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 .

In the last years, we have seen an increase in the number of Artificial Intelligence (AI)-powered applications for information retrieval and data science. This fact led to an increasing reliance on distributed computing infrastructures, including Cloud, Edge, and IoT environments. These architectures enable powerful and scalable solutions but also introduce new security and privacy risks that must be addressed at both the system and data levels. Even a single breach on any of the links of the data-service-infrastructure chain may seriously compromise the security of the end-user application. With such a wide attack surface, security must definitely be approached in a holistic way and addressed in any layer where concerns may potentially arise. SIoTEC solicits novel and innovative ideas, proposals, positions and best practices that address the modelling, design, implementation, and enforcement of security in Cloud/Edge/IoT environments. Workshop website: https://siotec.netsons.org/

With the significant increase in mobile users connected to the wireless network, coupled with the escalating energy consumption and the risk of network saturation, the search for resource management has become paramount. Managing several access points throughout a whole region is hugely relevant in this context. Moreover, a wireless network must keep its Service Level Agreement, regardless of the number of connected users. With that in mind, in this work, we propose four prediction models that allow one to predict the number of connected users on a wireless network. Once the number of users has been predicted, the network resources can be properly allocated, minimizing the number of active access points. We investigate the use of Particle Swarm Optimization and Genetic Algorithms to hyper-parameterize a Multilayer Perceptron neural network and a Decision Tree. We evaluate our proposal using a campus-based wireless network dataset with more than 20,000 connected users. As a result, our model can considerably improve network performance by intelligently allocating the number of access points, thereby addressing concerns related to energy consumption and network saturation. The results have shown an average accuracy of 95.18%, managing to save network resources effectively.

Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical applications, wherein an aero transfer function is correlated with the digital twins. Mapping functions are utilized in the monitoring of diverse parameters that are associated with the identification of objects inside data transmission networks, with the aim of minimizing uncertainty. The suggested system model is enhanced by incorporating analytical representations and deep learning methods, resulting in the provision of zero point twin functionalities. The present study investigates the aforementioned integrated procedure through the analysis of four different situations. In these settings, an aero communication tool box is employed to transform the device configuration into simulation outputs. The results obtained from the comparison of these scenarios reveal that the projected model significantly enhances the maintenance period while minimizing data errors.

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

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

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.

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.

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.

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.

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.

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

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

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.

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.

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

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

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

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

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

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

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

The 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.

With the explosion of Big Data, visualizing statistical data became a challenging topic that has involved many research efforts over the last years. Interpreting Big Data and efficiently showing information for good understanding are difficult tasks, especially in healthcare scenarios, where different types of data have to been managed and cross-related. Some models and techniques for health data visualization have been presented in literature. However, they do not satisfy the visualization needs of physicians and medical personnel. In this paper, we present a new graphical tool for the visualization of health data, that can be easily used for monitoring health status of patients remotely. The tool is very user friendly, and allows physician to quickly understand the current status of a person by looking at colored circles. From a technical point of view, the proposed solution adopts the geoJSON standard to classify data into different circles.

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

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

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

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.

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.