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Register a new userContract-based Methodology for Developing Resilient Cyber-Infrastructure in the Industry 4.0 Era
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Daniel Jun Xian Ng
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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As the industrial cyber-infrastructure become increasingly important to realise the objectives of Industry~4.0, the consequence of disruption due to internal or external faults become increasingly severe. Thus there is a need for a resilient infrastructure. In this paper, we propose a contract-based methodology where components across layers of the cyber-infrastructure are associated with contracts and a light-weight resilience manager. This allows the system to detect faults (contract violation monitored using observers) and react (change contracts dynamically) effectively.
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Industrial cyber-infrastructure is normally a multilayered architecture. The purpose of the layered architecture is to hide complexity and allow independent evolution of the layers. In this paper, we argue that this traditional strict layering results in poor transparency across layers affecting the ability to significantly improve resiliency. We propose a contract-based methodology where components across and within the layers of the cyber-infrastructure are associated with contracts and a light-weight resilience manager. This allows the system to detect faults (contract violation monitored using observers) and react (change contracts dynamically) effectively. It results in (1) improving transparency across layers; helps resiliency, (2) decoupling fault-handling code from application code; helps code maintenance, (3) systematically generate error-free fault handling code; reduces development time. Using an industrial case study, we demonstrate the proposed methodology.
There has been an intense concern for security alternatives because of the recent rise of cyber attacks, mainly targeting critical systems such as industry, medical, or energy ecosystem. Though the latest industry infrastructures largely depend on AI-driven maintenance, the prediction based on corrupted data undoubtedly results in loss of life and capital. Admittedly, an inadequate data-protection mechanism can readily challenge the security and reliability of the network. The shortcomings of the conventional cloud or trusted certificate-driven techniques have motivated us to exhibit a unique Blockchain-based framework for a secure and efficient industry 4.0 system. The demonstrated framework obviates the long-established certificate authority after enhancing the consortium Blockchain that reduces the data processing delay, and increases cost-effective throughput. Nonetheless, the distributed industry 4.0 security model entails cooperative trust than depending on a single party, which in essence indulges the costs and threat of the single point of failure. Therefore, multi-signature technique of the proposed framework accomplishes the multi-party authentication, which confirms its applicability for the real-time and collaborative cyber-physical system.
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.
Instilling resilience in critical infrastructure (CI) such as dams or power grids is a major challenge for tomorrows cities and communities. Resilience, here, pertains to a CIs ability to adapt or rapidly recover from disruptive events. In this paper, the problem of optimizing and managing the resilience of CIs is studied. In particular, a comprehensive two-fold framework is proposed to improve CI resilience by considering both the individual CIs and their collective contribution to an entire system of multiple CIs. To this end, a novel analytical resilience index is proposed to measure the effect of each CIs physical components on its probability of failure. In particular, a Markov chain defining each CIs performance state and a Bayesian network modeling the probability of failure are introduced to infer each CIs resilience index. Then, to maximize the resilience of a system of CIs, a novel approach for allocating resources, such as drones or maintenance personnel, is proposed. In particular, a comprehensive resource allocation framework, based on the tools of contract theory, is proposed enabling the system operator to optimally allocate resources, such as, redundant components or monitoring devices to each individual CI based on its economic contribution to the entire system. The optimal solution of the contract-based resilience resource allocation problem is analytically derived using dynamic programming. The proposed framework is then evaluated using a case study pertaining to hydropower dams and their interdependence to the power grid. Simulation results, within the case study, show that the system operator can economically benefit from allocating the resources while dams have a 60% average improvement over their initial resilience indices.
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.
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