Modern automotive systems are evolving into distributed cyber-physical platforms that demand secure, dynamic, and portable software deployment across heterogeneous embedded devices. Traditional embedded approaches, based on static firmware images and device-specific toolchains, struggle to meet these requirements at scale. This paper presents a Kubernetes-based orchestration architecture for deploying WebAssembly (Wasm) modules on resource-constrained microcontroller units (MCUs) in automotive cyber-physical systems (CPSs). Our orchestration framework manages the lifecycle of Wasm applications across heterogeneous MCU networks and addresses key automotive requirements including isolated execution through Wasm sandboxing and centralized management of distributed embedded systems. The proposed solution enables dynamic vehicle customization, over-the-air (OTA) updates and collaborative computation scenarios envisioned in next-generation automotive platforms.

The Industrial Revolution changed the picture of industrial systems by making them highly efficient and more manageable. The inclusion of the internet, smart devices, and various protocols has collectively developed Industrial Control Systems (ICS), which are responsible for the entire industrial activity management. However, the devices in ICS may also malfunction and show abnormal behavior, affecting industrial activities. Hence, it is necessary to have a good abnormal condition detection system in ICS, but the presence of a real environment for developing such a detection system is challenging. Therefore, the proposed work addresses this challenge by modeling the ICS to mimic the actual behavior of the industries and collecting the relevant data to train a model for detecting anomalous behavior in the system. The work employed Discrete Event System Specification (DEVS) as a modeling and simulation formalism for designing the ICS. Through this modeling system, the work collects the states associated with every device and trains a machine-learning model to deal with anomalies in ICS further. This work also aims to show the significance of the DEVS formalism in modeling the ICS and how its state-based data may be used further to make an artificial intelligence-oriented solution.

Sustainable urban planning manages cities to protect the environment and uses resources wisely for the needs of urban daily life. The Internet of Things (IoT) optimizes the usage of resources and the consideration of urban utilities, such as congestion and pollution. Furthermore, Blockchain supports transparent data collection and securely storing data. Immutability is a fundamental feature of Blockchain, which keeps the data unchanged; however, researchers have proposed mutable Blockchains for data modifications by putting away security. This paper studies mutable Blockchains for sustainable IoT urban planning. It aims to discuss the productivity of mutable Blockchain in IoT-driven Sustainable Urban Planning. To this end, it discusses the challenges of mutable blockchains, presents the analytical modeling in practical applications, and examines the security of mutable Blockchains in public and centralized private Blockchains. Our main goal is to discuss their potential and limitations in IoT-based urban planning environments. The analysis shows that mutable Blockchains reduce computational costs but increase security risks. Our study explains the need for carefully governed mutability to support privacy and adaptability in IoT-based urban planning.

The rapid evolution of industrial environments, driven by the proliferation of dynamic devices such as wireless robots and smart sensors, has introduced unprecedented complexity. Navigating this landscape requires not only adapting to internal system changes but also aligning with managers' visions. This paper proposes a new approach that enables real-time reconfiguration of industrial systems. At its core is a modeling framework that captures node-level dependencies, revealing how coordination complexity can exponentially degrade system performance. By leveraging the flexibility offered by virtualization and IT/OT (Information Technology and Operational Technology) infrastructure, the proposed Distributed Model Predictive Control (DMPC) method evaluates a multitude of operational scenarios, allowing the system to adapt dynamically. The workflow culminates in the reconfiguration of Industrial Internet of Things (IIoT) nodes through WebAssembly (WASM), seamlessly bridging the gap between virtual parameters and physical reality. A mathematical process formulation, including an exponential latency model, provides a robust framework that manages complexity while ensuring responsive system behavior.

Industrial Control Systems (ICSs) are increasingly vulnerable to cyber threats due to their reliance on legacy protocols like Modbus TCP/IP, which lack built-in security mechanisms. Despite these risks, replacing or upgrading ICS components remains costly and impractical for many critical infrastructures, such as manufacturing, power generation, and transportation. This highlights the urgent need for security solutions that enhance protection without requiring disruptive system overhauls. Building on our previous work, this paper introduces a decentralized security framework based on dedicated proxies that manage cryptographic operations for legacy devices and facilitate secure communication. The architecture leverages Decentralized Identifiers (DIDs) for node identity management, storing DID Documents containing post-quantum public keys in a Distributed Hash Table (DHT). The DHT, composed of proxy nodes, is specifically modified to function as a Verifiable Data Registry (VDR), ensuring data integrity and availability. To support authorization, Verifiable Credentials (VCs) are issued by an operator-controlled Issuer Node, activated solely during new device installations, or maintenance operations. The proposed solution eliminates reliance on a central authority, enhances communication security against quantum threats, and improves resilience through decentralized identity management. Performance evaluations on both physical testbeds and simulated environments analyze handshake latency and system efficiency. Results demonstrate that our approach effectively secures legacy ICSs with an acceptable operational impact, paving the way for more robust and future-proof industrial networks.

The rapid expansion of the Internet of Things (IoT) landscape, notably in smart cities, smart metering, environmental monitoring, and smart agriculture, highlights the critical need for seamless and efficient device roaming within Long Range Wide Area Network (LoRaWAN) infrastructures. Although LoRaWAN shows great potential, its deployment faces significant challenges, especially under the constraints of the version 1.0 specifications, to enable efficient device mobility. This study introduces a comprehensive approach to enhance LoRaWAN-supported mobility, using three key advancements: the decoupling of Gateways (GWs) from the core network infrastructures to facilitate flexible deployments, the adoption of an adaptive GW-to-network connection protocol responsive to real-time traffic demands, and the enhancement of GW interoperability to provide dynamic and efficient network configurations. A notable innovation of this research is developing a mechanism that enables packet forwarding through an unidentified GW, eliminating the need for pre-established network agreements. The empirical evaluations of the article detail the effectiveness of the architecture in IoT applications, showcasing significant improvements in LoRaWAN’s roaming capabilities and reporting average latencies of up to 0.39 s in experimental tests. Extensive simulations with up to 100 roaming devices and 100 stationary devices further confirmed scalability, keeping the average roaming-to-stationary latency gap below 0.16 s and the worst-case end-to-end delay below 1.2 s even in highly congested scenarios. This contribution addresses pivotal challenges within the current LoRaWAN networks and sets the background for future advancements in IoT network technologies.

Operational technology (OT) and information technology (IT) are the backbones of modern industrial control systems (ICS). The coupling of IT and OT is significant as it provides various benefits that lead to higher manageability, efficiency, and productivity in industries. However, the presence of an IT network may leads to cyber-security concerns in the OT networking system. Hence, in the emerging and growing field of Industry 4.0, making the system secure from cyber threats is very important. Therefore, the proposed work presents the multi-directional view of ICS concerning cyber-security by discussing possible cyber vulnerabilities and the methods to deal with cyber threats. It also discusses the various industrial protocols, standards, and ICS design frameworks. Last but not least, the work discusses various tools used for network monitoring, security analysis, and penetration testing.

This paper presents a study on the integration of cloud computing with UAV autopilot systems, aiming to significantly enhance operational efficiency, scalability, and autonomy in UAV operations. By leveraging the synergies between the IoT, cloud computing, and sophisticated UAV autopilot technologies, we introduce a novel architectural framework designed to overcome existing challenges in UAV operations, such as advanced mission planning, real-time data analytics, and dynamic resource management. The presented approach emphasizes the integration of the MAVLink protocol and the ROS with the PX4 autopilot system, marking a crucial step towards achieving autonomous UAV operations characterized by enhanced safety and reduced mission execution times. Empirical assessments, supported by detailed operational scenario analyses, demonstrate the effectiveness of this integration, revealing improvements in UAV performance metrics. The outcomes highlight the potential of cloud computing integration with UAV operational paradigms, paving the way for further exploration in autonomous systems and cloud-assisted IoT applications.

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support Data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/Data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

The burgeoning paradigms of Fog and Edge computing propose delegating Cloud-related tasks to the network’s periphery, thus placing computational resources closer to data producers. This shift promises to boost the performance of IoT-based services, providing swift response times while conserving bandwidth. Despite their potential, the current Edge/Fog computing platforms must provide the required flexibility for dynamic service orchestration within a data-oriented context. Addressing this gap, the Function-as-a-Service (FaaS) model emerges as an exceptional strategy for Edge/Fog deployments. Its ability to manage an ever-expanding ecosystem of devices with remarkable flexibility and efficiency holds considerable promise. This paper articulates a novel approach to enhancing the adaptability of IoT Edge/Fog deployments. We propose an innovative extension to OpenStack, an open-source Cloud management system, which pushes its functionality towards the network Edge. Our approach empowers OpenStack to facilitate FaaS services within a distributed IoT infrastructure, thus infusing unprecedented adaptability and efficiency into the Edge/Fog computing paradigms.

To fully exploit the capabilities of the Internet of Things (IoT), it is expected to assemble large-scale networks made of heterogeneous IoT devices (e.g., embedded systems, sensors, and actuators) and smart systems. These devices and systems should interact in a seamless fashion regardless of the underlying protocols. Yet, most of the actual IoT deployments are based on proprietary technologies and tightly coupled systems. To overcome 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 interact through Web protocols, in accordance to Web standards. In this paper, we extend our system for WoT meant for associating to the geographically distributed IoT resources publically resolvable URL/URI. In particular, we propose in this work a HATEOAS-enabled mechanism to discover IoT resources available on an IoT node. This approach makes developers, and users alike, able to discover available resources within a node without knowing them beforehand. A use case is also described, in the context of telemedicine.

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This applies also to resource-constrained IoT and edge devices, which will often benefit from relatively small – but smart – local anomaly detection tasks that aim at protecting the device, or the information they convey from sensors towards a central node. This provides the device with fault detection capabilities that are typically required when engineering dependable devices, services or systems. This paper overviews a pitfall-free process to provide small devices with anomaly detection capabilities, to make them self-aware of their health condition, and possibly take appropriate countermeasures. Our methodology applies to a wide range of Linux-based devices: we show an application to a specific ARANCINO device, which has already been successfully used in many smart cities and sensing applications. We craft anomaly detectors that are very effective in detecting most of the anomalies. Additionally, we comment on the beneficial impact of time-series analysis, which could help improve detection performance even further, allowing to equip any small device with responsive and accurate anomaly detection machinery.

The COVID-19 pandemic has emphasized the importance of responsive, efficient, and cost-effective production networks to manufacture essential goods rapidly. The Air Factories 2.0 project is a research-driven initiative designed to withstand the pandemic by leveraging advanced technologies, such as 3D printing, blockchains, and distributed manufacturing, to summon the maker community’s expertise and resources. However, this project’s potential extends beyond the medical field and can be actualized in several domains. This paper comprehensively analyzes the Air Factories 2.0 platform, its technological and scientific contexts, and its potential implications for the future of manufacturing, emergency response, and 3D printing-enabled decentralized supply chains.The study outlines the Air Factories 2.0 project’s operational and organizational structure, stakeholders and roles, participation and tokenization mechanisms, algorithms used to manage production and distribution, blockchains, and smart contracts employed to ensure transparency, security, and efficiency. Furthermore, this paper examines the advantages and limitations of the Air Factories 2.0 platform for the future of advanced manufacturing across various domains.

In industrial wireless sensors networks (IWSNs) deployments, data management and processing tasks are often delegated to centralized computing facilities. Albeit providing an abundance of resources, these solutions impose several drawbacks, stemming from the higher latency they may introduce, as well as from security/privacy issues. To deal with these shortcomings, in this article, we propose an architecture designed for industrial monitoring duties, based on the Fog computing paradigm. In particular, we devised a Fog layer to host a data aggregation algorithm based on singular value decomposition (SVD). Considering a real-world case study about a distributed monitoring system for industrial motors, the benefits of the proposed solution in terms of network performances, i.e., latency and packet delivery ratio, are analyzed using the OMNET++ simulator. Besides, the system implements a set of security mechanisms at different levels (i.e., IWSN, Fog, and Cloud). In particular, we propose a new lightweight and energy-efficient ciphering algorithm for resource-constrained industrial motes. According to our experiments, the new algorithm reduces energy consumption by a factor of three compared to Advanced Encryption Standard-128.

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

Intelligent Personal Assistants (IPA) are becoming an essential part of our life. The trend aiming at adopting IPA devices is driven by the uncountable applications enabled by the advent of the Internet of Things (IoT) and its role in home automation. Actually, people are able to use, for example, voice-controlled IPA devices (e.g., Amazon Alexa) to interact and control things in their surrounding environment. Yet, to enable such interactions in an environment with multiple devices, the user has to configure each device. Besides, it is not possible to apply the same operation in a specific environment used for a short period, such as a hotel room, i.e., for security reasons: the owner of a hotel cannot permit access to the devices from their customer as it may be risky. To deal with such a limitation, the adoption of a Software-Defined approach, such as Software-Defined I/O (SDI/O), is a relevant approach to decouple the access to IoT resources from their management duties. In the case of accommodation facilities, such as a hotel room, to connect an IPA to IoT devices, a set of repeated configurations has to be performed. This paper proposes “Everywhere IPA” (EIPA), an approach relying on the Software-Defined I/O approach to automatize the configuration of IoT devices.

The development of Low-Power Wide-Area Networks (LPWAN) has significantly boosted the uptake of Internet of Things technologies, fostering their adoption in such domains as Smart Cities and Industry 4.0. In the context of LPWANs, LoRa is one of the most promising developments, experiencing massive growth in recent years. This situation has led to the steadily growing trend of redundant deployments across locations, or scenarios, where it would be more natural to be able to leverage (e.g., rent) LoRa infrastructure belonging to a third party. Key to reversing this unsustainable trend is roaming approaches. Although the LoRa specifications define mechanisms for roaming, gaps and challenges make roaming difficult to deploy and operate in real-world scenarios. This paper analyzes these concerns and proposes a novel architecture compliant with the LoRa specification to design and accommodate roaming services in a consistent, efficient, and scalable way.

The huge growth in terms of the number of distributed devices connected to the Internet, the so-called Internet of Things (IoT), has led to the expansion of their domain of applicability by enabling advanced and futuristic environments such as the Smart City-like scenarios. In such a use case, a pressing need emerges to cope with the complexity and heterogeneity of the infrastructure by reusing as much tooling as possible without resorting to vertical ad-hoc solutions. Furthermore, having good decision support based on the colossal amount of data (i.e., Big Data) generated by these environments is absolutely critical to make them smart. In fact, the impediment to the smart city concept is to provide efficient aiding decisions whilst using a malleable solutions to meet the information technology evolution. In this context, we introduce in this paper our system for IoT infrastructure management and Big Data processing. In particular, our IoT infrastructure manager middleware Stack4Things (S4T) is integrated with the Elastic Stack (ELK) data management solution. This work thus describes the rationale, efforts, and results so far achieved, for an integration of the IoT paradigm with a Cloud-oriented environment focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.

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

Air pollution is one of today's most biggest problems, mostly due to greenhouse gas emissions that have increased exponentially since the mid-twentieth century. Today's society is the main culprit due to the emissions caused by the industriaV-manufacturing processes and the different transportation means. In particular, the cities, always affected by traffic congestion and traversed by uncountable vehicles, are crucial points where actuate solutions can mitigate the pollutants generated by the vehicles. The cities, enhanced by the Internet of Things (IoT) advent, are becoming “Smart” thanks to the data generated by IoT devices. The ubiquitous distribution and the possibility of correlating data from different sources (voluntary shared or freeware) have enabled the crowdsourced initiative to realize applications exploiting these data. In this work, a multilevel IoT/Cloud infrastructure lends itself to offering solutions to citizens to solve daily life problems. The architecture, putting in place means to conceive an exciting application, is investigated through different use cases linked to current cities issues: public mobility, free parking slot detection, and air quality monitoring.

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

A visit from qualified and authorized personnel to troubleshoot customer-premises equipment (CPE) on-site is an example of a situation where both local wireless connectivity and access to the Cloud may be needed, the former to discover proximal equipment to probe and diagnose upon, the latter to promptly send useful data back to the company providing services to the customer, e.g., without waiting for the repair session to be over and the personnel have left the premises. In such a scenario, the company support service may take advantage of a dynamic coupling and interaction among the Cloud, mobiles (as personnel-borne equipment), and IoT devices (as bolt-on addition to the, possibly legacy, CPE). For this purpose, a software architecture for the collection and transmission of metrics of interest through a combined system, featuring a Cloud instance and a mobile-based agent, has been implemented. The reference architecture is composed of IoT-Cloud and edge/mobile devices, specifically smartphones and embedded boards, the latter equipped with a Cloud-controlled agent as a software component, with the goal to collect useful metrics from hardware devices of an energy substation, as a way to support remote and on-site diagnostics through technologies such as Bluetooth Low Energy. We present the reference architecture, its implementation, and some preliminary results on the effectiveness of the proposed solution.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

Summary The Internet of Things is underpinned by the global penetration of network‐connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing – a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in‐memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in‐memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud‐enabled architectures, and has the potential to address a wide range of IoT‐related data‐intensive time‐critical scenarios.

In this innovative practice full paper we present the implementation of the distant laboratory for the Internet of Things teaching and training. The recent outbreak of the SARS-COV-2 virus and related COVID-19 pandemic throughout the world has caused governments across the world to shut down schools and universities, to slow down the spread of the coronavirus that is causing the disease. As a result, some universities and schools have switched from physical classrooms to virtual or online classrooms. This approach is working well for theoretical subjects and courses, but it is not straight forward in the case of laboratory subjects and courses that require access to hardware resources. The IOT-OPEN.EU remote laboratory infrastructure presented in this paper is a timely solution. In this paper, we present current advances in distant learning, distant laboratory models, and the IOT-OPEN.EU remote laboratory implemented as part of the IOT-OPEN.EU ERASMUS+ project, along with short analysis on current advances in distant learning, where students are interacting with physical hardware remote way.

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).

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 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.

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.

Considering the proliferation of smart devices connected to the Internet, typically going under the aegis of Internet of Things (IoT), a trend has arisen to promote the Cloud paradigm as a suitable management system for such a complex environment. In this context, an effort to extend the OpenStack ecosystem to make it able to support the management of the IoT infrastructure has been made by virtue of the I/Ocloud approach, leading up to its reference implementation, the Stack4Things (S4T) middleware. S4T provides a set of suitable capabilities and features to make the (remote) IoT devices able to join an edge-based IaaS/PaaS Cloud. In the interest of enhancing the S4T middleware scalability and explore new capabilities in particular, ones related to Fog and Edge paradigms, it is becoming a must to test new features in practice at a low financial cost and particular constraints for instance, number/type of devices, network conditions, etc. For this purpose, the use of network emulation tools is a practical and suitable approach. In this paper, we present an integration between the S4T middleware and an emulation tool namely Containernet. Through the integration approach, we model network conditions (e.g., latency, bandwidth, packet loss) and devices (in forms of containers) using Containernet, and we manage the devices (i.e., containers) by means of S4T.

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.

Cloud storage adoption, due to the growing popularity of IoT solutions, is steadily on the rise, and ever more critical to services and businesses. In light of this trend, customers of cloud-based services are increasingly reliant, and their interests correspondingly at stake, on the good faith and appropriate conduct of providers at all times, which can be misplaced considering that data is the "new gold", and malicious interests on the provider side may conjure to misappropriate, alter, hide data, or deny access. A key to this problem lies in identifying and designing protocols to produce a trail of all interactions between customers and providers, at the very least, and make it widely available, auditable and its contents therefore provable. This work introduces preliminary results of this research activity, in particular including scenarios, threat models, architecture, interaction protocols and security guarantees of the proposed blockchain-based solution.

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.

A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered by actually decentralizing any mechanism and process that can escape single-authority control and the grip of centrally-managed bureaucracies, while at the same time not relying on any single party trusting any other. In line with this key insight, in this paper, we have designed and implemented a decentralized, trustless system for (environmental) sensing data acquisition, storage, and consumption, in the context of a real-world Smart City deployment and with the participation of independent, institutional, stakeholders. This Open Data system has been extended with an end-user wizard for user-friendly and trustless (i.e., independent) data audit. The acquisition layer requires nodes to authenticate when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party.

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.

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 thisCompanion 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.

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.

The cover image, by Rustem Dautov et al., is based on the Research Article Metropolitan Intelligent Surveillance Systems for Urban Areas by Harnessing IoT and Edge Computing Paradigms, https://doi.org/10.1002/spe.2586. Photo Credit: Rustem Dautov.

Summary 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.

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.

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.

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.

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.

Many current endeavors dealing with IoT and Cloud topics suggest leveraging sensing resources as mere data producers whereas the authors follow a Sensing and Actuation as a Service (SAaaS) approach. Among core ideas of SAaaS there's the enablement of an Infrastructure-oriented (i.e. IaaS-like) provisioning model for sensors and actuators alike. Such model matches technological requirements and constraints arising within certain application domains inspired by research about Future Internet topics. Aim of this paper is to explain why adopting the SAaaS paradigm for pervasively available and geographically distributed IT infrastructure is key, especially when field deployment of custom functionality is needed. Smart devices featuring always-on connectivity are the backbone of the IoT vision, and core building blocks of such infrastructure. Cloud-powered monitoring of urban areas and public facilities are among the most compelling scenarios to be found in IoT, and we are here focusing on these scenarios for two use cases. Actors, workflows and activities are described here in order to explain the main interactions and follow through outlining the use cases.

Smart environments represent a relatively uncharted ICT territory where plenty of sensor and actuator devices can be enrolled on-demand in order to realize high value-added services. A few application scenarios, such as Smart Cities, have already been explored. However, in order to finally enable such a paradigm, several issues have to be dealt with. In particular, from a developer perspective the high degree of heterogeneity for devices (ranging from cheap sensors to smart phones) could represent a hurdle for software design. In this paper, we present an innovative architecture that aims at providing a common reference platform for repurposing of devices i.e., reshaping their operational behavior for emergent and unforeseen requirements. Thanks to its modular and plugin based design, the proposed architecture is poised to ease implementation of both low-level (e.g., device discovery, code compilation, binary deployment) and high-level (e.g., service composition, data management) duties. We present the general architecture, then focusing on device-side aspects, while also providing two simple use cases that demonstrate the suitability of the proposed approach.

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