This paper presents Dronuum, a drone-based application designed within the Computing Continuum to enable efficient, real-time wildfire detection and environmental monitoring. Addressing the inherent limitations of Unmanned Aerial Vehicles (UAVs), notably constrained computational resources, energy capacity, and intermittent connectivity, the proposed system leverages a distributed, microservice-oriented architecture spanning Edge (drone) and Cloud environments. By integrating principles of Osmotic Computing and leveraging Liquid Computing (LIQO)-enabled multi-cluster orchestration, Dronuum can migrate application components across heterogeneous resources in response to mission requirements. The system decomposes the wildfire detection pipeline into modular services, including image acquisition, preprocessing, inference, and alerting. A lightweight YOLOv8n-based classifier is employed for fire detection. Experimental evaluation, conducted on a testbed combining Raspberry Pi Edge nodes and Cloud Virtual Machines (VMs), demonstrates the effectiveness of the proposed approach. Results indicate that classification accuracy is independent of the deployment scenario. Energy measurements confirm that LIQO-based offloading reduces the per-image energy cost from 6.85 mWh to 4.58 mWh, enabling up to 49.6% more images per battery charge, while microservice migration incurs a service downtime below ≈ 170 m s .

The rapid advancements in Artificial Intelligence (AI) and edge devices have driven the proliferation of smart-world applications, many of which are deployed in distributed environments. In contexts where data privacy must be maintained, Federated Learning (FL) has emerged as a promising privacy-preserving collaborative learning paradigm, offering significant potential to harness the distributed data available at the network’s edge. However, the heterogeneity of devices and the non-Independent and Identically Distributed (IID) nature of data across them pose significant challenges, undermining the performance of FL systems. To address these issues, Knowledge Distillation (KD) has proven to be a valid solution. Initially developed as a teacher-student training paradigm, KD enhances the performance of smaller student networks by transferring knowledge from larger, more powerful teacher networks. When integrated with FL, KD offers additional benefits: it mitigates the effects of data and model heterogeneity and compensates for the absence of a centralized teacher model. This is achieved by creating a global knowledge representation derived from the aggregated knowledge of individual clients. Building on these principles, we propose Weighted-FD, a novel framework that introduces a quality-aware approach to global knowledge computation. Unlike conventional methods, Weighted-FD evaluates the quality of knowledge contributed by each client and dynamically adjusts their influence on the global knowledge representation. This ensures a more accurate and effective aggregation process. We detail the mathematical foundation of our framework and validate its efficacy through extensive experiments conducted on MNIST, FashionMNIST, and CIFAR-10 under different data heterogeneity settings (IID, weak non-IID, and strong non-IID). The results demonstrate that the proposed method consistently outperforms existing federated distillation approaches. In particular, Weighted-FD achieves substantial improvements under strong non-IID conditions, reaching accuracy gains of up to 57.12% over FedMD and 49.34% over Selective-FD on CIFAR-10, while maintaining low computational and memory requirements, making it well suited for deployment in resource-constrained edge environments. The source code for Weighted-FD is publicly available at the following link: https://anonymous.4open.science/r/weighted-fd-C6EC/ .

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.

Despite CNNs’ high accuracy in medical image analysis, their opaque nature limits widespread clinical adoption, as practitioners are skeptical of predictions lacking clear rationale. This critical trust barrier necessitates the development of new approaches to provide transparent, reliable, and actionable insights coming from CNN, thus to effectively integrate them into healthcare processes. This paper addresses this issue by proposing a novel hybrid diagnostic pipeline that combines the predictive power of CNNs with the interpretive capabilities of Large Language Models (LLMs). Utilizing the LLM’s ability to generate human-like text and drawing clinical reasoning, our solution generates transparent explanations for CNN-based diagnoses. The approach is demonstrated on retinal diseases, where a ConvNeXt V2 model and Contrastive Language–Image Pretraining (CLIP) feature extraction approach are integrated for clinical classification and interpretation. This hybrid Vision-Language strategy aims to deliver both high predictive accuracy and the necessary human-readable accountability to foster clinical trust.

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.

Artificial intelligence is increasingly pervasive in many sectors. In this regard, IT operations are having a big deal on extracting useful information from the large amount of resources' datasets available (e.g., CPU, memory, disk, energy). The issue is bigger if we consider multiple cloud tiers. Artificial intelligence is a key technology when the main goal is to improve microservice migration through offload management. However, it struggles to facilitate distributed contexts where both data transfer needs to be reduced and data privacy needs to be increased. There is therefore a need for novel solutions that resolve the problem of prediction resource utilization (e.g. CPU) while maintaining data privacy and reducing data communication. In this paper, we present a Bi-LSTM model with attention trained in Federated Learning on CPU historical data. The dataset comes from multiple Microsoft Azure trace. The results are compared with the literature and showcase a good generalization and prediction results for metrics collected by multiple virtual machines. The model is evaluated in terms of R-squared, MSE, RMSE and MAE.

Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden relationships and latent structures, representing valuable information for many applications. This paper introduces Osmotic Learning (OSM-L), a self-supervised distributed learning paradigm designed to uncover higher-level latent knowledge from distributed data. The core of OSM-L is osmosis, a process that synthesizes dense and compact representation by extracting contextual information, eliminating the need for raw data exchange between distributed entities. OSM-L iteratively aligns local data representations, enabling information diffusion and convergence into a dynamic equilibrium that captures contextual patterns. During training, it also identifies correlated data groups, functioning as a decentralized clustering mechanism. Experimental results confirm OSM-L’s convergence and representation capabilities on structured datasets, achieving over 0.99 accuracy in local information alignment while preserving contextual integrity.

Federated Learning (FL) combined with Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA) has emerged as a promising approach to address data scarcity challenges in fine-tuning Large Language Models (LLMs) while ensuring privacy and computational efficiency. However, when applying LoRA in traditional FL, separately averaging the adapters during aggregation results in non-exact aggregation. While recent research has investigated this issue, its application to heterogeneous data settings remains largely unexplored. Data heterogeneity across clients can significantly affect the effectiveness of parameter-efficient adaptations and complicate the aggregation process.In this work, we explore the concept of exact aggregation in heterogeneous federated fine-tune settings, specifically focusing on LoRA-based approaches. We propose HEXA (Heterogeneity-aware EXact Aggregation), a novel method that mitigates the effects of data heterogeneity while preserving the benefits of exact aggregation in LoRA-enabled FL. We present a comprehensive theoretical framework for extending exact aggregation to heterogeneous settings and validate our approach through extensive empirical evaluation on the GLUE benchmark. Our results show that HEXA improves model performance in heterogeneous contexts while maintaining the computational efficiency of PEFT methods.

Technological advancements in Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) have enabled innovative applications in education and cultural heritage. This study presents the development and implementation of a MR experience at the Zoological Museum “Francesco Cambria” of the University of Messina, aimed at enhancing visitor engagement and learning. The MR system integrates 3D models, interactive assets, and gesture-based controls through Microsoft HoloLens 2, providing users with an immersive, multisensory experience. The system offers two visit modes: an Autonomous Mode, where visitors interact with 3D reconstructions and contextual information, and a Guided Mode, which delivers audio-narrated content based on spatial triggers. Usability and user experience were evaluated based on the System Usability Scale (SUS) and the Simulator Sickness Questionnaire (SSQ).

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.

This work compares univariate and multivariate Long Short-Term Memory (LSTM) models for predicting delays in public transportation. Only historical delay data was used to train the univariate model, which is effective for last-minute predictions but limited in capturing wider temporal correlations because it is optimized for real-time inference with minimal data requirements. To better capture intricate patterns in transportation delays, the multivariate model incorporates extra contextual factors like time of day and topographical coordinates. Multiple multivariate models were trained on different time intervals (e.g., daily, weekly, and monthly) to evaluate the impact of training data selection on predictive accuracy. The findings show that multivariate models provide better long-term accuracy, especially when trained on properly segmented data, whereas univariate models are computationally efficient and excellent for short-term updates. These results suggest how to best adapt LSTM-based forecasting models for dynamic, practical transportation applications.

Edge computing’s rise demands efficient compression strategies for deploying Machine Learning (ML) models on resource-constrained devices. As Artificial Intelligence (AI) shifts from cloud to edge, optimizing models across heterogeneous layers is crucial. Quantization reduces numerical precision, improving model size, inference speed and energy efficiency, key for edge deployments. However, its complexity limits accessibility. To address this, we propose Quantization-as-a-Service (QaaS), a serverless framework that automates model quantization for both cloud and edge environments. Built on OpenFaaS and Kubernetes, QaaS enables on-demand execution with dynamic resource orchestration, implementing Layer 5 of Edge Intelligence (EI). Our evaluation compares quantization performance on edge devices in terms of CPU usage and execution time when performed as a service versus locally. Results demonstrate that deploying quantization workflows using Function-as-a-Service (FaaS) not only maintains computational efficiency but also reduces CPU consumption compared to standalone execution, showcasing the potential of serverless solutions in EI.

The growing diversity of data sources in modern organizations has made efficient querying and integration crucial for actionable insights. With data distributed across structured relational databases like MySQL and semistructured NoSQL systems such as MongoDB, querying heterogeneous environments has become more complex. Apache Drill and PrestoDB are two prominent query engines designed to address this challenge by providing federated querying across multiple data sources. This paper examines the architecture, design, and performance of Apache Drill and PrestoDB, focusing on their integration with MySQL and MongoDB. Using a synthetic smart city dataset that simulates urban mobility, traffic, air quality sensors, and public infrastructure usage, a series of queries, spanning from basic data retrieval to complex multi-source joins and aggregations, were executed to assess each engine’s performance across various workloads. This research provides insights into the trade-offs between flexibility and performance when querying heterogeneous data environments. It also offers guidance for optimizing data retrieval and analysis across various storage systems.

The COVID-19 pandemic was first identified in Wuhan (China) in late 2019, and quickly escalated into a global crisis, emphasizing the need for rapid and accurate diagnostic methods. Since chest X-rays play a key role in diagnosing COVID-19, we applied deep learning techniques to the COVID-19 Radiography Dataset to enhance diagnostic accuracy. These models are trained with augmented data to improve generalization across different radiographic presentations of the virus. The results demonstrate the speed and reliability of COVID-19 diagnosis, helping healthcare systems to make faster treatment decisions. This also supports ongoing efforts to manage COVID-19 and creates a foundation for using Artificial Intelligence to detect future respiratory diseases.

The management of natural disasters requires a multifaceted strategy that leverages advanced technologies to enhance preparedness, response, and recovery efforts. As extreme weather events become more frequent and intense, the ability to process and analyze large datasets in real time is transforming disaster management approaches. In urban environments, the complexity of infrastructure, high population density, and interdependencies among critical services introduce additional challenges. Smart cities provide new opportunities to address these risks by integrating interconnected sensor networks, cloud computing, and artificial intelligence for real-time situational awareness and datadriven decision-making. The application of Big Data analytics and Internet of Things (IoT) technologies strengthens early warning systems, improves resource allocation, and refines post-disaster assessments. By utilizing cloud-edge continuum architectures, the proposed solution supports scalable NDM-Analytics-as-a-Service (NDM-AaaS), integrating Big Data analytics for precise crisis assessment and automated response recommendations. The core components of the proposed solution optimize data management and analysis, enhancing disaster response capabilities in urban settings.

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.

Federated Learning (FL) aims to train artificial intelligence models without the need to share private raw data, thereby preserving privacy and security. Typically, it is assumed that all participating FL clients will act honestly to develop an accurate model. However, some clients may behave deceptively, manipulating their data to bias the model’s predictions and also degrade its generalization ability. This paper addresses the issue of fairness in FL from the perspective of client truthfulness. We introduce Federated Objective (FedObj), a novel aggregation method designed to minimize the impact of malicious clients and thereby improve the overall model’s robustness to such behavior. Our results show that FedObj achieves state-of-the-art performance in standard scenarios and outperforms conventional strategies when deceptive clients are involved. FedObj is a valuable approach for the collaborative development of trustworthy and fair AI systems, as it is significantly resilient to the misleading practices of malicious FL clients.

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.

Federated Learning exploits local model training to aggregate and create a global model without sharing raw data. Each client trains a local model and shares it to aggregate a global one. Several works demonstrate that starting from trained weights, it’s possible to reconstruct the original used data. For this reason, the research and industrial world introduced the Homomorphic Encryption technique to encrypt the transmitted local model’s weights. This approach protects the trained weights from a hypothetical malicious aggregator server that can not perform operations over plaintext weights. In the proposed work, we implement a Federated Learning solution applying Homomorphic Encryption using the Flower framework and the Tenseal library. Our solution follows best practices for custom aggregation strategies with the Flower framework, making it possible to provide it to the community.

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.

In last recent years, deep neural networks have seen success in Electric Vehicles (EVs) monitoring. Their scalability with large-scale data and numerous model parameters contributes to this success. However, EVs use resource-constrained devices that struggle with complex models and data privacy concerns prevent sharing outside the owning device. Thus, Knowledge Distillation (KD) and Federated Learning (FL) emerged as solutions for models simplification and for facilitating distributed training on private data. We propose and compare architectural solutions leveraging KD and FL to address challenges in EV environments, where resources are limited, and data privacy is crucial. By simplifying models with KD and enabling distributed training with FL, our architectures aim to integrate Machine Learning (ML) models into Internet of Things (IoT) devices within EVs, enhancing monitoring and predictive maintenance applications.

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.

Federated Learning lays its foundation on the computation and availability of data provided by the increasingly popular Edge devices. By performing on-device training, privacy over the data can be ensured and the computational capacity of the devices is directly exploited. On the other hand, the heterogeneous characteristics of such devices and the difficulty of establishing a proper communication network are often complex obstacles to overcome when implementing Federated Learning solutions. Leveraging ONNX Training Web, WebAssembly, and the Cloud-Edge-Client Continuum concept, we propose FLAT, a system that allows Federated Learning algorithms to run seamlessly on Web browsers, in the form of microservices, without the need for any dependencies and configuration. Through this approach, any device equipped with a browser, even in headless mode, can dynamically join a federated learning cluster in plug-and-play mode by simply connecting to a Web address. The effectiveness of the system is tested with the MNIST and CIFAR10 datasets, using respectively a DNN and a CNN with FedAvg as the aggregation strategy and considering different devices and browsers.

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.

Monitoring of infrastructures through sensor networks that acquire accelerations, tilts, angular velocities and environmental quantities can diagnose structural damage in time and prevent building collapse, thus becoming a key technology in today’s smart-cities. This paper presents the design and prototyping of a custom low-cost synchronous Structural Health Monitoring (SHM) system. The goal of data-driven modeling of infrastructures through Machine Learning techniques imposes the constraint of synchronous and continuous monitoring and requires the ability to control the system and off-load data remotely to an Edge-Cloud continuum. In the proposed system analysis of the acquired signals can take place through REST calls on the storage system and the use of tools built in Python language. At the end of the work, preliminary tests of acquisition, analysis of data writing times and measurement of times-of-flight of an elastic wave on a building under investigation are presented.

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.

In the domain of automatic speech recognition (ASR), we explore the usage of a state-of-the-art transformer-based sequence-to-sequence model to build a speaker-dependent isolated word recognizer for native Italian speakers with a speech disorder, such as dysarthria. In particular, this paper is concerned with a self-supervised learning approach, where the Wav2Vec2 has been fine-tuned on our private Italian corpus containing a total of 41 hours of speech contributions authored by 191 individuals with a disability and atypical speech. The discussed approach has been also evaluated thanks to collaboration of sixteen speakers with diverse degrees of speech disorders (mild, moderate, severe), and our analysis has shown a remarkable performance of our ASR system, with an overall word recognition accuracy of 97.4%.

Special issue on efficient management of microservice-based systems and applicationsThe advent of microservice architecture marks a transition from conventional monolithic applications to a landscape of loosely linked, lightweight, and autonomous microservice components.The primary objective is to ensure strong environmental uniformity, portability across various operating systems, and robust resource isolation.Leading cloud service providers such as Amazon, Microsoft, Google, and Alibaba have widely embraced microservices within their infrastructures.This adoption is geared toward automating application management and optimizing system performance.Consequently, addressing the automation of tasks like deployment, maintenance, auto-scaling, and networking of microservices becomes pivotal.This underscores the importance of efficient management of systems and applications built on microservices as a critical research challenge.Efficient management methods must not only ensure the quality of service (QoS) across multiple microservices units (containers) but also provide greater control over individual components.However, the dynamic and varied nature of microservice applications and environments significantly amplifies the complexity of these management approaches.Each microservice unit can be deployed and operated independently, catering to distinct functionalities and business objectives.Furthermore, microservices can interact and combine through lightweight communication techniques to form a complete application.The expanding scale of microservice-based systems and their intricate interdependencies pose challenges in terms of load distribution and resource management at the infrastructure level.Furthermore, as cloud workloads surge in resource demands, bandwidth consumption, and QoS requirements, the traditional cloud computing environment extends to fog and edge infrastructures that are in close proximity to end users.As a result, current microservice management approaches need further enhancement to address the mounting resource diversity, application distribution, workload profiles, security prerequisites, and scalability demands across hybrid cloud infrastructures.Keeping this in mind, this special issue addressed some of the aspects related to efficient management of microservice-based systems and applications with the focus on various challenges faced, and promising solutions to address such challenges by using software engineering, machine learning and deep learning techniques.We have received 21 submissions in this issue, and we accepted six high-quality submissions for publication after a rigorous review process with at least three reviewers for each paper.The authors are from diverse countries, including the USA, China, UK, Germany, India, Brazil, etc.Each of the accepted papers is summarized as follows.In the first article, Batista et al. 1 presented two strategies for handling asynchronous workloads associated with tax integration in a multi-tenant microservice architecture specific to the company's context.The initial approach utilizes polling, employing a queue as a distributed lock.The second approach, named the single active consumer, adopts a push-based technique, leveraging the message broker's logic for message delivery.These methodologies are designed to optimize resource allocation in scenarios involving an increasing number of container replicas and tenants.In the second article, Kumar et al. 2 introduced a resource allocation model designed to enhance the QoS in microservice deployment.Utilizing a Fine-tuned Sunflower Whale Optimization Algorithm, the model strategically deploys container-based services on physical machines, optimizing their execution capacity by efficiently utilizing CPU and memory resources.The primary objective of this proposed technique is to achieve an efficient distribution of workload, preventing resource wastage and ultimately enhancing QoS parameters.In the third paper, Zhu et al. 3 introduced RADF, a semi-automatic approach for decomposing a monolith into serverless functions by analyzing the inherent business logic present in the application's interface.The proposed method employs a two-stage refactoring strategy, initially performing a coarse-grained decomposition followed by a fine-grained one.This approach streamlines the decomposition process into smaller, more manageable steps, providing adaptability to generate a solution at either the microservice or function level.In the fourth paper, Würz et al. 4 identified the principal tasks and subtasks of the application for the purpose of partitioning.Subsequently, they outlined the program flow to ascertain which application tasks could be transformed into functions and elucidated their interdependencies.In the concluding step, they precisely specified individual functions

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.

EDGEmergency is a platform designed for disaster management that can dynamically leverage the edge infrastructure potentially already present within the emergency perimeter. Edge devices, from IoT to smartphones, possess an increasingly significant computational capacity that can be exploited, by changing their behavior in real-time and creating a pervasive local environment, capable of adapting perfectly to the specific context of reference. EDGEmergency, in fact, allows the creation of a unified computation environment leveraging the Cloud-Edge-Client Continuum concept, through which a computation cluster with zero configurations is created on-the-fly. The platform thus allows the deployment of distributed microservices on existing edge devices, installed by default for other purposes, through a modular and incremental logic that has the role of adapting best to the needs of the individual emergency, through advanced tools for analysis and monitoring, using artificial intelligence.

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.

The use of the Federated Learning paradigm could be disruptive in robotics, where data are naturally distributed among teams of agents and centralizing them would increase latency and break privacy. Unfortunately there are a lack of robot oriented framework for federated learning that use state of the art machine learning libraries. ROS2 (Robot Operating Systems) is a standard de-facto in robotics for building up teams of robots in a multi-node fully distributed manner. In this paper we presents the integration of ROS2 with PyTorch allowing an easy training of a global machine learning model starting from a set of local datasets. We present the architecture, the used methodology and finally we discuss the experimentation results over a well-known public dataset.

Internet of Things (IoT) became more and more popular because of the raise of ubiquitous internet connected devices. In this regard, IoT nodes are often organized in wireless sensor networks to facilitate communication and perform a coral computation. Such a network is often employed in urban or rural areas, i.e., for traffic, fires, and floods monitoring. Nodes are, therefore, deployed in remote areas, preventing the possibility to frequently access them, i.e., for firmware update. In this context, over-the-air (OTA) firmware update is used to remotely change the behavior of one or more nodes. In this paper, we firstly build a wireless mesh network with microcontrollers (i.e., ESP32) and, therefore, propose a secure filtered O TA firmware update involving firmware freshness (i.e., quarantine when firmware is not up-to-date), key pairing, and digital signature for data integrity and non-repudiation. The system is evaluated in terms of deactivation time $(s)$, energy consumption $(kWh)$, and greenhouse gases $(\mathrm{kgCO}_{2}\mathrm{e})$, highlighting good results in terms of scalability for grouped updates.

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.

Federated Learning (FL) is a cutting-edge technology for distributed solving of large-scale problems using local data exclusively. The potential of Federated Learning is nowadays clear in different context from automatic analysis of healthcare data to object recognition in video sources coming from public video streams, from distributed search for data breach and finance frauds to collaborative learning of hand typing on mobile phone. Multi-robot systems can also largely benefit from FL concerning resolution of problems like trajectory prediction, non colliding trajectory generation, distributed localization and mapping or distributed reinforcement learning. In this paper we propose a multi-robot framework that includes distributed learning capabilities by using Decentralized Stochastic Gradient Descent on graphs. First of all we motivate the position of the paper discussing the privacy preserving problem for multi robot systems and the need of decentralized learning. Then we build our methodology starting from a set of prior definitions. Finally we discuss in details the possible applications in robotics field.

Nowadays the process of anonymization of documents has been the subject of several studies and debates. By anonymization of documents, we mean the process of replacing sensitive data in order to preserve the confidentiality of documents without altering their content. In this work, we introduce Docflow, an open-source document anonymization engine capable of anonymizing documents based on specific filters chosen by the user. We applied Docflow to anonymize a set of legal documents and performed a processing performance analysis. By providing a Markdown input file to be anonymized, Docflow is able to redact all information according to users' choices, preserving the document content. Docflow will be integrated with NLP algorithms for the generation of the Markdown source file starting from documents already processed in different formats, but always with human supervision in the loop.

TEMA project is a Horizon Europe funded project that aims at addressing Natural Disaster Management by the use of sophisticated Cloud-Edge Continuum infrastructures by means of data analysis algorithms wrapped in Serverless functions deployed on a distributed infrastructure according to a Federated Learning scheduler that constantly monitors the infrastructure in search of the best way to satisfy required QoS constraints. In this paper, we discuss the advantages of Serverless workflow and how they can be used and monitored to natively trigger complex algorithm pipelines in the continuum, dynamically placing and relocating them taking into account incoming IoT data, QoS constraints, and the current status of the continuum infrastructure. Therefore we presented the Urgent Function Enabler (UFE) platform, a fully distributed architecture able to define, spread, and manage FaaS functions, using local IOT data managed using the Fiware ecosystem and a computing infrastructure composed of mobile and stable nodes.

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.

This work studies and defines the problem of providing extensive and opportunistic Edge AI-based area coverage in smart city application scenarios, by researching and determining the optimal configuration of sensing and computational resources for minimizing the environmental/technology footprint of the solution. A typical smart city computing continuum consists of statically installed multimodal sensing Internet-of-Things (IoT) nodes at various city locations, accompanied by interconnected computational Cloud/Edge/IoT nodes. This paper presents Optimal Trustworthy EdgeAI (OTE), an entirely novel research pipeline, that complements existing smart city infrastructure with intelligent drone Edge/IoT nodes (in the form of modularly equipped unmanned aerial vehicles), capable of autonomous repositioning according to individual/collective sensing and coverage criteria. Thereby, we envisage the emerging cutting-edge technologies of trustworthy sensing, perceiving, modelling technologies for predicting the behavior of moving targets (e.g., citizens/vehicles/objects), understanding natural phenomena (e.g., sea wave motion, urban flora/fauna, biodiversity) in order to anticipate events (people's bad habits, environmental changes), by exploiting novel continuous data processing services across the whole span of the enhanced Cloud-Edge-IoT computing continuum.

Nowadays Osmotic Computing is emerging as one of the paradigms used to guarantee the Cloud Continuum, and this popularity is strictly related to the capacity to embrace inside it some hot topics like containers, microservices, orchestration and Function as a Service (FaaS). The Osmotic principle is quite simple, it aims to create a federated heterogeneous infrastructure, where an application's components can smoothly move following a concentration rule. In this work, we aim to solve two big constraints of Osmotic Computing related to the incapacity to manage dynamic access rules for accessing the applications inside the Osmotic Infrastructure and the incapacity to keep alive and secure the access to these applications even in presence of network disconnections. For overcoming these limits we designed and implemented a new Osmotic component, that acts as an eventually consistent distributed peer to peer access management system. This new component is used to keep a local Identity and Access Manager (IAM) that permits at any time to access the resource available in an Osmotic node and to update the access rules that allow or deny access to hosted applications. This component has been already integrated inside a Kubernetes based Osmotic Infrastructure and we presented two typical use cases where it can be exploited.

The paper presents an approach to solve the software and hardware related failures in edge-cloud environments, more precisely, in cloud manufacturing environments. The proposed approach, called TOLERANCER, is composed of distributed components that continuously interact in a peer to peer fashion. Such interaction aims to detect stress situations or node failures, and accordingly, TOLERANCER makes decisions to avoid or solve any potential system failures. The efficacy of the proposed approach is validated through a set of experiments, and the performance evaluation shows that it responds effectively to different faults scenarios.

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.

The recent advancements in miniaturized smart data collecting devices pushed the need of securing communications between people and devices. Traditional approaches based on key exchange protocol can't be performed by resource-constrained embedded devices, and a novel approach, based on a robust decentralization of the eXtended Triple Diffie-Hellman (X3DH) protocol, has been proposed, namely the BlockChain-Based X3DH (BCB-X3DH) protocol. This work progresses the analysis further to fit a generic Smart City scenario with Edge and IoT nodes, performing intensive analysis on Raspberry Pi 3 model B+ and Raspberry Pi 4 to validate that the new protocol is not only resistant from well-known distributed attacks but can also be executed by miniaturized hardware with benefits in terms of resources, energy consumption and battery life-cycle.

The papers in this special section focus on sustainability of edge computing systems. Edge computing is an emerging architectural and technical approach aimed at addressing various shortcomings in traditional cloud computing paradigms and responding to today’s constantly increasing data-demanding services such as Internet-of-Things, 5G embedded artificial intelligence and smart cities. In Fog/Edge Computing, nodes at the edge of a network are equipped with processing, storage, networking, etc. capabilities to take over several tasks that were used to be sent to cloud services. Pre-filtering and aggregation of data as well as online processing and actuation are sample procedures envisaged/dedicated to fog/ edge 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.

With the growth in the Internet of things (IoT) paradigm, there has been a tremendous makeshift in how the distributed devices work to achieve a common goal. However, it remains essential that all these devices work in a coherent manner to perform a collective action. This makes the task of resource provisioning extremely important in such a paradigm. The end-user level in IoT mostly comprises of low computation and communication powered devices. Improper utilization of the available resources in such a scenario burdens the complete system and degrades the quality of service. In such a scenario, the use of cloud computing techniques can help to manage the resources effectively. More so, with the emergence of relatively newer cloud-based technologies such as edge and fog computing, resource management in the IoT has become far more effective. These technologies bring the computation and communication capabilities closer to the IoT devices where some of the services can be offloaded to the edge devices. These devices are called IoT edge devices and they provide a unique opportunity to tackle some of the existing and pertinent issues for resource management in IoT paradigms; yet at the same time, they face their own set of challenges. However, the use of IoT edge devices in a traditional IoT paradigm results in better utilization of the available resources as well as improving the overall quality of service. Keeping this in mind, this special issue addressed some of the aspects related to resource management in IoT edge devices with the focus on various challenges faced, and potential techniques and solutions to address such challenges by leveraging IoT edge devices. We received numerous submissions in the issue, and we accepted 13 high-quality submissions for publication as a result after following a rigorous review process. Each of the accepted papers is summarized as follows. In the first paper, Khan et al.1 presented "A cache-based approach toward improved scheduling in fog computing" for efficient resource allocation in the fog computing environment, while maintaining the quality of service. The authors use first-in first-out scheme to place the jobs in queue and cache the job type, fog server, arrival time, time to leave, and internal processing time. The jobs are then moved from the queue by the fog broker which selects fog server having sufficient required power and resources to execute the job. The authors' proposed cache-based scheme showed promising results in terms of reducing the execution time, latency, processing delays and power consumption as compared to the conventional first-come-first-serve and shortest job first policies. The second paper on "Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges" by Dewangan et al.2 sheds light on various types of resource provisioning schemes and classified those into different categories (to help understand them better) on the basis of the underlying technique and their overall objective. This survey helps to understand the optimal schemes for catering to different performance metrics such as time, cost, energy, service level of agreement rate, power consumption, resource utilization, etc. Moreover, the authors also highlighted some of the current research challenges in the domain of resource management. The next paper, "An energy efficient and low overhead fault mitigation technique for internet of thing edge devices reliable on-chip communication" by Ibrahim et al.3 presents a coding scheme to make the network-on-chip fault-tolerant. The network-on-chip provides communication backbone in the underlying network for which the proposed scheme handled both single and multibit adjacent bit errors. The next paper in this issue is on "Design and data analytics of electronic human resource management activities through Internet of Things in an organization" by Nasar et al.4 The authors focus on designing a data analytical human resource management system for IoT devices in an organization for ensuring the policies, strategies, and practices within the organization. The activities covered under this improved system include e-recruitment, e-Selection, e-performance management, e-learning, and e-compensation and the performance of the system was validated on four Kaggle databases. In the fifth paper on "A Mobile Data Offloading Framework based on a Combination of Blockchain and Virtual Voting", Hassija et al.5 enable mobile users to offload computation tasks to resource-rich mobile-devices in order to reduce energy consumption and enhance performance. The authors used directed acyclic graphs (DAGs) for mobile offloading algorithm where the users can securely submit a transaction (powered by blockchain) request for task offloading a DAG, while a game-theoretic scheme was employed in order to model the interactions between various mobile devices for bargaining cost and time. The sixth paper by Lu is on "Security of Internet of Things edge devices".6 The paper focuses on securing the edge nodes and edge gateways in IoT to meet its future security needs to eliminate the data leakage risk. The edge nodes were optimized by using a cache replacement algorithm, namely Max-PSN and the results illustrate that the proposed mechanism performed superiorly to the lead frequently used and least recently used algorithms with respect to the hit rate and average response speed of centralized and distributed systems. In the seventh paper, Balasubramanian and Jolfaei present "A scalable framework for healthcare monitoring application using the Internet of Medical Things".7 The authors made use of IoT for providing real-time alarm and assistance in order to ease the activities of pregnant women by merging the advantages of event-driven and assistive care loop framework architecture. In the next paper, Bodkhe and Tanwar shed some light on "Secure data dissemination techniques for IoT applications: Research challenges and opportunities".8 As the name suggests, the authors presented a comprehensive summary of secure data dissemination schemes present in the existing literature for IoT applications along with their potential research issues and possible countermeasures. The majority of the researched literature in this survey covers the Internet of Vehicles, Internet of Drones, and Internet of Battlefield things with respective open issues and challenges of each of these. As countermeasures, the authors researched opportunities in the directions of the requirement of secure dissemination protocols, efficient data aggregation methods, and cluster-based data dissemination. The ninth paper on "Comparative study of support vector machines and random forests machine learning algorithms on credit operation" by Teles et al.9 compares the support vector machine (SVM) and random forest (RF) scheme for their application to predict financial risks on credit operation. The outcomes of this paper suggest that while both can be effectively used for the specified task, RF has an advantage of the speed and operational simplicity over SVM; while SVM has the benefit of higher classification accuracy. The tenth paper presented by Zhao et al. titled "Message-Sensing Classified Transmission Scheme Based on Mobile Edge Computing in the Internet of Vehicles".10 The authors make use of mobile edge computing for secure message transmission by prioritizing secure messages using the analytic hierarchy process to guarantee a higher transmission level for urgent messages. Moreover, using the Lagrangian relaxation method, an optimal task offloading model was devised for delay and energy loss by assigning different weight factors to these parameters. The next paper is "FPFTS: A Joint Fuzzy PSO Mobility-aware Approach to Fog Task Scheduling Algorithm for IoT Devices" by Javanmardi et al.11 The authors build a fog task scheduler leveraging the particle swarm optimization along with fuzzy theory to assign tasks of the users to fog devices. The proposed task schedular was tested on iFogSim simulator and results show that it outperformed first-come-first-serve and delay-priority algorithms with respect to delay and network utilization. Zhang et al.,12 in their paper "Service offloading oriented edge server placement in smart farming" made use of the edge resources to support the real-time intelligent controls in smart farming. The authors presented a service offloading oriented architecture for reducing delay in data transmission from sensors to the edge servers while balancing the load on the servers and optimizing the energy consumption. The final accepted paper in this special issue is on "A metaheuristic optimization approach for energy efficiency in the IoT networks" by Iwendi et al.13 The authors proposed a hybrid metaheuristic algorithm, namely, WOA-SA, for optimizing the energy consumption of the sensors in IoT-based wireless sensor networks. The two metaheuristic approaches, namely, whale optimization algorithm and simulated annealing for choosing the cluster heads in order to optimize the energy consumption in the network. The proposed approach was found to be more effective than its counterparts in terms of load, temperature, residual energy, and cost function. We sincerely hope that after reading the accepted contributions in this special issue would help the readers of the journal and a wider research community to gain knowledge on the presented research challenges, techniques and solutions, and encourage them to further work on different aspects of resource management in IoT devices. We thank the editor-in-chief and editorial board members for providing us with the opportunity to conduct a special issue in Software: Practice and Experience. We also like to thank the administrative staff, reviewers and most importantly, the authors, for their help and contributions in successful organization of this issue.

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.

The Internet of Things (IoT) paradigm promises to make “things” such as physical objects with sensing capabilities and/or attached with tags, mobile objects such as smartphones and vehicles, consumer electronic devices, and home appliances such as fridge, television, health care devices, as part of the Internet environment. In cloud-centric IoT applications, the sensor data from these “things” is extracted, accumulated, and processed at the public/private clouds, leading to significant latencies. To satisfy the ever increasing demand for cloud computing resources from emerging applications such as IoT, academics and industry experts are now advocating for going from large-centralized cloud computing infrastructures to micro data centers located at the edge of the network. These micro data centers are often closer to a user (geographically and in access latency) compared to the centralized cloud data center. The aim of utilizing such edge resources is to off load computation that would have “traditionally” been carried out at the cloud data center to a resource that is closer to a user or edge devices. This vision also acknowledges the variation in network latency from an end-user to cloud data center. While the network around a data center is often high capacity and speed, that near the user device may have variable properties (in terms of resilience, bandwidth, latency, etc.). Referred to as “fog/edge computing,” this paradigm is expected to improve the agility of cloud service deployments in addition to bringing computing resources closer to end-users. The emergence of computing paradigms such as edge and fog computing supports the data analysis near the data sources for a wide range of applications. Edge computing is the middle layer between users and cloud data centers, and it plays an important role in the IoT use cases where applications required near real-time actions. The intermediate edge layer provides limited computing and storage resources, which consists of network gateways and micro data centers. Edge computing application orchestration is capable of big data processing and can be installed in heterogeneous hardware configurations. Due to the large-scale deployment and device heterogeneity, edge computing infrastructure designing and implementation are challenging, including model analysis, system integration, protocol designing, energy, and security modeling. In addition, since edge data centers are installed in network gateways with open network configuration, they are prone to several network threats, less trustworthy, and easy to compromise. On the one hand, the development of fog and edge clouds includes dedicated facilities, operating system, network, and middleware techniques to build and operate such micro data centers that host virtualized computing resources. On the other hand, the use of fog and edge clouds requires extension to current programming models and proposes new abstractions that will allow developers to design new applications that take benefit from such massively distributed systems. The use of this approach also opens up other challenges in security and privacy (as a user now needs to “trust” every micro data center they interact with), support for resource management for mobile users who transfer session from one micro data center to another, and support for “embedding” such micro data centers into devices (eg, cars, buildings, etc). The objective of this special issue is to disseminate original contributions and research findings concerning the challenges and changes (both evolutionary and disruptive) in edge and fog computing. It provides cutting-edge research from both academia and industry, with emphasis on current developments and future directions in security and privacy issues of emerging fog computing. The call for special issues received a number of submissions. Each paper was reviewed by at least three reviewers and went through at least two rounds of reviews. After a two-phase peer review process, we have accepted 14 high-quality papers related to the aforementioned areas of interest. The accepted papers focus on recent solutions by developing novel research ideas around edge and fog computing for several applications, such as health care, smart city, urban pollution monitoring, etc. The brief contributions of these papers are discussed in the following section. The first paper titled “Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance” by Xu et al proposes an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places. It utilizes an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. This approach has thoroughly experimented, and the experimental results demonstrate the efficiency of the proposed approach by achieving better abnormal event detection performance for a crowded scene of urban public places with an edge environment. The second paper titled “User allocation-aware edge cloud placement in mobile edge computing” by Guo et al studies the edge cloud placement problem, which is to place the edge clouds at the candidate locations and allocate the mobile users to the edge clouds. Further, it formulates as a multi-objective optimization problem with the objective to balance the workload between edge clouds and minimize the service communication delay of mobile users. The experiment results show the performance of the proposed approach in terms of workload balance and communication delay for validation. The third paper titled “A secure fog-based platform for SCADA-based IoT critical infrastructure” by Baker et al contributes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature, that is, a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. This paper also presents the implementation details of a prototype for our proposed secure fog-based platform and provides performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform. The fourth paper titled “Developing applications in large scale, dynamic fog computing: A case study” by Giang et al presents a case study in building fog computing applications using an open-source platform distributed node-RED. It shows how applications can be decomposed and deployed to a geographically distributed infrastructure using distributed node-RED, and how existing software components can be adapted and reused to participate in fog applications. This case study is implemented in a lab-based fog infrastructure and simulated for large-scale evaluation. The fifth paper titled “An osmotic computing infrastructure for urban pollution monitoring” by Longo et al focuses on the design and development of a middleware that integrates data coming from mobile and IoT devices specifically deployed in urban contexts using the osmotic computing paradigm. Moreover, a component of the osmotic membrane has been developed in this paper for security management. The sixth paper titled “Characterizing application scheduling on edge, fog, and cloud computing resources” by Varshney and Simmhan offers a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. The proposed model is divided into multiple steps, initially characterized by the resource capabilities and limitations of these infrastructures and offers a taxonomy of application models, quality-of-service constraints and goals, and scheduling techniques based on a literature review, followed by tabulated key research prototypes and papers using this taxonomy. It also highlights gaps in the literature and open problems remain. The seventh paper titled “Cloud-aided online electroencephalography (EEG) classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN” by Ke et al presents the design of an online EEG classification system aided by cloud centering on a lightweight convolutional neural network (CNN). The system incrementally trains the CNN on cloud and enables hot deployment of the trained classifier without the need to restart the gateway to adapt to the users' needs. The classifier maintains a high convolutional layer to gain the ability of processing high-dimensional EEG segments. Finally, the model is experimented to validate the contribution. The eighth paper titled “SEWMS: An Edge-based Smart Wearable Maintenance System in Communication Network” by Rui et al proposes a dynamic context-aware information push algorithm (DCAIP) by focusing on the current low level in information, complicated scenes, and various information on on-site maintenance. It also presents a smart wearable maintenance system (SEWMS), an edge computing-assisted IoT platform for the real-time guidance of technical experts and systems for on-site maintenance personnel, aiming to improve the efficiency and quality of on-site maintenance. The ninth paper titled “A crosswalk pedestrian recognition system by using deep learning and zebra-crossing recognition techniques” by Dow et al investigates a real-time pedestrian recognition system that ensures high accuracy by using a deep learning classifier and zebra-crossing recognition techniques. The proposed system was designed to improve pedestrian safety and reduce accidents at intersections. Environmental feature vectors were first used to detect zebra crossings and to determine crossing areas. An adaptive mapping technique was then used to map the pedestrian waiting area based on the crossing area. A dual-camera mechanism was used to maintain detection accuracy and improve system fault tolerance. Finally, the you-only-look-once model was used to recognize pedestrians at intersections. The 10th paper titled “Intelligent sentiment analysis approach using edge computing-based deep learning technique” by Sankar et al studies machine learning algorithms to extract the best features from the training review dataset. Then, the selected features are fed into the CNN and other fully connected layers for further processing. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a smartphone without the need for any cloud or server-side application programming interface. The 11th paper titled “Pipeline provenance for cloud-based big data analytics” by Wang et al proposes a solution, named LogProv toward realizing the functionalities for big data provenance, which needs to renovate data pipelines or some of big data software infrastructure to generate structured logs for pipeline events, and then stores data and logs separately in cloud space. The LogProv is implemented and deployed in Nectar Cloud, associated with Apache Pig, Hadoop ecosystem, and adopted Elasticsearch to provide query service. The 12th paper titled “Socially aware microcloud service overlay optimization in community networks” by Apolónia et al presents a model, named Select in Community Networks (SELECTinCN), which enhances the overlay creation for pub/sub systems over peer-to-peer (P2P) networks. Moreover, SELECTinCN includes social information based on cooperation within CNs by exploiting the social aspects of the community of practice. The model organizes the peers in a ring topology and provides an adaptive P2P connection establishment algorithm, where each peer identifies the number of connections needed based on the social structure and user availability. The 13th paper titled “DewSim: A trace-driven toolkit for simulating mobile device clusters in Dew computing environments” by Hirsch et al models and develops a trace-based toolkit built on modular software artifacts to speed up research in resource management techniques in Dew environments. A trace-driven methodology is adopted to assure the practical value of simulated scenarios. The toolkit comprises a device profiler application for Android to capture generic battery and central processing unit traces from real devices, a profile mixer to create user interaction baseline traces through generic ones, and an extensible engine to simulate the execution of workloads configurable via text files. The 14th paper titled “How to Place Your Apps in the Fog-State of the Art and Open Challenges” Borgi et al review the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview of the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog. The 15th paper titled ‘SELFNET 5G mobile edge computing infrastructure: Design and prototyping’ by Chirivella-Perez et al. presented the design and prototype implementation of the fifth-generation (5G) mobile edge infrastructure based on a mobile edge computing paradigm. This mobile edge infrastructure SELFNET is an amlganmation of cloud computing, software-defined networking, and network function virtualization to end up with a portable 5G infrastructure testbed which enabled the realistic execution and testing. Finally, in the last paper titled ‘SDN/NFV security framework for fog-to-things computing infrastructure’ by Krishnan et al. proposed DTARS which is a System for Distributed Threat Analytics and Response for an Edge/Fog and SDN integrated architecture. In this system, the detection scheme runs at the data plane wherein a coarse-grained behavioral, anti-spoofing, flow monitoring and fine-grained traffic multi-feature entropy-based algorithms are deployed. The proposed framework has been developed for defense applications on malware testbed. We hope that the research contributions and findings in this special issue would benefit the readers in terms of enhancing their knowledge and encouraging them to work on various aspects of edge and fog computing. We express our sincere thanks to the editor-in-chief for allowing us to organize this special issue. The editorial office staffs are excellent and thanks for their support. We are also thankful to all the authors who made this special issue possible, and to the reviewers for their thoughtful contributions.

Smart grid (SG) is an emerging technology which provides many services to the end users and utilities, such as load management, frequency regulation, and grid stability. Although many solutions exist to provide these services in a secure manner, but these solutions are not adequate keeping in view of the heavy cryptographic primitives execution on these devices. Hence, in this article, GUARDIAN, a blockchain-based secure demand response management scheme is presented so as to take energy trading decisions securely for managing the overall load of residential, commercial, and industrial sectors. In GUARDIAN, the miner nodes, which are block verifiers, are selected using their power consumption and processing power. These nodes are responsible for authenticating the energy transactions in SG. The energy transaction is initialized by an end user which creates the block of transaction to trade the energy. The miner nodes then validate these blocks and adds these in the blockchain. The successful energy trade occurs only for the blocks which are in the blockchain. The proposed scheme is lightweight in terms of communication and computation costs. Moreover, the results obtained demonstrate the effectiveness of proposed scheme for secure demand response management in the SG.

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.

In the last century, the automotive industry has arguably transformed society, being one of the most complex, sophisticated, and technologically advanced industries. Autonomous vehicles (AVs) are a main concept in the future of Intelligent Transportation Systems (ITS) since they provide an increase in safety and road efficiency. One of the most critical aspects of managing AVs is their behavior in proximity of intersections. Several research centers are developing algorithms to solve the intersections management, trying to avoid collisions and traffic congestion. As well as, given that many of these interactions transmit sensitive data such as identification, position, and speed of the vehicle, a high level of security and privacy insurance is a prerequisite for broad acceptation of these communication systems. In this paper, in order to address the issues those issues we propose a system that combines blockchain technology effectively to support the communication and the transaction between vehicles. The combination between FRFP and blockchain allows us to verify if all the AVs have the same ledger version (e.g the same priority list) to cross the intersection without collisions; as well as, in case of inconsistencies to establish an emergency situation to avoid any collision.

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.

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.

During the last decade, the healthcare topic has moved on the top of the development agenda not only for private organizations but also for public administrations. The information technology advances in the healthcare sector have designed an innovative model, the so-called eHealth, that leverages the Cloud Computing paradigm. Even more, with the medical devices (seen as IoT devices) proliferation, there have been introduced new challenges for the Cloud-IoT centric infrastructure. In this context, traditional Health Information Technology (HIT) systems present several limitations, such as scalability, faulttolerance, interoperability, data management and so on. Starting from the experience reported at ISASI-CNR (ME), a healthcare and research center, in this paper, we motivate the need to move the traditional HIT systems into innovative infrastructures based on the Osmotic Computing paradigm. In particular, we introduce two real uses cases, Ataxia and Dyslexia projects.

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.

In recent years, autonomous driving is becoming a very hot topic for both researchers and car manufacturers. Indeed, around the world new discoveries have been published. In this work we present an innovative Osmotic Computing solution for self adapting city traffic in autonomous vehicle environment. The Vehicular-to-Vehicular (V2V) and Vehicular to Edge-Cloud (V2EC) interactions inside specific areas of the City are considered: the interconnections. The Framework we are creating is able to adapt on a Dynamic Environment where Vehicles, Pedestrians and Physical Infrastructures can interact each other, offering continuous information on interconnections status and city traffic in general.

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.

This special issue asked for high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanisation. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability.

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.

Smart Internet of Things (IoT) applications will rely on advanced IoT platforms that not only provide access to IoT sensors and actuators, but also provide access to cloud services and data analytics. Future IoT platforms should thus provide connectivity and intelligence. One approach to connecting IoT devices, IoT networks to cloud networks and services is to use network federation mechanisms over the internet to create network slices across heterogeneous platforms. Network slices also need to be protected from potential external and internal threats. In this paper we describe an approach for enforcing global security policies in the federated cloud and IoT networks. Our approach allows a global security to be defined in the form of a single service manifest and enforced across all federation network segments. It relies on network function virtualisation (NFV) and service function chaining (SFC) to enforce the security policy. The approach is illustrated with two case studies: one for a user that wishes to securely access IoT devices and another in which an IoT infrastructure administrator wishes to securely access some remote cloud and data analytics services.

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.

Rapid urbanization is a global megatrend with 66 percent of the world’s population expected to live in urban areas by 2050. The staggering exponential increase in urbanization is leading to more people migrating to major cities in the search of better opportunities and quality of life. Cities need to increase the efficiency in which they operate and use their resources sustainability in order to meet the demands imposed by rapid urbanization. The challenge is to continue providing basic resources such as sufficient fresh water; cleaner energy; transportation alternatives to commute efficiently from one place to another; adaption to changing climatic conditions; safety and security; while also ensuring economical, social, and environment sustainability. These challenges represent a huge opportunity for a paradigm shift that will require the need for data processing, analysis, and security close to the connected "things" i.e., towards the edge of the network in-order to support the growing smart city ecosystem. This paradigm shift will lead to an explosive growth of independent, owned and operated things and services including gateways, repeaters, smart infrastructure, and systems. Such a paradigm needs to be architected in a way that is easy to operate and dramatically simplifies the management of service offerings through scalable orchestration and proper automation. It must allow management, integration, and deployment of different tenants (such as services and things independently owned) within the smart city ecosystem in a uniform way. It should also have a suitable policy framework, letting specific stakeholders have access to data produced by other tenants, and analyze and extract values from the data. In order to address these challenges, this special issue solicits high quality original research papers (including smart city experience papers) that made significant contributions to the state-of-the-art in "method and techniques to build sustainable smart city solutions" research area. The call for papers received a number of submissions. After a two-phase peer review process, we have accepted five high-quality papers related to the aforementioned areas of interest which will be published in the October-December 2017 as Part 1. The papers in this issue are briefly summarized.

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.

Capacity, availability or resilience of clouds can be increased by interconnecting two or more cloud computing environments to form a cloud federation and share resources. Shared resources include compute and storage resources but also networking resources. By integrating software defined networks/ virtual networks (SDN), network function virtualization (NFV) and network function chaining (SFC) technologies into cloud management platforms it is possible to create more advanced and flexible cloud federation mechanisms. In this paper we show how to secure federated cloud networks and how to customise the security of each individual federated cloud network running in a cloud federation. We propose an architecture for securing federated cloud networks by enforcing a global security policy to all network segments of a federation, and local security policies on each network of the federation. Cloud stakeholders can specify the required security virtual network functions (VNF), how to configure them, and how to chain them in a service manifest. The proposed architecture is illustrated with a deep packet inspection case study. Future work on implementing the proposed architecture in an OpenStack federation is briefly discussed.

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.

Federated cloud networks are formed by federating virtual network segments from different clouds, e.g. in a hybrid cloud, into a single federated network. Such networks should be protected with a global federated cloud network security policy. The availability of network function virtualisation and service function chaining in cloud platforms offers an opportunity for implementing and enforcing global federated cloud network security policies. In this paper we describe an approach for enforcing global security policies in federated cloud networks. The approach relies on a service manifest that specifies the global network security policy. From this manifest configurations of the security functions for the different clouds of the federation are generated. This enables automated deployment and configuration of network security functions across the different clouds. The approach is illustrated with a case study where communications between trusted and untrusted clouds, e.g. public clouds, are encrypted. The paper discusses future work on implementing this architecture for the OpenStack cloud platform with the service function chaining API.

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.

The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

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.

One of the main obstacles hindering wider adoption of storage cloud services is vendor lock-in, a situation in which large amounts of data that are placed in one storage system can not be migrated to another vendor, e.g., due to time and cost considerations. To prevent this situation we present an advanced on-boarding federation mechanism, enabling a cloud to add a special federation layer to efficiently import data from other storage clouds. This is achieved without being dependent on any special function from the other clouds. We design a generic, modular on-boarding architecture and demonstrate its implementation as part of a VISION Cloud, which is a large scale storage cloud designed for content-centric data. Our system is capable of integrating storage data from various clouds, providing a common global view of storage data. The users can access the data through the new cloud provider immediately after the setup, maintaining the normal operation of applications, so that they do not need to wait for the completion of the data migration process. Finally, we analyze the payment models of existing storage clouds, showing that transferring the data via on-boarding federation with a direct link between clouds can lead to significant time and cost savings.

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.

The right setup of a testbed is important for enabling the rapid development and deployment of cloud services, in particular all the times the testbed hosts more concurrent interactions. This work shows how to configure an environment where more developers insist on it. In particular what tools are indispensable for managing compelling distributed cloud environments. We looked at mature Open Source solutions, performing their smoothed integration. The paper describes all parts necessary for accomplishing complex scenarios, even in a bounded perimeter as the VISION Cloud EU project offering. VISION Cloud represents the reference architecture dealing with Cloud Storage, and our work attempts to provide solutions applied on VISION, but suitable for any type of framework.

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.

Nowadays, new and sophisticated technologies are quickly developing, taking advantage of the growing necessity of the companies to reduce operating costs, administrative time and to improve their productivity. Automatic Vehicle Monitoring (AVM) systems are one of the promising application fields where mobile technologies can be successfully adopted with specific care toward the definition of new services in the PTS (Public Transport Service) area.. The ability of an AVM system to control the street traffic represents a very important value for an advanced fleet management system. This paper proposes an AVM software platform, which is characterized by some innovative features able to manage multiple heterogeneous devices for fleet management and to monitor the street traffic. The architectural choices the system is based on are discussed and motivated. Specifically, this paper presents a case study of the implementation of the proposed system in the public transportation system of the city of Messina.

A cloud-enabled virtual environment called CLEVER is proposed in this paper. CLEVER simplifies the access management of private/hybrid clouds and provides simple and easily accessible interfaces to interact with different “interconnected” clouds, deploy Virtual Machines and perform load balancing through migration. The concept of interface is also exploited for integrating security, contextualization, VM disk image management and federation functionalities made available from higher level software components. Due to its pluggable design, CLEVER is able to grant high scalability, modularity and flexibility in the middleware architecture, while fault tolerance requirements are also satisfied. A prototype version of CLEVER has been developed to implement and test its main features, as discussed in the final part the paper.

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.

Grid computing represents a new challenge for enterprises looking for computing and storage resources. Business grid is the new frontier that, with the wide adoption of the notion of services, is paving the way through a real shift of the grid computing paradigm from the scientific to the commercial sector. This imposes among the others the solution of security issues, as well as the development of reliable mechanisms to access data. In this paper we address the problem of data fault tolerance in Grid through the adoption of the redundant residue number system. From each file to be stored, a set of n elements is generated which are allocated to n + r different containers. If a disrupting event occurs, the r new elements will allow a correct reconstruction of the original file. The user will autonomously select both the fragmentation number and the degree of replication, that best fit his/her needs.

Gateway node provides connectivity to the nodes inside MANET. Based upon the route to gateway node found during discovery phase; source node relies on intermediate nodes to forward packets to the gateway node. We have identified a vulnerability of the gateway discovery phase where adversaries can forcibly introduce routes having wormholes exploiting the routing race conditions. We have termed such routes having wormholes as the pseudo routes. The pseudo route attack is a more generalized form of the wormhole attack where worm-hole start and end points can be any two nodes in the route and even multiple wormholes can be present in the same route. We have proposed an end-to-end approach using localization information to detect and prevent pseudo route attacks. Simulation results are presented to show the impact of pseudo route attack under various scenarios as well as for performance of the proposed protocol. We found that attack impact is more when the gateway is at the corner of the network

Data dissemination is used in mobile ad hoc networks (MANETs) for activities like issuing commands, propagating alarms, and configuring nodes. Existing forms of data dissemination are based upon push or pull approaches. We present a novel data dissemination approach that supports push and pull approaches as well as direct node-to-node dissemination method. Our proposal is to develop a highly connected core based on two-hop neighbor information to assist in dissemination. We propose some algorithms for selecting a subset of network's nodes to perform data dissemination on behalf of the complete network. The core structure is characterized by its ability to work with limited neighbor information, connectivity maintenance, high connectivity with other features related to node degree. This property helps to lessen the control overhead as piggybacking can be used to gain partial neighbor information. We provide simulation results showing the performances of the proposed scheme

We present the Aipac protocol, which allows nodes in an ad hoc network to configure their own IP address automatically. Due to the mobility of nodes, different networks can overlap, and nodes with the same address can accidentally get in contact. Our protocol offers an innovative management of such merging of networks. In fact, it makes use of the features of reactive routing protocol to minimize the configuration and maintenance of unique address traffic. Moreover, it implements a gradual merging procedure that causes the merging process of networks according to their evolution. That allows to avoid to generate a huge amount of traffic on the wireless channels. We used the ns2 simulator to show that the protocol works correctly, and so as to analyze its performance.

This paper proposes a methodological approach for the performance analysis of Web-based searching applications on the Internet. It specifically investigates the behavior of the client/server (C/S), remote-evaluation (REV) and mobile-agent (MA) communication paradigms and describes how Petri-net models can be developed to derive performance indices which can help the designer to improve the efficiency of his distributed applications. Our purpose is that of identifying a set of models that can help to understand the environmental situations in which such paradigms should be preferred or combined in order to optimize the performances of a distributed system. In particular, we propose a modeling technique applied to an information retrieval application on the World Wide Web. An analytical evaluation through the solution of nonMarkovian Petri-net models is provided, which allows us to identify the main parameters, as well as the way they interact, to be taken into consideration when distributed applications are to be designed. An experimental environment is also studied in order to obtain real measurements used to validate the analytical models.

Wireless devices are becoming very popular and powerful to be commonly adapted to access distributed services. More and more sophisticated applications are being developed and a very promising market is quickly being established where mobile users might access multimedia data while roaming from a cell to another, anytime and everywhere. Such scenario requires a strong infrastructure in order to adequately manage all the different issues related with high-level service provisioning. QoS represents one of the most crucial issues as it involves many different aspects and directly impacts the user satisfaction. In order to achieve this target, we propose an architecture, which allows mobile devices to access advanced services available in the wired part of the network. This architecture, based on mobile agents technology, assumes the presence of a VOD virtual server. The strategy adopted is to hide all the basic mechanisms inside a middleware layer that provides necessary interfaces to allow a simple interaction between the user on the one side and the network and the distributed services on the other. The overall architecture, that is currently being implemented and tested, has been used as an experimental environment to provide mobile users with a new service to access MPEG-4 flows.

This paper provides an experimental and analytical evaluation of the client-server, remote evaluation and mobile agent communication paradigms. Our purpose is that of identifying the environmental situations in which such paradigms should be preferred or combined in order to optimize the performances of a distributed system. The scenario selected for the analysis is information retrieval on the WWW. An analytical evaluation throughout the solution of non-Markovian Petri net models is provided.

Wireless communication systems are becoming the new enabling technology for accessing, sharing and processing data. It is easy to foresee that in the near future users will access the Internet through wireless PDA, while roaming from one place to another. Mobile ad-hoc networks (MANET) are peer-to-peer wireless networks that do not rely on the presence of a wired interconnection infrastructure. Apart from more traditional applications in the military field as well as in disaster recovery and emergency situations, MANET may represent an interesting possibility for personal communications and interaction among mobile users. We identify some possible fields of application for such wireless systems, identifying advantages and drawbacks. We also present a prototype application where MANETs are used in order to provide a distributed image processing service.