Search

Abstract Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side. Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods.

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as “a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this context, entanglement denotes how well a DT mirrors its counterpart and the extent to which the behavior of the physical counterpart aligns with the commands issued by the DT. In this paper we propose a concise yet expressive and original metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness, i.e., the freshness of the collected data and the ratio between collected and total data, respectively. In addition, the paper describes how we have built our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.

Condition Monitoring (CM) is an extremely critical application of the Internet of Things (IoT) within Industry 4.0 and Smart City scenarios, especially following the recent energy crisis. CM aims to monitor the status of a physical appliance over time and in real time in order to react promptly when anomalies are detected, as well as perform predictive maintenance tasks. Current deployments suffer from both interoperability and management issues within their engineering process at all phases– from their design to their deployment, to their management–, often requiring human intervention. Furthermore, the fragmentation of the IoT landscape and the heterogeneity of IoT solutions hinder a seamless onboarding process of legacy devices and systems. In this paper, we tackle these problems by first proposing an architecture for CM based on both abstraction layers and toolchains, i.e., automated pipelines of engineering tools aimed at supporting the engineering process. In particular, we introduce four different toolchains, each of them dedicated to a well-defined task (e.g., energy monitoring). This orthogonal separation of concerns aims to simplify both the understanding of a complex ecosystem and the accomplishment of independent tasks. We then illustrate our implementation of a complete CM system that follows said architecture as a real Structural Health Monitoring (SHM) pilot of the Arrowhead Tools project, by describing in detail every single tool that we developed. We finally show how our pilot achieves the main objectives of the project: the reduction of engineering costs, the integration of legacy systems, and the interoperability with IoT frameworks.

A Digital Twin (DT) refers to a virtual representation or digital replica of a physical object, system, process, or entity. This concept involves creating a detailed, real-time digital counterpart that mimics the behavior, characteristics, and attributes of its physical counterpart. DTs have the potential to improve efficiency, reduce costs, and enhance decision-making by providing a detailed, real-time understanding of the physical systems they represent. While this technology is finding application in numerous fields, such as energy, healthcare, and transportation, it appears to be a key component of the digital transformation of industries fostered by the fourth Industrial revolution (Industry 4.0). In this paper, we present the research results achieved by IoTwins, a European research project aimed at investigating opportunities and issues of adopting DTs in the fields of industrial manufacturing and facility management. Particularly, we discuss a DT model and a reference architecture for use by the research community to implement a platform for the development and deployment of industrial DTs in the cloud continuum. Guided by the devised architectures’ principles, we implemented an open platform and a development methodology to help companies build DT-based industrial applications and deploy them in the so-called Edge/Cloud continuum. To prove the research value and the usability of the implemented platform, we discuss a simple yet practical development use case.

Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of “stochastic noise” in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge.

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.

Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

With the strong development of the Internet of Things (IoT), the definition of IoT devices’ intended behavior is key for an effective detection of potential cybersecurity attacks and threats in an increasingly connected environment. In 2019, the Manufacturer Usage Description (MUD) was standardized within the IETF as a data model and architecture for defining, obtaining and deploying MUD files, which describe the network behavioral profiles of IoT devices. While it has attracted a strong interest from academia, industry, and Standards Developing Organizations (SDOs), MUD is not yet widely deployed in real-world scenarios. In this work, we analyze the current research landscape around this standard, and describe some of the main challenges to be considered in the coming years to foster its adoption and deployment. Based on the literature analysis and our own experience in this area, we further describe potential research directions exploiting the MUD standard to encourage the development of secure IoT-enabled scenarios.

Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a pre-filtering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins.

Fog Computing is a recent and compelling paradigm that proposes to run information-processing services at the edge of the network. While interesting standardization efforts in Fog Computing are being currently pursued by many organizations, most of them focus on management and orchestration functions, and primarily propose the adoption and adaptation of programming models designed for Cloud applications. Instead, Fog Computing applications would significantly benefit from innovative solutions that, on the one hand, adopt an “acceptable lossiness” perspective and manage information processing/dissemination in a dynamic and integrated way and, on the other hand, support a Multi Layer Routing (MLR) approach to exploit multiple routing options at different abstraction levels at the same time. This paper presents an overview of the opportunities and challenges of Fog Computing-based Internet of Things (IoT) applications by jointly exploiting acceptable lossiness and MLR. In addition, the paper proposes the innovative Holistic pRocessing and NETworking (HORNET) Software Defined Networking (SDN) solution which leverages an information-centric and value-based service model and the MLR approach to support IoT applications. The reported preliminary experimental results show the feasibility and effectiveness of the proposed approach.

Distributed intrusion detection systems (IDS) are primarily deployed across the network to monitor, detect, and report anomalies, as well as to respond in real-time. Predominantly, an IDS is equipped with a set of rules that it needs to infer to be able to perform efficient detection. However, reducing the generation of false alarms is a major challenge in any IDS implementation. Additionally, the sheer number of IoT devices that generate alarms in a moderately large sensor network may be overwhelming. In order to reduce alarms, this paper contributes to the field by proposing an original framework that limits the number of generated messages without compromising detection accuracy. The primary idea is to exploit mid-level nodes called collectors where similar alerts are collected and analyzed independently. Priority is assigned to each alert and similar alerts are fused to respective collectors for more informed decision making. In addition, Kademlia based Distributed Hash Table (DHT) is used for efficient alert transportation and distributed fusion of similar alerts. In order to minimize false alarm rate, event correlation is used to find similarity between events fused by different detection sensors. The framework is implemented in a fog-based environment to assess and evaluate the efficiency of the proposed system in edge network. The architecture is evaluated with the recognized DARPA 1999 dataset; the reported results show that the proposed technique reduces message generation by 62% while achieving false positive accuracy over 80%.

In this article, we introduce a cooperative obstacle-aware surveillance system for virtual emotion intelligence which is supported by low energy configuration with the minimal wasted communication cost in self-sustainable network with 6G components. We make a formal definition of the main research problem whose goal is to minimize the wasted communication range of system members on condition that the required detection accuracy with the given number of obstacles is satisfied when the requested number of obstacle-aware surveillance low energy barriers are built in self-sustainable network. To solve the problem, we have originally designed and implemented two different approaches, and then thoroughly evaluated them through extensive simulations. Then, their performances based on numerical outcomes are demonstrated with detailed discussions.

The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality.

The recent COVID-19 pandemic in Italy has highlighted several critical issues in the management process of infected people. At the health level, the management of the COVID-19 positive was mainly delegated to the regional authorities and centrally monitored by the State. Despite requested common activities (such as diagnosis of virus positivity, active surveillance of infected people and contact tracing), Regional Health Departments were able to issue specific directives in their territories and establish priority levels for each activity according to the specific needs related to the emergency in their area. The development of novel digital tools for the management of infected people become an urgent necessity to foster more organized and integrated solutions, able to quickly process large amounts of data. Mobile Crowdsensing methodologies could effectively facilitate needed lateral interviewing activities as well as the monitoring of crowds in environments with a high concentration of virus-positive subjects (such are hospital wards but also other locations), facilitating the tracing of possible outbreaks of contagion due to advanced geolocation techniques and big data analysis methods. This paper analyzes the functionality of SWAPS (Supporting Workflows for Healthcare Personnel management), a modular and scalable web platform which facilitate and reduces the management time of COVID positive health personnel within healthcare facilities. It also analyzes the possible integrations between SWAPS and ParticipACT, an advanced MCS platform developed by the University of Bologna that can help set up the alert notification in case of entry into a COVID risk area. This article surveys the current literature on software platforms to address COVID-19 and related tracing issues and presents the practical issues and on-the-field results obtained from the research developed by the University of Bologna by assisting the deployment of the proposed solution for a big Regional Health Department in the city of Bologna.

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance.

The Multi-access Edge Computing (MEC) and Fog Computing paradigms are enabling the opportunity to have middleboxes either statically or dynamically deployed at network edges acting as local proxies with virtualized resources for supporting and enhancing service provisioning in edge localities. However, migration of edge-enabled services poses significant challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture that supports service migration with different options of granularity (either entire service/data migration, or proactive application-aware data migration) across heterogeneous edge devices (either MEC-based servers or resource-poor Fog devices) that host virtualized resources (Docker Containers). The most innovative elements of the technical contribution of our work include i) the possibility to select either an application-agnostic or an application-aware approach, ii) the possibility to choose the appropriate application-aware approach (e.g., based on data access frequencies), iii) an automatic edge services placement support with the aim of finding a more effective placement with low energy consumption, and iv) the in-lab experimentation of the performance achieved over rapidly deployable environments with resource-limited edges such as Raspberry Pi devices.

While the digital twins paradigm has attracted the interest of several research communities over the past twenty years, it has also gained ground recently in industrial environments, where mature technologies such as cloud, edge and IoT promise to enable the cost-effective implementation of digital twins. In the industrial manufacturing field, a digital model refers to a virtual representation of a physical product or process that integrates data taken from various sources, such as application program interface (API) data, historical data, embedded sensor data and open data, and that is capable of providing manufacturers with unprecedented insights into the product’s expected performance or the defects that may cause malfunctions. The EU-funded IoTwins project aims to build a solid platform that manufacturers can access to develop hybrid digital twins (DTs) of their assets, deploy them as close to the data origin as possible (on IoT gateway or on edge nodes) and take advantage of cloud-based resources to off-load intensive computational tasks such as, e.g., big data analytics and machine learning (ML) model training. In this paper, we present the main research goals of the IoTwins project and discuss its reference architecture, platform functionalities and building components. Finally, we discuss an industry-related use case that showcases how manufacturers can leverage the potential of the IoTwins platform to develop and execute distributed DTs for the the predictive-maintenance purpose.

The 5G vision is not restricted solely to the wireless domain and its challenging requirements cannot be fulfilled without the efficient integration of cutting‐edge technologies in all portions of the telecommunications infrastructure. The promoted architectures for next generation telecommunications systems involve high capacity network domains, which operate flexibly and seamlessly to offer full quality of experience to all types of subscribers. The proliferation of highly demanding multimedia services and the features of modern communication devices necessitate the development of end‐to‐end schemes which can efficiently distribute large amount of network resources anywhere and whenever needed. This study introduces a new resource allocation scheme for cutting‐edge fibre‐wireless networks is introduced that can be applied in the fronthaul portion of 5G‐enabled architectures. The adopted technologies are the forthcoming 25G‐Ethernet Passive Optical Network (EPON) for the optical domain and the 5G‐ready long‐term evolution ‐Advanced Pro for the wireless domain. The proposed scheme performs allocation decisions based on the outcome of an adjustable multi‐stage optimisation problem. The optimisation factors are directly related to the major considerations in bandwidth distribution, namely priority‐based traffic differentiation, power awareness, and fairness provision. The conducted evaluations prove that this approach is able to ensure high efficiency in network operations.

Abstract Traditionally, mobile ad hoc networks (MANET) and hybrid optical-wireless networks, also known as fiber-wireless (FiWi), have been considered disjointly and independently, with no synergic management solutions. The former has primarily focused on dispatching packets among mobile nodes in infrastructure-less and very dynamic environments, the latter on offering high-bandwidth and low-latency access to cellular-equipped mobile nodes. The recent advancements and penetration of software-defined networking (SDN) techniques have stimulated the adoption of SDN-based flexible monitoring and control also for the MANET and FiWi domains. In this perspective, the paper originally proposes a model and an architecture that loosely federate MANET and FiWi domains based on the interaction of MANET and FiWi controllers: the primary idea is that our MANET and FiWi federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of packet management over hybrid FiWi-MANET networks. Our model is applied to several relevant use cases to practically point out the benefits of the proposal in terms of both load balancing and fairness improvements.

These days, ubiquitous computing has radically changed the way users access and interact with services and content on the Internet: novel smart mobile devices and broadband wireless communication channels allow users to seamlessly access them anytime and anywhere. Middleware infrastructures to support ubiquitous computing need to support an extremely dynamic and ever-changing scenario, where novel contents/services, devices, formats, and media channels become available. Service-oriented architectures and service composition techniques have proven to be the key in designing flexible and extensible platforms that are able to reliably support ubiquitous computing. However, current trends in service composition for ubiquitous computing tend to be either too formal and, therefore, poorly used by average final users, or too vertical and poorly flexible and extensible. This paper proposes novel service composition middleware for ubiquitous computing that relies on a translucent composition model to achieve a flexible, extensible, highly-available, but also easily understandable and usable platform. The proposed system has been widely tested, benchmarked, and deployed on a number of different and heterogeneous ubiquitous scenarios.

Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: the primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness.

Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization; thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead.

The fifth‐generation (5G) mobile networks are expected to bring higher capacity, higher density of mobile devices, lower battery consumption and improved coverage. 5G entails the convergence of wireless and wired communications in a unified and efficient architecture. Mobile nodes, as defined in fourth‐generation era, are transformed in heterogeneous networks to make the front‐haul wireless domains flexible and intelligent. This work highlights a set of critical challenges in advancing 5G networks, fuelled by the utilisation of the network function virtualisation, the software defined radio and the software defined networks techniques. Furthermore, a novel conceptual model is presented in terms of control and management planes, where the inner architectural components are introduced in detail.

Cloud resource management research and techniques have received relevant attention in the last years. In particular, recently numerous studies have focused on determining the relationship between server-side system information and performance experience for reducing resource wastage. However, the genuine experiences of clients cannot be readily understood only by using the collected server-side information. In this paper, a cloud resource management framework with two novel turnaround time driven auto-scaling mechanisms is proposed for ensuring the stability of service performance. In the first mechanism, turnaround time monitors are deployed in the client-side instead of the more traditional server-side, and the information collected outside the server is used for driving a dynamic auto-scaling operation. In the second mechanism, a schedule-based auto scaling preconfiguration maker is designed to test and identify the amount of resources required in the cloud. The reported experimental results demonstrate that using our original framework for cloud resource management, stable service quality can be ensured and, moreover, a certain amount of quality variation can be handled in order to allow the stability of the service performance to be increased.

Vehicle target tracking is a sub-field of increasing and increasing interest in the vehicular networking research area, in particular for its potential application in dense urban areas with low associated costs, e.g., by exploiting existing monitoring infrastructures and cooperative collaboration of regular vehicles. Inspired by the concept of trap coverage area, we have originally designed and implemented an original protocol for vehicle tracking in wide-scale urban scenarios, called TCAP. TCAP is capable of achieving the needed performance while exploiting a limited number of inexpensive sensors (e.g., public-authority cameras already installed at intersections for traffic monitoring), and opportunistic vehicle collaboration, with high scalability and low overhead if compared with state-of-the-art literature. In particular, the wide set of reported results show i) the suitability of our TCAP tracking in the challenging urban conditions of high density of vehicles, ii) the very weak dependency of TCAP performance from topology changes/constraints (e.g., street lengths and speed limits), iii) the TCAP capability of self-adapting to differentiated runtime conditions.

Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

Nowadays, sensor-rich smartphones potentially enable the harvesting of huge amounts of valuable sensing data in urban environments, by opportunistically involving citizens to play the role of mobile virtual sensors to cover Smart City areas of interest. This paper proposes an in-depth study of the challenging technical issues related to the efficient assignment of Mobile Crowd Sensing (MCS) data collection tasks to volunteers in a crowdsensing campaign. In particular, the paper originally describes how to increase the effectiveness of the proposed sensing campaigns through the inclusion of several new facilities, including accurate participant selection algorithms able to profile and predict user mobility patterns, gaming techniques, and timely geo-notification. The reported results show the feasibility of exploiting profiling trends/prediction techniques from volunteers’ behavior; moreover, they quantitatively compare different MCS task assignment strategies based on large-scale and real MCS data campaigns run in the ParticipAct living lab, an ongoing MCS real-world experiment that involved more than 170 students of the University of Bologna for more than one year.

The smart grid is concerned with energy efficiency and with the environment, being a countermeasure against the territory devastations that may originate by the fossil fuel mining industry feeding the conventional power grids. This paper deals with the integration between the electromobility and the urban power distribution network in a smart grid framework, i.e., a multi-stakeholder and multi-Internet ecosystem (Internet of Information, Internet of Energy, and Internet of Things) with edge computing capabilities supported by cloud-level services and with clean mapping between the logical and physical entities involved and their stakeholders. In particular, this paper presents some of the results obtained by us in several European projects that refer to the development of a traffic and power network co-simulation tool for electro mobility planning, platforms for recharging services, and communication and service management architectures supporting interoperability and other qualities required for the implementation of the smart grid framework. For each contribution, this paper describes the inter-disciplinary characteristics of the proposed approaches.

Crowdsensing is emerging as a powerful paradigm capable of leveraging the collective, though imprecise, monitoring capabilities of common people carrying smartphones or other personal devices, which can effectively become real-time mobile sensors, collecting information about the physical places they live in. This unprecedented amount of information, considered collectively, offers new valuable opportunities to understand more thoroughly the environment in which we live and, more importantly, gives the chance to use this deeper knowledge to act and improve, in a virtuous loop, the environment itself. However, managing this process is a hard technical challenge, spanning several socio-technical issues: here, we focus on the related quality, reliability, and scalability trade-offs by proposing an architecture for crowdsensing platforms that dynamically self-configure and self-adapt depending on application-specific quality requirements. In the context of this general architecture, the paper will specifically focus on the Quasit distributed stream processing middleware, and show how Quasit can be used to process and analyze crowdsensing-generated data flows with differentiated quality requirements in a highly scalable and reliable way.

We claim the strong potential of data-centric communications in Unmanned Aircraft Systems (UAS), as a suitable paradigm to enhance collaborative operations via efficient information sharing, as well as to build systems supporting flexible mission objectives. In particular, this paper analyzes the primary contributions to data dissemination in UAS that can be given by the Data Distribution Service (DDS) open standard, as a solid and industry-mature data-centric technology. Our study is not restricted to traditional UAS where a set of Unmanned Aerial Vehicles (UAVs) transmit data to the ground station that controls them. Instead, we contemplate flexible UAS deployments with multiple UAV units of different sizes and capacities, which are interconnected to form an aerial communication network, enabling the provision of value-added services over a delimited geographical area. In addition, the paper outlines an approach to address the issues inherent to the utilization of network-level multicast, a baseline technology in DDS, in the considered UAS deployments. We complete our analysis with a practical experience aiming at validating the feasibility and the advantages of using DDS in a multi-UAV deployment scenario. For this purpose, we use a UAS testbed built up by heterogeneous hardware equipment, including a number of interconnected micro aerial vehicles, carrying single board computers as payload, as well as real equipment from a tactical UAS from the Spanish Ministry of Defense.

The relevance of effective and efficient solutions for vehicle traffic surveillance is widely recognized in order to enable advanced strategies for traffic management, e.g., based on dynamically adaptive and decentralized traffic light management. However, most related solutions in the literature, based on the powerful enabler of cooperative vehicular communications, assume the complete penetration rate of connectivity/communication technologies (and willingness to participate in the collaborative surveillance service) over the targeted vehicle population, thus making them not applicable nowadays. The paper originally proposes an innovative solution for cooperative traffic surveillance based on vehicular communications capable of: (i) working with low penetration rates of the proposed technology and (ii) of collecting a large set of monitoring data about vehicle mobility in targeted areas of interest. The paper presents insights and lessons learnt from the design and implementation work of the proposed solution. Moreover, it reports extensive performance evaluation results collected on realistic simulation scenarios based on the usage of iTETRIS with real traces of vehicular traffic of the city of Bologna. The reported results show the capability of our proposal to consistently estimate the real vehicular traffic even with low penetration rates of our solution (only 10%).

Nowadays there is growing interest in evolving the distributed sensors concept from the more traditional one of enabling technology to monitor the surrounding physical environment towards Cyber Physical Systems (CPS) sensors and actuators , that is, as a suitable tool to measure/influence the cyber activity of possibly worldwide communities of users (e.g., any geotagged operation leaving a cyber footprint and any cyber physical incentive to stimulate activity as in crowdsensing). To leverage this novel perspective, we propose a framework to integrate at best multilayer CPS sensors and actuators as the basis for autonomic management operations on both physical and cyber worlds. In this paper the specific application domain target is peer-to-peer content sharing based on social identities and relationships, but we claim that the proposed CPS framework is of general applicability. In particular, our original middleware solution adopts CPS actuators to move users' content temporarily from smart home environments to high-performance cloud resources to minimize the access time of a dynamically selected quota of contents. Then, based on social network sensors and connectivity/networking ones hosted at lightweight domestic Web servers, our CPS actuators can originally and dynamically move content back from the cloud to smart homes when appropriate, in order to both retain full ownership of user-generated content and reduce cloud hosting costs.

The spread of off-the-shelf mobile devices equipped with multiple wireless interfaces together with sophisticated sensors is paving the way to novel wireless Internet of Things (IoT) environments, characterized by multi-hop infrastructure-less wireless networks where devices carried by users act as sensors/actuators as well as network nodes. In particular, the paper presents Real Ad-hoc Multi-hop Peer-to peer-Wireless IoT Application (RAMP-WIA), a novel solution that facilitates the development, deployment, and management of applications in sparse Smart City environments, characterized by users willing to collaborate by allowing new applications to be deployed on their smartphones to remotely monitor and control fixed/mobile devices. RAMP-WIA allows users to dynamically configure single-hop wireless links, to manage opportunistically multi-hop packet dispatching considering that the network topology (together with the availability of sensors and actuators) may abruptly change, to actuate reliably sensor nodes specifically considering that only part of them could be actually reachable in a timely manner, and to upgrade dynamically the nodes through over-the-air distribution of new software components. The paper also reports the performance of RAMP-WIA on simple but realistic cases of small-scale deployment scenarios with off-the-shelf Android smartphones and Raspberry Pi devices; these results show not only the feasibility and soundness of the proposed approach, but also the efficiency of the middleware implemented when deployed on real testbeds.