Search

Industrial digitalization has led to the introduction of autonomous cyber-physical production systems, optimizing the production processes. Stakeholders, however, are becoming concerned about the trustworthiness of such systems, especially its safety and the safety of its users. Over the years, many model-based risk assessment techniques have been proposed to help mitigate the trustworthiness-related risks within the targeted systems. However, these techniques do not meet the requirements of the guidelines provided by the International Standardization Organization have. Moreover, these techniques fail to consider the possible risks from the machine learning component of autonomous cyber-physical production systems. As a contribution, this paper presents an analysis of the compliance of MATrICS with the relevant ISO standards, its adaptation to ensure compliance, and its validation.

An increase in consumer demand and scarcity of available resources has led industrialists to hunt for solutions related to the automation of traditional manufacturing and production processes, optimizing resource consumption while improving the overall efficiency of the process. The resultant revolution brought forward the concept of cyber-physical production systems. Furthermore, industries within the private sector have integrated artificial intelligence with their traditional production processes as Cobots (collaborative robots), thus introducing the concept of Autonomous Cyber-Physical Production Systems. Although these systems maximize the production or manufacturing process while efficiently using the available resources, the machine learning component integrated into the traditional cyber-physical production system brings about trust-related issues due to its possible lack of predictability and transparency. Implementing trust-related attributes within autonomous cyber-physical production systems alone cannot overcome the highlighted problem. Therefore, a detailed risk assessment is required to identify and assess any trust-related risks in the system, especially at the early stages of the software development life cycle, to avoid major incidents and reduce maintenance costs. Based on the above-stated facts, this research proposes a model-based risk assessment technique for evaluating the trustworthiness of autonomous cyber-physical production systems. The proposed technique focuses on the identification and assessment of trust-related risks originating from the dynamic behavior of the machine learning component in autonomous cyber-physical production systems. For this, we use existing standards and techniques proposed for risk assessment in cyber-physical production systems as common ground to facilitate better implementation of trustworthiness in autonomous cyber-physical production systems. The proposed technique is aimed at overcoming the structural and b

The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) have enabled increased autonomy and closer human-robot cooperation in the production and manufacturing industry, thus leading to Autonomous Cyber-Physical Production Systems (ACPPS) and paving the way to the fifth industrial revolution (i.e., Industry 5.0). ACPPS are increasingly critical due to the possible consequences of their malfunctions on human co-workers, and therefore, evaluating their trustworthiness is essential. This article reviews research trends, relevant attributes, modeling languages, and tools related to the model-based trustworthiness evaluation of ACPPS. As in many other engineering disciplines and domains, model-based approaches, including stochastic and formal analysis tools, are essential to master the increasing complexity and criticality of ACPPS and to prove relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.

The production industry is looking for new solutions to improve the reliability, safety and efficiency of traditional processes. Current developments in artificial intelligence and machine learning have enabled a high level of autonomy in smart-manufacturing and production systems within Industry 4.0, thus paving the way towards fully Autonomous Cyber-Physical Production Systems (ACPPS). Although ACPPS can have many advantages, there still remains a concern regarding how much we can trust those systems, due to limited predictability, transparency, and explainability, as well as emerging vulnerabilities related to machine learning systems. In this paper, we present the findings of a study conducted on the possible risks related to the trustworthiness of ACPPS, and the consequences they have on the system and its environment.

The increased need for constant connectivity and complete automation of existing systems fuels the popularity of Cyber Physical Systems (CPS) worldwide. Increasingly more, these systems are subjected to cyber attacks. In recent years, many major cyber‐attack incidents on CPS have been recorded and, in turn, have been raising concerns in their users' minds. Unlike in traditional IT systems, the complex architecture of CPS consisting of embedded systems integrated with the Internet of Things (IoT) requires rather extensive planning, implementation, and monitoring of security requirements. One crucial step to planning, implementing, and monitoring of these requirements in CPS is the integration of the risk management process in the CPS development life cycle. Existing studies do not clearly portray the extent of damage that the unattended security issues in CPS can cause or have caused, in the incidents recorded. An overview of the possible risk management techniques that could be integrated into the development and maintenance of CPS contributing to improving its security level in its actual environment is missing. In this paper, we are set out to highlight the security requirements and issues specific to CPS that are discussed in scientific literature and to identify the state‐of‐the‐art risk management processes adopted to identify, monitor, and control those security issues in CPS. For that, we conducted a systematic mapping study on the data collected from 312 papers published between 2000 and 2020, focused on the security requirements, challenges, and the risk management processes of CPS. Our work aims to form an overview of the security requirements and risks in CPS today and of those published contributions that have been made until now, towards improving the reliability of CPS. The results of this mapping study reveal (i) integrity authentication and confidentiality as the most targeted security attributes in CPS, (ii) model‐based techniques as the most used r

Cyber physical systems (CPSs) are safety‐critical, be it weapon systems, smart medical devices, or grid stations. This makes ensuring security of all the components constituting a CPS unavoidable. The rise in the demand of interconnectedness has made such systems vulnerable to attacks, ie, cyberattacks. Over 170 cases of cyber‐security breaches in CPS were reported over the past two decades. An increase in the number of cyberattack incidents on CPS makes them more exposed and less trustworthy. However, identifying the security requirements of the CPS to pinpoint the relevant risks may help to counteract the potential attacks. Literature reveals that the most targeted security requirements of CPS are authentication, integrity, and availability. However, little attention has been paid on certain crucial security attributes such as data freshness and nonrepudiation. One major reason of security breaches in CPS is the lack of custom or generalized countermeasures. Therefore, we propose a security risk mitigation framework for a CPS focused on constraints, ie, authentication, data integrity, data freshness, nonrepudiation, and confidentiality. Furthermore, we evaluate the proposed work using a case study of a safety critical system. The results show a decrease in the severity of the identified security risks, ie, man‐in‐the‐middle attack, spoofing, and data tempering.

Underwater Wireless Sensor Networks (UWSNs) serve as a proficient source to monitor aquatic environment. However, data communications and information routing within these systems offer many challenges. To ensure sufficient network lifetime, energy efficiency in routing protocols serves as a major concern in UWSNs. This paper presents an energy competent cooperative routing scheme known as Region Based Courier-nodes Mobility with Incremental Cooperative (RBCMIC) routing. The proposed scheme uses broadcast nature of wireless nodes and performs an incremental cooperative routing. A rigorous evaluation and verification of the proposed scheme with current state-of-the-art yield improved energy efficiency, resulting in extended network lifetime. The results show that an overall improvement of 20% is witnessed in energy usage, whereas a notable 89% improvement is achieved in end-to-end delay in comparison to DEADS protocol.

In agile software development, some of the formal project management activities are neglected due to heavy documentation and planning. Agile methodologies usually focus on rapid development over hefty documentation which is contrary to the nature of the risk management process. Literature studies show agile methods are used for the development safety critical systems as well. For that ignoring risk management process is inevitable. This intrigued us to explore more on the risk management practices followed in agile methods following industry. To further the investigations on industrial practices regarding risk management, in this paper we explore on (i) the risks faced by the agile practitioners, and (ii) the mitigation strategies used. For this, we conducted an online survey with 54 agile practitioners. The results showed that the project deadlines and varying requirements are the two most commonly faced risks by the practitioners. Furthermore, the results also revealed that risk management strategies are being followed to some extent, but usually in a non-systematic way.

Software architecture is an important part of the software systems showing its components and connection between the components. Small release time and quick delivery has become the main objective of the software industry, these days. This has led to the negligence of the development of the software architecture resulting software architecture erosion. The aim of this research is to provide a review of the solutions proposed regarding the recovery of the software architecture over the years.

The title compound, C11H17NS, is an important intermediate for the synthesis of biologically active adamantlythiazolo-oxadiazoles. The adamantyl residue is disordered about a twofold rotation axis over two sites with site-occupation factors of 0.817 (3) and 0.183 (3). The crystal structure is stabilized by intermolecular N-H...S hydrogen-bonding interactions. Key indicators: single-crystal X-ray study; T = 173 K; mean &963;(C–C) = 0.002 Å; disorder in main residue; R factor = 0.038; wR factor = 0.103; data-to-parameter ratio = 12.3.

The latest industrial revolution has introduced the concept of autonomous cyber-physical production systems by integrating machine learning components into smart manufacturing systems. Although it does help maximize the production process while efficiently managing the required resources, integrating machine learning into those systems has a major impact on trustworthiness due to less predictable and explainable behaviors.This paper proposes an overview of a novel model-based methodology for evaluating the trustworthiness of those systems. In order to develop the methodology, we conducted a study to understand the potential and limitations of model-based assessment and categorized limitations into structural, behavioral, and resource-related. According to those findings, we concluded that machine learning components within autonomous cyber-physical production systems are not adequately considered regarding risk identification and assessment. Moreover, the study revealed that using a single modeling approach can limit the evaluation process to specific layers or attributes. Therefore, based on the conclusion drawn from this study, we propose a new methodology to overcome current limitations in identifying and assessing risks originating from machine learning components within autonomous cyber-physical production systems.

Industrial digitalization has led to the introduction of autonomous cyber-physical production systems, optimizing the production processes. Stakeholders, however, are becoming concerned about the trustworthiness of such systems, especially its safety and the safety of its users. Over the years, many model-based risk assessment techniques have been proposed to help mitigate the trustworthiness-related risks within the targeted systems. However, these techniques do not meet the requirements of the guidelinesprovided by the International Standardization Organizationhave. Moreover, these techniques fail to consider the possible risks from the machine learning component of autonomous cyber-physical production systems. As a contribution, this paper presents an analysis of the compliance of MATrICS with the ISO, its adaptation to make it compliant, and its validation.