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This paper presents the design and implementation of an educational testbed developed for a course on industrial communication networks at the Engineering Faculty, University of Catania. The aim is to realize a platform capable of emulating various network configurations, thus enabling students to find out by themselves through practical experiments how different design choices, parameter settings, network configurations, and algorithms impact on the overall network performance. The testbed comprises a number of basic components (hosts, routers, and access points) implemented on nodes equipped with operating systems and open source software, which together make up a modular system. Each router can be loaded with data flows and monitored over preestablished time windows so as to evaluate its performance in a wide range of operating conditions. The wireless part makes it possible to configure environments with different levels of noise and bandwidth utilization so as to emulate a broad spectrum of real operating environments. The testbed can be used via remote access through a web interface that not only allows the operating conditions to be configured but also permits real-time monitoring. Students can configure the testbed on the basis of the network they are studying and can measure its performance for evaluation purposes. Index Terms--Communication engineering education, computer networks, measurement, scheduling, wireless LAN.

Industrial wireless sensor networks (IWSN) are expected to support in the near future a wide range of applications in which mobile nodes, for example, robots or humans, interoperate between them and with machinery. However, the real-time constraints imposed by the industrial processing make mobility support quite challenging. This article proposes an approach based on software-defined networking (SDN), herein named forwarding and time-slotted channel hopping scheduling over SDN (FTS-SDN), that handles transmission scheduling and node mobility in IWSN providing bounded end-to-end delays. The article presents the detailed design of the FTS-SDN and the implementation on commercial-off-the-shelf devices. In addition, the FTS-SDN performance are extensively discussed, providing simulation results and experimental measurements obtained in a real scenario. [ABSTRACT FROM AUTHOR]

Modern cars consist of a number of complex embedded and networked systems with steadily increasing requirements in terms of processing and communication resources. Novel automotive applications, such as automated driving, rise new needs and novel design challenges that cover a broad range of hardware/software engineering aspects. In this context, this paper provides an overview of the current technological challenges in on-board and networked automotive systems. This paper encompasses both the state-of-the-art design strategies and the upcoming hardware/software solutions for the next generation of automotive systems, with a special focus on embedded and networked technologies. In particular, this paper surveys current solutions and future trends on models and languages for automotive software development, on-board computational platforms, in-car network architectures and communication protocols, and novel design strategies for cybersecurity and functional safety. [ABSTRACT FROM AUTHOR]

Presents information on the Women in IES Workshop at the ISIE 2022 conference. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on telecommunication services in automation applicaitons. The evolution of automation systems has always been the result of continuous improvements to state-of-the-art systems, occasionally punctuated with significant paradigm and architectural changes that acted as innovation drivers. This happened with the industrial revolutions, which had in their origin the appearance of disruptive technologies, such as the steam engine and machine tools (first industrial revolution); electrification, mass production, and assembly lines (second industrial revolution); use of microprocessors and software (third industrial revolution). [ABSTRACT FROM PUBLISHER]

This paper proposes the Multilevel Gaussian Mixture Model (ML_GMM), a novel algorithm for online, unsupervised modeling of complex motion trajectories which allows for fast and reliable abnormal trajectory detection in video surveillance. The presented approach does not directly deal with target tracking or attribution of an observation to either one or another trajectories belonging to different targets. Instead, it receives as an input a sequence of observations and considers them as produced by a reliable, yet possibly noisy, tracker. As an output, it then produces a labeling of the input sequence, as a "normal" or "abnormal" whole trajectory. The normal and abnormal trajectory domains are learned in an unsupervised fashion during the training phase and can be updated while the classifier is running, thanks to its reduced computational complexity. The novelty of the contribution is twofold. First, ML_GMM is capable of representing short-term, as well as long-term, transitions in a consistent way, thus allowing for accurate representation of long and complex trajectories and their reliable labeling as normal or abnormal. Second, it allows the learning process to be run in parallel with the discrimination process, in order to support adaptability of the model to evolving operating conditions. Experimental results show that ML_GMM outperforms a number of comparable approaches in the literature. [ABSTRACT FROM AUTHOR]