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Leveraging the Internet of Things (IoT) in intensive domains, such as in the Industrial Internet of Things (IIoT) or Internet of Medical Things (IoMT), provides automation and sensing solutions for complex environments through the interconnection of different sensors and actuators. However, these scenarios usually demand to meet stringent Quality of Service (QoS) requirements to work properly. Fog computing, a paradigm that brings computation and storage closer to the edge, and Software-Defined Networking (SDN), a networking paradigm that enables for network scalability and flexibility, can be combined. To do so, fog nodes that integrate both, computation resources and SDN capabilities, are leveraged to meet these stringent needs. Clearly, the placement of such fog nodes plays a key role in the achieved QoS. In this paper, an optimal fog node placement formulation is evaluated in an emulated fog and SDN environment. Results show that an optimal fog node placement can achieve a reduction of up to 59% in the network latency with a minimal jitter compared with other well-known placement methods.

Industrial networked computing environments are expected to serve a wide range of applications with heterogeneous Quality-of-Service (QoS) requirements. This capability demands for novel, QoS-aware network management and configuration techniques resilient in the face of network changes. State-of-the-art approaches only focus on aspects related to the management of network devices. In this work, we move a step further, proposing an end-to-end QoS management approach in Time-Sensitive Networking (TSN) compliant networks, capable of handling reconfiguration events e.g., link-drop. Shedding some light on our proposal, we first discuss its functional building blocks, successively validating the approach on a real TSN testbed.

Recent research focuses on building Cloud-based solutions for big geospatial data analytics. Avalanches of georeferenced mobility data are being collected and processed daily. However, mobility data alone is not enough to unleash the opportunities for insightful analytics that may assist in mitigating the adverse effects of climate change. For example, answering complex queries such as follows: 'what are the Top-3 neighborhoods in Buenos Aires in terms of vehicle mobility where the index of PM10 pollutant is greater than 40'. Similar queries are necessary for emergent health-aware smart city policies. For example, they can provide insights to municipality administrators so that they foster the design of future city infrastructure plans that feature citizen health as a priority. For example, building mobile maps for daily dwellers so that to inform them which routes to avoid passing-through during specific hours of a day to avoid being subjected to high-levels PM10. However, answering such a query would require joining real-time mobility and environment data. Stock versions of the current Cloud-based geospatial management systems do not include intrinsic solutions for such scenarios. In this paper, we report the design and implementation of a novel system MeteoMobil for the combined analytics of information representing mobility and environment. We have implemented our system atop Apache Spark for efficient operation over the Cloud. Our results show that MeteoMobil can be efficiently exploited for advanced climate change analytics.

The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter. Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends. Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction. However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts. In this paper, we propose a context-aware deep learning-based recommender system, US-NCF, in support for social recommender systems. Our experiments show US-NCF outperforms state-of-art counterparts.

The diversity of sensing options that IoT offers imposed requirements to evolve stream processing engines so to cope with highly heterogeneous and fast-pace data streams challenging their computing capacities. Location intelligence applications aim at exploiting those geo-referenced data in generating visualizations and dashboards that provide deep insights for assisting decision making in smart cities and urban planning. As data arriving are mostly geo-referenced and the rate is fluctuating in pace and skewness, computations upon streams should depend on approximation by applying methods such as sampling. Representativeness in sampling designs remains the pivotal concern in the literature. In spatial data streams contexts, it loosely means selecting proportional counts of spatial tuples from each group of tuples that belong to the same real geometry (i.e., geographically residing in the same proximity) within each streaming time window. This is challenging in streaming settings because spatial data is parametrized, loosing hence it is real geometries, which requires costly geometric operations to project them back to maps. To close this void, we have designed SpatialSPE in a previous work and incorporated an efficient fine-grained spatial online sampling method (SAOS) transparently within its layers. In this paper, we extend SAOS (the novel method is termed ex-SAOS) by new features that allow efficient online spatial sampling on a coarser level, which is a requirement in smart city scenarios. Our results show that ex-SAOS is efficient and effectively extends SAOS for more general smart city and urban computing scenarios.

Compared to Cloud computing, Fog computing is proving to support challenging scenarios imposing stricter delay requirements, e.g., tactile Internet and Industrial Internet of Things (IIoT), and demanding increased flexibility, e.g., dynamic Smart Cities and users' follow-me provisioning cases. However, such scenarios are characterized by increased heterogeneity of nodes in terms of hardware/software characteristics, of time-varying services/applications possibly offered by multiple service providers, and frequent joining/leaving of nodes. The paper originally proposes Multi-Layer Advanced Networking Environment (Multi-LANE), a Multi-Layer Routing (MLR) solution based on Software Defined Networking (SDN). Multi-LANE dynamically selects and exploits routing strategies and mechanisms suitable for applications with heterogeneous capabilities and requirements. Based on application requirements and its centralized point of view, our SDN controller determines the most suitable path and configures the proper MLR forwarding mechanism, ranging from traditional IP and sequence-based overlay to more articulated ones based on payload content type and value inspection.

Edge Computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.

Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated scalability management of resources. Therefore, modern enterprise services are increasingly leveraging inter/intra-cloud deployments and the choice of the right cloud provider to support the execution of them becomes a fundamental operational choice. The paper presents our Audit4Cloud platform, an open-source tool for auditing the performance of virtual resources made available by various commercial cloud providers, with specific focus on cloud networking. In particular, we claim that Audit4Cloud is an enabling key in choosing the right cloud vendor as it not only offers the visibility of current values of some significant performance indicators about the offered cloud resources, but also provides users with a complete picture of those performance indicators over time. We have already performed a large experimental campaign by considering primary commercial cloud providers; the collected results show the feasibility of the approach and that Audit4Cloud can play the role of a solid third-party auditing tool to estimate real performance and costs of cloud resources.

The widespread adoption of ubiquitous IoT edge devices and modern telemetry spewing out unprecedented avalanches of spatially-tagged datasets that if could interactively be explored would offer deep insights into interesting natural phenomena, which might remain otherwise illusive. Online application of spatial queries is expensive, a problem that is further inflated by the fact that we, more than often, do not have access to a full dataset population in non- stationary settings. As a way of coping up, sampling stands out as a natural solution for approximating estimators such as averages and totals of some interesting correlated parameters. In any sampling design, representativeness remains the main issue upon which a method is regarded good or bad. In a loose way, in a spatial context, this means fairly sampling quantities in a way that preserves spatial characteristics so as to provide more accurate approximates for spatial query responses. Current big data management systems either do not offer over-the-counter spatial-aware online sampling solutions or, at best, rely on randomness, which causes too many imponderables for an overall estimation. We herein have designed a QoS- spatial-aware online sampling method that outperforms vanilla baselines by statically significant magnitudes. Our method sits atop Apache Spark Structured Streaming's codebase and have been tested against a benchmark that is consisting of millions-records of spatially- augmented dataset.

Consolidation of Internet of Things and Fog computing paradigms requires effective and efficient application-layer protocols between service seekers and providers. As most of these nodes run on batteries, discovering service providing devices in an IoT-Fog environment has to be performed in an energy-efficient way. In this paper, we propose Power Efficient Node Discovery (PEND), which is an MQTT-driven IoT-fog integration solution for the sustainability of object discovery in a publish/subscribe environment. By enabling the MQTT broker to serve as a fog node to trigger turning on/off of the Bluetooth interfaces of subscriber objects, Bluetooth Low Energy Scanner (BLE-S), and to monitor the trajectories of publishers/advertisers, Bluetooth low Energy Advertiser (BLE-A), we introduce significant reduction in the Bluetooth, CPU, and process- specific power consumption of the mobile devices. The reduction in the battery drain by the BLE interface under the proposed scheme can be as low as 10%-20% of a naive, locality based discovery benchmark whereas the process specific battery drain of the proposed scheme can be as low as 55%-62% of the node discovery benchmark. Furthermore, with the synchronization of the publishers and subscribers at the fog layer entity, i.e., MQTT broker, 100% node discovery can be achieved by the scanning/service subscriber devices.

The recent advances in telecommunications, wide availability of powerful and always-connected smart devices, and the wide adoption of cloud computing services are paving the way towards the concept of fog computing. An outstanding problem in this area is the availability of effective application-layer protocols between service seekers and providers at the Internet of Things (IoT) level because most IoT nodes run on batteries. Hence, IoT-Fog discovery service must be energy efficient to prolong the lifetime of smart objects. This paper presents a thorough study of our previously proposed MQTT-driven IoT-fog integration, namely the Power Efficient Node Discovery (PEND), and investigates the impact of dynamic arrival patterns on its performance. The MQTT broker serves as a fog node to trigger turning on/off the Bluetooth Low Energy (BLE) interfaces of the surrounding objects by monitoring their trajectories. Furthermore it leverages this additional location awareness to significantly reduce the power consumption of the device discovery process for mobile devices. With this motivation, we present a detailed performance study of PEND under various settings and provide in-depth discussions of some lessons about the obtainable energy saving and the effectiveness of the our enhanced BLE device discovery solution. The results we present are valuable for the fog community to design new optimizations and to refine the whole IoT device discovery process to the purpose of better efficiency and scalability.

Software Defined Networking (SDN), with its clear distinction of control and data planes, as well with its simple paradigm of logically centralized controller with global visibility of the whole targeted network status, is gaining momentum in different scenarios. However, its effective exploitation in Mobile Ad hoc Networks (MANETs) is still an open research issue, mainly due to the peculiar dynamicity of the related environments (e.g., in the case of spontaneous MANETs) and to the need to exploit locality considerations to achieve efficient solutions. Here we originally present an architecture and an associated prototype for SDN-based quality management of selected traffic flows, which advance the state-of-the-art in the field by i) allowing high flexibility via deployment of new flow management policies at provisioning time, ii) properly handling node join/leave events, and, most relevant, iii) determining MANET “islands”, usually managed by separated SDN controllers that can seldom interact to federate their management decisions. In addition to design/implementation insights and to the availability of the prototype code, this paper provides the community with a significant and novel contribution in terms of first experimental performance results, which show the feasibility and the effectiveness of the proposed approach.

The exponential amount of geospatial data that has been accumulated in an accelerated pace has inevitably motivated the scientific community to examine novel parallel technologies for tuning the performance of spatial queries. Managing spatial data for an optimized query performance is particularly a challenging task. This is due to the growing complexity of geometric computations involved in querying spatial data, where traditional systems failed to beneficially expand. However, the use of large-scale and parallel-based computing infrastructures based on cost-effective commodity clusters and cloud computing environments introduces new management challenges to avoid bottlenecks such as overloading scarce computing resources, which may be caused by an unbalanced loading of parallel tasks. In this paper, we aim to fill those gaps by introducing a generic framework for optimizing the performance of big spatial data queries on top of Apache Spark. Our framework also supports advanced management functions including a unique self-adaptable load-balancing service to self-tune framework execution. Our experimental evaluation shows that our framework is scalable and efficient for querying massive amounts of real spatial datasets.

First research activities are starting to recognize the suitability of edge computing approaches in several Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) application domains, for instance, to better address scalability issues and to improve reactivity via local control decisions and actuation. The paper originally proposes the design and implementation of an extended edge computing platform, specifically designed for the support of mobile services when provisioned in so-called hostile environments. In particular, our original platform is capable of proactive migration of virtualized functions in response to provision-time device mobility. The paper provides a practical contribution in terms of design and implementation of proactive migration as an extension of the Elijah platform, available for the community of researchers/practitioners in the field. In addition, the paper contributes with lessons learnt from deployment and experimentation, including experimental results about the performance achievable when adopting simple but effective strategies for proactive migration.

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application- level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word- embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.

The technical challenges associated with multihoming management in mobile systems and applications have attracted relevant research activities, as demonstrated by the wide related literature of the recent years. However, only very recently some multihoming solutions and techniques have started to be applied in industrially-relevant platforms and cases, often in a limited and very controlled way. This paper has the specific and focused objective of reporting a fresh state-of-the-art overview of the maturity of multihoming solutions for Android and to describe our practical experience of multihoming configuration and evaluation over off-the-shelf Android devices. In particular, we report the experience made while considering the relevant Locator/Identifier Separation Protocol (LISP) and especially LISPmob support solutions, by i) showing how to efficiently configure LISPmob on non-rooted Android devices; and ii) thoroughly analyzing its supported features towards the abstraction of seamless mobility. In addition, the paper includes a qualitative and quantitative comparison of different multihoming support approaches, as well as original experimental results about the performance of LISPmob over Android terminals.

Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their service infrastructures towards converged and all-IP next generation networks providing typical telco services within LTE (and soon 5G) and also fixed network environments, e.g., often still adopting IP Multimedia Subsystem (IMS) architecture solutions. This telco service infrastructure evolution requires a significant upfront investment in the necessary hardware and software, thereby slowing down the adoption process significantly more than any other Internet application. Cloud computing applied to telco infrastructures can allow pay-per-use business models and significantly lower investment risks by providing telco infrastructure functionality as Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) platform. For this purpose we propose a quality audit and resource brokering framework that is fully NFV-compliant. Its current and reported implementation specifically targets IMS services because of the still relevant role played by IMS in converged provisioning and the wide availability of IMS deployment testbeds to validate the proposal. In particular, the proposed solution can monitor the quality offered by VNFs and scale in/out depending on dynamic requirements; it is fully based on industrial standards and open-source reference implementations, thus enabling rapid adoption in real industrial environments.

Reducing traffic congestion and improving the efficiency of urban vehicular mobility are widely recognized as central objectives for smart cities. In particular, given the economic/time costs of developing an infrastructure for traffic monitoring and surveillance, there is growing interest in the exploitation of V2V communication technologies to foster new forms of peer-to-peer cooperation and to achieve even coarsegrained estimations of vehicular traffic, also with no need for communication towards global data collection centers. In addition, in realistic scenarios for the next years, it is necessary that such solutions can reasonably work with limited penetration rates of vehicles equipped with V2X traffic surveillance capabilities. In this perspective and within the framework of the ongoing EU FP7 COLOMBO project, we have investigated, developed, and thoroughly evaluated some innovative locality-based cooperation protocols for the determination of traffic characteristics in proximity of intersections, with the goal of offering concise monitoring indicators to optimize traffic light management. The reported results ( obtained through realistic simulations based on real traffic traces and the real road topology of the city of Bologna) show that it is possible to achieve reasonable estimations of vehicular traffic, suitable for traffic light control optimization, even if with limited penetration rates of our solution.