradigm proposes a new elastic management model of microservices, where deployment and migration strategies are strongly related to the underlaying infrastructure requirements (i.e., load balancing, reliability, availability, and so on) and applications (i.e., anomalies detection, awareness of the context, proximity, QoS, and so on). Specifically, knowing that an Osmotic application must have a failover behavior (highly horizontally/vertically scalable, 24 hours 24 available, fault-tolerant and secure), this paper highlights the Osmotic ecosystem platform focusing on the implementation of a blue-green mechanism for the fast re-deployment of microservices, exploiting emerging technologies, such as Docker, Kubernetes, Agento and MongoDB. Experiments shows the time required to arrange, deploy and destroy microservices.

The study of DNA sequences has become indis-pensable for basic biological research, and in numerous applied fields such as comparative genomics, evolutionary biology, pan genomics, genetics of disease, regulation of gene expression, oncology and many others, all supported by bioinformatics. In the era of Cloud computing, federating the Cloud systems of different genetics research organisations paves the way towards a new era of data sharing and new mashup services and applications. However, due to the huge amount of genomics data (genomics Big Data) that have to be managed, a parallel distributed NoSQL DataBase Management System (DBMS) approach becomes fundamental. Specifically, due to the textual nature of genomics data, a NoSQL DBMS appears to be the most suitable solution. In this paper, by considering the whole human genome, we present a preliminary study comparing this latter using MongoDB with a SQL-like database solution, i.e., MySQL in order to look for DNA sequences. Moreover, in order to optimize the research of genomics codes, we adopt hash functions that allow mapping nucleotides sequences of arbitrary size onto data of a fixed smaller size. Experiments, shows that MongoDB apart simplifying the management of genomics data provides better performances.

Nowadays, Healthcare Social Networks (HSNs) offer the possibility to enhance patient care and education. However, they also present potential risks for users due to the possible distribution of poor-quality or wrong information along with their bad interpretation. In recent years several discordant information have been diffused in social networks regarding potential risks of flu vaccines. In this paper, by considering a Twitter datasets, we study the accuracy of users' opinions comparing different Machine Learning approaches including Bayesian, Linear and Support Vector Machine (SVM) classifiers.

Over the past years, in order to care neurolodical diseases, beyond conventional physical treatments, robotics rehabilitation has been widely adopted for improving the patients' therapies. Recent scientific works were mainly aimed at personalizing treatments, according to the patient's clinical conditions. On the contrary, this scientific work aims to propose an alternative approach based on big data analytics coming from the sensors of robotic rehabilitation devices in order to improve the patient's therapy in the perspective of a healthcare Cloud of Things scenario. We perform an exploratory analysis considering big data coming from sensors installed in Lokomat, i.e., one of the major robotic rehabilitation devices, in order to study the data model and the predictors that will allow clinical operators to forecast the best treatment personalizing the therapy. Data analysis proves that there are moderate correlations among features referring to stance and swing biofeedbacks of hip and knee. From the analytical point of view, these values may approximate the stance and swing phases of knee and hip. This can be compared with the normal gait pattern of healthy individuals, so as to point out those patients having a closer normal ambulation. Obtained results are comparable with the Lokomat therapy outcomes of patients with neurological injuries by means of pattern recognition techniques.

The papers in this special section focus on cloud computing, fog computing, the Internet of Things, and Big Data analytics for the future healthcare industry, or Healthcare 4.0. Healthcare Industry 4.0 allows increasing flexibility in production, speeding up both manufacturing and market processes, increasing both the product quality and productivity, and changing business models modifying the interaction with value chain, competitors, and clients. Healthcare Industry 4.0 requires investments and mind-set change for cross-industry collaboration, agreements on data ownership, security, legal issue solving, product registration standards, new machine-to-machine communication protocols, and employment/skills development. Furthermore,Healthcare Industry 4.0 is revolutionizing the market of health service provisioning to patients and clinical operators.

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Nowadays, we are observing an explosion on recording and transmitting of videos from multiple sources such as Social Media (Periscope, Facebook, Youtube etc.) and owner of trains/coaches (Trenitalia-Frecce-Italy, TGV-France, Ryanair-Bus-Travels, etc.). In this paper, we investigate how to support the provisioning of videos to heterogeneous end user devices in different contexts, adapting the content to the specific requirements of the used end devices. In particular, we present a new Cloud-Edge Service for vIdeO delivery (CESIO) architecture, that exploits Cloud and Edge virtual resources to improve the delivery video contents at different quality resolutions. The paper describes architecture components and their behaviour in the system. Moreover, a possible application scenario is discussed to well explain how the proposed solution works.

In recent years, the rapid growth of new Cloud technologies acted as an enabling factor for the adoption of microservices based architecture that leverages container virtualization in order to build modular and robust systems. As the number of containers running on hosts increases, it becomes essential to have tools to manage them in a simple, straightforward manner and with a high level of abstraction. Osmotic Computing is an emerging research field that studies the migration, deployment and optimization of microservices from the Cloud to Fog, Edge, and Internet of Things (IoT) environments. However, in order to achieve Osmotic Computing environments, connectivity issues have to be addressed. This paper investigates these connectivity issues leveraging different network overlays. In particular, we analyze the performance of four network overlays that are OVN, Calico, Weave, and Flannel. Our results give a concrete overview in terms of overhead and performances for each proposed overlay solution, helping us to understand which the best overlay solution is. Specifically, we deployed CoAP and FTP microservices which helped us to carry out these benchmarks and collect the results in terms of transfer times.

Nowadays, the possibility of using social media in the healthcare domain is attracting the attention of many clinical professionals all around the world. In this panorama, many Healthcare Social Network (HSN) platforms are emerging with the purpose to enhance patient care and education. However, many clinical operators are reluctant to use them because they do not fulfil their requirements and are looking at the possibility to develop their own HSN platforms in order to perform social science studies. In this context, one of the major issue is the management of generated big data presenting a huge amount of relations and for this reason, traditional Relational Database management Systems (RDBMSs) are not adequate. The objective of this preliminary scientific work is to prove that a NoSQL graph DBMS can address such an issue, paving the way toward future social science studies. Experiments results show that Neo4j, i.e., one of the major NoSQL graph DBMS, simplifies the management of HSN data also guaranteeing acceptable performances in the perspective of future social science studies.

This paper presents an innovative solution to manage security and trustiness in Osmotic Computing. Osmotic Computing dynamically manages Cloud, Edge and IoT resources across federated environments set up by different and cooperating stakeholders. In this context, the Software Defined Membrane (SDMem) is the main component responsible to orchestrate the osmotic transfer of microelements (MELs) across different environments straightening the security needs of such a complex ecosystem. The basic idea presented in this paper is to leverage Private Blockchain technologies in SDMem implementation over federated systems. Data access activities will be logged in a private distributed Blockchain-based ledger. This will allow to have a certified, non-repudiable record of all the data accessed performed by distributed computing, thus assuring the overall ownership and integrity of data and processes running in MELs. The resulting SDMem solution allow us to isolate data and workflows in distributed environments where heterogeneous resources and devices are exploited.

The Open Archive Information System (OAIS) is a reference model for organizing people and resources in a system, and it is already adopted in care centers and medical systems to efficiently manage clinical data, medical personnel, and patients. Archival storage systems are typically implemented using traditional relational database systems, but the relation-oriented technology strongly limits the efficiency in the management of huge amount of patients' clinical data, especially in emerging cloud-based, that are distributed. In this paper, we present an OAIS healthcare architecture useful to manage a huge amount of HL7 clinical documents in a scalable way. Specifically, it is based on a NoSQL column-oriented Data Base Management System deployed in the cloud, thus to benefit from a big tables and wide rows available over a virtual distributed infrastructure. We developed a prototype of the proposed architecture at the IRCCS, and we evaluated its efficiency in a real case of study.

Nowadays, in order to enable future medical decision making, in the healthcare panorama there is the need of efficient Cloud-systems able to acquire and integrate Big e-health Data, coming from heterogeneous sources, through smart clinical workflows. Indeed, during the treatment at hospital, patients use medical devices generating a huge amount of data that have to be automatically stored into the Cloud storage system. In this paper, we specifically discuss an automated Machine-To-Machine clinical workflow able to manage the migration of Big e-health Data coming from medical devices to a Cloud NoSQL storage system. To validate our solution, we also present and test a real use case in which a clinical workflow is considered to manage big robotic rehabilitation datasets of the IRCCS Messina (Italy) Institute. Experiments prove the goodness of our approach in terms of data acquisition and integration.

Nowadays, Next Generation Sequeencing (NGS) is a catch-all term used to describe different modern DNA sequencing applications that produce big genomics data that can be analysed in a faster fashion than in the past. For this reason, NGS requires more and more sophisticated algorithms and high-performance parallel processing systems able to analyse and extract knowledge from a huge amount of genomics and molecular data. In this context, researchers are beginning to look at emerging deep learning algorithms able to perform efficient big data analytics. In this paper, we analyse and classify the major current deep learning solutions that allow biotechnology researchers to perform big genomics data analytics. Moreover, by means of a taxonomic analysis, we provide a clear picture of the current state of the art also discussing future challenges.

Patients' data security and privacy is fundamental in the perspective of moving clinical data over the Cloud. Indeed, this concern has slowed down the adoption of Cloud services in the healthcare context. In fact, clinical operators are reluctant to open Hospital Information Systems (HIS) to external Cloud services. In this paper, we discuss system developed at the IRCCS “Bonino Pulejo” clinical and research centre (Italy) that is able to solve this concern. Such a system is based on two software components that are anonymizer and splitter. The first collects anonymize clinical data, whereas the second obfuscates and stores data in multiple Cloud storage providers. Thus, only authorized clinical operators can access data over the Cloud. A case of study considering real Magnetic Resonance Imaging (MRI) data is analysed in order to assess the performance of the whole system.

Nowadays, Edge computing allows to push the application intelligence at the boundaries of a network in order to get high-performance processing closer to both data sources and end-users. In this scenario, the Horizon 2020 BEACON project - enabling federated Cloud-networking - can be used to setup Fog computing environments were applications can be deployed in order to instantiate Edge computing applications. In this paper, we focus on the deployment orchestration of Edge computing distributed services on such fog computing environments. We assume that a distributed service is composed of many microservices. Users, by means of geolocation deployment constrains can select regions in which microservices will be deployed. Specifically, we present an Orchestration Broker that starting from an ad-hoc OpenStack-based Heat Orchestraton Template (HOT) service manifest of an Edge computing distributed service produces several HOT microservice manifests including the the deployment instruction for each involved Fog computing node. Experiments prove the goodness of our approach.

Summary Nowadays, remote desktop technologies are widely used to control remote computers. In this context, more and more Desktop as a Service (DaaS) solutions that allow users to directly control Virtual Machines through HTML5 web applications are emerging. However, currently, DaaS is at an early stage because several issues have to be addressed. In this paper, we aim to provide software architects a 360‐degree study on the major web‐based remote desktop client systems for the development of next generation DaaS solutions in both OpenStack‐based private and public Clouds. In particular, we compare and test different native and customized web‐based remote desktop solutions, specifically focusing on audio and video redirection performances in both Cloud environments. Exhaustive experiments highlight, from an objective point of view, the behavior of different DaaS solutions, providing useful hints to software architects and developers. Copyright © 2017 John Wiley & Sons, Ltd.

The advent of Cloud computing is changing the way of conceiving information and communication systems in different application fields including Biotechnology. In this context, an emerging research field is Next-Generation Sequencing (NGS) that includes several recent technologies allowing sequencing DNA and that have revolutionized the study of genomics and molecular biology. These cutting-edge sequencing systems produce big datasets that require significant scalable computing resources. In this paper, we analyse and classify the major current NGS Cloud-based solutions adopted in scientific laboratories according to different Cloud service levels. Moreover, by means of a taxonomy, we discuss the challenges and advantages of possible future NGS Cloud-based systems.

OpenStack is one of the major open-source solutions for creating and managing Infrastructure as a Service (IaaS) cloud providers. In this paper, we explore how the Horizon dashboard allows us to access virtual machines via a web-based remote desktop client. Besides the default remote desktop clients (based on the noVNC software), we propose to integrate in the OpenStack dashboard other alternative solutions testing both audio and video redirection feature. We consider different protocols including Virtual Network Computing (VNC) and Remote Desktop Protocol (RDP), while our experiments aim to evaluate which the best solution currently available on the market is, also providing indications to developers on how to extend the OpenStack dashboard in order to provide the best user experience from an objective point of view.

Google Cloud Vision is an image recognition technology that allows us to remotely process the content of an image and to retrieve its main features. By using specialized REST API, called Google Cloud Vision API, developers exploit such a technology within their own applications. Currently, this tool is in limited preview and its services are accessible for trusted tester users only. From a developer's perspective, in this paper, we intend to use such software resources in order to achieve assistive technology solutions for people with disabilities. Specifically, we investigate some potential benefits of Cloud Vision tool towards the development of applications for users who are blind.

Advances in database technologies are moving the attention of data managers from well known structured relational databases (SQL-Like DBs) towards NoSQL approaches, especially to address big data issues. However, changing technologies in consolidated data management system is hard and requires great investments. Deploying hybrid SQL-NoSQL approaches could be a good solution to speed up the transition in many domains and information systems, but a formal data model to design hybrid databases is necessary. This paper presents a new data model aimed at solving this issue. Starting from the well known E-R model, we introduce some additional components to identify data and “big data” in the system, in order to drive the implementation of SQL-like solutions to manage data, and NoSQL solutions to manage big data. The paper also discusses a Hospital Information System use case, to clearly show how the proposed enriched E-R model can be successfully adopted.

The advent of both Cloud computing and Internet of Things (IoT) is changing the way of conceiving information and communication systems. Generally, we talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart objects, e.g., single board computers running Linux- based operating systems, interconnected with a remote Cloud infrastructure, platform, or software through the Internet and able to provide IoT as a Service (IoTaaS). In this context, container-based virtualization is a lightweight alternative to the hypervisor-based approach that can be adopted on smart objects, for enhancing the IoT Cloud service provisioning. In particular, considering different IoT application scenarios, container-based virtualization allows IoT Cloud providers to deploy and customize in a flexible fashion pieces of software on smart objects. In this paper, we explore the container-based virtualization on smart objects in the perspective of a IoT Cloud scenarios analyzing its advantages and performances.

Nowadays, the combination between Cloud computing and Internet of Things (IoT) is pursuing new levels of efficiency in delivering services, representing a tempting business opportunity for IT operators of increasing their revenues. However, security is considered as one of the major factors that slows down the rapid and large scale adoption and deployment of both IoT and Cloud computing. In this paper, considering such an IoT Cloud scenario, we present an architectural model and several use cases that allow different types of users to access IoT devices.

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

FIWARE represents a new European Cloud platform that aims to land on the international ICT market bringing prominent novel advantages for societies. In fact, it provides new compelling and novel software components, available through APIs, able to give developers new valuable Cloud platform functionalities. The main contribution of this work consists in providing software architects an useful experience regarding the adoption of FIWARE for the design of a Cloud and Internet of Things (IoT) architecture. More specifically, we describe how can be possible to use the FIWARE Cloud platform to speed up the design of a real e-health Remote Patient Monitoring (RPM) architecture with an agile software development methodology. Our architecture aims to allow care givers to improve remote assistance to patients at home, optimizing the management of the workflow of doctors, physicians, medical assistants, and other involved hospital operators. In this paper, we specifically describe the main FIWARE components that we have adopted to design our architecture and how they have been integrated.

Cloud federation is an emerging topic towards new dynamic scenarios in smart ecosystems, where new more flexible energy management strategies are needed than the traditional, in order to optimize mobility and energy efficiency. In this paper we focus both on cloud federation and energy efficiency to enforce a dynamic energy management strategy for the whole ecosystem, in order to reduce carbon dioxide emissions. More specifically, starting from our two-step approach, we evaluate a cloud federation ecosystem by moving computational resources among federated cloud DCs in order to maintain, for a forecast period, the related workload at the best Green Destination (GD) powered by renewable energy sources. To this end we present and discuss two simulated scenarios, and their experimental results, thus to proving the goodness of our approach. Moreover, an energy comparison between the transfer and the maintenance phases for the computational workload is made.

Cloud federation offers new business models to enforce more flexible energy management strategies. Independent Cloud providers are exclusively bounded to the specific energy supplier powering its Data Centers. The situation radically change if we consider a federation of cooperating Cloud providers. In such a context a proper migration of virtual machines among providers can lead to a global energy cost-saving strategy. In this paper, we present an approach to reduce energy cost in a federated Cloud ecosystem. More specifically, we propose an algorithm that allows providers to determine a map of possible destinations for cost-evaluation. Furthermore, we introduce an additional algorithm to determine the optimum energy cost migration path, and, consequently, the best Cloud Data Center where virtual machines should be migrated in order to push down energy costs.

In this paper we investigate the applicability of the federation among several Cloud platform, demonstrating that a federated environment provides evident benefits despite the costs for the setup and maintainance of the federation itself. Also, we propose a new solution able to manage resource allocation in federated Clouds where resource requests occur in a dynamic way. We adopt such a solution to setup distributed Hadoop nodes of virtual clusters for the parallel MapReduce processing of large data sets. To increase their capabilities, Cloud Providers establish a federation relationship, making the Hadoop-based Cloud platforms much more performing than in the isolate case, adding a further level of parallelization in service provisioning. The results analyzed in the referece use case, that is a video transcoding using the MapReduce paradigm in a federated fashion, show how the federation costs in terms of delays and overhead are low in comparison with the service provisioning costs, and also highlight how federation makes the offered Cloud service more streamlined and fast.

Nowadays, Assistive Technology (AT) systems are closely tied to the devices that they control. Considering a smart environment where a person with a disability needs to interact with multiple devices, the user is forced to rely on AT software tools available on the each used platform. Therefore, computer skills are required to adjust any different computing environment configuration according to the user's needs and preferences. To address such issues, in this paper, we discuss the usage of embedded systems able to interface sensors and existing AT software tools running on user's personal equipments, in order to natively interact with many platforms. Thus, our work aims to decouple AT software tools from the accessed computer systems, allowing us to control various kinds of computer systems, even those that do not provide any AT features, by using just a personal assistive equipment.

The advent of the cloud computing paradigm offers different ways both to sell services and to exploit external computational resources according to a pay-per-use economic model. Cloud computing offers more and more business opportunities, and thanks to the concept of virtualization, different types of cost-effective cloud-based services have been rising. There is another perspective which represents a further business opportunity for small/medium providers which hold physical datacenters, known as Cloud federation. The cloud computing ecosystem includes hundreds of independent and heterogeneous clouds. Most of such cloud platforms can be considered as “islands in the ocean of the cloud computing” and do not present any form of federation. A possible future alternative scenario is represented by the promotion of cooperation among small/medium cloud providers, thus enabling the sharing of computational and storage resources. Federation of clouds can happen at the three typical levels of abstraction, i.e., infrastructure, platform, and software as a service (IaaS, PaaS, and SaaS). In this paper, we provide an analysis of the requirement for the establishment of an IaaS Cloud federation.

Hadoop is widely used in many application scenarios, where a massive computation is required. The Hadoop framework is an open-source distributed computing system adopting the Map-Reduce paradigm for data processing, which is gaining more and more popularity. Indeed, recently, many Big Data solutions benefited from the Hadoop framework. One of the main issues in using Hadoop is related to its impossibility to dynamically scale and re-configure the environment, e.g, adding/removing nodes in a cluster for an efficient resource usage. This paper presents a new approach to dynamically setup Hadoop using a Message Oriented Middleware for Cloud computing (MOM4C), in order to make the system much more suitable to Cloud providers' requirements.

This paper deals with Cloud federation issues, where Clouds are both providers and clients of virtual resource at the same time. Specifically, we have designed a solution based on a XMPP communication platform, which allows to set up federated environments and to easily manage virtual shared resources. Heterogeneous and dynamic Clouds can interact in near-real time, making available their own resources according to specific agreement policies. We are thinking to implement the proposed solution using the Hadoop framework, a well-known Cloud Map-Reduce middleware designed to offer different types of services. Here, we analyze the basic and useful elements necessary to making up federated clouds. By mean of Cloud federation, two or more Hadoop clusters can be connected in order to increase service scalability, overcoming some technical limitations of the Hadoop framework itself.

In this paper, we address several critical security issues that bottle up the wide adoption of the Cloud Computing technology. In the last years the Cloud Security Alliance (CSA) has identified many security issues that should be considered during the design of Cloud systems. We experienced them developing the Security-Enhanced (SE) CLEVER, the secure release of CLEVER, a Message Oriented Middleware for Cloud computing based on the well-known XMPP protocol. SE CLEVER provides a secure inter-module and inter-Cloud communication system useful for managing Federated environments. The secure communication system of SE CLEVER was designed leveraging the XMPP flexibility. The experimental results show how the secure capabilities introduced in SE CLEVER do not affect the overall performances of the middleware.

Nowadays, a typical scenario in the panorama of cloud computing includes an IaaS cloud provider offering on-demand VM hosting services to its clients. In this field, famous examples of large scale commercial providers are Amazon and Rackspace. However, how to arrange analogous providers with open source tools is not totally clear. Moreover, the integration between an IaaS cloud middleware with third party legacy software systems, today, represents a difficult task to accomplish. CLEVER is an open source cloud IaaS middleware allowing the allocation and management of VMs. In this paper, through the development of a REST interface, we discuss how a CLEVER-based cloud provider can be integrated with third party systems, hence satisfying their VM allocation requests.

Nowadays, the cloud computing ecosystem includes hundreds of independent and heterogeneous clouds. Most of such cloud platforms can be considered as “islands in the ocean of the cloud computing” and do not present any form of federation. At the same time several clouds are beginning to use the cloud-based services of other clouds, but there is still a long way to go toward the establishment of a worldwide ecosystem including thousands of cooperation federated clouds. This paper aims to investigate the existing cloud middleware solutions able to address all the potential issues involved in these new cloud scenarios. In particular, the CLEVER cloud middleware will be analyzed, highlighting its design and features, and explaining the motivations that allow us to consider it suitable to the different phases of the evolution of federated cloud computing.

Nowadays, the cloud computing ecosystem is more and more distributed and heterogeneous. Cloud service providers begin to build their services using cloud-based services offered by other service providers. This raises several issues due to integration between services and provider themselves. In this paper, we propose a practice addressing such a concern in a "Vertical Supply Chain" scenario of distributed clouds.

The near future evolution of the cloud computing can be hypothesized in three subsequent stages: stage 1 "Monolithic" (now), cloud services are based on independent proprietary architectures; stage 2 "Vertical Supply Chain", cloud providers will leverage cloud services from other providers; stage 3 "Horizontal Federation", smaller, medium, and large cloud providers will federate themselves to gain economies of scale and an enlargement of their capabilities. Currently, the major clouds are planning the transition to the stage 2, but how to achieve the stage 3 is unclear because some architectural limitations have to be overcome. In this paper, considering a general cloud architecture, we highlight such limitations and propose some enhancements which add new federation capabilities. In order to address such concerns we propose a solution based on the Cross-Cloud Federation Manager, a new component placeable inside the cloud architectures, allowing a cloud to establish the federation with other clouds according to a three-phase model: discovery, match-making and authentication.

Cloud environments offer a variety of concrete and abstracted entities which need to be identified. An example of cloud environment is the European project RESERVOIR, which similarly to other platforms characterized by a high level of dynamism, needs to identify and resolve resources. RESERVOIR has to manage allocation, deallocation and migration of virtual machines from an execution context to another. Such tasks could trigger identity and name alterations; in addition, a virtual machine may hold one or more names, identifiers, and representations in various execution environments. Nowadays, the Internet is using the Domain Name System (DNS), that is not suitable to such emerging scenarios. This paper aims to explore several RESERVOIR use cases, demonstrating how a flexible cloud naming system allows organizations to simplify the management of their assets deployed into the cloud.

Cloud computing is a highly dynamic environment where resources can be composed with other ones to provide many kinds of services to clients. In such scenario naming and resource location become critical issues and the existing Domain Name System (DNS), considered alone, is not able to address the new emerging problems. A cloud environment offers a variety of concrete and abstracted entities which need to be identified, whose states can frequently change: a virtual resource, could be allocated, deallocated or moved from a context to another. Moreover, a cloud entity could hold one or more names, identifiers, and representations in various cloud contexts where name alterations could frequently occur. In such environment, the management and integration of independent cloud name spaces is then becoming more compelling. This paper aims to propose a cloud naming system able to address such problems, providing an implementation practice in a cloud federation use case.