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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.

Nowadays Cloud computing permeates almost every domain in Information and Communications Technology (ICT) and, increasingly, most of the action is shifting from large, dominant players toward independent, heterogeneous, private/hybrid deployments, in line with an ever wider range of business models and stakeholders. The rapid growth in the numbers and diversity of small and medium Cloud providers is bringing new challenges in the as-a-Services space. Indeed, significant hurdles for smaller Cloud service providers in being competitive with the incumbent market leaders induce some innovative players to “federate” deployments in order to pool a larger, virtually limitless, set of resources across the federation, and stand to gain in terms of economies of scale and resource usage efficiency. Several are the challenges that need to be addressed in building and managing a federated environment, that may go under the “Security”, “Interoperability”, “Versatility”, “Automatic Selection” and “Scalability” labels. The aim of this paper is to present a survey about the approaches and challenges belonging to the “Automatic Selection” category. This work provides a literature review of different approaches adopted in the “Automatic and Optimal Cloud Service Provider Selection”, also covering “Federated and Multi-Cloud” environments.

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.

SmartME has been one of the first initiatives in Italy to realize a Smart City through the use of open technologies. Thanks to the use of low cost sensor-powered devices scattered over the city area, different “smart” services have been deployed having the Stack4Things framework as the common underlying middleware. In this paper, we present the results obtained after 2 years of project highlighting the vertical solutions that have been proposed in different areas ranging from environmental monitoring to parking management.

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.

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.

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.

Recently, Industrial Wireless Sensor Networks (IWSN) have gained a lot of interest from both research and industry communities as an evolution of WSN specifically tailored for Industry 4.0 applications and their requirements. Thanks to the set of benefits IWSN technology introduces, it is considered to be a sustainable solution for industrial system monitoring. Yet, this approach has several drawbacks stemming from the limited resources (i.e., compute and storage) available on-board network devices. Although Cloud-based WSN data management solutions are widely adopted, issues related to this approach persist (e.g., high latency, bandwidth consumption, storage costs). This paper introduces an innovative platform enhancing IWSN architectures by adding a processing layer at the network edge: an approach that follows the Fog Computing paradigm. Remote management and programmability of the Fog layer are only some of the most challenging requirements. We show how our approach is suitable for industrial scenarios by applying it to a representative use case, i.e., the monitoring of asynchronous electric motors.

In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.

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.

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber-physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city-wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off-load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off-loading pattern, where processing tasks are pushed “vertically” to the cloud.

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.

The integration between the Internet of Things (IoT) and fog computing can pave the way to a plethora of applications. Fog computing indeed allows IoT devices to offload complex tasks to computing resources, known as fog nodes, that are in their proximity (e.g., at the network edge). Fog proximity enables important advantages, first and foremost low latency. However, IoT device mobility endangers those advantages, as the IoT device gets farther away from the serving fog node. Migrating the fog service among fog nodes, following the device route, permits to maintain proximity and preserve low latency. In this work, we propose an OpenStack-based platform that implements a fog service as a container and migrates the latter to support device mobility. We performed experiments over a real testbed to: (i) evaluate the impact of hardware resources of fog nodes on migration performance; (ii) validate our platform. Results are encouraging, as the average round-trip latency between the mobile device and the fog layer was as low as 10 ms and exceeded the maximum value allowed by the considered application (i.e., 20 ms) in 1.5% of the experiment duration.

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.

A Smart City can be envisioned as an ecosystem of smart environments that can be federated to interact one another, making infrastructure suitable to host innovative services for citizens and improve quality of city life. In such a context, interoperability and the presence of different administrative domains are the main challenges. In this paper, we propose to extend the Software-Defined City paradigm to both I/O and networking functions for implementing this vision. Through a motivating use case, we show how this approach can help in making city infrastructure a fully programmable ecosystem of resources that can be rewired any time at will.

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.

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.

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name this Companion Fog Computing (CFC), since the Fog service behaves as a "companion" of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations.

In the last decades, the impact of Information and Communication Technologies (ICT) on transportation systems radically changed them, identifying in the Intelligent Transportation Systems (ITS) a new research area. A problem often addressed in ITS is vehicle routing, for which plenty of solutions have been already defined in literature. Vehicle routing problems are usually NP hard, therefore these are mainly heuristic solutions. A requirement for them is to be deployed and run in navigation systems, ready to react to sudden changes in a (quasi) real-time way. Hence, to reduce the latency is still an open issue, not only depending on the complexity of the solution but also on other parameters, such as the traffic update latency in traffic-aware vehicle routing. A way to solve them is by exploiting distributed, collaborative approaches, establishing a proper collaboration platform and algorithms able to use it. Mobile Crowdsensing, on the one hand, and collective and swarm intelligence approaches, on the other, can fill this gap. This paper is a first attempt in this direction, aiming at defining a new class of (swarm) the Intelligent Transportation Systems (SITS), on top of a crowdsourcing-based infrastructure.

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 Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes IoT vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. BC capabilities like immutability, transparency, auditability, data encryption and operational resilience can help solve most architectural shortcomings of IoT. This article presents a comprehensive survey on BC and IoT integration. The objective of this paper is to analyze the current research trends on the usage of BC-related approaches and technologies in an IoT context. This paper presents the following novelties, with respect to related work: (i) it covers different application domains, organizing the available literature according to this categorization, (ii) it introduces two usage patterns, i.e., device manipulation and data management (open marketplace solution), and (iii) it reports on the development level of some of the presented solutions. We also analyze the main challenges faced by the research community in the smooth integration of BC and IoT, and point out the main open issues and future research directions. Last but not least, we also present a survey about novel uses of BC in the machine economy.

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.

Cloud and Fog computing have established a convenient and widely adopted approach for computation offloading, where raw data generated by edge devices in the Internet of Things (IoT) context is collected and processed remotely. This vertical offloading pattern, however, typically does not take into account increasingly pressing time constraints of the emerging IoT scenarios, in which numerous data sources, including human agents (i.e., Social IoT), continuously generate large amounts of data to be processed in a timely manner. Big data solutions could be applied in this respect, provided that networking issues and limitations related to connectivity of edge devices are properly addressed. Although edge devices are traditionally considered to be resource-constrained, main limitations refer to energy, networking, and memory capacities, whereas their ever-growing processing capabilities are already sufficient to be effectively involved in actual (big data) processing. In this context, the role of human agents is no longer limited to passive data generation, but can also include their voluntary involvement in relatively complex computations. This way, users can share their personal computational resources (i.e., mobile phones) to support collaborative data processing, thereby turning the existing IoT into a global cyber-physical-social system (CPSS). To this extent, this paper proposes a novel IoT/CPSS data processing pattern based on the stream processing technology, aiming to distribute the workload among a cluster of edge devices, involving mobile nodes shared by contributors on a voluntary basis, and paving the way for cluster computing at the edge. Experiments on an intelligent surveillance system deployed on an edge device cluster demonstrate the feasibility of the proposed approach, illustrating how its distributed in-memory data processing architecture can be effective.

Some words mark an era, and "Smart City" is definitely one of these. A Smart City is an urban area where the Information and Communication Technologies (ICT) are employed to improve citizens' Quality of Life (QoL) in areas such as: mobility, urban surveillance, and energy management. Throughout this paper, we present the #SmartME project, which aims to create an infrastructure and an ecosystem of "smart" services by exploiting existing devices, sensors, and actuators distributed in the city of Messina. We also present the Stack4Things framework, which is the management core of the #SmartME project.

The health monitoring component is the essential block, a pillar of several e-health systems. Plenty of health tracking applications and specific technologies such as smart devices, wearables, and data management systems are available. To be effective, promptly reacting to issues, a health monitoring service must ensure short delays in data sensing, collection, and processing activities. This is an open problem that distributed computing paradigms, such as Internet of Things (IoT), Cloud, and Edge computing, could address. The solution proposed in this paper is based on Stack4Things, an IoT-Cloud framework to manage edge nodes such as mobiles, smart objects, network devices, workstations, as a whole, a computing infrastructure allowing to provide resources on-demand, as services, to end users. Through Stack4Things facilities, the health tracking system can locate the closer computing resource to offload processing and thus reducing latency per the Edge computing paradigm.

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.

In the roadmap for the implementation of ubiquitous computing, ubiquitous sensing and actuation is a milestone still to be reached. It refers to providing sensing and actuation facilities anytime and everywhere. This does not just imply to interconnect sensors and actuators through the Internet, but also and mainly to provide this facilities. IoT-Cloud computing paradigms such as the sensing and actuation as a service one could be a proper way to address this problem. In past work we developed an SAaaS framework extending OpenStack with specific functionalities for resource constrained nodes, Stack4Things. In this paper we focus on access control, authorization and delegation mechanisms which are basic mechanisms for the implementation of the UbSA vision. Thus starting from Stack4Things, we describe how we adapted and extended mechanisms provided by OpenStack, with specific regard to Keystone, with new functionalities for delegation and access control. A use case in the smart city scenario of #SmartME describes the proposed solution in practice.

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 Cloud computing paradigm applied to Internet of Things (IoT) has recently found an interesting and innovative application area in the Smart City context. #SmartME is a crowdfunding project that aims at exploring such a possible synergy with the intent of morphing Messina into a Smart City. However, in the process of integrating Cloud computing and IoT related technologies, a data oriented approach has been mainly considered in the past, employing Cloud infrastructures as mere repositories for data collected by scattered devices. In the #SmartME project, the University of Messina Mobile and Distributed Systems Lab (MDSLab) research group is focusing on a different approach in which sensing and actuation resources are considered as extensions of the data center and the Cloud is adopted as a virtual infrastructure manager providing the infrastructure administrator with a management and monitoring surface. In this paper, details about the first year of the #SmartME project are provided with particular emphasis to the social, bureaucratic, and technical steps that conducted to the current deployment of tens of boards throughout Messina. In particular, the Stack4Things framework, which represents the core and soul of the #SmartME project, is presented; describing the underlying technologies and providing an overview of the Web portals that are used for administration and data retrieval.

Cloud computing is becoming a relatively mature paradigm in the ICT landscape. In light of the growing appetite for resources and service levels on par with user expectations, multi-cloud scenarios are becoming the next frontier in the usage of distributed datacenters for private and hybrid Cloud scenarios. Application deployment in particular is a noteworthy feature to be evaluated as microservices become mainstream in adoption. Especially so when considered jointly with orchestration services; indeed OpenStack, as the most widely adopted Cloud middleware among the OpenSource community, features an orchestration subsystem, and may orchestrate the deployment of applications and services. In this work the authors will describe an architecture, developed within the H2020 BEACON project, for a standardized approach to orchestrated application deployment in multi-Cloud OpenStack- based setups, with TOSCA providing the specifications.

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.

As embedded and mobile systems grow pervasive in people lives and expand their reach, paradigms related to mobile crowdsensing (MCS) are going to play an ever more prominent role. Innovative methodologies and applications able to unlock such a huge potential are required. A domain where MCS really fits is mobility and transportation. However, for a full exploitation of this paradigm in intelligent transportation systems (ITS), distributed and self-management capabilities for the involved nodes-vehicles have to be provided. This paper is a first step in this direction, laying out an optimization system by exploiting feedback-driven patterns in a distributed-opportunistic way. In this sense, collective intelligence and stigmergic, swarm-based paradigms are adapted to an innovative decentralized MCS pattern toward new approaches in ITS. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modeled and evaluated by Markovian agents.

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.

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.

Consumer-centric mobile devices, such as smartphones, are an emerging category of devices at the edge of the Internet. Leveraging volunteers and their mobiles as a (sensing) data collection outlet is known as Mobile Crowd Sensing (MCS) and poses interesting challenges, with particular regard to the management of sensing resource contributors, dealing with their subscription, random and unpredictable join and leave, and node churn.To facilitate and expedite the (commercial) exploitation of this trend, in this paper we propose to adopt a service-oriented approach to cope with MCS application deployment into a sensing Cloud infrastructure, decoupling the MCS application domain from the infrastructure one. To this purpose we provide the building blocks for implementing such a novel take on MCS, which from a Cloud layering perspective can be identified as a platform service, i.e., an MCS as a service (MCSaaS). A prototype implementation that serves as a blueprint and a proof-of-concept of the proposed framework is presented, while an evaluation of the effectiveness of the MCSaaS paradigm has been provided using suitable mobility-related use cases for a validation of the concept, as well as a modeling approach through the adoption of generalized stochastic Petri nets. Emulation of deployment comparing (i) prototype of MCSaaS and (ii) current practices.Modeling of MCSaaS paradigm by Petri nets and evaluation against conventional MCS.Description of architectural elements of the platform and their interactions.MCSaaS platform for mass deployment of MCS apps, and their elastic user base adaptation.Mobile crowdsensing as a service paradigm for MCS apps design and deployment.

This paper presents the BEACON Framework, which will enable the provision and management of cross-site virtual networks for federated cloud infrastructures in order to support the automated deployment of applications and services across different clouds and datacenters. The proposed framework will support different federation architectures, going from tightly coupled (datacenter federation) to loosely coupled (cloud federation and multi-cloud orchestration) architectures, and will enable the creation of Layer 2 and Layer 3 overlay networks to interconnect remote resources located at different cloud sites. A high level description of the main components of the BEACON framework is also introduced.