Ubiquitous deployment of embedded systems is having a substantial impact on our society, since they interact with our lives in many critical real-time applications. Typically, embedded systems used in safety or mission critical applications (e.g., aerospace, avionics, automotive or nuclear domains) work in harsh environments where they are exposed to frequent transient faults such as power supply jitter, network noise and radiation. They are also susceptible to errors originating from design and production faults. Hence, they have the design objective to maintain the properties of timeliness and functional correctness even under error occurrences. Fault-tolerance plays a crucial role towards achieving dependability, and the fundamental requirement for the design of effective and efficient fault-tolerance mechanisms is a realistic and applicable model of potential faults and their manifestations. An important factor to be considered in this context is the random nature of faults and errors, which, if addressed in the timing analysis by assuming a rigid worst-case occurrence scenario, may lead to inaccurate results. It is also important that the power, weight, space and cost constraints of embedded systems are addressed by efficiently using the available resources for fault-tolerance. This thesis presents a framework for designing predictably dependable embedded real-time systems by jointly addressing the timeliness and the reliability properties. It proposes a spectrum of fault-tolerance strategies particularly targeting embedded real-time systems. Efficient resource usage is attained by considering the diverse criticality levels of the systems' building blocks. The fault-tolerance strategies are complemented with the proposed probabilistic schedulability analysis techniques, which are based on a comprehensive stochastic fault and error model.

With the advent of autonomous vehicles, the issue of safety-evaluationhas become key. ISO26262 recommends using Markov chains. However, in their most common form, Markov chains lack the flexibility required to model non- exponential probability distributions and systems displaying parallelism. In these cases, generalized semi-Markov processes arebetter suited. Though, these are significantly more taxing to analyze mathematically.  This thesis instead explores the option of simulating these systemsdirectly via MATLAB’s Simulink and Stateflow. An example system, here called CASE, currently under study by Scania was used as an example. The results showed that direct simulation is indeed possible, but the computational times are significantly greater than those from standard MATLAB-functions. The method should therefore be employed on parallel systems when results with a high level of fidelity are needed, and alternative methods are not available.

Yes / In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architecture reconfiguration. Given the safety-critical nature of the applications involved, an approach for addressing safety in the context of reconfiguration impacting functional and non-functional properties at runtime is needed. In this paper, we introduce a concept for runtime safety analysis and decision input for open adaptive systems. We combine static safety analysis and evidence collected during operation to analyse, reason and provide online recommendations to minimize deviation from a system’s safe states. We illustrate our concept via an abstract vehicle platooning system use case. / DEIS H2020 Project under Grant 732242.

Yes / The advances in the Internet of Things (IoT) has substantially contributed to the automation of modern societies by making physical things around us more interconnected and remotely controllable over the internet. This technological progress has inevitably created an intelligent society where various mechatronic systems are becoming increasingly efficient, innovative, and convenient. Undoubtedly, the IoT paradigm will continue to impact human life by providing efficient control of the environment with minimum human intervention. However, despite the ubiquity of IoT devices in modern society, the dependability of IoT applications remains a crucial challenge. Accordingly, this paper systematically reviews the current status and challenges of IoT dependability frameworks. Based on the review, existing IoT dependability frameworks are mainly based on informal reliability models. These informal reliability models are unable to effectively evaluate the unified treatment safety faults and cyber-security threats of IoT systems. Additionally, the existing frameworks are also unable to deal with the conflicting interaction between co-located IoT devices and the dynamic features of self-adaptive, reconfigurable, and other autonomous IoT systems. To this end, this paper suggested the design of a novel model-based dependability framework for quantifying safety faults and cyber-security threats as well as interdependencies between safety and cyber-security in IoT ecosystems. Additionally, robust approaches dealing with conflicting interactions between co-located IoT systems and the dynamic behaviours of IoT systems in reconfigurable and other autonomous systems are required.

No / In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architecture reconfiguration. Given the safety-critical nature of the applications involved, an approach for addressing safety in the context of reconfiguration impacting functional and non-functional properties at runtime is needed. In this paper, we introduce a concept for runtime safety analysis and decision input for open adaptive systems. We combine static safety analysis and evidence collected during operation to analyse, reason and provide online recommendations to minimize deviation from a system’s safe states. We illustrate our concept via an abstract vehicle platooning system use case. / This conference paper is available to view at http://hdl.handle.net/10454/17415.

No

Yes / Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty.