A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies

Zhou, Peng; Zuo, Decheng; Hou, Kun Mean; Zhang, Zhan; Dong, Jian; Li, Jian-Jin; Zhou, Haiying

doi:10.3390/s19051033

Cited by 19 publications

(4 citation statements)

References 209 publications

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“…In addition, many systems need real-time responses, given that they work in critical contexts, or even are responsible for attending requirements on which people's lives depend, and cannot be stopped whenever a change happens. Therefore, tolerance mechanisms must have ways to provide adaptation to new characteristics and requirements at runtime [23,45].…”

Section: Ch2-adaptability and Evolutionmentioning

confidence: 99%

“…Fault tolerance (FT) plays an essential role in maintaining a system's regular operation, even in the presence of failures, leading to less faults, errors, and failures, improving the dependability [14]. However, applying fault tolerance techniques is a complex task, posing these challenges: IoT systems are often loaded with multiple heterogeneous "things" to support multi-purpose applications [15][16][17]; they often have particular requirements regarding fault-tolerance schemes, including specific fault characteristics [18,19]; complexity due to large-scale and ubiquitous applications [6,10,20]; and they are in constantly evolving [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems

Melo

Aquino

2021

Sensors

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Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.

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Section: Ch2-adaptability and Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems

Melo

Aquino

2021

Sensors

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“…Human generally provides additional degrees of freedom for the CPS. In [20], in order to avoid human from making mistakes and simplify management, a self-managed CPS is proposed; human factors are further simulated in a mixed environment of CPS. Reference [21] proposed an architecture that seamlessly integrates factory workers in an industrial cyber physical production environment.…”

Section: Human-in-the-loopmentioning

confidence: 99%

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

Chen

Wang

et al. 2020

IEEE Access

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With the development of industrial Internet and artificial intelligence, data fusion in crossdomains and cross-layers have become an inevitable trend. Most of the data fusion involved in the production process of hot rolling are concentrated on the level of sensors, Internet of Things (IoT) and the Internet; but human data are not well integrated. In order to avoid the human factor from becoming the bottleneck of the entire production schedule, this paper proposes a ternary data fusion model based on reinforcement learning algorithm. The related data source from human-cyber-physical space includes: social network, Internet and IoT. By merging the ternary data, a variety of data (including humans') can be quickly calculated to obtain better and faster decisions. In order to achieve automated fusion from ternary data, this paper proposes a method based on reinforcement learning: firstly, the domain ontology used for associating ternary data is reduced and tessellated (dimension reduction), and then the reinforcement learning model is used to form "the new ontology". Compared with resource-intensive global calculations (which may cost a few days), the new method can complete the calculations in minutes. This means that the new method optimizes the data source required for decision-making and improves the efficiency. Finally, the production scheduling of hot rolled steel is used as an example to verify the feasibility of the proposed method. INDEX TERMS human-cyber-physical data fusion, ternary data fusion, cyber-physical system, domain ontology, reinforcement learning

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“…The use of redundancy can improve CPS reliability. However, redundancy can have a significant impact on CPS performance, size, weight, power consumption, and reliability[91][92][93][94][95].…”

mentioning

confidence: 99%

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

Boi-Ukeme¹

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The technological advancement in Cyber-Physical Systems (CPS) has seen more sophisticated hardware, leading to systems that are complex, interconnected, and require automation. This trend has made modern CPS fragile and susceptible to faults. Traditional methods for fault detection and diagnosis are unable to adequately scale up to handle the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS. Also, real-time requirements present new challenges that are not sufficiently addressed by traditional fault-tolerant design approaches, therefore more intelligent methods are now needed to deal with these faults. To address these issues, we propose an approach to formally define faults, detect, and diagnose faults, and accommodate faults for fault tolerance.For fault detection and diagnosis, we propose a generic fault detection and diagnosis (FDD) scheme capable of diagnosing CPS faults in real-time. The scheme is developed to accommodate different modeling methods but for clarity of explanation, we adapt it to the DEVS formalism. To test the scheme, we implemented a library to store fault codes in a data structure and developed intelligent logic to ensure faults are correctly detected and isolated. We also propose a purely data-driven approach to detect faults in CPS where we have no control over the design of the control system. Our data-driven methods thrive on setting rigorous processes and workflows to ensure that representative data is collected for CPS to ensure that FDD techniques perform optimally.

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A Comprehensive Technological Survey on the Dependable Self-Management CPS: From Self-Adaptive Architecture to Self-Management Strategies

Cited by 19 publications

References 209 publications

FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems

FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems

Top-Down Human-Cyber-Physical Data Fusion Based on Reinforcement Learning

A Robust Discrete Event Method for the Design of Cyber-Physical Systems

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