Efficient urban monitoring requires methods able to estimate traffic flows on road segments with limited or no sensor coverage. This study validates a traffic flow-scaling model that combines fixed sensors and Floating Car Data in Bologna across 11 sections. Tree-based Machine Learning models, particularly XGBoost, demonstrated excellent performance after hyperparameter optimization. Using the scaled data, a short-term forecasting task (+1h and +4h) showed that tree-based models outperform a neural network based Long Short-Term Memory model in terms of accuracy and robustness. Results indicate that the integration of floating car data and fixed sensors enables accurate spatio-temporal traffic estimation, providing a scalable and cost-effective strategy for urban mobility planning and proactive traffic management.

Water utilities increasingly require a deeper understanding of users' water demand to optimize resource management and meet customer needs. The adoption of smart metering solutions enables the collection of detailed consumption data. This study presents a methodology for identifying usage patterns by analysing smart meter data. In particular, the authors introduce a machine learning methodology to identify user profiles from smart meter water consumption data. Clustering results are graphically interpreted and compared, providing insights into consumption habits and assessing the methodology's effectiveness and stability.

Rapid urbanization and the growth of cities worldwide have led to increasing traffic congestion, deteriorating air quality, and heightened safety concerns for both drivers and pedestrians. At the same time, advances in technology have provided traffic management organizations with more sophisticated tools to plan and manage urban transport networks. However, deploying sensors across every road in a metropolitan area remains currently cost prohibitive. To address this challenge, this paper proposes a two-stage approach to modelling traffic on roads without sensors. In the first phase, detailed in this study, Floating Car Data is used to estimate traffic flows on sensor-less roads. By employing Machine Learning techniques, the Floating Car Data is scaled to approximate actual traffic volumes and flows along these routes. The second phase builds upon these estimates to forecast future traffic conditions.

The rapid urbanization of our world has led to an ever-increasing urban population, resulting in significant challenges in managing transportation systems. Traffic congestion, with its associated environmental pollution, safety hazards, and prolonged travel times, continues to plague urban areas. Despite numerous efforts to mitigate these issues, the problem persists and hampers urban development. This paper addresses the pressing need for effective traffic flow forecasting of the city of Catania as a critical element in traffic management. Catania’s rapid urbanization has created a complex transportation network as the city’s population has expanded into suburbs and neighbouring areas, causing congestion, pollution, and increased individual motorization due to the high demand for mobility. This has led to severe traffic congestion in the central district, necessitating an efficient traffic management strategy, particularly through the utilization of traffic flow forecasting. A central challenge in traffic flow prediction is the cost and practicality of sensors deployment on every road. To address this issue, the authors propose a novel two-level machine learning approach. The first level employs an unsupervised clustering model to extract patterns from big data generated by sensors, while the second level employs supervised machine learning models for traffic flow forecasting within each cluster. Importantly, this approach enables predictions for roads lacking sensor data by utilizing a really small subset of these new data from alternative sources and assigning roads to appropriate clusters.

Industry 4.0 is really changing the manufacturing through the convergence of physical systems and their digital counterparts with Information Technology as an enabler. The Asset Administration Shell is core to Industry 4.0 components. Very recently a relevant metamodel has been defined. The paper presents its mapping with OPC UA achieved extending the relevant information model.

The paper presents a web-based platform able to offer access to OPC UA Servers. The proposed platform may be used by web-users to exchange data with OPC UA Server without any knowledge of the standard. The software solution described in the paper is available on GitHub.

Paper presents a solution providing OPC UA connectivity from a common web user using web technologies. Integration of OPC UA with the web may represent an important contribution to enhance its interoperability, allowing integration with web applications, IoT and WoT. The main difference with the solutions available in literature is that user may have no knowledge of OPC UA standard. A web-based platform is presented and described in details, including its implementation available on GitHub.

The increased need of flexibility of automation systems and the increased capabilities of sensors and actuators paired with more capable bus systems, pave the way for the reallocation of IEC 61131-3 applications away from the field level into so-called compute pools. Such compute pools are decentralised with enough compute power for a large number of applications, while providing the required flexibility to quickly adapt to changes of the applications requirements. The paper proposes a framework able to deploy IEC 61131-3 applications to multiple computing platforms. It is based on CLR VM and does not require any modifications of the IEC 61131-3 applications. The paper presents also an analysis about the capability of the proposed framework to respect real-time constraints of the industrial processes.

The need to deploy IEC 61131-3 - based application on multiple target platforms is becoming more and more urgent at this moment. The increased need of flexibility of automation systems and the increased capabilities of sensors and actuators paired with more capable bus systems, pave the way for the reallocation of applications, such as control loops, away from the field level into so-called compute pools. Such compute pools are decentralised with enough compute power for a large number of applications, while providing the required flexibility to quickly adapt to changes of the applications requirements. The aim of the paper is propose a framework able to deploy IEC 61131-3-based application to multiple computing platforms. It is based on CLR VM and does not require any modifications on the IEC 61131-3 applications; all additional overhead is handled by the framework here defined. As applications in the automation domain often come with real-time requirements, the paper presents also an analysis about the capability of the proposed solution to respect typical real-time constraints in factory automation.

BACKGROUND: Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics, and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. RESULTS: We propose a novel methodological approach that is based on high level PN, and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. CONCLUSIONS: The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software.

IEC 61850 has been recognised as one of the fundamental components for reference Smart Grid architectures. IEC 61850 engineering process is based on engineering tools for configuration of substation automation systems. Issues about interoperability of the configuration process are not manageable with current IEC 61850 SCL-based engineering capabilities and require an alternative approach that is capable of handling Smart Grids integration principles. The authors believe that engineering process for Smart Grid environment may be improved by the integration of IEC 61850 and OPC UA. A proposal is presented in the paper based on the mapping of the entire set of IEC 61850 SCL elements to the OPC UA information model.

Generally, interoperability in all the levels of a communication architecture (field, unit, plant and management level) is a feature that cannot be easily satisfied because equipments are based on different technologies and communication protocols. OPC Unified Architecture (OPC UA) can provide a cohesive, secure and reliable cross platform framework for interoperability, as shown by current literature. This paper deals with the integration of KNX information model with OPC UA.

KNXnet/IP allows integration of different KNX networks through IP; this is achieved by the use of a particular device called KNXnet/IP Router. Due to the different transmission speeds among KNX network and the IP medium, a flow control mechanism to prevent the congestion of KNXnet/IP Router has been recently foreseen by the standard. In this paper, the authors present some preliminary results about a performance evaluation of the KNXnet/IP flow control mechanism, based on the use of a particular kind of stochastic Petri Nets, called Stochastic Activity Networks (SAN).

KNXnet/IP communication system allows integration of different KNX networks through IP by a particular device called KNXnet/IP Router. Due to the different transmission speeds among KNX networks and the IP medium, a flow control mechanism to prevent the congestion of KNXnet/IP Router has been foreseen by the KNX standard. In this paper we analyse the performance of the KNXnet/IP flow control mechanism in congestion condition, through simulation of a model based on Stochastic Activity Networks (SAN), capable to implement all the specifications foreseen by the KNX standard.

The widespread use of standard, worldwide and vendor-independent OPC UA specifications in industrial environment introduces many benefits as it allows to keep open the market of the industrial applications. On the other hand, OPC UA adopts a very complex software infrastructure to realise the data exchange between industrial applications and devices (e.g. mainframes, PLC, microcontroller); this complexity may impact on the overall performance of the communication between industrial applications. The aim of this paper is to deal with the performance evaluation of OPC UA, pointing out all the main features which could influence performance in the client/server exchange of information.

This paper deals with the problem of securing data transmission in home and building networks, discussing a solution to introduce both confidentiality and authentication, based on classical security mechanism and algorithms. To validate the approach the authors applied the proposed techniques to KNX, the European and international standard for home and building automation, which doesn't provide for any security mechanisms.

The paper deals with the problem of making secure data transmission inside home and building automation environment; as known, here data exchanged may be critical, as they may regard commands to actuators and/or private and secret information. The need to protect critical data has led to introduce secure transmissions inside communication systems used for home/building automation applications. The paper deals with this problem taking into account the KNX communication system, which, at this moment, doesn't foresee any security and authentication mechanisms. A security and authentication solution will be presented and assessed comparing it with the current state of the art.

Use of pattern matching algorithm for location detection in a wireless LAN is well known in literature. It allows to achieve good performances in terms of localisation errors, but it may require a very long and time-consuming start-up phase, during which a set of measurements of the radio signal strength values received in different positions inside the area involved in the localisation, must be taken. The complexity of this procedure increases with the size of the area involved in the localisation; this clearly happens for large outdoor areas, where a huge set of measurements must be taken during the start-up phase. The paper aims to present an approach for localisation in outdoor areas covered by a wireless network; this approach is still based on the use of pattern matching algorithm, but use of a signal propagation model during the start-up phase has been introduced in order to reduce its complexity. Performances of the proposal have been evaluated for outdoor localisation and the main results achieved will be shown in the paper

Literature presents many approaches for location detection of a mobile user using IEEE 802.11b wireless communication standard; generally localisation is limited to indoor scenarios, where GPS cannot be used, and is based on pattern matching algorithm. The paper aims to reach two different goals. The first is the proposal of the use of IEEE 802.11b standard for outdoor location detection; this choice is mainly justified by the large availability of communication cards at very low costs and by the limits of GPS, which may introduce not negligible errors in location detection. The second aim of the paper is the proposal of a new approach for outdoor wireless localisation, aimed to reduce the complexity featuring by the well-known localisation approaches based on pattern matching algorithm

This paper deals with the problem of information flow scheduling in a communication system based on CAN ISO IS-11898 physical medium access mechanism. It mainly features a bus access arbitration protocol based on a priority assigned to each message to be transmitted; if two or more messages are transmitted at the same time by different communication nodes, only the message with the highest priority continues to be transmitted, the other being stopped. In real-time applications, messages are relevant to process variables which must be transmitted within strict time constraints; according to the CAN ISO IS-11898 bus arbitration protocol, respect of real-time constraints depends on the priority assigned to each variable. The aim of the paper is to propose a procedure for dynamic assignment of priorities to variables to be transmitted, in such a way the relevant real-time requirements are fulfilled. Although many other approaches can be found in literature, the proposal is original as it is based on standard full CAN communication stacks.

The paper deals with integer linear programming problems. As is well known, these are extremely complex problems, even when the number of integer variables is quite low. Literature provides examples of various methods to solve such problems, some of which are of a heuristic nature. This paper proposes an alternative strategy based on the Hopfield neural network. The advantage of the strategy essentially lies in the fact that hardware implementation of the neural model allows for the time required to obtain a solution so as not depend on the size of the problem to be solved. The paper presents a particular class of integer linear programming problems, including well-known problems such as the Travelling Salesman Problem and the Set Covering Problem. After a brief description of this class of problems, it is demonstrated that the original Hopfield model is incapable of supplying valid solutions. This is attributed to the presence of constant bias currents in the dynamic of the neural model. A demonstration of this is given and then a novel neural model is presented which continues to be based on the same architecture as the Hopfield model, but introduces modifications thanks to which the integer linear programming problems presented can be solved. Some numerical examples and concluding remarks highlight the solving capacity of the novel neural model.

The solution of constrained satisfaction and constrained optimization problems using a Hopfield model requires determination of the values of a certain number of coefficients linked to the surrounding conditions of the problem. It is quite difficult to determine these values, mainly because a heuristic search is necessary. This is not only time-consuming but may lead to solutions that are far from optimal, or even nonvalid ones. So far, there have been no works in literature offering a general method for the search for coefficents with will guarantee optimal or close to optimal solutions. This paper proposes a fuzzy approach which allows automatic determination of Hopfield coefficients.

Full exploitation of available resources in a flexible manufacturing system is extremely important to optimize its productivity. The parameters on which to act in order to maximize exploitation of the resources are the sequence of activities performed by each resource and the number of parts processed in each production cycle (WIP). The aim of the paper is to propose a performance optimization strategy which takes both these parameters into account. The strategy is based on a genetic algorithm, whose aim is to explore as far as possible sequences of activities performed by each resource. For each sequence, a heuristic algorithm, the adjustment heuristic algorithm, is applied to find the minimum WIP. In this way, the sequence scenario which offers the minimum WIP is found.

The aim of this paper is to discuss the problems relating to communication in distributed process control, highlighting the limits of traditional centralized communication systems based on point-to-point connections. The paper shows the real advantages that can be obtained by using a distributed common-bus communication system in time-critical process control applications, highlighting its impact on the architecture of control systems from the point of view of both hardware and software. Particular reference is made to the International Electrotechnical Commission/Instrument Society of America (IEC/ISA) communication system which is close to becoming an international standard. In particular, the solution based on the use of the distributed common-bus communication protocol is applied to the control of an orange-picking robot, which had previously been implemented in a traditional way using point-to-point communications. The choice of a case study referring to a robot control system seems an appropriate benchmark for the real potential of the fieldbus because the processes involved in a robot control system have highly critical time constraints which may stress the fieldbus communication system. The paper presents a qualitative analysis of the advantages that can be obtained by introducing the fieldbus into the orange picking robot control system. The analysis gives a detailed illustration of the hardware and software modification needed to replace the traditional point-to-point control systems of the orange picking robot with the fieldbus. Then an evaluation of the performance of fieldbus system is provided. The evaluation is made by modeling and simulating the distributed communication system with transition timed Petri nets (TTPNs). The results obtained in the study as a whole confirm the authors' conviction that, by careful analysis, it is possible and advantageous even in difficult cases to replace classical centralized point-to-point control systems with distributed systems based on use of the fieldbus.

This paper presents the use of Hopfield-type neural networks for process scheduling in the area of factory automation, where bus-based communication systems, called FieldBuses, are widely used to connect sensors and actuators to the control systems. We show how it overcomes the problem of the computational complexity of the algorithmic solution. The neural model proposed allows several processes to be scheduled simultaneously; the time required is polynomial with respect to the number of processes being scheduled. This feature allows real-time process scheduling and makes it possible for the scheduling table to adapt to changes in process control features. The paper presents the neural model for process scheduling and assesses its computational complexity, pointing out the drastic reduction in the time needed to generate a schedule as compared with the algorithmic scheduling solution. Finally, the authors propose an on-line scheduling strategy based on the neural model which can achieve real-time adaptation of the scheduling table to changes in the manufacturing environment.

The paper deals with the problem of scheduling the transmission of periodic processes in a distributed FieldBus system, defining the conditions guaranteeing correct transmission. The scheduling of periodic processes fixes the transmission times for each process in a table, whose length is equal to the Least Common Multiple (LCM) of all the periods. This involves great memorization problems when some periods are relatively prime. The authors identify the theoretical conditions which allow the length of the scheduling table to be drastically reduced, but still guarantee correct transmission. On the basis of the theoretical conditions given, the authors present a pre-run-time scheduling algorithm which determines a transmission sequence for each producing process within the desired scheduling interval. An online scheduling algorithm is also proposed to schedule new transmission requests which are made while the system is functioning. The reduction in the schedule length may increase the number of transmissions, thus reducing the effective bandwidth and increasing the communication overload. In order to make as complete an analysis as possible of the scheduling solution, the authors also present an analysis of both the computational complexity of the algorithms proposed and the communication overload introduced.>

The authors deal with the problem of the information exchange between processes in extended fieldbus systems. As is known, although most of the traffic in these systems is within a single bus, there is always a certain amount transiting from one fieldbus to another, often covering a certain distance. There is therefore the problem of finding ways to interconnect the various fieldbuses in such a way as to meet the time constraints imposed by the applications. The paper proposes and then assesses a way of interconnecting even a large number of fieldbuses, introducing delays which are compatible with process control requirements.>

In this paper, the authors adopt a neural approach to deal with the problem of routing in a packet switching network. The aim is to define a routing strategy which will combine the advantages of both the centralized and the distributed approaches. The neural approach presented is based on the idea of inserting a neural network (N/N) into each node in the computer network which will be responsible for computing the route between its node and the immediately adjacent one. Two distributed routing solutions are presented in the paper based on an optimizing network and a mapping network. The routing obtainable and the implementation resources needed for the two approaches are evaluated. Finally, the performance offered by the neural strategies proposed is compared with that offered by classical distributed and centralized routing solutions. As a parameter of merit, the effect of overloading caused by the additional traffic present in each solution is used.

The work presented is a case study of the application on the FieldBus protocol to motor cars. The problem of the management of traffic made up of the various actuation and control signals present in a car is addressed, and criteria for allocation of the available bandwidth and assessing the behavior of the FieldBus in various traffic conditions are discussed. The results obtained show that, in the particular case considered, the FieldBus proved to be perfectly capable of meeting the communication requirements of car control systems.>