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.

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.

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.

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.

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.

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.

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.

Low-cost light-scattering particulate matter sensors are often advocated for dense monitoring networks. Recent literature has focused on evaluating their performance. Nonetheless, low-cost sensors are also considered unreliable and imprecise. Consequently, exploring techniques for anomaly detection, resilient calibration, and improvement of data quality should be more discussed. In this study, we analyze a year-long acquisition campaign by positioning 56 low-cost light-scattering sensors near the inlet of an official particulate matter monitoring station. We use the collected measurements to design and test a data processing pipeline composed of different stages, including fault detection, filtering, outlier removal, and calibration. These can be used in large-scale deployment scenarios where the quantity of sensors data can be too high to be analyzed manually. Our framework also exploits sensor redundancy to improve reliability and accuracy. Our results show that the proposed data processing framework produces more reliable measurements, reduces errors, and increases the correlation with the official reference.