Using run-time checking to provide safety and progress for distributed cyber-physical systems

2013 ◽  
Author(s):  
Stanley Bak ◽  
Fardin Abdi Taghi Abad ◽  
Zhenqi Huang ◽  
Marco Caccamo
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Run Time

scholarly journals A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Designs ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Imre Horváth
Keyword(s):  
Performance Enhancement ◽  
Cyber Physical Systems ◽  
Comprehensive Description ◽  
Computational Framework ◽  
Learning Mechanism ◽  
Physical Systems ◽  
Knowledge Based ◽  
System Adaptation ◽  
Ampliative Reasoning ◽  
Run Time

To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

A Study on Run Time Assurance for Complex Cyber Physical Systems

2013 ◽  
Author(s):  
Matthew Clark ◽  
Xenofon Koutsoukos ◽  
Joseph Porter ◽  
Ratnesh Kumar ◽  
George Pappas ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Run Time

Design and Run-Time Aspects of Secure Cyber-Physical Systems

2019 ◽  
pp. 357-382 ◽  
Author(s):  
Apostolos P. Fournaris ◽  
Andreas Komninos ◽  
Aris S. Lalos ◽  
Athanasios P. Kalogeras ◽  
Christos Koulamas ◽  
...  
Keyword(s):  
Cyber Physical Systems ◽  
Physical Systems ◽  
Run Time

Designing Run-time Evolution for Dependable and Resilient Cyber-Physical Systems Using Digital Twins

2021 ◽  
pp. 1-32
Author(s):  
Luis F. Rivera ◽  
Miguel Jiménez ◽  
Gabriel Tamura ◽  
Norha M. Villegas ◽  
Hausi A. Müller
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomic Computing ◽  
Cyber Physical Systems ◽  
Transportation Systems ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Physical Systems ◽  
Self Adaptation ◽  
Run Time ◽  
Physical Assets

The proliferation of Smart Cyber-Physical Systems (SCPS) is increasingly blurring the boundaries between physical and virtual entities. This trend is revolutionizing multiple application domains along the whole human activity spectrum, while pushing the growth of new businesses and innovations such as smart manufacturing, cities and transportation systems, as well as personalized healthcare. Technological advances in the Internet of Things, Big Data, Cloud Computing and Artificial Intelligence have effected tremendous progress toward the autonomic control of SCPS operations. However, the inherently dynamic nature of physical environments challenges SCPS’ ability to perform adequate control actions over managed physical assets in myriad of contexts. From a design perspective, this issue is related to the system states of operation that cannot be predicted entirely at design time, and the consequential need to define adequate capabilities for run-time self-adaptation and self-evolution. Nevertheless, adaptation and evolution actions must be assessed before realizing them in the managed system in order to ensure resiliency while minimizing the risks. Therefore, the design of SCPS must address not only dependable autonomy but also operational resiliency. In light of this, the contribution of this paper is threefold. First, we propose a reference architecture for designing dependable and resilient SCPS that integrates concepts from the research areas of Digital Twin, Adaptive Control and Autonomic Computing. Second, we propose a model identification mechanism for guiding self-evolution, based on continuous experimentation, evolutionary optimization and dynamic simulation, as the architecture’s first major component for dependable autonomy. Third, we propose an adjustment mechanism for self-adaptation, based on gradient descent, as the architecture’s second major component, addressing operational resiliency. Our contributions aim to further advance the research of reliable self-adaptation and self-evolution mechanisms and their inclusion in the design of SCPS. Finally, we evaluate our contributions by implementing prototypes and showing their viability using real data from a case study in the domain of intelligent transportation systems.

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