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.

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.

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.

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.

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.

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.

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.