Real-Time Performance of Industrial IoT Communication Technologies: A Review

With the growing need for automation and the ongoing merge of OT and IT, industrial networks have to transport a high amount of heterogeneous data with mixed criticality such as control traffic, sensor data, and configuration messages. Current advances in IT technologies furthermore enable a new set of automation scenarios under the roof of Industry 4.0 and IIoT where industrial networks now have to meet new requirements in flexibility and reliability. The necessary real-time guarantees will place significant demands on the networks. In this paper, we identify IIoT use cases and infer real-time requirements along several axes before bridging the gap between real-time network technologies and the identified scenarios. We review real-time networking technologies and present peer-reviewed works from the past 5 years for industrial environments. We investigate how these can be applied to controllers, systems, and embedded devices. Finally, we discuss open challenges for real-time communication technologies to enable the identified scenarios. The review shows academic interest in the field of real-time communication technologies but also highlights a lack of a fixed set of standards important for trust in safety and reliability, especially where wireless technologies are concerned.Comment: IEEE Internet of Things Journal 2023 | Journal article DOI: 10.1109/JIOT.2023.333250

Offloading Real-Time Tasks in IIoT Environments under Consideration of Networking Uncertainties

Offloading is a popular way to overcome the resource and power constraints of networked embedded devices, which are increasingly found in industrial environments. It involves moving resource-intensive computational tasks to a more powerful device on the network, often in close proximity to enable wireless communication. However, many Industrial Internet of Things (IIoT) applications have real-time constraints. Offloading such tasks over a wireless network with latency uncertainties poses new challenges. In this paper, we aim to better understand these challenges by proposing a system architecture and scheduler for real-time task offloading in wireless IIoT environments. Based on a prototype, we then evaluate different system configurations and discuss their trade-offs and implications. Our design showed to prevent deadline misses under high load and network uncertainties and was able to outperform a reference scheduler in terms of successful task throughput. Under heavy task load, where the reference scheduler had a success rate of 5%, our design achieved a success rate of 60%.Comment: 2nd International Workshop on Middleware for the Edge (MiddleWEdge '23). 2023. AC

Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems

Manufacturing, automotive, and aerospace environments use embedded systems for control and automation and need to fulfill strict real-time guarantees. To facilitate more efficient business processes and remote control, such devices are being connected to IP networks. Due to the difficulty in predicting network packets and the interrelated workloads of interrupt handlers and drivers, devices controlling time critical processes stand under the risk of missing process deadlines when under high network loads. Additionally, devices at the edge of large networks and the internet are subject to a high risk of load spikes and network packet overloads. In this paper, we investigate strategies to detect network packet overloads in real-time and present four approaches to adaptively mitigate local deadline misses. In addition to two strategies mitigating network bursts with and without hysteresis, we present and discuss two novel mitigation algorithms, called Budget and Queue Mitigation. In an experimental evaluation, all algorithms showed mitigating effects, with the Queue Mitigation strategy enabling most packet processing while preventing lateness of critical tasks.Comment: EdgeSys '2

Towards a Staging Environment for the Internet of Things

Internet of Things (IoT) applications promise to make many aspects of our lives more efficient and adaptive through the use of distributed sensing and computing nodes. A central aspect of such applications is their complex communication behavior that is heavily influenced by the physical environment of the system. To continuously improve IoT applications, a staging environment is needed that can provide operating conditions representative of deployments in the actual production environments -- similar to what is common practice in cloud application development today. Towards such a staging environment, we present Marvis, a framework that orchestrates hybrid testbeds, co-simulated domain environments, and a central network simulation for testing distributed IoT applications. Our preliminary results include an open source prototype and a demonstration of a Vehicle-to-everything (V2X) communication scenario

Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds

As a result of the many technical advances in microcomputers and mobile connectivity, the Internet of Things (IoT) has been on the rise in the recent decade. Due to the broad spectrum of applications, networks facilitating IoT scenarios can be of very different scale and complexity. Additionally, connected devices are uncommonly heterogeneous, including micro controllers, smartphones, fog nodes and server infrastructures. Therefore, testing IoT applications is difficult, motivating adequate tool support. In this paper, we present Héctor, a framework for the automatic testing of IoT applications. Héctor allows the automated execution of user-defined experiments on agnostic IoT testbeds. To test applications independently of the availability of required devices, the framework is able to generate virtual testbeds with adjustable network properties. Our evaluations show that simple experiments can be easily automated across a broad spectrum of testbeds. However, the results also indicate that there is considerable interference in experiments, in which many devices are emulated, due to the high resource demand of system emulation

A Priority-Aware Multiqueue NIC Design for Real-Time IoT Devices

Low-level embedded systems are used to control cyber-phyiscal systems in industrial and autonomous applications. They need to meet hard real-time requirements as unanticipated controller delays on moving machines can have devastating effects. Modern developments such as the industrial Internet of Things and autonomous machines require these devices to connect to large IP networks. Since Network Interface Controllers (NICs) trigger interrupts for incoming packets, real-time embedded systems are subject to unpredictable preemptions when connected to such networks. In this work, we propose a priority-aware NIC design to moderate network-generated interrupts by mapping IP flows to processes and based on that, consolidates their packets into different queues. These queues apply priority-dependent interrupt moderation.First experimental evaluations show that 93 % of interrupts can be saved leading to an 80 % decrease of processing delay of critical tasks in the configurations investigated

Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems

When IP-packet processing is unconditionally carried out on behalf of an operating system kernel thread, processing systems can experience overload in high incoming traffic scenarios. This is especially worrying for embedded real-time devices controlling their physical environment in industrial IoT scenarios and automotive systems. We propose an embedded real-time aware IP stack adaption with an early demultiplexing scheme for incoming packets and subsequent per-flow aperiodic scheduling. By instrumenting existing embedded IP stacks, rigid prioritization with minimal latency is deployed without the need of further task resources. Simple mitigation techniques can be applied to individual flows, causing hardly measurable overhead while at the same time protecting the system from overload conditions. Our IP stack adaption is able to reduce the low-priority packet processing time by over 86% compared to an unmodified stack. The network subsystem can thereby remain active at a 7x higher general traffic load before disabling the receive IRQ as a last resort to assure deadlines

Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems

Manufacturing, automotive, and aerospace environments use embedded systems for control and automation and need to fulfill strict real-time guarantees. To facilitate more efficient business processes and remote control, such devices are being connected to IP networks. Due to the difficulty in predicting network packets and the interrelated workloads of interrupt handlers and drivers, devices controlling time critical processes stand under the risk of missing process deadlines when under high network loads. Additionally, devices at the edge of large networks and the internet are subject to a high risk of load spikes and network packet overloads. In this paper, we investigate strategies to detect network packet overloads in real-time and present four approaches to adaptively mitigate local deadline misses. In addition to two strategies mitigating network bursts with and without hysteresis, we present and discuss two novel mitigation algorithms, called Budget and Queue Mitigation. In an experimental evaluation, all algorithms showed mitigating effects, with the Queue Mitigation strategy enabling most packet processing while preventing lateness of critical tasks

Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of low-carbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and costs. In this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available