Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems

Bandyszak, Torsten; Daun, Marian; Tenbergen, Bastian; Kuhs, Patrick; Wolf, Stefanie; Weyer, Thorsten

doi:10.1109/tase.2020.2980726

Cited by 17 publications

(14 citation statements)

References 58 publications

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“…, s n }, the set of all smart sensor nodes. 1 An action, submit the estimate to the Web server. 2 An action, do not submit the estimate to the Web server.…”

Section: Nomenclature Meementioning

confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by/4.0/ the server network traffic at the required level. r Number of smart sensor nodes that must take 1 . P(r ) = P π (P(r ) is used in the Appendix).…”

Section: πmentioning

confidence: 99%

“…W ITH the capability to enable communication between machines via the Worldwide Web, Web services have infiltrated into various Internet-of-Things (IoT) applications to bridge different components, such as Web server and smart sensor nodes [1]- [4], where the latter differs from the traditional sensor nodes by having the intelligence to make decisions [5].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Web Service-Based Data-Collection System With Smart Sensor Nodes for Balance Between Network Traffic and Sensing Accuracy

Hou

Zhao

Başar

2021

IEEE Trans. Automat. Sci. Eng.

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Web services integrate various components in the Internet of Things (IoT). In a Web service-based data-collection system with multiple smart sensor nodes periodically sampling and estimating the same unknown physical parameter of interest, the smart sensor nodes first submit their estimates to the Web server, and then, the server picking the one with the minimum error seems to be a practical way to arrive at a minimum error estimate (MEE). More submissions provide the Web server with more candidates to consider, which can maximize the probability of the server guaranteeing the MEE, while also leading to more network traffic. Therefore, how to make the optimal tradeoff between network traffic and sensing accuracy arises as an interesting problem. This article proposes a network traffic-dependent probability threshold policy within an intended underlying optimization-theoretical framework to address this problem. The policy is such that the smart sensor nodes submit their estimates and corresponding estimation errors (ECEEs) to the Web server within a tolerable network traffic threshold while maximizing the probability of the server delivering the MEE. Theoretical analysis, simulation, and field experiments document and illustrate its performance.Note to Practitioners-This article addresses the interesting tradeoff between sensing accuracy and network traffic demand in the Web service-based data-collection system that operates in some remote areas with limited network traffic. It helps to improve the operation efficiency of the Internet-of-Things (IoT) systems that employ Web service technology to enable the Web server to deliver minimum error estimate with maximum probability while keeping the network traffic within a given range. Our simulation and experimental investigations show that the solution developed here outperforms existing solutions.

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“…, s n }, the set of all smart sensor nodes. 1 An action, submit the estimate to the Web server. 2 An action, do not submit the estimate to the Web server.…”

Section: Nomenclature Meementioning

confidence: 99%

Section: πmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Web Service-Based Data-Collection System With Smart Sensor Nodes for Balance Between Network Traffic and Sensing Accuracy

Hou

Zhao

Başar

2021

IEEE Trans. Automat. Sci. Eng.

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“…In the following, we present a modeling technique [Bandyszak et al 2018b], [Bandyszak et al 2020] to support the systematic identification and documentation of runtime uncertainties during the…”

Section: Orthogonal Uncertainty Modelingmentioning

confidence: 99%

“…This relationship, pointing from a base artifact element to a corresponding OUM uncertainty element, captures possible mitigations that either counteract uncertainty once it has occurred (i.e., to reduce possible negative effects), or prevent uncertainty from becoming active. [Bandyszak et al 2020] Example Figure 7-3 shows an example OUM (middle part) as well as some exemplary base artifacts (upper and lower part). The latter include a structural context model, a functional model, and a behavioral model of the exemplary CACC system.…”

Section: Mitigation Relationmentioning

confidence: 99%

Handling Uncertainty in Collaborative Embedded Systems Engineering

Bandyszak

Jöckel

Kläs

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

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As collaborative embedded systems operate autonomously in highly dynamic contexts, they must be able to handle uncertainties that can occur during operation. On the one hand, they must be able to handle uncertainties due to the imprecision of sensors and the behavior of data-driven components for perceiving and interpreting the context to enable decisions to be made during operation. On the other hand, uncertainties can emerge from the collaboration in a collaborative group, related to the exchange of information (e.g., context knowledge) between collaborative systems. This chapter presents methods for modeling uncertainty early in development and analyzing uncertainty during both design and operation. These methods allow for the identification of epistemic uncertainties that can occur when various, potentially heterogeneous systems are required to collaborate. The methods also enable graphical and formal modeling of uncertainties and their impact on system behavior (e.g., in the course of dynamic traffic scenarios). Furthermore, this chapter investigates the quality of outputs issued by data-driven models used to equip collaborative embedded systems with uncertainty-resilient machine learning capability.

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Uncertainty handling in cyber–physical systems: State‐of‐the‐art approaches, tools, causes, and future directions

Asmat

Khan

Hussain

2022

J Software Evolu Process

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Cyber-Physical System (CPS) is the set of heterogeneous physical units linked to a network and performs complex operations to achieve a goal. Uncertainty increases with the increase in complexity of CPS. Thus, uncertainty needs to be mitigated to assure the quality and reliability of a CPS. This study aims to identify current stateof-the-art approaches, tools, root causes, and metrics for uncertainty in the domain of CPS. We performed a systematic literature review and employed keyword-based search on publisher sites to find potential studies. After applying the devised inclusion and exclusion criteria on identified potentially relevant studies, selection of studies is validated using an index engine. The core contributions of this study are (i) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, (ii) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, and (iii) to identify the stateof-the-art methods that lack the ability to elaborate the metrics to measure the uncertainty in CPS. The results of the proposed study are beneficial in guiding future research on devising new approaches or tools to mitigate the causes of uncertainty in CPS.

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Orthogonal Uncertainty Modeling in the Engineering of Cyber-Physical Systems

Cited by 17 publications

References 58 publications

Optimization of Web Service-Based Data-Collection System With Smart Sensor Nodes for Balance Between Network Traffic and Sensing Accuracy

Optimization of Web Service-Based Data-Collection System With Smart Sensor Nodes for Balance Between Network Traffic and Sensing Accuracy

Handling Uncertainty in Collaborative Embedded Systems Engineering

Uncertainty handling in cyber–physical systems: State‐of‐the‐art approaches, tools, causes, and future directions

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