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With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production - namely, using data as a resource to interconnect the elements in the production system for a more resource-efficient and sustainable production. Selection and integration of digital technologies are crucial steps to ensure that suitable technology is chosen and properly introduced in the production system. However, having one digital technology is not enough; rather there is a need to combine several synergising technologies for smart production. There are many challenges when selecting and integrating a combination of synergising digital technologies for smart production. Therefore, the purpose of this paper is to support manufacturing companies in systematically selecting and integrating suitable digital technologies for efficiently benefiting data value chains for smart production. This paper employed a multiple case study involving manufacturing companies within different industries and of different sizes. The paper analyses the current challenges related to the selection and integration of digital technologies and proposes a data flow framework with possible ways of combining digital technologies. The proposed framework shows alternative data flows between a combination of technologies depending on what digital technologies are selected and how they are integrated.

This paper identifies that existing industrial automation standards, such as IEC/IEEE 60802 and IEEE 802.1Q, often have inconsistent definitions of traffic types. In the context of utilizing time-sensitive networking (TSN) standards for future automation systems, clear and consistent traffic characteristics and use cases should be defined to benefit from TSN features. Besides that, to facilitate the integration of TSN into the automation systems, the current standards provide a recommendation for mapping the automation traffic to the TSN traffic classes. In this paper, we propose an alternative mapping methodology for automation traffic to TSN traffic classes after presenting the existing automation traffic and their characteristics. Finally, through a case study, we show the potential of the new mapping methodology compared to the standard mapping strategy.

Multi-Access Edge Computing (MEC) is a paradigm that enables Internet-of-Things (IoT) applications and devices to run tasks in different locations, from IoT devices to servers in the Cloud. This way, less capable devices can offload computation loads to more powerful or available servers. However, choosing the optimal location for a particular task can be complex due to the features of each location and restrictions of the task. For this, numerous approaches in the literature adopt a centralized strategy for the computation offloading decision, which introduces a single point of failure and can be a bottleneck for resource-demanding applications. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to solve the choice of computing locations, and its assessment in a real testbed. This testbed is formed of an end-user device running the agent, which connects to a MEC server and a Cloud server through 5G. We compare the algorithm against four alternatives, one based on another DRL approach, and analyze their performance in terms of meeting the computing tasks’ requirements and energy consumption in the User Equipment (UE), where synthetically generated tasks are executed or offloaded. DRL algorithms are shown to provide the best tradeoff between performance and energy consumption in changing conditions.

Integrating Time-Sensitive Networking (TSN) with 5G cellular networks facilitates high-bandwidth, low-latency end-to-end communication in networked embedded systems. An integrated TSN-5G network has the potential to support predictable and deterministic end-to-end communication, as well as to significantly enhance scalability, particularly in industrial automation, by providing flexibility, efficiency, and responsiveness. This paper aims to facilitate the end-to-end (E2E) communication over the integrated TSN-5G networks, by addressing two of the main challenges: (1) design and implementation of an effective TSN-5G gateway that not only ensures an effective forwarding mechanism to translate the traffic among both networks, but also maps the TSN traffic to the corresponding 5G quality-of-service profiles to ensure the timing requirements of highly critical traffic, and (2) establishment of clock synchronization across all network components to support the E2E communication in TSN-5G networks. The paper outlines the design and implementation of a robust TSN-5G gateway that bridges TSN and 5G network architectures, ensuring seamless interoperability between them. We utilize a standalone private 5G network within a controlled lab environment to establish the E2E communication for the TSN-5G network, with a particular emphasizes on analyzing latencies and jitter to provide valuable insights for industrial implementation. Moreover, the gateway facilitates a time synchronization approach, to enable time coordination between network components that supports E2E communication on TSN-5G networks considering the hardware limitation. Our findings indicate that achieving latencies below 20 ms is feasible in an integrated TSN-5G network using the proposed configuration of a private 5G setup with a channel bandwidth of 40 MHz.

The Internet of Things (IoT) is increasingly being adopted in diverse domains, many of which require strict timing constraints and predictable behavior. Despite the growing importance of timing characteristics in IoT applications, current approaches to address timing requirements are often fragmented, context-specific, and lack a unified understanding. Consequently, addressing timing aspects in IoT remains largely ad hoc and dependent on individual applications, making it challenging to generalize findings or systematically apply established solutions. The goal of this study is to provide a comprehensive understanding of how timing is defined, characterized, and measured within the IoT community. We conducted this study through a systematic and structured mix methods research approach. First, we performed a systematic review of the literature, extracting and analyzing information from 38 primary studies, selected from a rigorous process involving 1176 studies. Second, to complement the literature findings, we conducted an expert survey involving 28 respondents from academia and industry, representing a variety of roles with specialized expertise in IoT systems and timing-related issues. We identified two primary characterizations of timing within the IoT: time-criticality and predictability. Additionally, we collected and categorized 113 distinct timing metrics from literature into commonly found layers of an IoT system. The majority of the surveyed practitioners and researchers (75%) agree with our categorization and consider this research useful and relevant (71.5%). We believe that our study provides practitioners and researchers with insights into timing characteristics and metrics in IoT applications, towards the ultimate goal of standardization.

Time-Sensitive Networking (TSN) has emerged as a key technology for ensuring reliable and deterministic communication in vehicular networks. However, the complexity of evaluating, configuring, and analyzing TSN mechanisms has hindered its widespread adoption. This paper introduces CONAN-TSN, an integrated toolchain designed to address these challenges by providing a comprehensive solution for the configuration, analysis, and evaluation of TSN networks. CONAN-TSN includes tools for traffic generation, traffic mapping, scheduling, and timing analysis, enabling seamless integration and practical implementation of TSN in vehicular systems. The toolchain’s effectiveness is demonstrated through its application to an industrial use case, showcasing its ability to enhance network schedulability and performance. The results highlight CONAN-TSN’s potential to bridge the gap between theoretical benefits and real-world deployment of TSN, offering a valuable resource for researchers and practitioners in the field.

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints. 

The increasing complexity of modern embedded systems highlights the limitations of Controller Area Network (CAN) in terms of transmission speed and scalability. The IEEE Time-Sensitive Networking (TSN) task group developed a set of standards to enhance switched Ethernet with high bandwidth, low jitter, and deterministic communication. Despite these advances, CAN will likely co-exist with TSN in, e.g., the automotive industry due to factors such as cost-effectiveness and legacy of CAN. This paper presents an experimental evaluation of a CAN-toTSN gateway implementation, focusing on the impact of different forwarding and scheduling strategies on network performance. We analyze various queuing techniques and scheduling mechanisms in a realistic experimental setup and assess their impact on end-to-end delay and TSN bandwidth utilization. The evaluation results demonstrate that encapsulating only a single CAN frame within a TSN frame effectively minimizes the end-to-end delay of CAN frames, in particular when a high-speed TSN network is used. Furthermore, we perform a comparative evaluation of the Time-Aware Shaper (TAS) and Weighted Round Robin (WRR) mechanisms in the TSN network. Interestingly, WRR leads to lower delays for CAN frames in the TSN network compared to TAS, which we attribute to the lack of synchronization between CAN and TSN.

Industrial real-time embedded systems run complex software applications with strict timing constraints to ensure efficient operation. Increasing computational demands often strain device resources, leading to missed deadlines and disrupted task execution. This paper proposes a novel decision-making mechanism for offloading soft real-time tasks to edge or cloud servers. The mechanism contains two major components: (i) a monitoring agent to observe and collect task-level performance parameters during run-time, and (ii) a decision-making component to decide, based on the collected parameters, which tasks should be offloaded to improve the overall system performance. We implement the proposed mechanism in the FreeRTOS real-time operating system to provide evidence of its feasibility. We also perform a set of experiments to show performance of the proposed mechanism. The results demonstrate that the proposed mechanism ensures predictable execution of the system with minimal overhead, offering a scalable solution for resource-constrained soft real-time systems.

This paper presents the development and evolution of an educational module centered on collaborative problem-based learning, model-based software development, and end-to-end timing analysis for vehicular embedded systems, highlighting our experiences in integrating state-of-the-art research and industry practices over the years. For the past eight years, the module has been taught to both industry professionals and academic students. In the industry, it has been presented through seminars and workshops organized within the industrial settings of two vehicle manufacturers and a provider of vehicular software development tools. In an academic context, this module has been delivered as part of a PhD course. Furthermore, it has been incorporated into 11 instances of master's courses across four European universities. When offered in an industry context, the module is kept concise and more focused on hands-on activities and practical use cases. In contrast, when the module is delivered in an academic setting, it is supplemented with additional lectures and discussions on its topics. Interestingly, the feedback received from participants, especially those from the industry, has not only contributed to refining this educational module but has also advanced the state of the art in modeling and timing analysis of embedded software architectures.