The aggressive increase in the pervasiveness of Internet of Things (IoT) technologies has introduced substantial security challenges, particularly regarding device authentication. In this scenario, covert channels are increasingly being used to support malware with stealthy behaviors in IoT deployments, aiming at exfiltrating data or secretly orchestrating botnets. Although concerning, such covert communication strategies can be harnessed in a beneficial way, supporting lightweight message authentication mechanisms which can not only remain undetected to the prying eye, but also mitigate any kind of ill intended exploitation of such covert techniques. In parallel, these techniques can become an effective alternative, particularly where traditional cryptographic approaches often struggle due to resource constraints, protocol diversity, and the risk of protocol-level observation or manipulation. In this work, we present a novel authentication architecture that leverages multi-layer covert channels to embed authentication techniques within seemingly benign IoT communications. Our approach dynamically injects and modifies network traffic at runtime using Frida, a powerful dynamic instrumentation toolkit, enabling covert embedding of authentication data without requiring changes to the IoT application's codebase.

The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particularly Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithms, have shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real-world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state-of-charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation-to-reality gap. The framework incorporates EnergAIze, a MADDPG-based multi-agent control strategy, and specifically addresses challenges related to real-world data collection, system integration, and user behavior modeling. Preliminary results collected from a real-world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9% reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behaviors. These outcomes underscore the framework’s effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.

The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents Percepta, a lightweight Data Stream Processing (DSP) system tailored to support AI workloads at the edge, with a particular focus on such as Reinforcement Learning (RL). It introduces specialized features such as reward function computation, data storage for model retraining, and real-time data preparation to support continuous decision-making. Additional functionalities include data normalization, harmonization across heterogeneous protocols and sampling rates, and robust handling of missing or incomplete data, making it well-suited for the challenges of edge-based AI deployment.

The increasing number of IoT deployment scenarios and applications fostered the development of a multitude of specially crafted communication solutions, several proprietary, which are erecting barriers to IoT interoperability, impairing their pervasiveness. To address such problems, several middleware solutions exist to standardize IoT communications, hence promoting and facilitating interoperability. Although being increasingly adopted in most IoT systems, it became clear that there was no “one size fits all” solution that could address the multiple Quality-of-Service heterogeneous IoT systems may impose. Consequently, we witness new interoperability challenges regarding the usage of diverse middleware. In this work, we address this issue by proposing a novel architecture - the PolyglIoT, that can effectively interconnect diverse middleware solutions while considering the delivery QoS requirements alongside the proposed translation. We analyze the performance and robustness of the solution and show that such Multiprotocol Translator is feasible and can achieve a high performance, thus becoming a fundamental piece to enable future highly heterogeneous IoT systems of systems.

Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerful platforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach.

The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.

The emergence of new Information and Communication Technologies, such as the Internet of Things and big data and data analytics provides opportunities as well as challenges for the domain of interest, and this paper discusses their importance in condition monitoring and maintenance. In addition, the Open system architecture for condition-based maintenance (OSA-CBM), and the Predictive Health Monitoring methods are gone through. Thereafter, the paper uses bearing fault data from a simulation model with the aim to produce vibration signals where different parameters of the model can be controlled. In connection to the former mentioned a prototype was developed and tested for purposes of simulated rolling element bearing fault systems signals with appropriate fault diagnostic and analytics. The prototype was developed taking into consideration recommended standards (e.g., the OSA-CBM). In addition, the authors discuss the possibilities to incorporate the developed prototype into the Arrowhead framework, which would bring possibilities to: analyze various equipment geographically dispersed, especially in this case its rolling element bearing; support servitization of Predictive Health Monitoring methods and large-scale interoperability; and, to facilitate the appearance of novel actors in the area and thus competition.

The Event Handler - a publish-subscribe broker implemented over REST/HTTP(S) - is an auxiliary system of the Arrowhead framework for IoT applications. During this work we found that the existing implementation of the Event Handler suffers from serious performance issues. This paper describes the reengineering effort that ultimately enabled it to reach much more acceptable levels of performance, by using appropriate software configurations and design patterns. Additionally, we also illustrate how this enhanced version of the Event Handler can be modeled using Petri nets, to depict the performance impact of different thread pool configurations and CPU core availability. The main objective of this modeling process is to enable the estimation of the system's performance to guarantee the required quality of service.

The Arrowhead Framework, a SOA-based framework for IoT applications, provides the Event Handler system: a publish/subscribe broker implemented with REST/HTTP(S). However, the existing implementation of the Event Handler suffers from message latency problems that are not acceptable for industrial applications. Thus, this paper describes the refactoring process of this system that enabled it to reach acceptable levels of latency.

Nowadays, collecting complex information regarding a machine status is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Proactive Maintenance (PM), which is the most advanced one. The paper discusses the sensors that can be used to support maintenance, as pertaining to different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. The paper proceeds then to detail three different real world examples of project pilots that make use of the described sensors, and draws a comparison between them. In particular, each scenario has got unique characteristics and prefers different families of sensors, but on the other hand provides similar characteristics on other aspects. In fact, the paper concludes with a discussion regarding how each scenario can benefit from PM and from advanced sensing.

Cyber-Physical Systems (CPS) are creating new market opportunities and business models for all kind of European Industries. CPS-based platforms are increasing in their size and target application areas in a steady manner. However, even if progress is made every day supported by continuous technological advancements, CPS application and deployment is still challenging. Many solutions have been made available or is currently under development in several research projects/ initiatives. Typically, these solutions show no interoperability between each other and are tailored to a specific application context. Thus, there is an urgent need for a clear definition of what a CPS-populated system actually is. This will provide a common ground for designing and building interoperable CPS-populated systems. Interoperability represents one of the most challenging problems for such systems essentially due to their intrinsic characteristics: heterogeneity, distribution and networked. These must be addressed to allow the cooperation and collaboration between all the actors of the system. In this landscape, the MANTIS project is aimed to provide a reference model for interoperable and interconnected CPS-populated systems for maintenance-related ecosystems, which is the focus of this paper.

This paper presents an architecture that supports Quality of Service (QoS) in an Arrowhead-compliant System of Systems (SoS). The Arrowhead Framework supports local cloud functionalities for automation applications, provided by means of a Service Oriented Architecture (SOA), by offering a number of services that ease application development. On such applications the QoS guarantees are required for service fruition, and are themselves requested as services from the framework. To fulfil this objective we start by describing the Arrowhead architecture and the components needed to dynamically in run-time negotiate a system configuration that guarantees the QoS requirements between application services.

This paper presents how the ICT infrastructure developed in the European ENCOURAGE project, centered around a message oriented middleware, enabled energy savings in buildings and households. The components of the middleware, as well as the supervisory control strategy, are overviewed, to support the presentation of the results and how they could be achieved. The main results are presented on three of the pilots of the project, a first one consisting of a single household, a second one of a residential neighborhood, and a third one in a university campus.

The Arrowhead Framework is a European effort that aims to apply Service Oriented Architecture to the embedded systems' world. The Event Handler system is a component that supports the handling of events, and in that sense it enriches service-oriented applications with the capabilities of interacting via the publish/subscribe paradigm. In fact, the Event Handler system is in charge of the notification of events that occur in a given Arrowhead compliant installation, manages producers and consumers of events, allows filtering of messages, and manages historical data regarding events. This latter capability is performed either on local files, on a database, or through another component of the Arrowhead Framework - the Historian system. The net result of the integration of the Event Handler in an Arrowhead Framework simplifies and empowers the communication of its components, as it is demonstrated in the paper with two examples: the management of application faults, and the support to quality of service of orchestrated services.

Modern distributed real-time embedded applications have high processing requirements associated with strict deadlines. For some applications, such constraints cannot be fulfilled by existing single-core embedded platforms. A solution is to parallelize the execution of the applications, by allowing networked nodes to distribute their workload to remote nodes with spare capacity. In that context, this paper presents a holistic timing analysis for fixed-priority fork-join parallel/distributed tasks. Furthermore, we extend the holistic approach to consider the interaction between parallel threads and messages interchanged through a flexible time triggered switched Ethernet network, and we show how the pessimism on the worst case response time computation of such tasks can be reduced by considering the pipeline effect that occurs in such distributed systems. To evaluate the performance and correctness of the holistic model, this paper includes a numerical evaluation based on a real automotive application. The obtained results show that the proposed method is effective in distributing the load by different nodes, allowing a significant reduction of the worst case response time of the tasks. Moreover, the paper also reports an implementation of the model on a Linux library, called parallel/distributed real-time, as well as the corresponding results obtained on a real testbed. The obtained results are in accordance with the predictions of the holistic timing analysis.

This paper presents a holistic timing analysis for fixed-priority fork-join Parallel/Distributed tasks (P/D tasks) over a Flexible Time Triggered - Switched Ethernet (FTT-SE) network. The holistic approach considers both time-triggered and event-triggered tasks/messages.

The evolution of the electrical grid into a smart grid, allowing user production, storage, and exchange of energy; remote control of appliances; and, in general, optimizations over how the energy is managed and consumed, is an evolution into a complex information and communication technology (ICT) system. With the goal of promoting an integrated and interoperable smart grid, a number of organizations all over the world started uncoordinated standardization activities, which caused the emergence of a large number of incompatible architectures and standards. There are now new standardization activities that have the goal of organizing existing standards and produce best practices to choose the right approach(es) to be employed in specific smart grid designs. This paper follows the lead of the National Institute of Standards and Technology (NIST) and the European Telecommunications Standards Institute/European Committee for Standardization/European Committee for Electrotechnical Standardization (ETSI/CEN/CENELEC) approaches in trying to provide taxonomy of existing solutions; our contribution reviews and relates current ICT state of the art with the objective of forecasting future trends based on the orientation of current efforts and on relationships between them. The resulting taxonomy provides guidelines for further studies of the architectures, and highlights how the standards in the last mile of the smart grid are converging to common solutions to improve ICT infrastructure interoperability.

Industrial processes use energy to transform raw materials and intermediate goods into final products. Many efforts have been done on the minimization of energy costs in industrial plants. Apart from working on “how” an industrial process is implemented, it is possible to reduce the energy costs by focusing on “when” it is performed. Although, some manufacturing plants (e.g. refining or petrochemical plants) can be inflexible with respect to time due to interdependencies in processes that must be respected for performance and safety reasons, there are other industrial segments, such as alumina plants or discrete manufacturing, with more degrees of flexibility. These manufacturing plants can consider a more flexible scheduling of the most energy-intensive processes in response to dynamic prices and overall condition of the electricity market. In this scenario, requests for energy can be encoded by means of a formal structure called flex-offers, then aggregated (joining several flex-offers into a bigger one) and sent to the market, scheduled, disaggregated and transformed into consumption plans, and eventually, into production schedules for given industrial plant. In this paper, we describe the flex-offer concept and how it can be applied to industrial and home automation scenarios. The architecture proposed in this paper aims to be adaptable to multiples scenarios (industrial, home and building automation, etc.), thus providing the foundations for different concept implementations using multiple technologies or supporting various kinds of devices.

The Arrowhead project aims to address the technical and applicative issues associated with cooperative automation based on Service Oriented Architectures. The problems of developing such kind of systems are mainly due to the lack of adequate development and service documentation methodologies, which would ease the burden of reusing services on different applications. The Arrowhead project proposes a technical framework to efficiently support the development of such systems, which includes several tools for documentation of services and to support the development of SOA-based installations. The work presented in this paper describes the approach which has been developed for the first generation pilots to support the documentation of their structural services. Each service, system and system-of-systems within the Arrowhead Framework must be documented and described in such way that it can be implemented, tested and deployed in an interoperable way. This paper presents the first steps of realizing the Arrowhead vision for interoperable services, systems and systems-of-systems.

In this paper we analyze some of the existing solutions for Message-Oriented Middleware (MOM), which can be used on industrial environments, and that are, at the same time, capable of handling large quantities of data and of providing adequate Quality-of-Service (QoS) levels for its supported applications. We also make a proposal for the generic structure of a middleware layer supported on a MOM.

The IEEE 802.15.4 standard provides appealing features to simultaneously support real-time and non realtime traffic, but it is only capable of supporting real-time communications from at most seven devices. Additionally, it cannot guarantee delay bounds lower than the superframe duration. Motivated by this problem, in this paper we propose an Explicit Guaranteed time slot Sharing and Allocation scheme (EGSA) for beacon-enabled IEEE 802.15.4 networks. This scheme is capable of providing tighter delay bounds for real-time communications by splitting the Contention Free access Period (CFP) into smaller mini time slots and by means of a new guaranteed bandwidth allocation scheme for a set of devices with periodic messages. At the same the novel bandwidth allocation scheme can maximize the duration of the CFP for non real-time communications. Performance analysis results show that the EGSA scheme works efficiently and outperforms competitor schemes both in terms of guaranteed delay and bandwidth utilization.

Smartphones and other internet enabled devices are now common on our everyday life, thus unsurprisingly a current trend is to adapt desktop PC applications to execute on them. However, since most of these applications have quality of service (QoS) requirements, their execution on resource-constrained mobile devices presents several challenges. One solution to support more stringent applications is to offload some of the applications' services to surrogate devices nearby. Therefore, in this paper, we propose an adaptable offloading mechanism which takes into account the QoS requirements of the application being executed (particularly its real-time requirements), whilst allowing offloading services to several surrogate nodes. We also present how the proposed computing model can be implemented in an Android environment.

Mobile applications are becoming increasingly more complex and making heavier demands on local system resources. Moreover, mobile systems are nowadays more open, allowing users to add more and more applications, including third-party developed ones. In this perspective, it is increasingly expected that users will want to execute in their devices applications which supersede currently available resources. It is therefore important to provide frameworks which allow applications to benefit from resources available on other nodes, capable of migrating some or all of its services to other nodes, depending on the user needs. These requirements are even more stringent when users want to execute Quality of Service (QoS) aware applications, such as voice or video. The required resources to guarantee the QoS levels demanded by an application can vary with time, and consequently, applications should be able to reconfigure themselves. This paper proposes a QoS-aware service-based framework able to support distributed, migration-capable, QoS-enabled applications on top of the Android Operating system.

In the last years, several solutions have been proposed to extend PROFIBUS in order to support wired and wireless network stations in the same network. In this paper we compare two of those solutions, one in which the interconnection between wired and wireless stations is made by repeaters and another in which the interconnection is made by bridges. The comparison is both qualitative and numerical, based on simulation models of both architectures.

The marriage of emerging information technologies with control technologies is a major driving force that, in the context of the factory-floor, is creating an enormous eagerness for extending the capabilities of currently available fieldbus networks to cover functionalities not considered up to a recent past. Providing wireless capabilities to such type of communication networks is a big share of that effort. The RFieldbus European project is just one example, where PROFIBUS was provided with suitable extensions for implementing hybrid wired/wireless communication systems. In RFieldbus, interoperability between wired and wireless components is achieved by the use specific intermediate networking systems operating as repeaters, thus creating a single logical ring (SLR) network. The main advantage of the SLR approach is that the effort for protocol extensions is not significant. However, a multiple logical ring (MLR) approach provides traffic and error isolation between different network segments. This concept was introduced in, where an approach for a bridge-based architecture was briefly outlined. This paper will focus on the details of the inter-Domain Protocol (IDP), which is responsible for handling transactions between different network domains (wired or wireless) running the PROFIBUS protocol.

Technological developments are pulling fieldbus networks to support a new wide class of applications, such as industrial multimedia applications. To enable its use in this kind of applications the TCP/IP suite of protocols can be integrated within a fieldbus stack, leading to a dual-stack approach that is briefly outlined in the paper. One important requirement that must be fulfilled by this approach is that the hard real-time guarantees provided to the control-related traffic ("native" fieldbus traffic) are kept. At the same time it must also provide the desired quality of service (QoS) to IP applications. The focus of the paper is on how, in such a dual-stack approach, QoS can be efficiently provided to IP applications requiring quasi-constant bandwidth.