arXiv subjects

Stefano Scanzio

Publications and source records attributed to Stefano Scanzio.

At least 19 recordsLinked to original sources

arXiv2025

QRmap: executable QR codes for Navigation in Industrial Environments and Beyond

QR codes are nowadays customarily used for embedding static data such as web hyperlinks or plain text. The sQRy technology (executable QR codes) permits to embed executable programs in QR codes, enabling people to interact with them even without an internet connection. In this work we present QRmap, a specific dialect that permits the inclusion of geographic maps in sQRy and supports interaction with the user to provide indications to reach the destination of interest. The QRmap technology facilitates navigation in large industrial plants where internet connectivity is absent, due to either environmental limitations or company policies. The proposed technology can have interesting applications in non-industrial contexts as well.

cs.HC↗

arXiv2025

Performance Evaluation of Parallel Wi-Fi Redundancy with Deferral Techniques

Wireless communication is increasingly used in industrial environments, since it supports mobility of interconnected devices. Among the transmission technologies operating in unlicensed bands available to this purpose, Wi-Fi is certainly one of the most interesting, because of its high performance and the relatively low deployment costs. Unfortunately, its dependability is often deemed unsuitable for real-time control systems. In this paper, the use of parallel redundancy is evaluated from a quantitative viewpoint, by considering a number of performance indices that are relevant for soft real-time applications. Analysis is carried out on a large dataset acquired from a real setup, to provide realistic insights on the advantages this kind of approaches can provide. As will be seen, deferred parallel redundancy provides clear advantages in terms of the worst-case transmission latency, at limited costs concerning the amount of consumed spectrum. Hence, it can be practically exploited every time a wireless connection is included in a control loop.

cs.NI↗

arXiv2025

Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks

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.

cs.NI↗

arXiv2025

Wi-Fi Rate Adaptation for Moving Equipment in Industrial Environments

Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.

cs.NI↗

arXiv2025

Widening the Coverage of Reference Broadcast Infrastructure Synchronization in Wi-Fi Networks

Precise clock synchronization protocols are increasingly used to ensure that all the nodes in a network share the very same time base. They enable several mechanisms aimed at improving determinism at both the application and communication levels, which makes them highly relevant to industrial environments. Reference Broadcast Infrastructure Synchronization (RBIS) is a solution specifically conceived for Wi-Fi that exploits existing beacons and can run on commercial devices. In this paper, an evolution of RBIS is presented, we call DOMINO, whose coverage area is much larger than the single Wi-Fi infrastructure network, potentially including the whole plant. In particular, wireless stations that can see more than one access point at the same time behave as boundary clocks and propagate the reference time across overlapping networks.

cs.NI↗

arXiv2025

Analysis of the operation of a TSN switch and other devices using executable QR codes

Executable QR codes, also known as sQRy, are a technology aimed at inserting executable programs in a QR code. Through a concrete example, in this paper, we demonstrate their usage in the context of industrial networks in order to assess the operation of a TSN switch by analyzing its status LEDs even in the absence of an internet connection. The entire generation chain that is used to create the sQRy, as well as the corresponding execution chain that, starting from the sQRy, runs it on a mobile device, has been detailed through examples.

cs.NI↗

arXiv2025

On the Prediction of Wi-Fi Performance through Deep Learning

Ensuring reliable and predictable communications is one of the main goals in modern industrial systems that rely on Wi-Fi networks, especially in scenarios where continuity of operation and low latency are required. In these contexts, the ability to predict changes in wireless channel quality can enable adaptive strategies and significantly improve system robustness. This contribution focuses on the prediction of the Frame Delivery Ratio (FDR), a key metric that represents the percentage of successful transmissions, starting from time sequences of binary outcomes (success/failure) collected in a real scenario. The analysis focuses on two models of deep learning: a Convolutional Neural Network (CNN) and a Long Short-Term Memory network (LSTM), both selected for their ability to predict the outcome of time sequences. Models are compared in terms of prediction accuracy and computational complexity, with the aim of evaluating their applicability to systems with limited resources. Preliminary results show that both models are able to predict the evolution of the FDR with good accuracy, even from minimal information (a single binary sequence). In particular, CNN shows a significantly lower inference latency, with a marginal loss in accuracy compared to LSTM.

cs.NI↗

arXiv2025

Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning

Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This paper presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the paper evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.

cs.NI↗

arXiv2025

Improving Wi-Fi Network Performance Prediction with Deep Learning Models

The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods including convolutional neural networks and long short-term memory were analyzed on datasets acquired from a real Wi-Fi setup across multiple channels. The models were compared in terms of prediction accuracy and computational complexity. Results show that the frame delivery ratio can be reliably predicted, and convolutional neural networks, although slightly less effective than other models, are more efficient in terms of CPU usage and memory consumption. This enhances the model's usability on embedded and industrial systems.

cs.NI↗

arXiv2025

On the Suitability of Wi-Fi for Interconnecting Moving Equipment in Industrial Environments

To ensure an unprecedented degree of flexibility, next-generation Industry 4.0/5.0 production plants increasingly rely on mobile devices, e.g., autonomous mobile robots and wearables. In these cases, a major requirement is getting rid of cables through the adoption of wireless networks. To this purpose, Wi-Fi is currently deemed one of the most promising solutions. Achieving reliable communications over the air for distributed real-time control applications is, however, not devoid of troubles. In fact, bounded transmission latency must be ensured for most of the exchanged packets. Moreover, for devices powered on batteries, energy consumption also needs to be taken into account. In this paper, a joint simulated analysis of these aspects is carried out to quantitatively evaluate what we can practically expect from Wi-Fi technology.

cs.NI↗

arXiv2025

Compression of executable QR codes or sQRy for Industry: an example for Wi-Fi access points

Executable QR codes, or sQRy, is a technology dated 2022 that permits to include a runnable program inside a QR code, enabling interaction with the user even in the absence of an Internet connection. sQRy are enablers for different practical applications, including network equipment configuration, diagnostics, and enhanced smart manuals in industrial contexts. Many other non-industry-related fields can also benefit from this technology. Regardless of where sQRy are used, text strings are among the most commonly embedded data. However, due to strict limitations on the available payload, the occupancy of strings limits the length of the programs that can be embedded. In this work, we propose a simple yet effective strategy that can reduce the space taken by strings, hence broadening sQRy applicability.

cs.HC↗

arXiv2025

Robust Multicast Origin Authentication in MACsec and CANsec for Automotive Scenarios

Having everything interconnected through the Internet, including vehicle onboard systems, is making security a primary concern in the automotive domain as well. Although Ethernet and CAN XL provide link-level security based on symmetric cryptography, they do not support origin authentication for multicast transmissions. Asymmetric cryptography is unsuitable for networked embedded control systems with real-time constraints and limited computational resources. In these cases, solutions derived from the TESLA broadcast authentication protocol may constitute a more suitable option. In this paper, some such strategies are presented and analyzed that allow for multicast origin authentication, also improving robustness to frame losses by means of interleaved keychains. A flexible authentication mechanism that relies on a unified receiver is then proposed, which enables transmitters to select strategies at runtime, to achieve the best compromise among security, reliability, and resource consumption.

cs.CR↗

arXiv2024

Packet Steering Mechanisms for MLO in Wi-Fi 7

Besides extremely high throughput, Wi-Fi 7 is also aimed at providing users a more deterministic behavior, characterized by shorter average latency and smaller jitters. A key mechanism to achieve this is multi-link operation, which brings simultaneous multi-band communication to client stations as well. In this paper, traffic steering policies are briefly reviewed and grouped into general classes, each one with its advantages and limitations. A basic mechanism for supporting dynamic steering is then described, which is simple enough to allow implementation in real Wi-Fi chipsets but highly flexible at the same time. Its operation can be driven by the host on a per-packet basis, and this permits to optimize spectrum usage depending on the requirements of applications and the traffic pattern they generate.

cs.NI↗

arXiv2024

Executable QR codes with Machine Learning for Industrial Applications

Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program is directly encoded in binary form within the QR code, it can be executed even when the reading device is not provided with Internet access. The applications of this technology are manifold, and range from smart user guides to advisory systems. The first programming language made available for eQR is QRtree, which enables the implementation of decision trees aimed, for example, at guiding the user in operating/maintaining a complex machinery or for reaching a specific location. In this work, an additional language is proposed, we term QRind, which was specifically devised for Industry. It permits to integrate distinct computational blocks into the QR code, e.g., machine learning models to enable predictive maintenance and algorithms to ease machinery usage. QRind permits the Industry 4.0/5.0 paradigms to be implemented, in part, also in those cases where Internet is unavailable.

cs.NI↗

arXiv2024

On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality

The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions.

cs.NI↗

arXiv2024

Wireless Sensor Networks based on TSCH/TDMA with Power Consumption and Latency Constraints

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.

cs.NI↗

arXiv2024

A Software Platform for Testing Multi-Link Operation in Industrial Wi-Fi Networks

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability. In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach.

cs.NI↗

arXiv2024

Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry.

cs.NI↗