Gianluca Cena

Having everything interconnected through the Internet is making security a primary concern in the automotive and industrial domains as well. Vehicles and machinery often consist of embedded devices (smart sensors/actuators, electronics control units, human-machine interfaces) locally interconnected through low-cost multidrop buses like Single Pair Ethernet and CAN XL. Although these protocols provide link-level security based on symmetric cryptography, they do not support origin authentication (OA) for multicast frames. 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 multicast OA strategies are presented and analyzed that improve robustness to frame losses by means of a dual keychain. 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.

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.

Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This article 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 article 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.

Wireless sensor networks are characterized by low power consumption because motes are typically battery-powered. Time-slotted channel hopping (TSCH) relies on a fixed transmission schedule, which enables the receiver module of wireless motes to be switched off every time it is not needed. Unfortunately, in many practical contexts, most of the reserved slots remain unused, which leads to appreciable energy waste. For periodic traffic, proactive reduction of idle listening (PRIL) techniques have been proven able to mitigate this problem. In this article, PRIL multihop (PRIL-M) is introduced with the aim to improve existing PRIL techniques, by lowering energy waste further in large real-world mesh networks. PRIL-M is advantageous in all those contexts where ultralow power consumption is more important than end-to-end latency. Applications that can benefit from PRIL-M include, e.g., environmental monitoring, where sensors are deployed over the target area and must operate for years without maintenance. A thorough simulation campaign showed that, in these scenarios, energy consumption of PRIL-M is 75% less than standard TSCH, while the average latency is about 20 times larger.

QR codes are increasingly used in a plurality of scenarios, and research activities are being successfully carried out to improve this technology and widen its contexts of applicability. After an extensive survey of the state of the art on the subject, this work presents the new, promising possibility to embed a programming language in a QR code. This new kind of executable QR codes, we named eQR codes, enable interaction with end users even in the absence of an Internet connection, and provide a sort of Internet of Things paradigm where intelligence is embedded in the object tag in the form of a program. Among all the possible languages that can be embedded, this work focuses on a powerful but compact (in terms of QR code storage occupation) dialect, termed QRtree, which is aimed at implementing decision trees. The eQR code technology makes a new class of applications possible, e.g., providing hints for navigation or instructions for using rescue devices in places with no network coverage like mountains and caves. Smart interactive user manuals are enabled as well. Besides defining the QRtree language and eQR code structure, this article describes all the steps needed to generate eQR codes and to manage their execution in end-user devices. A simple yet realistic example and the related code are also presented, to practically show how this technology can be used to solve real-world problems. For the example, the QRtree version of the code takes 234B, less than one-half the size of an equivalent program in Python bytecode (634B).

One of the aspects that mainly characterize wireless networks is their apparent unpredictability. Although several attempts were made in the past years to define for them deterministic medium access techniques, for instance by having data exchanges scheduled by an access point, as a matter of fact they remain a partial solution and are unable to ensure the same behavior as wired infrastructures, since interference may also come from devices outside the network, which obey different rules. A possible way to cope with disturbance on air, both internal and external to the network, is to obtain some knowledge about it by analyzing what happened in the recent past. This information, usually expressed in terms of suitable metrics, is then employed to optimize network operation, for example by prioritizing time-sensitive traffic when needed. In the simplest approaches such metrics coincide with statistical indices evaluated on transmission outcomes, like the failure rate. In this paper we analyze a more sophisticated solution that relies on machine learning, and in particular on artificial neural networks, to predict the behavior of a Wi-Fi link in terms of its frame delivery ratio. Results confirm that more accurate predictions than simpler methods (e.g., moving average) are possible, even when training is partially independent from the specific conditions experienced on the different channels.

Time slotted channel hopping is an enhanced access mechanism for IEEE 802.15.4 that improves many aspects of the original protocol, like determinism and power consumption. Besides preventing collisions, time slotting allows motes to stay asleep for most of the time. In addition, channel hopping achieves more stable communication, in terms of packet losses and transmission latency, in spite of wireless spectrum variations. In this paper, the specific effects of the latter mechanism are investigated from both qualitative and quantitative points of view. In particular, a thorough experimental campaign was carried out on real devices, deployed in a real environment and performing realistic data exchanges, when they are exposed to different interference conditions, to assess the benefits it brings over the case when transmissions are performed on a fixed channel. Experimental results confirm that, when the transmission frequency is kept repeatedly changing, communication quality is sensibly less affected by co-located Wi-Fi infrastructures, which makes the network behavior more predictable and hence intrinsically more dependable

New generation electrified and self-driving vehicles require much higher performance and flexibility for onboard digital communications than Controller Area Networks may offer. For this reason, automotive Ethernet is often regarded as the next de facto standard technology in these contexts, and by extension for networked embedded systems as well. However, an abrupt and drastic move from CAN to Ethernet is likely to cause further cost increases, which can be hardly tolerated by buyers. This paper analyzes the third generation of CAN, termed CAN XL, and studies how interoperability can be ensured with Ethernet. Likely, composite CAN XL-Ethernet networks are the key for getting the best of both worlds, not only in the automotive domain but also for sensing and control in scenarios like building automation, wired sensor networks, and low-cost networked embedded systems with real-time constraints.

By removing wire harness, Wi-Fi is becoming increasingly pervasive in every aspect of our lives, in both the consumer and industrial worlds. Besides flexibility, the recent high efficiency and extremely high throughput versions managed to close the performance gap with Ethernet. However, it still lags behind Ethernet for what concerns dependability. To this aim, the ultra high reliability study group has been recently formed. This paper reports on some preliminary ideas and proposals about the ways seamless redundancy can be exploited to make Wi-Fi more reliable, yet retaining a good degree of backward compatibility with existing network infrastructures.