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Managing Multi-Processor Systems-on-Chip (MPSoCs) is becoming increasingly complex as demands for advanced capabilities rise. This complexity is due to the involvement of more processing elements and resources, leading to a higher degree of heterogeneity throughout the system. Over time, management schemes have evolved from simple to autonomous systems with continuous control and monitoring of various parameters such as power distribution, thermal events, fault tolerance, and system security. Autonomous management integrates self-awareness into the system, making it aware of its environment, behavior, and objectives. Self-Aware Cyber-Physical Systems-on-Chip (SA-CPSoCs) have emerged as a concept to achieve highly autonomous management. Communication infrastructure is also vital to SoCs, and Software-Defined Networks-on-Chip (SDNoCs) can serve as a base structure for self-aware systems-on-chip. This paper presents a survey of the evolution of MPSoC management over the last two decades, categorizing research works according to their objectives and improvements. It also discusses the characteristics and properties of SA-CPSoCs and explains why SDNoCs are crucial for these systems.

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At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems.

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Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.

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Objective: This article presents a Scoping Review (ScR) identify the approaches to automatically generate test cases from Cyber-Physical Systems (CPS) models, more specifically, Medical Cyber-Physical Systems (MCPS) models. Method: ScR was performed by identifying indexed articles in five electronic databases using a specific search string and selection criteria, defined in a review protocol. Results: When protocol was executed, 467 studies were returned, from which 12 were summarized. Several formal and semi-formal notations used in CPS modeling were identified, as well as tools for generating test cases for such systems. Furthermore, we present an overview of the state-of-the-art regarding automatic test case generation for such systems models. Conclusion: Based on the results, we conclude there is a research gap with regard to tools for the fully automatic test case generation in MCPS.

Objetivo: Este artigo apresenta uma Revisão de Escopo (RE) para identificar as abordagens para gerar automaticamente casos de testes a partir de modelos de Sistemas Físico-Cibernéticos (SFC), mais especificamente, Sistemas Médicos Físico-Cibernéticos (SMFC). Método: A RE foi realizada pela identificação de trabalhos indexados em cinco bases eletrônicas de dados usando termos de busca e critérios de inclusão, definidos em um protocolo de revisão. Resultados: Ao executar o protocolo foram retornados 467 estudos, dos quais sumarizaram-se 12. Foram identificadas várias notações formais e semi-formais usadas na modelagem de SFC, bem como ferramentas para gerar casos de teste para esses sistemas. Além disso, foi apresentada uma visão geral do estado da arte em relação à geração automática de casos de teste para esses modelos de sistemas. Conclusão: Com base nos resultados obtidos, conclui-se que ainda há uma lacuna de pesquisa no que diz respeito às ferramentas para a geração totalmente automática de casos de teste para SMFC.

Objectivo: En este artículo se presenta una Revisión de Alcance (RA) para identificar los enfoques para generar automáticamente casos de prueba a partir de modelos de Sistemas Físico-Cibernéticos (SFC), más específicamente, Sistemas Médicos Físico-Cibernéticos (SMFC). Método: La RA se realizó mediante la identificación de artículos indexados en cinco bases de datos electrónicas utilizando términos de búsqueda y criterios de selección, definidos en un protocolo de revisión. Resultados: Al ejecutar el protocolo se devolvieron 467 estudios, de los cuales se resumieron 12. Se han identificado varias notaciones formales y semiformales utilizadas en el modelado de SFC y SMFC, así como herramientas para generar casos de prueba para estos sistemas. Además, se presentó una descripción general del estado del arte en relación a la generación automática de casos de prueba para estos modelos de sistema. Conclusión: Con base a los resultados obtenidos, se concluye que hay una brecha de investigación con respecto a las herramientas para la generación de casos de prueba totalmente automática en MCPS.

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Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research.

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In recent years, advanced threats against Cyber-Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier.

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In this paper, we present an approach to assess the schedulability and scalability of CPS Networks through an algorithm that is capable of estimating the load of the network as its utility grows. Our approach evaluates both the network load and the laxity of messages, considering its current topology and real-time constraints while abstracting environmental specificities. The proposed algorithm also accounts for the network unreliability by applying a margin-of-safety parameter. This approach enables higher utilities as it evaluates the load of the network considering a margin-of-safety that encapsulates phenomena such as collisions and interference, instead of performing a worst-case analysis. Furthermore, we present an evaluation of the proposed algorithm over three representative scenarios showing that the algorithm was able to successfully assess the network capacity as it reaches a higher use.

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This article presents the design and implementation of an event-triggered control approach, applied to the leader-following consensus and formation of a group of autonomous micro-aircraft with capabilities of vertical take-off and landing (VTOL-UAVs). The control strategy is based on an inner-outer loop control approach. The inner control law stabilizes the attitude and position of one agent, whereas the outer control follows a virtual leader to achieve position consensus cooperatively through an event-triggered policy. The communication topology uses undirected and connected graphs. With such an event-triggered control, the closed-loop trajectories converge to a compact sphere, centered in the origin of the error space. Furthermore, the minimal inter-sampling time is proven to be below bounded avoiding the Zeno behavior. The formation problem addresses the group of agents to fly in a given shape configuration. The simulation and experimental results highlight the performance of the proposed control strategy.

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Robots, or in general, intelligent vehicles, require large amounts of data to adapt their behavior to the environment and achieve their goals. When their missions take place in large areas, using additional information to that gathered by the onboard sensors frequently offers a more efficient solution of the problem. The emergence of Cyber-Physical Systems and Cloud computing allows this approach, but integration of sensory information, and its effective availability for the robots or vehicles is challenging. This paper addresses the development and implementation of a modular mobile node of a Wireless Sensor Network (WSN), designed to be mounted onboard vehicles, and capable of using different sensors according to mission needs. The mobile node is integrated with an existing static network, transforming it into a Hybrid Wireless Sensor Network (H-WSN), and adding flexibility and range to it. The integration is achieved without the need for multi-hop routing. A database holds the data acquired by both mobile and static nodes, allowing access in real-time to the gathered information. A Human⁻Machine Interface (HMI) presents this information to users. Finally, the system is tested in real urban scenarios in a use-case of measurement of gas levels.

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Medical Cyber-Physical Systems (MCPS) are context-aware, life-critical systems with patient safety as the main concern, demanding rigorous processes for validation to guarantee user requirement compliance and specification-oriented correctness. In this article, we propose a model-based approach for early validation of MCPS, focusing on promoting reusability and productivity. It enables system developers to build MCPS formal models based on a library of patient and medical device models, and simulate the MCPS to identify undesirable behaviors at design time. Our approach has been applied to three different clinical scenarios to evaluate its reusability potential for different contexts. We have also validated our approach through an empirical evaluation with developers to assess productivity and reusability. Finally, our models have been formally verified considering functional and safety requirements and model coverage.

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