Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities to the Ethernet layer. Safety-critical realtime applications based on TSN require both timeliness as well as fault-tolerance guarantees. The TSN standard 802.1CB introduces seamless redundancy mechanisms for time-sensitive data whereby each data frame is sequenced and duplicated across a redundant link to prevent single points of failure (most commonly, link failures). However, a major shortcoming of 802.1CB is the lack of fault detection mechanisms which can result in unnecessary replications even under good link conditions -clearly inefficient in terms of bandwidth use. This paper proposes a machine learning-based intelligent configuration synthesis mechanism that enhances bandwidth utilization by replicating frames only when a link has a higher propensity for failure.
Low latency and on demand resource availability enable fog computing to host industrial applications in a cloud like manner. One industrial domain which stands to benefit from the advantages of fog computing is robotics. However, the challenges in developing and implementing a fog-based robotic system are manifold. To illustrate this, in this paper we discuss a system involving robots and robot cells at a factory level, and then highlight the main building blocks necessary for achieving such functionality in a fog-based system. Further, we elaborate on the challenges in implementing such an architecture, with emphasis on resource virtualization, memory interference management, real-time communication and the system scalability, dependability and safety. We then discuss the challenges from a system perspective where all these aspects are interrelated.
Electric drives are used to control electric motors, which are pervasive in industrial applications. In this paper we propose enhancing the electric drives to fulfil the role of fog nodes within a Fog Computing Platform (FCP). Fog Computing is envisioned as a realization of future distributed architectures in Industry 4.0. We identify the system-level requirements of such an FCP, including requirements that are extracted from the current architecture of drives, which we consider as a baseline. These requirements are then used to design a system-level architecture, which we model using the Architecture Analysis & Design Language (AADL). We identify the "technology bricks" (components such as hardware, software, middleware, services, methods and tools) needed to implement the FCP. The proposed fog-based architecture is then used to implement a Conveyor Belt industrial use case. We evaluate the resulting use case on several aspects, demonstrating the usefulness of the proposed fog-based approach. By developing the electric drives as fog nodes, that we call fogification, new offerings like programmability, analytics and connectivity to customer Clouds are expected to increase the added value. Increased flexibility allows drives to assume a larger role in industrial and domestic control systems, instrumenting thus also legacy systems by using drives as the data source.
The Fog computing paradigm employing multiple technologies is expected to play a key role in a multitude of industrial applications by fulfilling futuristic requirements such as flexible and enhanced computing, storage, and networking capability closer to the field devices. While performance aspects of the Fog paradigm has been the central focus of researchers, safety aspects have not received enough attention so far. In this paper, we identify various safety challenges related to the Fog paradigm and provide specific safety design aspects as a step towards enhancing safety in industrial automation scenarios. We contextualize these ideas by invoking a distributed mobile robots use-case that can benefit from the use of the Fog paradigm. CCS CONCEPTS• Computer systems organization → Embedded and cyberphysical systems; Fault-tolerant network topologies; Robotics; Robotic autonomy.
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