A Self-Healing Framework for Building Resilient Cyber-Physical Systems

Ratasich, Denise; Höftberger, Oliver; Isakovic, Haris; Shafique, Muhammad; Grosu, Radu

doi:10.1109/isorc.2017.7

Cited by 23 publications

(17 citation statements)

References 10 publications

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“…SHSA considers the currently available information in the network, i.e., can be applied in dynamic systems like the IoT (components may be added and removed during runtime). The knowledge base, in particular the relationships between the communicated information, can be defined by the application's domain expert or learned (approximated by, e.g., neural networks, SVMs or polynomial functions, see also [200]).…”

Section: Case Study: Resilient Smart Mobilitymentioning

confidence: 99%

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A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems

et al. 2019

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The Internet of Things (IoT) is a ubiquitous system connecting many different devices -the things -which can be accessed from the distance. The cyber-physical systems (CPS) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. This paper summarizes state-of-the-art of existing work on anomaly detection, fault-tolerance and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for CPS which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable "Trustworthy IoT for CPS". Finally, this framework is illustrated on a selected use case: A smart sensor infrastructure in the transport domain.

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Section: Case Study: Resilient Smart Mobilitymentioning

confidence: 99%

“…Alternatively, the monitor and diagnose task may be installed in the cloud analyzing the logged tracks to trigger maintenance of radar sensors. The requirements needed by SHSA regarding the architecture of the system (e.g., communication network) and a reference implementation of SHSA can be found in [200].…”

Section: Case Study: Resilient Smart Mobilitymentioning

confidence: 99%

A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems

et al. 2019

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“…In [11], Höftberger introduced an ontology (a knowledge base) defining physical relations or semantic equivalences between variables (e.g., laws of physics) to substitute failed observation services. We applied this technique in [14] and we extended the knowledge base with properties and utility theory in [13]. However, this knowledge base has been only exploited for failure recovery, but not yet for fault detection.…”

Section: Related Workmentioning

confidence: 99%

“…A relation r : v o = f (V I ) is a function or program (e.g., math, pseudo code or executable python code) to compute an output variable v o from a set of input variables V I . The relations can be defined by the application's domain expert or learned (approximated) with neural networks, SVMs or polynomial functions (see [14]).…”

Section: Variables and Relationsmentioning

confidence: 99%

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Adaptive Fault Detection Exploiting Redundancy with Uncertainties in Space and Time

Ratasich

Platzer

Grosu

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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The Internet of Things (IoT) connects millions of devices of different cyber-physical systems (CPSs) providing the CPSs additional (implicit) redundancy during runtime. However, the increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. Self-healing is an increasingly popular approach for ensuring resilience, that is, a proper monitoring and recovery, in CPSs. This work encodes and searches an adaptive knowledge base in Prolog/ProbLog that models relations among system variables given that certain implicit redundancy exists in the system. We exploit the redundancy represented in our knowledge base to generate adaptive runtime monitors which compares related signals by considering uncertainties in space and time. This enables the comparison of uncertain, asynchronous, multi-rate and delayed measurements. The monitor is used to trigger the recovery process of a self-healing mechanism. We demonstrate our approach by deploying it in a real-world CPS prototype of a rover whose sensors are susceptible to failure.

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