Industry 5.0 envisions manufacturing systems that are human-centric, sustainable, and resilient. In this context, the Internet of Everything (IoE) enables integration of devices, people, and processes into a unified digital ecosystem. This paper presents a modular, semantically enriched framework that supports this transition by managing heterogeneous data sources—such as IoT sensors, wearable devices, and smart objects—through a layered architecture. The platform enables real-time data stream processing, semantic interoperability, and secure, context-aware access. Anomaly detection is enabled through a privacy-preserving mechanism based on behavioral fingerprinting and federated learning. The platform supports immersive human-machine interaction via gesture recognition, empowering workers to control and interact with industrial systems. Use cases demonstrate the system’s ability to support gesture-based control and intelligent monitoring, highlighting its potential to enhance adaptability, security, and worker empowerment in Industry 5.0 environments.

Human Activity Recognition (HAR) is becoming a key component in contemporary settings like Industry 5.0 and advanced smart home systems. In this study, we propose the use of a time-triggered, probabilistic Extended Finite-State Machine (EFSM) to build a modular Digital Twin (DT) of the system made by a moving person and the corresponding environment - including the sensors for their monitoring - to realistically reproduce the daily activities of the person and the signals generated by the sensors. The use of an EFSM allows to model the details of user’s behaviors and to easily address the trade-off between accuracy and complexity of the model. In particular, the probabilistic nature of the EFSM allows to introduce variability in the simulations while maintaining the model simple. Simulations performed using the DT generate accurate extended data that can be used to feed and train HAR algorithms, while the corresponding ground truth is used to label the data for the evaluation of the algorithms. The empirical analysis of the generated patterns shows that closely capture the behavior of the occupants in a simulated indoor environment. Moreover, a simple model based on a Long-Short Term Memory (LSTM) neural network was devised to show the usage of the synthetic dataset in the inference of a person’s position based on motion sensor signals.

Boot time of embedded devices is a major constraint the automotive domain, and it is thus fundamental to evaluate the impact of the available security schemes on this metric. This paper addresses the problem by evaluating the boot time of a device running the Linux Operating System for automotive applications, with different security hardening approaches at boot level. The tested device is the SK-AM62P-LP starter kit evaluation module running a Yocto-based Linux image. Testing has been automated in order to acquire a sufficiently large number of measurements. Two different measurement techniques have been explored for comparison. A first method leverages a built-in estimation feature provided by the U-Boot boot loader. A second approach relies on the use of the grabserial dump and timing program. Focus is put on the time required to verify the integrity and authenticity of Flattened Image Tree (FIT) images by U-Boot. For each security feature, an extended set of boot time estimations have been acquired, providing an accurate statistical evaluation of its impact on performance. The results show that the introduction of SHA-512 for integrity verification leads to a non negligible increase in boot time. The addition of RSA-4096 for signature verification results in a slight further increase.

Vibration monitoring is one of the most widely used approaches to detect infrastructural deterioration. This work presents an ongoing project focused on vibration-based structural health monitoring (SHM) of bridges using dense sensor networks and artificial neural networks (ANNs). A key challenge in this context is to manage the large volumes of high-frequency data generated by distributed triaxial accelerometers deployed across bridge spans. To address this, two data-centric contributions are proposed and evaluated: (1) a lightweight, threshold-based event filtering method that selectively retains high-intensity vibration segments while discarding low-amplitude non-informative vibrations, and (2) the evaluation of the Parquet data format, which significantly improves storage efficiency and compatibility with scalable analytical pipelines. Experimental results demonstrate that the proposed filtering method can achieve substantial data storage reduction retaining as little as 20–40% of the original data while maintaining low signal distortion.

Wireless sensor networks (WSNs) are employed across a wide range of industrial applications where ultra-low power consumption is a critical prerequisite. At the same time, these systems must maintain a certain level of determinism to ensure reliable and predictable operation. In this view, time slotted channel hopping (TSCH) is a communication technology that meets both conditions, making it an attractive option for its usage in industrial WSNs.This work proposes the use of machine learning to learn the traffic pattern generated in networks based on the TSCH protocol, in order to turn nodes into a deep sleep state when no transmission is planned and thus to improve the energy efficiency of the WSN. The ability of machine learning models to make good predictions at different network levels in a typical tree network topology was analyzed in depth, showing how their capabilities degrade while approaching the root of the tree. The application of these models on simulated data based on an accurate modeling of wireless sensor nodes indicates that the investigated algorithms can be suitably used to further and substantially reduce the power consumption of a TSCH network.

The deployment of autonomous mobile robots in Industry 4.0 environments requires reliable and adaptable SLAM (Simultaneous Localization and Mapping) techniques. This paper presents a comparative evaluation of multiple SLAM algorithms that take advantage of both LiDAR and visual inputs on a Clearpath Jackal robot using ROS2 Humble. Unlike existing studies limited to sensor-specific SLAM approaches, our work systematically benchmarks algorithm performance in a testbed at SmartFactoryOWL, Lemgo, under varying sensor configurations and environmental conditions to assess their applicability in real-world industrial scenarios. We evaluated traditional algorithms such as GMapping, Hector, and Cartographer, along with Visual SLAM frameworks such as ORB-SLAM3 and RTAB-Map. Experiments are conducted in both Gazebo simulation and real-world environments modeled on SmartFactoryOWL. Key performance indicators including mapping accuracy, localization accuracy, robustness to dynamic changes, and real-time performance are systematically analyzed. This study aims to identify suitable SLAM solutions tailored for smart factory applications fostering better human-robot collaboration, thereby contributing towards robust indoor autonomy for mobile robot navigation.

Modern Artificial Intelligence (AI) research is having a huge impact in many technological domains. As in many other research areas, the application of AI in smart factories has been a key factor in the contribution to the “smartness”. Every aspect of industrial automation has been affected by the introduction of AI: the usage of AI solutions allows to introduce advanced capabilities for optimizing processes, increasing efficiency, and reducing costs. This paper analyzes some relevant aspects of the application and the impact of AI solutions on the current scenario of smart factories and industrial automation. We identify a list of significant topics related to this domain, and we report the main aspects related to them. The dissertation includes an initial quantitative analysis of the relevance of these topics in the scientific publications, a detailed description of the characteristics of the topics, and a discussion of the related challenges.

In recent years, the escalating challenges of climate change and the imperative for sustainable development have catalyzed the need for more energy-efficient buildings. The use of digital twins has been accelerated by advances in Internet of Things (IoT) sensors, data analytics, and novel computational methods. A new digital twin system is introduced in this paper for the modeling of Air Handling Units (AHUs), designed to accurately simulate the energy behavior and consumption of the system. The digital twin, called AHUSim, leverages an accurate mathematical model of the AHU, a modular architecture, a simple organization of the code and the use of modern tools and approaches for the implementation. This paper details the development, implementation, and validation of AHUSim, underlining its potential impact on reducing energy consumption and operational costs in buildings, thereby contributing to the broader goals of energy efficiency and sustainability. Preliminary results show the usage of AHUSim to model the physical variables that are involved in the functioning of an AHU, and the results that can be obtained in terms of assessment of energy consumption under different working conditions.

Mobile robots are essential in various industries, with Simultaneous Localization and Mapping (SLAM) technology playing a crucial role in their autonomy. This work-in-progress paper lays the foundation for evaluating 2D LiDAR-based SLAM algorithms for implementation on a Clearpath Jackal robot in a smart factory environment. The study focuses on three SLAM algorithms: GMapping, Cartographer, and Hector SLAM. A real-world smart factory, serving as a case-study location, is modelled in the Gazebo simulator to evaluate the selected algorithms according to mapping quality, location accuracy, and performance consistency. The simulation uses a hardware-in-the-loop approach, where LiDAR data is processed by the physical Jackal robot, ensuring realistic testing conditions. The findings from the simulation, including the key factors influencing the performance metrics, are validated through real-world testing. This paper outlines the methodology for both simulation and real-world deployment, setting the stage for determining the most suitable SLAM algorithm for efficient and accurate mapping and localization within the operational constraints and requirements of a smart factory environment. Additionally, preliminary insights into factors affecting SLAM performance in the real-world and the relative strengths and weaknesses of each framework are discussed.

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique.

With the growing diffusion of the Internet of Things (IoT) technology across most of the aspects of people daily lives, security concerns have become critical to ensure the exploitation of advantages introduced by this technology. This is even more true in the context of Industry 4.0, for which the IoT is becoming an important driver for automation. The detection of anomalies in IoT systems to ensure the capability of such systems to tolerate attacks to single devices is a crucial aspect. Behavioral fingerprinting is a recent and promising security solution in this context, which still requires research efforts to embrace new challenges in such a complex environment. Existing solutions focus mostly on modeling the behavior of IoT devices by analyzing the information extracted from the header of exchanged networking packets. However, in many application contexts, also attacks on the content of the packets can lead to disruptive results. Our proposal focus on these approaches by addressing a fully distributed scenario in which computation is directly handled by IoT devices, also through delegation, and describes a novel behavioral fingerprinting approach based on features suitably engineered from packet payloads. The effective-ness of our proposed method is assessed by both simulated and experimental results.

In the latest years, Light Emitting Diode (LED)-based lighting systems have revolutionarized architectural and design applications. The setup of a complex lighting system is a typically time-consuming task due to the number of manual operations that can it requires. In this paper, we introduce a LED-display system that aims at an automatic self-configuration while allowing a simple and effortless deployment. The proposed system is based on the careless deployment (in terms of positioning) of LED strips where each LED can be individually controlled and enlightened with the desired color. Since the position of every LED is not known during the deployment, we devised an automatic configuration procedure based on computer vision to determine the position of each LED, so that the LED can act as pixels to display a generic image. The different components of the system interact by exchanging messages with the Message Queue Telemetry Transport (MQTT) protocol. An example of application is provided that shows simple images displayed using the proposed display system.

With the recent advances in the Blockchain technology, and due to its decentralized nature, it has been a much considered approach for solving issues in the Internet of Things (IoT) sector, in particular, for IoT payment platforms. As Machine-to-Machine (M2M) payments are fundamental in the IoT economy, the development of Blockchain-based payment platforms, using cryptocurrency, is continuously increasing as it enables a pure M2M, secure and private financial transactions. Unlike traditional assets, cryptocurrencies have a higher index of volatility, which makes it essential to understand the movement of their prices, as a first step to optimize Blockchain-based M2M payment transactions. In this paper, we propose a novel hybrid model that deals with this challenge from a descriptive, as well as predictive points of view. We use Hidden Markov Models to describe cryptocurrencies historical movements to predict future movements with Long Short Term Memory networks. To evaluate the proposed hybrid model, we have chosen 2-minute frequency Bitcoin data from Coinbase exchange market. Our proposed model proved its effectiveness compared to traditional time-series forecasting models, ARIMA, as well as a conventional LSTM.

The need for powerful and flexible platforms for teaching and learning computer programming concepts is increasing due to the growing number of interested attendees. Although implementing a complete platform from scratch would achieve the best flexibility in terms of possible features, it also requires a huge time-consuming effort. This paper proposes Coderiu, a platform built on top of Free and Open Source Software (FOSS) packages suitably integrated to implement the desired learning platform. Such features include platform independence of code editing, which is achieved by leveraging a web-based Integrated Development Environment (IDE). The environment is extended with custom features to enable the automated testing of the solutions and the automatic remote backup of the working directories. Moreover, the architecture was made suitable to be used in classroom exams, which require a controlled environment. A critical aspect of the Coderiu platform, beside the integration of its components and modules, is represented by the scalability. The Coderiu platform is used by hundreds of students in case of courses in relatively small classes, while it is ready to serve a larger user base in the future. For this reason, requirements and performance are studied on a pilot installation to derive insights regarding the resources required for larger deployments.

Unintentional falls can cause severe injuries to a person, and even death, especially when no immediate assistance is provided. The aim of Fall Detection Systems (FDSs) is to detect the occurrence of a fall and to automatically and promptly request the necessary assistance. This work proposes a FDS based on wearable sensors - i.e., accelerometers and gyroscopes - and Machine Learning (ML), for sensor signal processing and detection. The process extracts a number of features on portions of the signal and classifies them as falls or regular daily activities. The classifier is a Support Vector Machine (SVM) that is trained using a manually labelled dataset, where human activities are distinguished between falls and regular activities. The method is assessed on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained in the literature for the same dataset; the proposed method largely outperforms the original analysis technique proposed for the SisFall dataset, with an F1 score higher than 97% and a recall higher than 99.7%.

Received Signal Strength Indication (RSSI) is commonly used to provide distance estimates in range-based localization. In most cases, the localization systems use RSS at short range where the distance estimates are more reliable or use RSS alongside other techniques such as Time of Flight (ToF). This is so, since RSSI measurements have relatively high variance at long range and are strongly influenced by occlusions and interference in the deployment region of the Radio Frequency (RF) devices. This paper presents an overview of common filtering techniques that can be used to process RSSI readings in order to improve the accuracy of range computation from raw RSSI with minimal computational overhead. The range estimates computed from the filtered data are compared with expected values of the perturbed range/distance expressed in terms of the Cramér-Rao Lower Bound (CRLB) for RSS distance estimation. Results show that filtering can significantly improve the accuracy of range estimation, highlighting the pros and cons of the presented filtering methods at different range values.

Multidimensional Scaling (MDS) can be used to localize a set of nodes (tags) by evaluating their distances from another set of nodes having known location (anchors). Node localization with MDS generally requires that the proximity graph be fully connected. This implies that matrices generated from tag-anchor ranging for which tag-to-tag distances are missing can not be used directly with the MDS algorithm without the use of estimates for the missing data. These estimates, however, unavoidably introduce some approximations in the localization process, which can become relatively large depending on the number of missing measurements and the amount of noise in the pair-wise distance measurements. This paper proposes a specialized form of the anchored MDS algorithm that undermines missing tag-to-tag distances in the connectivity matrix. We show that decoupling tag-to-tag interactions in the Scaling by MAjorizing a COmplicated Function (SMACOF) algorithm can undermine the effects of missing tag-to-tag distances and produce tag configurations that are inferred directly from only anchor-tag pairwise distances.

Advanced building materials are nowadays an active research domain. The luminous tile is a new smart device that combines traditional ceramic tiles with state-of-the-art large area electronic circuits, LED based lighting, embedded sensory and communication capabilities. Luminous tiles can self-illuminate and sense the neighbor environment by means of dedicated sensors. Once installed, a cover made by luminous tiles forms a cyber-physical network of adjacent components where each tile can communicate with adjacent tiles to spread control commands and to collect sensory information by means of a multi-hop message routing. This paper describes goals and technical solutions adopted for the development of the multi-hop communication and coordination protocol that enables the desired features of a cover made by luminous tiles. Notable features include: flexibility in the installation, allowing arbitrary shapes, topologies and orientation; smart self-configuration capabilities; fault-tolerance. Theoretical bounds and simulation results are carried out to assess the performance of the protocol in terms of latencies of the core operations.

Fuzzy logic is a powerful modelling approach to build control applications and to generate knowledge-based evaluation indices.In both cases, however, the applicability to complex systems is limited by the effort required to formulate the rules, whose number grows rapidly with the number of input variables and membership functions.This work presents a framework that implements the F-IND fuzzy model to simplify the formulation of fuzzy indices, where the rules are automatically generated on the basis of the specification of best and worst cases on the membership functions of each input variable.The paper discusses the method and presents the organization of the framework that allows automatic code generation, targeting the efficient execution of the calculations on an embedded system.The framework has been tested and validated on real hardware.

This article addresses the application of real-time scheduling to the reduction of the peak load of power consumption generated by electric loads in Cyber-Physical Energy Systems (CPES). The goal is to reduce the peak load while achieving a desired Quality of Service of the physical system under control. The considered physical processes are characterized by integrator dynamics and modelled as sporadic real-time activities. Timing constraints are obtained from physical parameters and are used to manage the activation of electric loads by a real-time scheduling algorithm. As a main contribution, an algorithm derived from the multi-processor real-time scheduling domain is proposed to efficiently deal with a high number of physical processes (i.e., electric loads), making its scalability suitable for large CPES, such as smart energy grids. The cyber-physical nature of the proposed method arises from the tight interaction between the physical processes operated by the electric loads, and the applied scheduling. To allow the use of the proposed approach in practical applications, modelling approximations and uncertainties on physical parameters are explicitly included in the model. An adaptive control strategy is proposed to guarantee the requirements on physical values under control in presence of modelling and measurement uncertainties. The compensation for such uncertainties is done by dynamically adapting the values of timing parameters used by the scheduler. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads. The performance of the method is evaluated by means of physically accurate simulations of thermal systems, showing a remarkable reduction of the peak load and a robust enforcement of the desired physical requirements.

Advanced building materials are nowadays an active research domain. The integration of traditional materials and technologies in the field of electronics, photonics and computer science are leading to a new class of smart components that provide advanced functionalities and enable original applications. The LUMENTILE H2020 EU funded Project aims at the integration of existing and state-of-the-art technologies in the domain of large area electronic circuits, LED based lighting, embedded systems and communication. These technologies are blended with advancements in the manufacturing of ceramic tiles to obtain a new building component that can be managed as a common tile, while providing the possibility to self-illuminate and to sense the neighbor environment by means of dedicated sensors. The applications of these new material and technologies include indoor and outdoor architectural design, smart environments (also targeting improved safety and security issues), smart and high-efficiency lighting and art installations. State-of-the-art advancements are expected in the field of large area circuits and successful integration of heterogeneous materials, mainly focusing on ceramics and electronics.

Data acquisition systems are fundamental components of modern distributed monitoring and control systems. The wide-spreading use of standard networking technologies in industrial scenarios, such as Ethernet, and the consolidation of web-based communication protocols, architectures and tools suggest the possibility to integrate out-of-the-box components to build a robust and reliable data acquisition system. This paper describes the design and implementation of a data acquisition system suitable to collect data from distributed embedded devices equipped with sensors based on a client-server architecture. The main feature of the proposed design is the integration of a set of technologies and tools widely adopted in the development of modern web services. The server component is based on Django, a popular Python web framework. While the backend runs a PostgreSQL database, the frontend includes data visualization tools leveraging the D3 JavaScript library. Metering points can be any embedded device able to interface with the desired sensors. The client-server communication architecture supports a RESTful API, as well as the ZeroMQ communication library. Messages are encapsulated in a human-readable JSON format. An important common characteristic of aforementioned technologies and tools is to be Free/Libre and Open Source Software (FLOSS). The proposed system thus represents a successful example of integration of FLOSS components to build a web-centric data acquisition system.

The coordination of appliances in a smart building to limit the peak load is one of the common objectives of power load management approaches such as the Demand-Side Management (DSM). The DSM, in turn, is an important research challenge in the field of smart energy systems and smart grids. This paper investigates the use of the limited-preemption scheduling approach to the coordination of a set of household appliances in a smart building. This approach is enabled by the application of a real-time scheduling framework to manage the activation of electric loads. The limited-preemption technique aims to reduce the number of stop/restart operations applied to interruptible devices, while ensuring the same performance in terms of peak load reduction. The original contribution of this paper w.r.t. to previous works on real-time scheduling with limited-preemption is to present some peculiar issues related to preemptions of electric loads and to assess suitability and benefits of this approach when applied to interruptible household appliances. Simulated results show the effectiveness of this method.

Innovative building materials are behind the development of smart environments. Such environments include rooms, apartments, buildings, streets, squares, etc. The integration of embedded computing devices within traditional building materials is one of the most appealing direction to create next generation smart environments. This paper presents a distributed self-configuration protocol suitable for the automatic set up of a cover made by smart tiles. The smart tile is an advanced building material where a classical tile is integrated by an embedded device. The main goal of the proposed configuration protocol is to allow the installation of the cover without the need of specialized skills or configuration requirements.

This paper addresses the application of real-time scheduling for the reduction of the peak load of power consumption generated by electric loads in a power system. The considered physical processes are characterized by integrator dynamics and modeled as sporadic real-time activities. To enable the applicability in realistic scenarios, modeling approximations and uncertainties on physical parameters are explicitly included in the model. A feedback control strategy is proposed to guarantee the requirements on physical values under control in presence of modeling and measurement uncertainties. To compensate for such uncertainties, the value of timing parameters used by the scheduler are dynamically adapted. Formal results have been derived to put into relationship the values of quantities describing the physical process with real-time parameters used to model and to schedule the activation of loads.

This paper describes the application of Real-Time Physical Systems (RTPS) as a novel approach to model the physical process of Cyber-Physical Systems (CPS), with specific focus on Cyber-Physical Energy Systems (CPES). The proposed approach is based on the real-time scheduling theory which is nowadays developed to manage concurrent computing tasks on processing platforms. Therefore, the physical process is modeled in terms of real-time parameters and timing constraints, so that real-time scheduling algorithms can be applied to manage the timely allocation of resources. The advantage is to leverage the strong mathematical background of real-time systems in order to achieve predictability and timing correctness on the physical process behind the considered CPS. The paper provides an introduction to the possible application of RTPS to energy systems. The analogy between real-time computing systems and energy systems is presented; moreover, the relationship between RTPS and related research fields is traced. Finally, the introduced techniques are proposed to optimize the peak load of power consumption in electric power systems. This method is suitable for systems spanning from small networks to smart grids.

This paper presents an innovative approach to use real-time scheduling techniques for the automation of electric loads in Cyber-Physical Power Systems. The goal is to balance the electric power usage to achieve an optimized upper bound on the power peak load, while guaranteeing specific constraints on the physical process controlled by the electric loads. Timing parameters derived from the scheduling discipline of real-time computing systems are used to model electric devices. Real-time scheduling algorithms can be exploited to achieve the upper bound by predictably and timely switching on/off the devices composing the electrical system. The paper shows the relevance of electric load balancing in power systems to motivate the use of real-time techniques to achieve predictability of electric loads scheduling. Real-Time Physical Systems (RTPS) are introduced as a novel modeling methodology of a physical system based on real-time parameters. They enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. The model of the physical process considered in this work is characterized by uncertainties that are compensated by a suitable feedback control policy, based on the dynamic adaptation of real-time parameter values. A number of relevant relationships between real-time and physical parameters are derived.

Audio and video applications over wireless sensor networks have recently emerged as a promising research field. However, the limits in terms of communication bandwidth and transmission power have withstood the design of low-power embedded nodes for voice communication. In this work we describe the implementation details of an embedded system for the wireless broadcasting of audio signals over the low datarate IEEE 802.15.4 standard, which is widely adopted to build Wireless Personal and Sensor Networks. The resulting device has been developed from scratch by combining several techniques with the goal of obtaining the most suitable implementation on a low-cost and low-power 16-bit microcontroller. We used a realtime operating system, a well-known psychoacoustic model based on FFT signal decomposition and the Haar wavelet transform to create a novel audio compression algorithm targeted to embedded systems with limited computational capabilities. The result is a fully-functional embedded system which is able to stream voice in real-time over IEEE 802.15.4 with an acceptable audio quality.

The Power Line technology has received an increasing attention in the last decades due to its inherent benefits, mainly related to the reduction of cabling and associated costs. Power Line Communication (PLC) was first employed in power utilities, and since the 80s in home automation, too. However, its use in the automotive field received relatively little attention. This paper extends previous works towards assessing PLC technology for communications within the automotive domain. In particular, it focuses on the real-time behavior of such technology, namely the DCB500 adaptors supplied by the Yamar company, showing experimental results of transmission delays, communication overheads and effectiveness of the medium access control policies.

The platooning is a coordination technique for teams of mobile units that aims at letting each unit to move closely to its preceding neighbour, thus forming the so-called platoon. This paper describes the design and implementation of a distributed robotics application where a team of autonomous mobile robots are coordinated to move as a platoon. The focus will be on the on-board real-time computing that allows a predictable robot's behavior. Experimental results are shown to assess the performance of the proposed platform.

Token passing is a channel access technique used in several communication networks. Among them, one of the most effective solution for supporting both real-time traffic (synchronous messages) and non real-time traffic (asynchronous messages), is the so-called timed-token protocol. Recently, a new token passing protocol, called budget sharing token protocol (BuST), was proposed to improve the existing timed-token approaches in terms of synchronous bandwidth guarantee, while guaranteeing a minimum throughput for the asynchronous traffic. This paper analyzes the ability of BuST to manage realtime and non real-time traffic in comparison with the classic timed-token protocol and its modified version, under the normalized proportional allocation (NPA) scheme. We will show that BuST achieves higher guaranteed realtime bandwidth than the original timed-token protocol, and improves the service for the non real-time traffic respect to its modified version.

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly difficult when obstacles are present in the manipulatorpsilas workspace. In this paper it is assumed that the obstacles can be approximated in a conservative way with discs. The goal is to represent the obstacles in the robot configuration space, in order to allow an efficient and accurate trajectory planning and tracking. Moreover, the paper provides the methods for checking the collision between the n-joint manipulator and the obstacles. Trajectory planning depends on tracking accuracy. In this paper an adequate tracking accuracy is guaranteed assuming the use of a suitably designed robust controller.

Token passing channel access mechanisms are used in several communication networks. An important class of token passing approaches are the so-called timed token protocols, which are able to manage both real-time traffic and non real-time traffic. Recently, a new token passing protocol, called Budget Sharing Token protocol (BuST), was proposed to improve the existing timed token approaches in terms of real-time bandwidth guarantee. This paper analyzes the ability of BuST to manage real-time and non real-time traffic under three different budget allocation schemes, and compares the performance of BuST with the original timed-token protocol (FDDI) and its modified version (FDDI-M). It is shown that BuST provides an higher guaranteed bandwidth for real-time traffic than FDDI, and improves the service for non real-time traffic with respect to FDDI-M. Moreover, new properties of the analyzed budget allocation schemes are provided for BuST, FDDI and FDDI-M. Finally, a set of simulation results are carried out to assess the performance of the three considered protocols.

The distributed control of industrial plants, where the control algorithm runs on a machine that receives information from sensors and sends actuation commands over a communication network is a challenging task, due to the delays introduced by communication. This paper presents the networked control of a robotic anthropomorphic manipulator based on a second order sliding mode technique. The control objective is to track a desired trajectory for the manipulator. The adopted control scheme allows an easy and effective distribution of the control algorithm over two networked machines. While the predictability of real-time tasks execution is achieved by the S.Ha.R.K. real-time operating system, the communication is established via a standard Ethernet network. The performances of the control system are evaluated under different experimental system configurations using a COMAU SMART3-S2 industrial robot, and the results are analyzed to put in evidence the robustness of the proposed approach against the possible network delays.

Timed-token networks, such as FDDI, support both synchronous real-time traffic and non real-time traffic (asynchronous messages). The medium access scheme of FDDI guarantees up to one half of the total network bandwidth for synchronous communication. Further enhancements, such as FDDI-M, improve the bandwidth dedicated to real-time messages. However, the ability of timed-token protocols to guarantee synchronous message deadlines highly depends on specific Synchronous Budget Allocation (SBA) schemes. This paper introduces BuST, the Budget Sharing Token protocol which improves the management of periodic real-time traffic, while guaranteeing a minimum throughput for non real-time messages, with respect to existing techniques. We evaluate the performance of BuST, in comparison with FDDI and FDDI-M, considering a Synchronous Budget Allocation (SBA) scheme proposed in the literature, using the Worst-Case Achievable Utilization (WCAU) as performance metrics. We demonstrate that the performance achieved by BuST is better or, in a few cases, equal to FDDI and FDDI-M.

Low-level support of peripheral devices is one of the most demanding activities in a real-time operating system. In fact, the rapid development of new interface boards causes a tremendous effort at the operating system level for writing and testing low-level drivers for supporting the new hardware. The possibility of reusing legacy drivers in real-time systems would offer the great advantage of keeping the rate of changes with a small programming effort. Since typical legacy drivers are written to execute in a non-preemptive fashion, a suitable operating system mechanism is needed to protect real-time application tasks from unpredictable bursty interrupt requests. In this paper, we present a novel approach suitable for scheduling interrupt service routines. Main features of the method include: high priority of the handler, non preemptive execution, bandwidth reservation for the application tasks, and independence of the interrupt service policy from the scheduling policy adopted for the application tasks.

The management of ad-hoc networks raises interesting problems, that are particularly challenging for networks of mobile nodes. Considering the inherent complexity of these systems, the development of distributed applications relying on wireless communication protocols would be greatly simplified by the use of specific tools for supporting testing and step-by-step debugging. In this paper we describe WISE, a flexible interactive simulation environment for the development of wireless ad-hoc networks consisting of mobile units. A graphical interface allows the user to create/delete nodes, change their positions and parameters, and select specific mobility models in order to verify the network behavior in dynamic conditions. The simulator also provides a useful support for the verification of agreement protocols, synchronization algorithms and distributed scheduling, allowing the user to display a step-by-step evolution of the algorithms in a suitable graphical representation

Wireless communication technology is spreading quickly in almost all the information technology areas as a consequence of a gradual enhancement in quality and security of the communication, together with a decrease in the related costs. This facilitates the development of relatively low-cost teams of autonomous (robotic) mobile units that cooperate to achieve a common goal. Providing real-time communication among the team units is highly desirable for guaranteeing a predictable behavior while operating autonomously in unstructured environments. This paper proposes a MAC protocol for wireless communication that supports dynamic resource reservation for small teams of cooperative robots. The protocol uses a slotted time-triggered medium access transmission control that is collision-free, even in the presence of hidden nodes. The transmissions are scheduled according to the earliest deadline first scheduling policy. An adequate admission control guarantees the timing constraints of the team communication requirements, including when new nodes dynamically join or leave the team. The paper describes the protocol focusing on the consensus procedure that supports coherent changes in the global system. Finally, a set of simulation results are shown that illustrate the effectiveness of the proposed protocol.