CLAIR: A Contract-Based Framework for Developing Resilient CPS Architectures

Andalam, Sidharta; Ng, Daniel Jun Xian; Easwaran, Arvind; Thangamariappan, Karthikeyan

doi:10.1109/isorc.2018.00013

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Safety via contracts: Andalam, Sidharta, et al [29] have discussed CLAIR, a contract-based framework for developing resilient CPS architectures. This work is predominantly a contract-based framework for components in different levels of abstraction.…”

Section: Related Workmentioning

confidence: 99%

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Ramakrishna

Dubey

Burruss

et al. 2019

2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)

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Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving (∼ 0.40 m/s) than the original system (using only LECs).

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Section: Related Workmentioning

confidence: 99%

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Ramakrishna

Dubey

Burruss

et al. 2019

2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)

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“…For a given contract, runtime observers check whether the expected behaviour is generated by the component or subsystem of concern. Observers can be expressed using different formalisms like finite state machines and timed and hybrid automata [7]. Upon failure of a contract, a fault is triggered, and the manager performs an analysis to determine whether any solutions within its scope can be applied.…”

Section: Framework Detailsmentioning

confidence: 99%

“…In contrast to redundancy, self-adaptive software-based resilience management approaches are more flexible and space-energy efficient [4][5][6][7][8]. Such approaches provide on-demand resilience services by assessing the status of the components and adapting them to meet requirements.…”

mentioning

confidence: 99%

“…However, this is generally impractical in large-scale CPS, and hence existing decentralised approaches such as RIAPS [5] and iLand [6] attempt recovery without complete fault information. For example, the discovery service application in RIAPS does not consider the expected recovery time to a fault which incurs unnecessary communication overheads and delays [7]. Similarly, the reconfiguration manager in iLand is unaware of fault types and their recovery times which incurs unnecessary reconfigurations.…”

mentioning

confidence: 99%

“…Furthermore, RMs communicate information about the faults to enable more meaningful fault-recovery. We introduced the use of contracts for fault detection in [7], but that proposal neither includes a hierarchical framework nor does it consider the use of contracts with parameters; as discussed in the following sections these features are vital for scalability and efficient fault-recovery. Hierarchy allows decomposition of the resilience management functions, aiding scalability.…”

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confidence: 99%

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Contract-Based Hierarchical Resilience Management for Cyber-Physical Systems

Haque

Easwaran

et al. 2018

Computer

Self Cite

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Orchestrated collaborative effort of physical and cyber components to satisfy given requirements is the central concept behind Cyber-Physical Systems (CPS). To duly ensure the performance of components, a software-based resilience manager is a flexible choice to detect and recover from faults quickly. However, a single resilience manager, placed at the centre of the system to deal with every fault, suffers from decision-making overburden; and therefore, is out of the question for distributed large-scale CPS. On the other hand, prompt detection of failures and efficient recovery from them are challenging for decentralised resilience managers. In this regard, we present a novel resilience management framework that utilises the concept of management hierarchy. System design contracts play a key role in this framework for prompt fault-detection and recovery. Besides the details of the framework, an Industry 4.0 related test case is presented in this article to provide further insights.

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Contract-Based Design for Low-Code Development in Industrial Edge Applications

Qu,

Zhang,

et al. 2023

2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)

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CLAIR: A Contract-Based Framework for Developing Resilient CPS Architectures

Cited by 4 publications

References 18 publications

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Contract-Based Hierarchical Resilience Management for Cyber-Physical Systems

Contract-Based Design for Low-Code Development in Industrial Edge Applications

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