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A Smart Factory exploits information and communication technologies (ICT) to improve the production process and the working environment, usually addressing safety concerns. To this concern, factory-grade fire protection systems are governed by several procedures and standards whose application often becomes definitely challenging when the factory premises are dispersed across multiple administrative domains. In such contexts, the Smart Factory approach can prove very effective in the management and coordination of the factory-level fire protection system. However, a catastrophic event may compromise the ICT infrastructure, affecting communication among factory domains and therefore its smart services. A strategy to cope with the latter may be the introduction of mechanisms to handle data analysis on-site for a prompt response while enabling seamless data distribution and processing among neighbouring (federated) ICT infrastructures and emergency operators. In this work, a novel software-defined approach for the adaptive management of a Smart Factory infrastructure is proposed, centered around business logic rewiring and reconfiguration at run-time across different factory domains. Thereby, even in the case of catastrophic (e.g., potentially disruptive) events, working devices of the emergency system can go on with their operations, including transferring data to rescuers and others emergency control systems. To demonstrate the effectiveness of the proposed Software Defined Factory approach, a Federated Fire Protection System operating in an industrial setting is implemented as a case study, able to promptly react and adapt to infrastructure-critical fires and their consequences by leveraging all information and computing facilities pooled over Cloud/Fog/Edge devices spanning the premises.

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In this paper, we present an authorization and delegation model for the IoT-Cloud based on blockchain technology. We focus on smart city’s design and simulation of smart contracts to address the specific characteristics of smart environments and analyze access control and delegation mechanism in IoT. In particular, the scheme is implemented for the Ethereum platform in the form of smart contracts. We present the theoretical analysis of the proposed solution. We perform experiments on local testnet Ganache and public testnet Rinkeby to evaluate the performance of the presented models. Finally, we discuss the results and limitations of the system and possible solutions to issues.

The Internet of Medical Things (IoMT), combined with interconnected wearable devices and medical-grade sensors, can play an essential role in healthcare evolution. By exploiting the data generated by the plethora of interconnected devices (vital parameters, location-based info, patients activity and more), advanced ICT systems can be put in place with predicting capabilities. This way potentially critical situations, that may evolve in serious complications to patients’ well-being, can be promptly recognized and successfully addressed, first of all, to save lives and secondarily to limit economical damages. To support continuous patient monitoring in public and private healthcare, this paper proposes "DILoCC", an architecture to manage wearable devices, sensors and applications, that uses a Distributed Incremental Learning (DIL) approach to exploit cooperation among the sensing devices and increase the overall system efficiency through the mitigation of "Catastrophic Forgetting" consequences.

To enable and support smart environments, a recent ICT trend promotes pushing computation from the remote Cloud as close to data sources as possible, resulting in the emergence of the Fog and Edge computing paradigms. Together with Cloud computing, they represent a stacked architecture, in which raw datasets are first pre-processed locally at the Edge and then vertically offloaded to the Fog and/or the Cloud. However, as hardware is becoming increasingly powerful, Edge devices are seen as candidates for offering data processing capabilities, able to pool and share computing resources to achieve better performance at a lower network latency—a pattern that can be also applied to Fog nodes. In these circumstances, it is important to enable efficient, intelligent, and balanced allocation of resources, as well as their further orchestration, in an elastic and transparent manner. To address such a requirement, this article proposes an OpenStack-based middleware platform through which resource containers at the Edge, Fog, and Cloud levels can be discovered, combined, and provisioned to end users and applications, thereby facilitating and orchestrating offloading processes. As demonstrated through a proof of concept on an intelligent surveillance system, by converging the Edge, Fog, and Cloud, the proposed architecture has the potential to enable faster data processing, as compared to processing at the Edge, Fog, or Cloud levels separately. This also allows architects to combine different offloading patterns in a flexible and fine-grained manner, thus providing new workload engineering patterns. Measurements demonstrated the effectiveness of such patterns, even outperforming edge clusters.

Thanks to the impact of the advancement in the hardware field, the network size and usage scope of the Internet are continuously growing. Indeed, new smart devices, e.g., sensors, actuators, home appliances are becoming strong enough to communicate and exchange data over the Internet. Accordingly, this distributed ecosystem with sensing/actuation capabilities is introducing new market opportunities with innovative services including, e.g., environmental monitoring, traffic monitoring, homes/buildings control. To conceive new IoT services, enabling the smart devices to join the Internet and expose their capabilities/data through the Web is fundamental. For this purpose, Web Application Programming Interfaces (APIs) or what we refer to also as RESTful Web Services is a paradigm that can enhance the IoT application scope by making smart things part of the Web. In this paper, based on our Stack4Things (S4T) Cloud middleware, we introduce a new approach for exposing services running on IoT devices to the Web so that they become reachable using globally resolvable Uniform Resource Locators (URLs). We emphasized security issues as well by implementing, on the devices, an automated mechanism capable of managing X.509 certificates issuance to enable secure communications using Hypertext Transfer Protocol Secure (HTTPS).

With the great impact of the advancement in the hardware field, major efforts have been made to create large-scale networks of IoT devices (e.g., embedded systems, sensors, and actuators) that can interact with each other as well as their surrounding environments. However, most of the deployments are based on proprietary and tightly-coupled systems. To overwhelm the application layer interoperability issues, the Web of Things (WoT) paradigm aims at making smart things an integral part of the Web and thus, enabling them to communicate through Web standards/protocols. Therefore, applications/services making use of smart things become easier to conceive by means of Web standards/protocols (e.g., HTTP, Websockets). This paper proposes an approach enabling the smart devices to join the Web using features provided by the Stack4Things (S4T) OpenStack-based middleware. Thereby, user agents, typically, Web browsers and/or smart devices can make use of the resources of (other) IoT devices using globally resolvable Uniform Resource Locators (URLs) regardless of their underlying used protocol stacks.

With the widespread emergence of the Internet of Things (IoT), our environment and locations are turning progressively into smart environments ranging from individual houses/offices to schools, factories, and hospitals. Even more interesting, with the rise of Fog/Edge paradigms, the IoT application scope has been extended to provide critical services. By pushing resources such as compute and storage to the network edge, IoT-based services are taking benefits from their proximity to provide better performances. However, albeit an exciting development in and by itself, Edge/Fog computing platforms currently do not provide a convenient level of flexibility and efficiency to support the dynamic composition of services with a data-oriented approach. In this context, the Function-as-a-Service computing paradigm rises as a convenient/suitable paradigm to be adopted in the IoT landscape. For the sake of providing flexible IoT Edge/Fog deployments, this paper introduces a system providing FaaS services based on a distributed IoT infrastructure. Besides, we provide a dashboard based on Node-RED that exploits, in the backend, the FaaS system to make the users able to conceive customized applications using the resources (i.e., sensors and actuators) that the IoT devices can host.

Nowadays, the study of nucleic acids (DNA/RNA) has become a digital science thanks to the advent of modern massive parallel sequencing technologies, better known with the acronym NGS standing for next-generation sequencing, and to the availability of a vast amount of genetic data easily accessible from publicly available databases. Due to the quantity and complexity of such data, its processing requires strong computer science knowledge and skills. This background includes topics such as programming and scripting languages, command-line interfaces, low-level data management tools, which are not always part of the toolbox of molecular biologists and geneticists. The need to adapt to entirely new IT tools and workflows slow down even the more experienced researchers, thus dedicated and customizable GUIs would be much more preferable and conducive. In this paper, we tackle this issue by proposing a preliminary architecture for a framework providing the following benefits: i) it supports the post-NGS analysis process definition phase (commonly called pipeline definition) via a graphical dashboard designed with NodeRED; ii) it automatically deploys the workflows on top of a cluster of computational resources, according to the Function-as-a-Service paradigm, i.e., treating each step of the pipeline as a function to be executed within Linux-based containers, pre-configured with all the necessary dependencies; iii) it runs such containers taking care automatically of resource load balancing. Finally, the framework is thought to include human feedback in the loop, thanks to the availability of a smart notification system, allowing the researcher to monitor the workflows and make any decision needed for its continuation.

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at pushing computation from the Cloud to be much close as possible to data sources, raising in the evolution of new paradigms namely Fog and Mist computing. Specifically, the Fog computing paradigm exploits powerful nodes such as servers, routers, and cloudlets that are coupled with the end devices or their access networks accordingly; they are ”relatively” close by the data sources. Whereas Mist computing, which is a lightweight form of Fog computing, pushes the resources even closer. Precisely, Mist computing uses particular nodes that could reside within the same network (e.g., Local Area Network (LAN)) as the end-devices. Considering the advancement that the hardware is knowing nowadays, Fog and Mist nodes are seen suitable to provide resources such as processing, storage, and networking in the proximity of data sources; thereby, the requirements of the new services could be met. Together with the Cloud, the Fog and Mist paradigms introduce a stacked architecture for data processing where a data pre-processing could be performed at the Mist level, then offloaded vertically to the upper layers (i.e., Fog nodes or the Cloud). In these circumstances, it is fundamental to build a management system able to provision efficiently the Fog/Mist-based applications. For this purpose, the Operating System (OS)-level virtualization using containerization technologies, considering its light footprint, fits as a suitable solution to provide Fog/Mist services. The industrial-grade Cloud middlewares, such as OpenStack, which is a reference architecture for Infrastructure-as-a-Service solutions, are still far away from incorporating this new trend. This article proposes an OpenStack-based middleware platform through which containers can be deployed/managed at the Fog/Mist levels.

Infrastructure as a service is a successful cloud-based utility paradigm. Its effectiveness stems from employing virtualization and enabling cloud providers to leverage elastic datacenter capabilities. To expand the reach and benefits of IaaS, the cloud must embrace the Internet of Things infrastructure as virtualized I/O resources and first-class objects at the edge.

Highly distributed smart environments, such as Smart Cities, require scalable architectures to support a large number of stakeholders that share Internet of Things (IoT) resources and services. We focus on authorization solutions that regulate access of users to smart objects and consider scenarios where a large number of smart objects owners want to share the resources of their devices in a secure way. A popular solution is to delegate third parties, such as public Cloud services, to mediate authorization procedures among users and smart objects. This approach has the disadvantage of assuming third parties as trusted proxies that guarantee correctness of all authorization procedures. In this paper, we propose a system that allows to audit authorizations managed by third parties, to detect and expose their misbehaviors to users, smart objects owners and, possibly, to the public. The proposed system is inspired by the transparency projects used to monitor Web Certification Authorities, but improves over existing proposals through a twofold contribution. First, it is specifically designed for IoT devices, provided with little resources and distributed in constrained environments. Second, it complies to current standard authorization protocols and available open-source software, making it ready to be deployed.

Orchestration is one of the cornerstone facilities in Cloud computing, and it has become critical with the advent of the Software-as-a-Service (SaaS) paradigm. It allows service providers to automatize the deployment of their software in Cloud computing infrastructure, thus making the process swift and scalable. However, trust remains a paramount concern still. In particular, transparency, provenance, and security present significant challenges for SaaS. Blockchain possesses the needed qualities to address these challenges. We propose a system capable of creating transparent, provenance-assured, and secure SaaS. The proposed solution addresses these concerns by dividing the orchestration process, i.e., the SaaS deployment, into self-contained steps, each of which is related to a specific domain, e.g., networking, access rules. The input for these steps, and the output of their execution, are both recorded into the blockchain, creating a trail of trust. An external user requiring to validate the orchestration process can query the blockchain. In this way, if the (infrastructure) Cloud provider is fully trusted, end users do not need to trust the service provider: the SaaS deployment becomes fully transparent, and the provenance of the deployed software stack can be ensured. This capability also guarantees security because it allows, e.g., source code auditing for any security threats. The proposed solution presents a generic ledger interface to interact with several blockchain solutions. The approach gives to the Cloud provider the freedom to select the blockchain technology to be used. We present an early evaluation of the overhead of our system against a standard orchestration framework, also presenting a discussion about the limitations of the current approach and possible solutions.

The internet of things (IoT) is essential for the implementation of applications and services that require the ability to sense the surrounding environment through sensors and modify it through actuators. However, IoT devices usually have limited computing capabilities and hence are not always sufficient to directly host resource-intensive services. Fog computing, which extends and complements the cloud, can support the IoT with computing resources and services that are deployed close to where data are sensed and actions need to be performed. Virtualisation is an essential feature in the cloud as in the fog, and containers have been recently getting much popularity to encapsulate fog services. Besides, container migration among fog nodes may enable several emerging use cases in different IoT domains (e.g., smart transportation, smart industry). In this paper, we first report container migration use cases in the fog and discuss containerisation. We then provide a comprehensive overview of the state-of-the-art migration techniques for containers, i.e., cold, pre-copy, post-copy, and hybrid migrations. The main contribution of this work is the extensive performance evaluation of these techniques that we conducted over a real fog computing testbed. The obtained results shed light on container migration within fog computing environments by clarifying, in general, which migration technique might be the most appropriate under certain network and service conditions.

Operating system-level virtualization using containerization technologies have changed the world of application development and software by bringing flexibility, efficiency and new methods for managing and distributing software. Edge/Fog computing complements nowadays powerful centralized approach leveraging datacenters resources with a number of distributed fog nodes with relatively capable resources in order to provide advanced services in proximity to end users and data sources. In fact, this emerging paradigm provides ubiquitous processing abilities through scattered heterogeneous hardware with different energy availability and computational capabilities. This paper aims at presenting an extension of an IoT centric infrastructure Cloud framework, named Stack4Things, towards the edge through an integration with two of the OpenStack subsystems (i.e., Zun and Kuryr) that deal with containers management.

The adoption of Internet of Things devices and, more in general, embedded systems, endowed with sensors and actuators, keeps rising globally, and the scope of their involvement broadens, for instance in e-Health applications. This work describes our integration of IoT paradigms and resource ecosystems with a tailored Cloud-oriented device-centric environment, by focusing on an e-Health scenario, featuring monitoring and early treatment of hospitalized patients, by focusing on Cloud-enabled event detection coupled with coordinated reaction.

A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered to cooperate for the greater good of the respective communities, and one of the most powerful ways to attain that is by actually decentralizing any process that can escape single-authority control and the grip of centrally-managed bureaucracies, whilst at the same time not relying on any single party trusting any other. In line with this key insight, in this paper we have designed and implemented a decentralized, trustless DLT-based system for (environmental) sensing, data acquisition, storage, and consumption, in the context of a real-world Smart City deployment, #SmartME, and with the participation of independent, institutional, stakeholders. This Open Data system has been devised to be user-friendly, with an end-user wizard for trustless (i.e., independent) data audit. Indeed, the data collection layer does not trust any IoT node by default, but requires nodes to authenticate themselves when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party. In this work we extend our preliminary proof-of-concept design, and explore the expected outcomes of scaling the system to comprise an even greater number of active institutions participating in the validation process, by carrying out simulation experiments. Results from these experiments help us in validating our approach and assessing its scalability.

Integration of the Internet of Things (IoT) with the Cloud may lead to a range of different architectures and solutions. Our efforts in this domain are mainly geared toward making IoT systems available as service-oriented infrastructure. Under Infrastructure-as-a-Service (IaaS) scenarios, network virtualization is a core building block of any solution, even more so for IoT-focused Cloud providers. Enabling mechanisms are required to support virtualization of the networking facilities for IoT resources that are managed by the Cloud. This work describes an approach to network virtualization based on popular off-the-shelf tools and protocols in place of application-specific logic, acting as a blueprint in the design of the Stack4Things architecture, an OpenStack-derived framework to provide IaaS-like services from a pool of IoT devices. We quantitatively evaluate the underlying mechanisms demonstrating that the proposed approach exhibits mostly comparable performance with respect to standard technologies for virtual private networks, or at least good enough for the kind of underlying hardware, e.g., smart boards, while still representing a more flexible solution.

As for the Cloud, essential features of Fog Computing are both virtualization and the capability to migrate virtual environments among nodes. In this paper, we thoroughly report both the state-of-the-art virtualization and migration techniques and their available implementations. In particular, we investigate the aptness of such technologies for a specific layer of the Fog hierarchy, namely the network edge. Indeed, this layer presents some characteristics that distinguish it from the Cloud so that a virtualization or migration technique that represents a good compromise for the Cloud might not be likewise suitable for the edge.

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In comparison to datacenter-oriented Cloud middleware, the administrator and the owner of the infrastructure are not one and the same. This translates into the requirement to support delegation-enabled authorization. In this paper, the authors investigate an authorization and delegation model for the IoT-Cloud based on blockchain technology. In particular, the scheme is implemented in the form of smart contracts over the Ethereum platform. Indeed, this approach represents an enhancement, over a function previously designed in a centralized fashion, by enabling the user to audit authorization operations and inspect how access control is actually performed, without blindly trusting the Cloud as a proxy for access to resources.

Cloud-controlled virtual networking at the edge can be considered a critical feature, highly in demand in the IoT infrastructure management domain. Fostering the vision of the Cloud as a suitable control surface for IoT, an integration between the two ecosystems is achievable at a genuinely Infrastructure-as-a-Service level. This work thus describes an integration design between an IoT-centric infrastructure Cloud framework, already capable of limited networking functionality, and Neutron, the networking subsystem belonging to the core services of the OpenStack platform. Design considerations and trade-offs are detailed in the paper.

The Software Defined Networking (SDN) paradigm, according to the most popular definition, proposes the ambitious vision of making network infrastructure (e.g., routers and switches) fully programmable. This approach introduces suitable levels of abstraction, in order to adaptnetwork infrastructure functions at runtime through powerful and expressive APIs. In this context, the concept of network virtualisation is of particular importance, namely the idea to create virtual partitions of the physical network infrastructure. When virtualization is applied in this domain, it allows several controller instances and their applications to populate and manage the assigned partitions. Even more useful are the aforementioned concepts when dealing with wide-area networks of cloud-hosting datacenters, especially when trying to provide, e.g., cloud-agnostic and transparent QoS, i.e., cloud-bursting. Scientific research is tackling these new trends following two approaches: multi-cloud and federated cloud. In this work we will pursue the latter, because it leaves the end-users (application owners or companies) free to focus their efforts on application-related activities. Load balancing is among the best practices to distribute user workloads fairly and dynamically among all the nodes in a scaling group, either in a data-center or across clouds. This strategy becomes even more relevant in a scenario featuring federated cloud networks. This is the context where this work finds its place, namely the design of a Load Balancing as a Service (LBaaS) approach for SDN in a OpenStack-based brokered cloud federation.

In the current era Internet is the most used medium for sharing and retrieving the information for building applications which are commonly developed for enhancing the user experience in terms of comfort, communication. For this, the need of real-time sensor data gains importance. The data collected from the physical objects should be easily available for different applications. Semantic representation of the sensor data directly addresses the problem of storing it in logical, easily accessible and extensible manner. Our paper works towards converting the already collected sensor data of the #SmartME project into semantic format and also proposes real-time storage of semantically enriched sensor data. To build applications using these sensor data the authors consider mainly three kinds of sensors, i.e., Temperature, Humidity, Pressure. Predicting the observed value of any sensor data is the main aim of this work. The analysis leverages other sensors & environmental parameters such as Date, Time, Longitude, Latitude, Altitude etc. Correlation among these parameters and the accuracy of the predicted results showed the suitability of our proposed idea.

Smart boards are triggering the IoT revolution, allowing to make common objects and things smart through their network, storage and processing capabilities. Arduino boards have a prominent role in this revolution, due to their customizability and programming freedom. From a different perspective, another strategic development for the IoT is towards Cloud, allowing to properly manage things and data ubiquitously, on demand, as services. Pushing in this direction, we can pave the way to the Cloud of Things, where real things, rather than the data they produce, can be provided to third parties, by adopting a “device-centric” approach. To this purpose, virtualization of physical resources becomes an essential step and core mechanism. In this paper we focus on smart board virtualization, implementing a flexible solution for Arduino boards based on Stack4Things, allowing to create, multiplex, migrate and deploy virtual boards in IoT-Cloud contexts. The results obtained by a preliminary implementation and experiments on the #SmartME testbed are shown in the paper to demonstrate the feasibility and the effectiveness of the proposed solution.

Smart Cities are the perfect ecosystem where IoT technology could be applied. In particular, to allow a direct interaction with objets scattered over a city (e.g, parking slots, light posts, gates) a powerful and scalable architecture has to be envisioned. In this paper, using Bluetooth Low Energy as underlying technology, we extend the concept of Personal Area Networks to include smart city objects so that a user can easily interact with them in a geolocalized and real time manner. A prototype implementing a smart gate has been developed in our University Campus in order to demonstrate the feasibility of the proposed approach.

Integration of the Internet of Things (IoT) with the Cloud may lead to a range of different architectures and solutions. Our efforts in this domain are mainly geared towards making IoT systems available as service-oriented infrastructure. Under Infrastructure-as-a-Service (IaaS) scenarios, network virtualization is a core building block of any solution, even more so for IoT-focused Cloud providers. Enabling mechanisms are required to support virtualization of the net- working facilities for IoT resources that are managed by the Cloud. This work describes an approach to network virtualization based on popular off-the-shelf tools and protocols in place of application-specific logic, acting as a blueprint in the design of the Stack4Things architecture, an OpenStack- derived framework to provide IaaS-like services from a pool of IoT devices. We quantitatively evaluate the underlying mechanisms demonstrating that the proposed approach exhibits mostly comparable performance with respect to standard technologies for virtual private networks, or at least good enough for the kind of underlying hardware, e.g., smart boards, whilst still representing a more flexible solution.

The IoT ecosystem is taking the whole ICT world by storm and, in particular for currently hot topics such as Smart Cities, it is becoming one of the key enablers for innovative applications and services. When talking about end users, or even citizens, mobiles enter the picture as the ultimate personal gadget, as well as relevant outlets for most of the duties (sensing, networking, edge computing) IoT devices are typically envisioned in the first place. Smartphones, tablets and similar accessories are even more powerful in terms of hardware capabilities (and function diversity) than typical embedded systems for IoT, but it is typically the software platform (e.g., the OS and SDK) which limits choices for the sake of security and control on the user experience. Even a relatively open environment, such as Android, exhibits these limits, in stark contrast to the otherwise very powerful and feature-complete functionalities the underlying system (i.e., Linux) natively supports. In this work the authors describe a fully user-friendly and platform-compliant approach to let users break free from some of these limitations, in particular with regard to network virtualisation, for the purpose of extending an IoT-ready Smart City use case to mobiles.

When pieces of information originate from the physical world through the sensing infrastructure, there is a pressing need to cope with the overhead and inherent limitations lying in merely shifting huge amounts of aggregated data across the net. In this scenario, a key point is the minimization of wasted bandwidth to accommodate for ever-growing demands of sensing data. For effective treatment of sensing data, BigData principles and approaches should be adopted, particularly the one by which computing has to be brought as near as possible to data. In this paper, we propose a new approach to deal with sensing data inspired by this principle, injecting intelligence on the device instead of just using it as source of data, thus reversing the trend from the current data-centric paradigm toward a device-centric one. This way, we shift the focus from the application level onto the infrastructure one, adopting a Cloud-oriented approach to abstract and virtualize sensor-hosting boards ready to be reconfigured with custom logic, such as MapReduce, by providing resources on demand, as a service. Theoretical, design, and technical aspects have been addressed in this paper through the evaluation of a device-centric sensing infrastructure-as-a-service (IaaS) stack implementation. In particular, a prototype for mobiles is described, getting into platform-dependent details where needed. The facilities so far implemented under the Android platform have been put under preliminary testing through a mobile application.

Smart Cities can be viewed as a complex ecosystem where thousands of “things” (e.g., smart objects, vehicles, mobiles) interact with people in order to provide innovative services. Following this view, the #SmartME project aims at creating an horizontal Cloud-based framework enabling developers and users to manage such a complex ecosystem, remotely controlling things as well as virtualizing their functions implementing a provisioning model to scatter the application logic on top of the involved smart objects and to choose with fine granularity which specific tasks to delegate to centralized Cloud infrastructure. After one year from the project kickoff, we present the main architecture by highlighting how advance services can be deployed.

To kickstart the process of morphing Messina into a "smart" city, an explicit mission for the crowdfunded #SmartME project, it is essential to set up an infrastructure of smart devices embedding sensors and actuators, to be scattered all over the urban area. An horizontal framework coupled with the Fog computing approach, by moving logic toward the "extreme" edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed, is a suitable solution for data- intensive services with time-bound constraints as those usually required by citizens. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. We present some of the core Stack4Things functionalities implementing a Fog computing approach towards a run- time "rewireable" Smart City paradigm, by outlining node management and contextualization mechanisms, also describing its usage in terms of already supported and developed verticals, as well as a specific example related to environmental data collection through #SmartME.

Recent developments in Cloud computing technology provide capabilities for an extensible, reliable, effective and dynamic infrastructure to technology-enabled enterprises, in order to efficiently leverage (or even monetize) their on-premise equipment. Furthermore, the virtualization technologies powering the Cloud revolution expand their reach by the day, and are nowadays commonly available, nearly household, capabilities. In this light, the intersection between volunteering and Cloud computing may bring massive and ubiquitous compute power for IaaS users. For instance, scientists and researchers, as a category of very demanding users, may benefit from such an enlargement of the pool of resources to tap into for high complexity computational workloads and big data problems without concern for the setup and maintenance of the underlying infrastructure. We have investigated this concept in the past under the Cloud@Home project, aimed at implementing a desktop-powered Cloud. In this paper we propose a blueprint of a Cloud@Home implementation starting from OpenStack, a well-known platform for Cloud solutions, a de-facto standard with variety of features, high interoperability and Open Source support. The reference, layered architecture and the preliminary implementation of a Cloud@Home framework based on OpenStack are discussed in the paper.

As part of a wider effort in integrating Internet of things (IoT) with the Cloud under the guise of infrastructure to be provided as-a-Service, network virtualization plays an essential role, both as an enabler of Infrastructure-as-a-Service scenarios and as a basic building block of the solution for the IoT-focused Cloud provider. Virtualization of the networking facilities for Cloud-managed IoT resources needs mechanisms to deal with the inherent complexity. This work outlines an implementation-agnostic approach to such a problem, reflected in our evolving Stack4Things architecture, derived from OpenStack, and implemented starting from such codebase, by leveraging also a choice of modern tooling and protocols. A specific use case and the discussion that follows are provided to frame the benefits of this strategy.

In the wake of the massive adoption of embedded systems, mobiles, and other smart devices, as the scope of their involvement keeps broadening, complexity may quickly become overwhelming and vertical ad-hoc solutions will not cut it anymore. We propose to reuse as much tooling as possible, taking into account suitable options with regard to infrastructure management, then piggybacking as much advanced functionalities as possible in such kind of environment. In this sense, a widely used and competitive framework for Infrastructure-as-a-Service such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks like fitting the bill. This work therefore describes the approach and the solutions so far preliminary implemented for enabling Cloud-mediated interactions with droves of sensor- and actuator-hosting nodes by proposing Stack4Things, a framework for Sensing-and-Actuation-as-a-Service. In particular, we focused on describing the subsystem of Stack4Things devoted to resource control and management, highlighting relevant requirements and justifying how our proposed framework addresses them, while also opening up possibilities for a range of future extensions towards complete fulfillment of the Sensing-and-Actuation-as-a-Service vision.

A Smart City represents an improvement of today cities that strategically exploits many smart factors to increase the city sustainable growth and strengthen city functions, while ensuring citizen quality of life and health. Cities can be perceived as an ecosystem of "things" which citizens daily interact with: street furniture, public buildings, transportation, monuments, public lighting as well as personal smartphones. Thanks to recent advances in ICT such things can be considered always interconnected also providing sensing and actuating facilities according to the Internet of Things and Cyber Physical Systems models. Creating smart services that exploit such a complex infrastructure is a fundamental and current challenge. To this end, aim of this paper is the design and implementation of the Software Defined Cities approach: a Cloud-based infrastructure that, starting from the well known concept of Software Defined paradigms, is able to transform this complex ecosystem in a simple and "programmable" environment where municipalities, companies, scientists, and citizens can easily collaborate in developing innovative smart services. The overall architecture is presented focusing on both the function virtualization and infrastructure aspects also giving details about the software stacks used (e.g., Open Stack) while a use case is laid out to demonstrate the advantages of the proposed approach.

CloudWave is an Integrated Project funded by the European Commission whose aim is to provide a new powerful foundation for the development, deployment, and management of Cloud-based services. CloudWave will develop a set of software components that have to be integrated into a single multi-layer Cloud stack based on OpenStack. During the project, a testbed environment allows project partners to exert full control over deployed componentry and collaborate on development. Among testbed requirements, the most interesting ones include providing a flexible infrastructure capable of emulating several multi-node Cloud environments and enabling the automatic deployment of CloudWave artifacts into such environment during integration phases. This paper provides a high-level description of the testbed design and implementation focusing on the technologies exploited to meet such requirements.

In light of an overarching scheme about extending the capabilities of Internet of things (IoT) with Cloud-enabled mechanisms, network virtualization is a key enabler of infrastructure-oriented IoT solutions. In particular, without network virtualization infrastructure cannot really be considered flexible enough to meet emerging requirements, and even administrative duties, such as management, maintenance and large-scale automation, would turn out to be brittle and addressed by special casing, leading to loss of generality and a variety of corner cases. We propose a Cloud-based network virtualization approach for IoT, based on the OpenStack IaaS framework, where its networking subsystem, Neutron, gets extended to accomodate virtual networks and arbitrary topologies among virtual machines and globally dispersed smart objects, whichever the setup and constraints of the underlying physical networks. This work outlines a motivating use case for our approach, and the ensuing discussion is provided to frame the benefits of the underlying design.

Smart devices, and mobiles in particular, are at the forefront of several hot new trends in ICT, such as the Internet of Things and service computing. Cloud computing is another paradigm generating a great deal of offshoots, some of which are aimed at enabling novel services and applications by exploiting its ubiquity and flexibility in combination with sensors and the (meta)data they produce about phenomena, events and other interesting items about the physical world. In this context, the authors, propose a new way to orchestrate devices, in particular SNs and mobiles, as resources to build up Clouds of sensors, reverting the current wisdom about mobile Clouds, i.e. the integration of feature-rich devices into the Cloud fabric as mere clients to one where personal / wearable devices are actively involved into a "sensing" Cloud, forming a fully feedback-enabled ecosystem. The main aim of the Sensing and Actuation as a Service (SAaaS) approach is therefore to implement such a Cloud by enrolling and aggregating sensing resources from sensor networks and personal, mobile devices. A device-centric approach is embraced as in IaaS Clouds: once collected, the physical (sensing) resources are abstracted and virtualised and then provided elastically, on-demand, as a service to end users, including facilities for customization of the (hosting) embedded platform. A key point of the SAaaS approach is the abstraction of resources, i.e. providing a uniform way to access to and interact with the underlying physical nodes. In this paper we focus on the low-level interaction with sensing resources in SAaaS, restricting the scope to mobiles, thus providing details on theoretical and design aspects as well as technical and implementation ones. In particular, we report on an implementation of the SAaaS low-level modules on Android devices, the SAaaS4Mobile one, providing architectural descriptions of the main modules, implementation guidelines and discussing through a preliminary implementation evaluation the effectiveness of the approach.

As the adoption of embedded systems, mobiles and other smart devices keeps rising, and the scope of their involvement broadens, for instance in the enablement of Smart City-like scenarios, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad-hoc solutions, while at the same time taking into account valid options with regards to infrastructure management, and other more advanced functionalities. In this sense, a widely used and competitive framework for Infrastructure as a Service such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks like fitting the bill. This work thus describes rationale, efforts, and results so far achieved, for an integration of IoT paradigms and resource ecosystems with such a kind of Cloud-oriented environment, by focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.

Cloud computing has emerged as one of the most effective paradigms to enable cheap, elastic and user-friendly access to computing, storage and networking resources. However, a key drawback of current Cloud solutions lies in the lack of avenues for interaction with the physical world. This work aims at contributing the design of an architecture for pervasive ICT infrastructure, where new generation services leverage the surrounding environment, collecting data and actuating control strategies. In accordance with such vision computing, storage, networking and sensing fit as a whole in an all-round encompassing Cloud-inspired provisioning model, paving the way for innovative and value-added services, to be implemented by bridging Clouds with the Internet of Things. Heterogeneous resources should be aggregated and abstracted according to tailored thing-like semantics, thus enabling a Things as a Service paradigm and eventually disclosing what we call a "Cloud of Things" (CoT).

Most of the current shortcomings in relation to Clouds made up of sensing resources can be addressed, as detailed in past work from the authors, following a Sensing and Actuation as a service (SAaaS) approach, i.e. enabling an Infrastructure-oriented (IaaS-like) provisioning model for sensors and actuators. The aforementioned approach matches the technological requirements and constraints springing around certain application domains belonging to the Future Internet research area. One of the most prominent scenarios is computer-assisted data treatment and automation of urban areas and public facilities, going under the umbrella term of a so-called Smart City. Aim of this paper is to provide specific processes, rules and guidelines for the adoption of the SAaaS paradigm in IT infrastructure powering Smart Cities. Such artifacts are for assessing the feasibility of deploying Smart City applications over SAaaS, moreover the design of some specific use cases is presented and discussed.

The lack of support and the shortcomings of Cloud computing in relation to pervasive applications can be addressed through the Sensing and Actuation as a Service (SAaaS) paradigm. In SAaaS, sensors and actuators, from both mobile devices and sensor networks, can be discovered, aggregated and elastically provided as a service according to the Cloud provisioning model. Nevertheless, managing a large set of sensing and actuation resources, characterized by volatility and heterogeneity, rises the need for specific mechanisms and strategies. In this paper we focus on management, abstraction and virtualization of sensing resources. More specifically, we describe the lowest level module of the SAaaS architecture, the hypervisor, that takes care of communication with devices and orchestrates their resources. The hypervisor operates according to policies and strategies coming from higher layers, and includes customization facilities that ease the integration of heterogeneous devices.

Cloud computing is among the hottest trends in ICT, aiming at providing on-demand computing and storage resources with guarantees on the quality of service. A limit of current Cloud implementations is the absence of mechanisms to effectively manage inputs from the physical world. Our idea is to move towards a pervasive Cloud, providing facilities and solutions able to interact with the surrounding environment enabling development of new and value added services. In this vision also mobile devices, such as PDAs, usually equipped with several sensors and actuators, have to be included in the overall picture. Mobile devices and their respective owners can decide whether, how and when to contribute to the Cloud, thus introducing further unknowns. In order to deal with all such issues, in this paper we propose a solution that gives way to the Sensing and Actuation as a Service (SAaaS) paradigm, a step towards the creation of a Cloud of sensors and actuators. This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the SAaaS. An ad-hoc architecture and some preliminary background on this challenging vision are provided and discussed.

This paper intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, collecting data and applying management strategies. According to such perspective computing, storage and sensing become complementary aspects to be coordinated. In this way innovative and value-added services can be implemented by bridging Clouds with the Internet of Things. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling a Things as a Service paradigm, a way to build a "Cloud of Things" (CoT). This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the CoT. An ad-hoc architecture and some preliminary background of this challenging view are provided and discussed, identifying guidelines and future directions.