A Statistical Approach to Response-Time Analysis of Complex Embedded Real-Time Systems

In recent years, the complexity of real-time embedded systems has increased dramatically. For those modern real-time systems, the limitations of original static Response Time Analysis (RTA) become more and more conspicuous. Most static analysis methods not only require much detailed system information, but also only target to some specific system model with non-realistic assumptions. As a result, those methods may produce overly pessimistic results, making them unsuitable to be applied on a complex industrial system. The best system model may be the system itself. Therefore, statistical RTA, which can produce probabilistic analysis results based on samples provided by real systems or simulators, may become more expedient. Statistical RTA usually requires more relaxed assumptions and less system information than static RTA. In this paper, we present an Extreme Value Theory (EVT) based method to compute Worst-Case Response Time (WCRT) targeting complex real-time systems. In the evaluation phase, we have applied this method to the calculation of worst-case transmission delays of messages over Controller Area Network (CAN), and some comparisons with static RTA are also provided. According to the experimental results, as the system complexity increases, our approach performs much more stable and less pessimistic.

In scheduling algorithms based on the Rate Monotonic (RM) method which are widely used in development of real-time systems, tasks with shorter periods have higher priorities. In contrast, ones with longer periods are likely to suffer from increased response times and jitter due to their lower priorities. We propose an Execution Right Delegation (ERD) method based on RM where a high-priority server for particular (or important) task is introduced to shorten response time and jitter of the task. In the evaluation, it is confirmed that response times and jitter of a particular (important) task are reduced. We also show Response Time Analysis (RTA), which assures worst-case response time of the task. This paper shows the algorithm and RTA of ERD and evaluates it by comparing it to a Deadline Monotonic method. The evaluation by simulation shows that ERD can reduce the average worst-case response time by 13.45% at maximum compared to the Deadline Monotonic scheduling. In addition, we confirm that the RTA provides a worst-case response time close to the simulation results.

A number of methods have been proposed for the response-time analysis (RTA) of controller area networks (CANs), which are the industry standard for in-vehicle network protocols. However, most of these methods are unsuitable for analyzing nonperiodically transmitted messages because they assume a constant transmission period for each message. Moreover, the calculated evaluation criteria are limited and comprise many over-pessimistic estimations, such as the worst-case response time (WCRT). To tackle these problems, this paper proposes an RTA method for CANs with randomly occurring messages, which replaces the constant transmission period with the average number of transmission instances per unit time. This approach finds the approximate probability distribution of the response time to any message in a CAN, providing a more general measure than the WCRT. To this end, a mathematical model based on M/G/1 queueing theory is developed. The model accurately emulates the behavior of nonperiodic CAN messages and enables RTA with minimal pessimistic estimations. When tested against the benchmarks, the proposed method accurately obtained response time distributions within several seconds to several tens of seconds.

Software plays a crucial role in robot control systems, and its efficient, flexible development is essential for production. Such software must be customized to specific production processes, requiring developers with specialized expertise in these areas—the high development threshold results in reduced efficiency and increased costs. Recently, significant progress has been made in automated problem-solving through societies of agents based on large language models (LLMs). To automate software development for industrial robot control systems, this paper introduces an Industrial Robot Control Software Auto-Development (IRCSAD) framework with multi-agent collaboration, and a Low-code Industrial Software Platform (LISP) for validating IRCSAD-developed software. IRCSAD automates software development and iterative optimization of prompts through the collaboration of multiple LLM-based agents. This work also proposes a software testing methodology for robot control systems based on reliability assessment. An experimental study that develops software for the robot control system for assembly, sorting, and inspection tasks is implemented. The results show that collaboration with LISP enhances IRCSAD's ability to solve complex problems in the development process, saves development costs, and improves development efficiency.

Despite the potential of quantum computing for drastically accelerating suitable real-time applications, response time analysis is still required to guarantee that quantum programs running on quantum computers satisfy application-specific timing requirements. This paper describes an inaugural project to determine whether a quantum program running on a quantum computer satisfies the timing constraints of a realtime application, that is, is quantum computing punctual and reliable in this time-sensitive application domain? Can this timing guarantee be formally verified? We leverage the existing work on the functional reactive programming (FRP) model to predict the worst-case response time (WCRT) of fault-tolerant classical computing systems since the timing analysis of re-executions for fault recovery plus transient-faults-induced wasted execution times is similar to determining the response time of FRP tasks. Ongoing work shows that accounting for wasted execution times due to errors in quantum computers resulting from quantum decoherence and state fidelity can be treated similarly and develops a mapping from quantum programs to FRP programs for efficient timing analysis.

The well-known model of Vestal aims to avoid excessive pessimism in the quantification of the processing requirements of mixed-criticality systems, while still guaranteeing the timeliness of higher-criticality functions. This can bring important savings in system costs, and indirectly help meet size, weight and power constraints. This efficiency is promoted via the use of multiple worst-case execution time (WCET) estimates for the same task, with each such estimate characterised by a confidence associated with a different criticality level. However, even this approach can be very pessimistic when the WCET of successive instances of the same task can vary greatly according to a known pattern, as in MP3 and MPEG codecs or the processing of ADVB video streams. In this paper, we present a schedulability analysis for the multiframe mixed-criticality model, which allows tasks to have multiple, periodically repeating, WCETs in the same mode of operation. Our work extends both the analysis techniques for Static Mixed-Cricality scheduling (SMC) and Adaptive Mixed-Criticality scheduling (AMC), on one hand, and the schedulability analysis for multiframe task systems on the other. Our proposed worst-case response time (WCRT) analysis for multiframe mixed-criticality systems is considerably less pessimistic than applying the SMC, AMC-rtb and AMC-max tests obliviously to the WCET variation patterns. Experimental evaluation with synthetic task sets demonstrates up to 63.8% higher scheduling success ratio (in absolute terms) compared to the best of the frame-oblivious tests.

This paper describes the design and implementation of a platform for fast external sensor integration in an industrial robot control system. As an application and motivating example, the implementation of force controlled grinding and deburring within the AUTOFETT-project (EU Growth Programme) is reported.

Recently, human–robot interaction technology has been considered as a key solution for smart factories. Surface electromyography signals obtained from hand gestures are often used to enable users to control robots through hand gestures. In this paper, we propose a dynamic hand-gesture-based industrial robot control system using the edge AI platform. The proposed system can perform both robot operating-system-based control and edge AI control through an embedded board without requiring an external personal computer. Systems on a mobile edge AI platform must be lightweight, robust, and fast. In the context of a smart factory, classifying a given hand gesture is important for ensuring correct operation. In this study, we collected electromyography signal data from hand gestures and used them to train a convolutional recurrent neural network. The trained classifier model achieved 96% accuracy for 10 gestures in real time. We also verified the universality of the classifier by testing it on 11 different participants.

Optimizing security and quality of service in a real-time operating system using multi-objective Bat algorithm

One marker of quality emergency medical services care is measured by meeting an 8-minute response time guideline. This guideline was based on results of paramedic response times for nontraumatic cardiac arrest patients and has not been studied in unselected patients. The objective of this study was to evaluate the effect of paramedic response time on survival to hospital discharge in unselected patients in a large urban setting while controlling for a number of potentially important confounders, including level of illness severity. This was a retrospective cohort study performed in an urban 911-based ambulance service system. Patients transported by paramedics to a single urban county teaching hospital from January 1, 1998, to December 31, 1998, were included. Data collected included patient demographics; paramedic response, scene, and transport times; the nature of the medical complaint; and whether the patient survived to hospital discharge. Multivariable logistic regression models were developed using response time as the primary independent variable and survival to hospital discharge as the dependent variable. Covariates included scene time, transport time, age, gender, and level of illness severity. Of 34,111 calls involving emergency response, 11,078 patients (32%) were transported to the study institution and 10,382 (94%) had response time data available. Of these, 9,559 patients (92%) had data available to categorize them into groups based on their level of illness severity and were thus included in the study. A survival benefit was identified for response times <or=4 minutes (odds ratio [OR], 0.70; 95% confidence interval [CI] = 0.52 to 0.95). No survival benefit was identified when response time was modeled as a continuous variable (OR, 1.01; 95% CI = 0.98 to 1.04) or when dichotomized at 8 minutes (OR, 1.06; 95% CI = 0.80 to 1.42). A paramedic response time within 8 minutes was not associated with improved survival to hospital discharge after controlling for several important confounders, including level of illness severity. However, a survival benefit was identified when the response time was within 4 minutes for patients with intermediate or high risk of mortality. Adherence to the 8-minute response time guideline in most patients who access out-of-hospital emergency services is not supported by these results.

Detecting cyberattacks in industrial control systems using online learning algorithms

The need for detection, identification and prediction of anomalies emerged just a short time ago. This task brings multiple challenges that are mainly associated with recording and detection of novel attacks and influences. It draws attention of experts in network security and diagnostics of information and industrial systems. Detection of anomalies in dynamic dataflows largely determines the efficiency of management of computer network information security within information and industrial systems. At the same time, the technology of dynamic dataflow prediction is also very important for building systems intended to detect anomalies in data protection within industrial control systems. Any automated systems are based on available computer capabilities and advances in management theory, mathematical modeling and optimizations methods. Processes that occur in industrial automated systems and that are reflected in dataflows being observed constitute complex multi-component processes, thus making it more complicated to predict such processes. In this case, complex multi-component time series data should be predicted at different (short-term, medium-term and long-term) time scales. A neural network architecture matching the structure of the multi-component time series being predicted should be built for generating a multi-component prediction. It is proposed to decompose the complex multi-component time series into several basic components using the digital signal processing technology, i. e. to perform a preliminary structural analysis of multi-component time series within the observed range of all time series that reflect operation of the industrial control system. Separate predictions with different time horizon are formed for each basic component of the multi-component time series using the available neural network architecture and machine learning taking into account dynamic characteristics of the above components. Anomalies in the observed range of multiple time series that reflect operation of the industrial control system are detected (identified) through component-wise comparison of each component (resulted from the above preliminary digital processing) of any time series within the observed range of all time series, with each prediction of the relevant component of the above time series within the observed range of all time series. This approach that implies component-wise comparison will allow to detect anomalies within the range of observed time series of the industrial control system separately by their different dynamic characteristics, and thus will improve the efficiency of management of information security within information and industrial systems.

As a convenient data source from computerized tests, response time could also be very informative evidence for the validity of test scores, offering an opportunity for insights into parts of the test that test takers linger over and other parts of the test where they glide through the material. Given these hopes for, and expectations of, response time data, we should critically evaluate how such data are studied and more importantly, to what extent this type of data lives up to its promise. We begin this chapter by defining response time and briefly discussing its use as validity evidence. We then describe the typical uses of response time data and review the different approaches to studying response time data in various research. The chapter closes with an evaluation of response time data as validity evidence and suggests the conditions that would facilitate better use of response data for validation purposes.

Recent world events and geopolitics have brought the vulnerability of critical infrastructure to cyberattacks to the forefront. While there has been considerable attention to attacks on Information Technology (IT) systems, such as data theft and ransomware, the vulnerabilities and dangers posed by industrial control systems (ICS) have received significantly less attention. What is very different is that industrial control systems can be made to do things that could destroy equipment or even harm people. For example, in 2021 the US encountered a cyberattack on a water treatment plant in Florida that could have resulted in serious injuries or even death. These risks are based on the unique physical characteristics of these industrial systems. In this paper, we present a holistic, integrated safety and security analysis, we call Cybersafety, based on the STAMP (System-Theoretic Accident Model and Processes) framework, for one such industrial system—an industrial chiller plant—as an example. In this analysis, we identify vulnerabilities emerging from interactions between technology, operator actions as well as organizational structure, and provide recommendations to mitigate resulting loss scenarios in a systematic manner.

With the development of multiprocessor technology, multiple processors are increasingly used in embedded real-time systems. The parallelism of multiple processors makes the worst-case response time (WCRT) analysis of multiprocessor real-time systems complicated. In the widely used global fixed-priority (GFP) scheduling, some effective approximate WCRT analysis methods have been proposed. These methods mainly focus on optimizing overall procedure or carry-in workload and assume that the whole carry-out workload of a high-priority task cause interference on the analyzed job. However, through our careful observation of specific examples, the partial carry-out workload of a high-priority task and the analyzed job may be executed in parallel. In other words, the part of parallel carry-out workload of a high-priority task could not cause interference on the analyzed job. In this article, we propose an improved WCRT analysis method by optimizing carry-out interference estimation for GFP scheduling, resulting in a more accurate upper bound of WCRT than the recent advanced methods. Experimental results show that our proposed method is superior than the recent advanced methods in terms of the acceptance rate of the system.